Geometry design for acquiring surface das data to monitor carbon storage sites

Abstract

The disclosed method includes: determining a subsurface space of interest; arranging a source sensor array relative to a receiver sensor array of a Surface Distributed Acoustic Sensing (S-DAS); transmitting, from the source sensor array, a first set of seismic signals indicating baseline measurements; receiving, using the receiver sensor array, the baseline measurements and thereby generate baseline data; injecting fluid into the subsurface space of interest; transmitting, using the source sensor array, a second set of seismic signals indicating fluid plume measurements; receiving, using the receiver sensor array, the fluid plume measurements and thereby generate fluid plume data; determining, based on the baseline data and the fluid plume data, time-shift data and thereby generate fluid plume extent data; formatting the fluid plume extent data into data values that characterize an extent or rate of spread of the fluid plume; and visualizing the extent or rate of spread of the fluid plume.

Description

GEOMETRY DESIGN FOR ACQUIRING SURFACE DAS DATA TO MONITORCARBON STORAGE SITESCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to US Provisional Application No. 63 / 658674, filed on June 11, 2024, titled “Geometry Design For Acquiring Surface DAS Data To Monitor Carbon Storage Sites,” and to US Provisional Application No. 63 / 602884, filed on November 27, 2023, titled “Geometry Design For Acquiring Surface DAS Data To Monitor Carbon Storage Sites,” all of which are incorporated herein by reference in their entirety for all purposes.TECHNICAL FIELD

[0002] This disclosure is directed to equipment configurations for carbon storage operations.BACKGROUND

[0003] Interest in carbon capture and storage (CCS) has grown significantly in recent years. In particular, support of the societal goal of meeting a net zero carbon emission target has refocussed attention on carbon capture and storage (CCS). In the International Energy Agency (IEA) Sustainable Development Scenario in which global fluid (e.g., CO2) emissions from the energy sector are expected to fall to zero on a net basis by 2070, CCS accounts for nearly 15% of the cumulative reduction in emissions as of 2021. For example, it is estimated by the IEA that between 5700 mega-tonne (Mt) of carbon dioxide (CO2) need to be sequestrated or stored in, for example, subsurface storage structures, by 2025 to achieve the net zero goal.

[0004] Sequestrating or storing fluids such as CO2 in, for example, subsurface storage structures is a significant challenge because of the rapid increases in scale of both carbon capture process and the storage of carbon or other greenhouse gases. The Global CCS Institute describes this as a rapid scale up. In 2022, 61 new CCS facilities were announced, resulting in 196 CCS projects in development as of September 2022. This is a 44% year-on-year increase in capture capacity, but, importantly, the projects in development will still only store 244 Mt per year of fluid (e.g., CO2) according to the Global CCS Institute in 2022. For example, the IEA indicates that CO2 capture plants take between three and five years to build, while theassessment and development process for CO2 storage can take much longer times. Thus, the surmountable task ahead of the energy transition industry includes that of scaling up CCS or fluid storage operations.

[0005] Safe subsurface storage of fluids such as hydrogen, methane, and carbon dioxide, for example, is a major challenge facing the energy industry. In particular, subsurface fluid storage has uncertainties that place both development e.g., financial feasibility) and environmental safety constraints on fluid storage projects thereby negatively impacting project execution and completion timelines for fluid storage.

[0006] Moreover, conformance requirements for fluid storage systems may be based on time-lapse 4-dimensional (4D) image data. This can be acquired using streamers or less commonly seabed nodes. The costs associated with this approach are not only very high but also include significant redundancies with regard to monitoring fluid plume migration (e.g., CO2 plume migration) activity that relies on developing 4D image for interpretation.

[0007] There is therefore a need to develop cost-effective mechanisms that monitor or track fluid storage structures to: effectively manage the aforementioned uncertainties; comply with fluid storage requirements from regulatory bodies; and accurately report carbon storage parameters to stakeholders including said regulatory bodies.SUMMARY

[0008] Disclosed are methods, systems, and computer programs for detecting or monitoring fluid plume in a subsurface space of interest. According to an embodiment, a method for detecting or monitoring fluid plume in a subsurface space of interest comprises: determining a subsurface space of interest for storing fluid; arranging a source sensor array of a Seismic Distributed Acoustic Sensing (S-DAS) sensor relative to a receiver sensor array of the S-DAS sensor on a trajectory going through the source sensor array and an injector well associated with the subsurface space of interest, the injector well being configured to inject fluid into the subsurface space of interest; prior to fluid injection into the subsurface space of interest, transmitting, from the source sensor array of the S-DAS sensor, a first set of seismic signals indicating baseline measurements that reflect an absence of fluid in the subsurface space of interest; receiving, using the receiver sensor array of the S-DAS sensor, the baseline measurements and thereby generate baseline data for the subsurface space of interest; injectingfluid into the subsurface space of interest via the injector well; transmitting, using the source sensor array of the S-DAS sensor, a second set of seismic signals indicating fluid plume measurements that reflect the presence fluid plume associated with the subsurface space of interest; receiving, using the receiver sensor array of the S-DAS sensor, the fluid plume measurements and thereby generate fluid plume data for the subsurface space of interest; determining, based on the baseline data and the fluid plume data, time-shift data between the baseline data and the fluid plume data and thereby generate fluid plume extent data; formatting the fluid plume extent data into a first set of data values that characterize a first extent or rate of spread of the fluid plume relative to the subsurface space of interest; and visualizing the first extent or rate of spread of the fluid plume relative to the subsurface space of interest on a graphical display device.

[0009] In other embodiments, a system and a computer program can include or execute the method described above. These and other implementations may each optionally include one or more of the following features.

[0010] The above method further comprises: analyzing the time-shift data to select or pick onset datapoints of one or more locations where a time-shift associated with at least the second set of seismic signals originates; and positioning plume datapoints associated with the fluid plume extent data based on the onset datapoints to indicate at least the first extent or rate of spread of the fluid plume relative to the subsurface space of interest.

[0011] In some embodiments, the fluid plume extent data is used to configure one or more of: fluid injection rate of equipment injecting fluid into the subsurface space of interest; and sensitivity settings or resolution settings associated with the source sensor array of the S- DAS sensor or the receiver sensor array of the S-DAS sensor.

[0012] Furthermore, the source sensor array may be located outside the fluid plume at a distance greater than a critical offset value, such that the critical offset value indicates a distance between a first location of the fluid plume relative to a second location of the source sensor array. In some embodiments, the source sensor array is located outside the fluid plume at a distance less than a critical offset value, such that the critical offset value indicates a distance between a first location of the fluid plume relative to a second location of the source sensor array.

[0013] According to one embodiment, the above method further comprises: generating, based on the critical offset value and at least the fluid plume extent data, a computing model indicating or characterizing a spatio-temporal multi-dimensional path or trajectory of the fluid plume associated with the subsurface space of interest; adapting or varying data values associated with at least the critical offset value and the fluid plume data or the time-shift data between a first time and a second time in a plume extent computing simulation; determining, based on the plume extent computing simulation, a projection of the fluid plume extent data from the first time to the second time and thereby generate an updated fluid plume extent data; formatting the updated fluid plume extent data into a second set of data values that characterize a second extent or rate of spread of the fluid plume relative to the subsurface space of interest between the first time and the second time; and visualizing the second extent or rate of spread of the fluid plume relative to the subsurface space of interest on a graphical display device.

[0014] In some embodiments, the updated fluid plume extent data is used to configure or determine one or more: fluid injection rate of equipment injecting fluid into the subsurface space of interest; and sensitivity settings or resolution settings associated with source sensor array of the S-DAS sensor or the receiver sensor array of the S-DAS sensor.

[0015] According to one embodiment, detecting or monitoring fluid is based on a measure, monitor, and verify (MMV) plan, such that the MMV plan comprises a digital document indicating one or more of: conformance data configured to establish fluid storage parameters associated with time lapse data measurements that confirm that fluid plume data including fluid plume edge data associated with the subsurface space of interest is conformant relative to a subsurface model; and containment data configured to indicate fluid measurements associated with the subsurface space of interest to verify the presence or absence of fluid leakage of fluid stored in the subsurface space of interest relative to a surface or subsurface environment surrounding the subsurface space of interest.

[0016] In some cases, the MMV plan accounts for: an atmosphere domain associated with fluid storage within the subsurface space of interest; a biosphere domain associated with the fluid storage within the subsurface space of interest; a hydrosphere domain associated with the fluid storage within the subsurface space of interest; and a geosphere domain associated with the fluid storage with thin the subsurface space of interest.

[0017] Furthermore, the MMV plan, according to some embodiments, accounts for a plurality of phases associated with injecting fluid into the subsurface space of interest, the plurality of phases including: a pre-injection phase; an injection phases; and a post-injection phase.

[0018] In addition, the source sensor array of the S-DAS sensor can be arranged to have a geometry comprising at least one of: a spiral geometry; a spoke geometry; or a linear geometry.BRIEF DESCRIPTION OF THE DRAWINGS

[0019] This disclosure is illustrated by way of example, and not by way of limitation in the figures of the accompanying drawings in which like reference numerals are used to refer to similar elements. It is appreciated that various features may not be drawn to scale and the dimensions of various features may be arbitrarily increased or reduced for clarity of discussion.

[0020] FIG. 1A shows an exemplary carbon capture and storage (CCS) project lifecycle and impacts of using seismic data during each stage of the CCS project lifecycle according to some embodiments.

[0021] FIG. IB shows seismic data changes associated with carbon or fluid storage operations according to some embodiments according to some embodiments.

[0022] FIG. 1C shows a characterization and assessment overview of a potential storage complex and surrounding area according to some embodiments.

[0023] FIG. ID shows an exemplary uplift in a static image when reprocessing legacy seismic data according to some embodiments.

[0024] FIG. IE provides a summary of monitoring techniques under monitoring objectives according to some embodiments.

[0025] FIG. IF shows an exemplary output derived from a 3-dimensional (3D) Surface Distributed Acoustic Sensing (S-DAS) field experiment according to some embodiments.

[0026] FIG. 1G provides a comparison between a predicted plume extent and a true or model-based plume extent according to some embodiments.

[0027] FIG. 1H shows an exemplary workflow for building monitoring scenarios for fluid storage and containment operations at a resource site according to some embodiments.

[0028] FIG. 2 shows a cross-sectional view of a resource site for which the process ofFIG. 1H may be executed according to some embodiments.

[0029] FIG. 3 shows a network system illustrating a communicative coupling of devices or systems associated with the resource site of FIG. 2 according to some embodiments.

[0030] FIG. 4 shows an exemplary comparison of analysis data used to direct the choice of metrics needed for optimally detecting fluid storage events at a resource site according to some embodiments.

[0031] FIG. 5 shows an exemplary fluid detection map according to some embodiments.

[0032] FIG. 6 shows an exemplary workflow for optimizing fluid storage (GS) operations at a resource site according to some embodiments.

[0033] FIG. 7 shows an exemplary dataflow indicating a departure from discrete data acquisition for CCS operations according to some embodiments.

[0034] FIG. 8 shows an exemplary dataflow for implementing the disclosed adaptive monitoring regime according to some embodiments.

[0035] FIG. 9 shows an implementation with a spoke receiver geometry that has a single source in the center according to some embodiments.

[0036] FIG. 10 includes a configuration with a central source that has a spiral cable layout according to some embodiments.

[0037] FIG. 11 depicts a parallel receiver geometry with a single source at the center according to some embodiments.

[0038] FIG. 12 shows an exemplary implementation of a detection coverage based on a central shot with reflection and diving waves being recorded along the spokes with increasing offset according to some embodiments.

[0039] FIG. 13 includes a spoke receiver geometry with additional line of shots around the outside of the spoke pattern according to some embodiments.

[0040] FIG. 14 depicts an example of detection coverage from line of shots acquired around the edge of the outer circumference of the spoke geometry according to some embodiments.

[0041] FIG. 15 shows an exemplary reflection or imaging coverage from line of shots acquired around circumference of a receiver geometry for both PP and PS coverage according to some embodiments.

[0042] FIG. 16 comprises a spoke geometry with additional spiral shots according to some embodiments.

[0043] FIG. 17 shows a small spiral shot line that may be adequate for determining early stages of plum migration data according to some embodiments.

[0044] FIG. 18 shows subsurface coverage for primary wave (PP) and / or secondary wave (PS) seismic analysis when a spiral shot line extends to the outer circumference of a receiver array according to some embodiments.

[0045] FIG. 19 depicts a sensor configuration using hardware for interrogating one or more fiber optic cables according to some embodiments.

[0046] FIG. 20 provides an exemplary comparison of time durations associated with using the configurations of various embodiments disclosed.

[0047] FIGS. 21-33 provide an example of injected plume edge detection according to some embodiments.

[0048] FIGS 34A and 34B show plots where the time-shifts are plotted as a function of offsets for each spokes.

[0049] FIG. 35 shows a visualization including a 2D ray tracing used to back propagate a ray path of the refraction from the location picked at the onset detection point of the surface relative to the plume edge at the top of a reservoir.

[0050] FIG. 36 shows a plurality of plots indicating shots aligned with spokes.

[0051] FIG. 37 shows results including adjacent shots for a 10-year injection period.

[0052] FIG. 38 clarifies aspects of the results shown in FIG. 37.

[0053] FIGS. 39A and 39B provide exemplary detailed workflows 3900a and 3900b for methods, systems, and computer programs for detecting or monitoring fluid plume in a subsurface space of interest.DETAILED DESCRIPTION

[0054] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of this disclosure. However, it will be apparent to one of ordinary skill in the art that this disclosure may be practiced without these specific details. In other instances, exemplary methods, procedures, components, circuits and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0055] The disclosed systems and methods may be accomplished using interconnected devices and systems that obtain a plurality of data associated with various parameters of interest at a resource site. The workflows / flowcharts described in this disclosure, according to some embodiments, implicate a new processing approach (e.g., hardware, special purpose processors, and specially programmed general-purpose processors) because such analyses are too complex and cannot be done by a person in the time available or at all. Thus, the described systems and methods are directed to tangible implementations or solutions to specific technological problems in developing and / or storing resources such as oil, gas, water, and other minerals. More specifically, the systems and methods presently disclosed may be applicable to operations associated with fluid storage at a resource site.

[0056] Attention is now directed to methods, techniques, infrastructure, and workflows for operations that may be carried out at a resource site. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined while the order of some operations may be changed. Some embodiments include an iterative refinement of one or more data associated with a resource site via feedback loops executed by one or more computing device processors and / or through other control devices / mechanisms that make determinations regarding whether a given action, template, or resource data, etc., is sufficiently accurate.Overview

[0057] The disclosed methods and systems, according to some embodiments minimizes uncertainties associated with development and safety constraints for fluid storage operations, including but not limited to storage of methane, hydrogen and carbon dioxide. For example, actively preparing for high-risk scenarios associated with fluid storage operations may be accomplished via the design and implementation of effective monitoring campaigns. This ensures efficient fluid containment as fluid leakage events can jeopardize project viability and license acquisition to operate workflows and mechanisms for fluid storage. Moreover, conforming field measurements with well-designed and implemented fluid storage monitoring campaigns can ensure optimal reservoir production operations that enable quantifying and improving fluid storage models with associated predictability data.

[0058] According to some embodiments, monitoring of a fluid storage site must continue even after injection operations end (e.g., fluid injection operations) which significantly increases operational bottlenecks (e.g., costs) compared with other hydrocarbon exploratory activities. Additionally, when, for example, an injection operation into a storage complex at a resource site ends, the subsurface model generated (e.g., fluid storage model) for the storage complex needs to have a high level of predictability to support the long-term responsibility for monitoring the stored fluid within the storage complex. The disclosed technology innovatively supports the design of fluid capture and storage monitoring campaigns by quantifying sitespecific uncertainties across multiple scenarios and determining or predicting monitoring methods that detect specific risk events. Additionally, the workflows discussed may be automatic, semi-automatic, or a combination thereof and may be based on feedback from realtime or near real-time sensor measurements and / or from historic sensor measurements at the fluid storage site in order to improve the predictability of the fluid storage model, enhance storage site integrity in the long term, and iteratively optimize fluid storage and monitoring campaigns at the fluid storage site.

[0059] Furthermore, regulations associated with carbon capture and storage (CCS) operations often require monitoring to demonstrate to regulators that a given carbon storage activity is safe and proceeding as planned. In some cases, determining that a given carbon storage operation is optimal comprises a process including measuring relevant carbon storage parameters, monitoring said parameters, and verifying that said parameters are performing as intended. This according to some implementations, is referred to as a measure, monitor, verify (MMV) plan.

[0060] The MMV plan can be driven by two objectives. The first objective can include adherence to planned guidelines associated with a given MMV plan and requires time lapse measurements that confirm that a stored fluid (e.g., carbon dioxide, methane, hydrogen) plume migration is conforming or otherwise aligning to a generated model (e.g., surface or subsurface) associated with the MMV plan. The secondary objective can include containment operations and can require measurements to be undertaken to verify the absence of effects or factors external to (e.g., outside of) a given storage complex under consideration.

[0061] In exemplary implementations, the MMV plan blends together a number of discreet measurements acquired using a variety of tools. For example, the MMV plan canaccount for several domains including an atmosphere domain, a biosphere domain, a hydrosphere domain, and a geosphere domain. In addition, the MMV plan can also account for the different phases of the injection (e.g., fluid injection) process including a pre-inj ection phase, an injection phase, and a post-injection and closure phase associated with storing and / or conveying fluid. Furthermore, operating costs associated with the MMV plan can be large and span several decades. As such the disclosed embodiments beneficially enable developing MMV plans or strategies that reduce the foregoing costs and achieve regulatory objectives efficiently.

[0062] According to one embodiment, this disclosure includes adaptive monitoring mechanisms associated with using deployed fiber optic cable e.g., distributed acoustic sensing (DAS)) sensors to record seismic data. When the fiber optic cable is deployed at a surface location at, for example, a resource site, such implementations can be referred to as Surface- DAS (S-DAS) sensor implementations. In addition, the disclosed approach addresses issues associated with acquisition geometry data required to generate S-DAS data for use as part of adaptive monitoring operations (dependently or independently of adaptive monitoring associated with an MMV plan) associated with a given MMV plan. It is appreciated that the disclosed S-DAS sensors or systems can rely on, or be associated with data recordings (e.g., seismic data recordings or S-DAS recordings) using a fiber optic cable. In some cases, the disclosed S-DAS sensors or systems comprise hydrophone sensors and / or geophone sensors.

[0063] A relevant consideration involved in designing and implementing a fluid capture and storage campaign is determining that a likelihood that method A associated with the fluid capture and storage campaign will detect event B within a range of subsurface uncertainties. Monitoring techniques (e.g., monitoring techniques involving the use of sensors to capture realtime and / or near-real time data and / or historic sensor data) for detecting important site-specific risks in the subsurface domain may be optimized for longevity and low operational costs. For the fluid capture and storage operations disclosed herein, a plurality of different monitoring configurations may be compared against site-specific conditions captured by sensors in realtime or near-real-time or site-specific conditions historically captured by sensors. For example, up to 45 different monitoring configurations may be compared against site-specific conditions according to some embodiments. Two distinct monitoring goals (e.g., containment and conformance) may increase the complexity of the disclosed monitoring systems. When themonitoring objective is to confirm conformance to an existing subsurface model, a positive outcome from this may include confirming the accuracy of generated fluid or gas storage (GS) models using acquired data from monitoring systems. If the acquired data confirms that the GS model is inaccurate or for example, the GS model has a substantially high degree of uncertainty, then additional data may need to be acquired to verify and / or reduce the uncertainty in the GS model. Given, for example, a monitoring timeframe of about 20 to 50+ years, the process of confirming that the GS model is accurate can lend itself to automation or semi-automation operations (e.g., simulations) as the case may require. In some instances, at least one aspect of the disclosed techniques involves manually configuring or initiating modeling operations associated with the GS model. For example, the process of ascribing quantitative parameters to a given GS model may be a process initiated by a user. Initiating tests or simulations using a configured or parameterized GS model, for example, may involve a user activating one or more visual indicators (e.g., test / simulation icon) on a graphical user interface device.

[0064] FIG. 1A depicts an exemplary dataflow 100a of a CCS project lifecycle as well as impacts of using seismic data during the decision process within each stage of the CCS project lifecycle. The CCS project lifecycle can comprise a multifaceted and complex process requiring significant investment and technical expertise at each of the key steps depicted in FIG. 1A. As can be seen in this figure, the CCS project lifecycle can comprise a planning phase 102 and an operations phase 104. The planning phase may include a data processing and imaging stage 102a that helps generate data to characterize or otherwise screen one or one or more CCS sites at indicated at stage 102b. In some cases, the operations phase 104 comprises leveraging CCS monitoring technologies from both the planning phase 102 and the operations phase 104 to determine CCS monitoring strategies and or other data processing or data analytics associated with subsurface fluid storage as indicated at stage 104a.

[0065] It is appreciated that this disclosure uses seismic data (e.g., seismic data acquired at the surface) for CCS or fluid storage operations, according to some embodiments. The development of carbon capture facilities may be based on a largest capital expenditure (CAPEX) within the CCS lifecycle process. However, a significant proportion of risks associated with CCS fluid storage operations lie within the carbon storage (CS) process itself. CCS lifecycle project decisions may be underpinned by the subsurface screening and characterisation (e.g., see stage 102b) work undertaken ahead of the final CCS development operations based in parton the accuracy of predicted injectivity rates associated with the fluid to be stored as well as capacity of the storage volume required to store the fluid, and the operational expenditure (OPEX) associated with monitoring measurement and verification (MMV) strategies stipulated by a regulatory framework. Each of these stages can rely on geophysics and in particular, on seismic data. The balance of cost control and technical excellence can prove useful to one or more of the disclosed methods and systems. However, these aspects are within the monitoring domain, and in particular the post injection and long-term monitoring stages of CCS projects- - a space in the industry which has the greatest opportunity to innovate and evolve.

[0066] Geophysics including the acquisition and analysis of seismic data has played some role in the exploration of hydrocarbon and mineral resources. For example, seismic data may be used as an exploration tool to provide information on (amongst other things) fluid migration pathways, trapping mechanisms, and accurate structural information in the subsurface. According to one embodiment, seismic data may be used to execute screening operations associated with carbon or fluid storage. As with hydrocarbon exploration, the type of seismic data (e.g., acquisition geometry, technology, or processing sequence and age of last reprocessing attempt) used for carbon or fluid storage operations may be dependent on the stage of exploration as depicted in FIG. IB.

[0067] FIG. IB shows seismic data changes 100b associated with carbon or fluid storage operations according to some embodiments. In the regional reconnaissance stage 106, 2-dimensional (2D) or 3-dimensional (3D) volume data of a resource site or a subsurface associated with the resource site may be used. During a license round application or during the focused screening stage 106b, the 3D volume data may be merged and / or reprocessed to provide a contiguous data set. In the final site appraisal stage 106c, new data may be considered with target orientated acquisition parameters. Alternatively, the 2D or 3D data (e.g, existing data) may be taken through an advanced broadband reprocessing sequence culminating in high resolution depth imaging data.

[0068] In the early stages of understanding gross fluid storage volumes associated with a resource site, available data may be used to help with reconnaissance at a regional or basin scale. The challenges of these data sets can be numerous, including: irregular data coverage; limited data bandwidth; residual noise and multiple energy data; inaccurate imaging; and variable illumination. However, the use of legacy data, combined with fast, targetedreprocessing may be used to meet the aspects of the objective of assessing the fluid storage potential at a regional scale. In the case of data rich basins, once an area has been identified as a potential storage site, 2D and / or 3D seismic data may be collected (e.g. from national data repositories, multi-client libraries and third-party data libraries) and reprocessed to provide a contiguous post-stack merge with signal enhancement that provide consistent phase as well as amplitude and seismic characteristics to enable structural interpretation across the area. In the site appraisal stage 106c, the area of interest may be reduced to allow for more costly processing sequences to be applied to the data to improve upon data quality issues of vintage data.

[0069] Acquisition of new seismic data may depend on a number of factors. In the data rich basin scenario, new seismic data may be linked to uncertainties within the structural interpretation and storage risk identification process and to the chosen MMV strategy as well as the suitability of legacy data sets to provide an adequate seismic baseline reference including proximity data associated with areas having existing or planned third-party activity, such as dual land use considerations like wind farms. Within certain principal projects, for example, a preference to acquire new, purpose designed seismic data ahead of injection may be preferable. This data has the dual use of characterizing the fluid storage volume and overburden and / or characterizing suitable seismic baseline data set to match the geometry and acquisition parameters that will be deployed during the MMV program.

[0070] Furthermore, in the case where the proximity to the emitter is critical to the siting of the storage unit, or in basins that are not rich in legacy 2D and 3D seismic data, the acquisition of purposed designed seismic data is required early in the site screening and feasibility process. This data, according to some implementations, can be designed and acquired for future MMV plan development.

[0071] FIG. 1C shows a characterization and assessment overview 100c of a potential storage complex and surrounding area. This overview can comprise data collection 108a, model generation 108b, modeling computing operations 108c, sensitivity analysis 108d, and risk assessment 108e. It is appreciated that the processes illustrated in FIG. 1C including container focused seismic interpretation, geomechanical modelling of the storage site, petrophysical assessment of the storage site, and CO2 storage capacity estimations may be combined to provide a geological site summary and ranking of potential CCS sites. This may be further leveraged together with other non-geological criteria (e.g., proximity to an emitter orsource of CO2, presence and condition of existing wells in the area, and perhaps most importantly, the acceptance of CS operations by regulatory bodies. This can comprise a complex ranking operation requiring tools that: assist in this dynamic process; collect information on capacity and containment including injectivity data (e.g., seal assessment, layer quality assessment, injection pressure), distance to pipeline data, number of nearby wells data, and project cost benchmarking.

[0072] According to one embodiment, application of contemporary seismic data- processing and imaging techniques to heritage seismic datasets can provide significant improvements for storage targets. Exemplary implementations include reprocessing of heritage datasets that indicate uplift data in a static image when reprocessing legacy seismic data with signal -processing and imaging workflows. Exemplary depictions lOOd of such uplift data 110a and 110b are shown in FIG. ID.

[0073] In some embodiments, seismic data-processing and imaging technologies which may reduce interpretation uncertainty and improve the integrity of subsurface characterisation may be applied to analyzing a given subsurface for energy development. For example, Leastsquares migration in the image domain (LSMi) and depth-domain inversion (DDI) can provide an illumination-corrected reflectivity estimate improving resolution and amplitude fidelity of a characterized subsurface. DDI, for example, can further provide more rock properties, providing crucial information for geomechanics and flow simulations. Full-waveform inversion (FWI) computing operations can also be used in high fidelity velocity model building and to improve both quality and efficiency. For example, FWI can be run to high frequencies, improving images and allowing ‘FWI imaging.’ The integration of gravity data can reduce uncertainty in a generated surface or subsurface computing model associated with the limited information provided by legacy seismic datasets. The siting of CS sites in shallow water bodies can highlight the need to adequately remove shallow- water multiples from the image and indeed, those multiples may be used to improve near-surface coverage through the use of the Imaging with Multiples (TWM) technique. For example, in very shallow water bodies, the limitations of towed (e.g., streamer geometries typically mean that small) angle primary reflections may not be recorded, resulting in a poor near surface image. According to some embodiments, creating images using IWM allows the recovery of small-angle reflectivity and improved shallow images for overburden characterisation.

[0074] The disclosed methods and systems automate the design process for fluid or gas storage (GS) operations at a resource site by directly simulating multiple geological scenarios and monitoring techniques (e.g., seismic, electromagnetic, gravity) thereby providing high- level objectives with computationally intensive low-level details being automated and executed via computational tests or simulations. According to some embodiments, a storage complex or site may be modeled or otherwise digitized in a modeling package using a tool such as Petrel with relevant information associated with the storage complex being available for parameterization to conform to relevant monitoring objectives. The resulting interactive report generated from said modeling may provide stakeholders with an understanding of GS storage considerations at the storage complex together with the option to drill-down into specific simulation and / or testing scenarios. By designing open simulator interfaces that can aggregate and / or configure GS models for a given resource site, cross-domain factors across multiple domains associated with a given resource site may be factored in the GS campaigns for a given resource site. The cross-domain factors may include interactions between drilling, extracting, and / or safety operations at the resource site.

[0075] Furthermore, regulation can require monitoring of carbon storage systems to demonstrate to regulatory bodies that activities and structures surrounding said carbon storage systems are compliant, safe, and proceeding as planned. This cycle, according to some embodiments, is termed the measure-monitor-verify (MMV) cycle or plan. In particular, the MMV plan can comprise conformance operations with associated time lapse measurements undertaken to confirm that fluid migration activity (e.g., fluid such as CO2 plume migration) conforms to a developed surface and / or subsurface model. Furthermore, the MMV plan can also include containment operations comprising obtaining measurements that verify the stability of the developed subsurface computing model and / or robustness of an implemented storage complex based on the subsurface and / or surface computing model. The MMV plan could be built or developed to blend together a number of discreet measurements acquired from an implemented storage complex using a variety of tools. In particular, the MMV plan, according to one embodiment, is implemented to account for several domains including an atmosphere domain, a biosphere domain, a hydrosphere domain, and a geosphere domain associated with the storage complex under consideration. Furthermore, the MMV plan can also account for the different phases of the injection (e.g., fluid injection) including pre-inj ectionoperations, injection operations, post-injection operations, and closure operations. The costs associated with the MMV plan can be large and span several decades. The disclosed subjectmatter provides methods, systems, and computer programs that reduce these costs and also satisfy regulatory requirements in an optimal and cost-effective fashion. In some instances, the disclosed methods and systems may be applied to one or more of the aforementioned domains including a geosphere domain, the atmosphere domain, the biosphere domain, and the hydrosphere domain as previously noted.

[0076] According to one embodiment, the disclosed methods and systems develop a monitoring system that re-imagines 4D seismic acquisition and moves away from a 4D imaging objective to a 4D detection objective. In particular, a sparse acquisition geometry may be generated that samples data associated with the subsurface within which fluid is to be stored such that the sampled data enables verification of whether a stored fluid (e.g., CO2, CH4, and / or some other gas) is in place or not. Thus, this approach does not require creating an image of the subsurface, but simply verifies whether a stored fluid is present within a given sector of the storage complex (e.g., subsurface storage complex) or not.

[0077] According to one embodiment, the disclosed methods and systems links sampled data to a subsurface model on which a storage complex is based. Specifically, measurement s) including sampling subsurface data provides adequate information to not only confirm the presence or absence of the stored fluid plume (e.g., gas plume) but also feeds back into the subsurface computing model and thereby refines and adjusts the subsurface computing model throughout the lifecycle of the storage complex. In some implementations, the disclosed methods include: designing geometry parameters; selecting appropriate sensors to track or measure the designed geometry parameters; developing a mechanism to update the properties of the subsurface computing model based on the tracking; executing history matching operations on the subsurface model using said mechanism; executing update or optimization operations on the subsurface model based on the history matching operations; and executing verification operations on the updated subsurface computing model to ensure that the subsurface computing model is within a specified tolerance threshold value. If the subsurface computing model is not within the tolerance threshold value, the measurements from sensors may not be accurately matched to the subsurface computing model. This mismatch can be triggered or flagged to initiate a corrective process that leverages additional data acquisitionoperations or efforts that drive the optimization and design of the subsurface computing model and / or the storage facility based on same. According to one embodiment, sensors used for sampling and / or tracking and / or measuring the designed geometry parameters include distributed acoustic sensors (DAS). For example, the DAS sensors may sample the designed geometry parameters associated with the subsurface and thereby create a subsurface layout that meets sampling requirements associated with the subsurface computing model update operations. In addition, the disclosed methods and systems also leverage real-time or near-real time data associated with 4D full waveform inversion (FWI) operations and / or from 4D Machine Learning computing operations associated with plume property prediction from prestack data associated with a stored fluid. Furthermore, the disclosed embodiments also incorporate prototype workflows that leverage machine learning techniques to enable multiple realizations of the subsurface computing model, which is fungible, organic, or otherwise elastic or tweakable to ensure convergence on an optimal subsurface computing model within a short time (e.g., in near-real time, between 1 and 10 hours, or at least 10 hours, etc.) and that accurately characterizes the subsurface for fluid storage operations. A plurality of verification operations may be executed on the optimal subsurface model as further discussed elsewhere herein.Storage Complex Characterization

[0078] According to some embodiments, steps required to adequately characterize aCCS site for fluid or carbon storage includes creating a static geological computing model. Dynamic computer modelling coupled with geomechanical simulations can be implemented using the created static geological computing model with sensitivity analysis computing operations being further executed in response to the geomechanical simulations to identify and / or quantify risks relating to the integrity of the storage complex site. In addition, dynamic simulators may be used to provide answers to questions such as storage capacity of the storage complex under consideration, injectivity of said storage complex, and fluid containment of the storage complex. In one embodiment, these simulators consider different storage scenarios for subsurface geological structures such as aquifers, depleted oil and gas fields / reservoirs, or other enhanced oil recovery (EOR) schemes. Furthermore, these simulators may also consider the complex multiphase nature of the fluid to be stored (e.g., CO2) as well as simulate over longtimescales and integrate or couple the simulators with geomechanical analysis. Geomechanical simulations by the simulators may be used to evaluate the strength of, for example, rocks in the subsurface given pore pressure changes for different injection scenarios as well as mitigate residual risks associated with failure and / or fault reactivation associated with the subsurface. As discussed, captured seismic data may be used to create the static model. It is also worthy of note that the validation of the dynamic simulations may form part of the MMV processes. Rock physics may also bridge the gap between the simulator and seismic domains.Monitoring measurement and verification

[0079] According to some implementations, monitoring strategies for stored fluid may be incorporated into the design phase (see FIG. 1A) of a storage complex. When considering the design of the MMV strategy, objectives of the measurements being captured may be reviewed as further discussed below.

[0080] FIG. IE illustrates exemplary technologies 1 OOe applied within a carbon or fluid or gas storage MMV with groupings under multiple objectives. In particular, FIG. IE provides a summary of monitoring techniques, summarised under monitoring objectives (e.g., assurance objectives 112a and / or verification objectives 112b), area of investigation (e.g., surface, borehole, near borehole or reservoir) and, phase of project (e.g., baseline, injection, post injection and long term). According to one embodiment, the first objective or the assurance objective 112a may include assuring key stakeholders that the carbon or fluid storage related activity is not having any unwanted or detrimental outcome to the environment. In this way a link to containment of the fluid or carbon may be established. The second objective or the verification objective may include verifying that carbon or fluid storage related activity is progressing as planned based on acquired measurements that confirm that the carbon or fluid storage activity is conforming to the predicted activity and that the subsurface computing model associated with the storage complex is sufficiently accurate. Under these two objectives are listed a plurality of measurement types that can be combined to verify that these objectives have been achieved and therefore address regulatory requirements. In some embodiments, this comprises providing a comparison between the actual and modelled behaviour of stored fluid (e.g., CO2) within the storage complex relative to subterranean structures including a water formation and / or a storage site as well as detecting significant irregularities; detecting fluidmigration activity; detecting fluid leakage activity of stored fluid; detecting significant adverse effects for the surrounding environment; assessing the effectiveness of any corrective measures; and updating assessment of the safety and integrity of the storage complex in the short and long term.

[0081] It is appreciated that not every technique is valid for every site, and the design process, according to some embodiments, may be configured using a risk-based approach whereby the site-specific risks are identified, and the appropriate blend of technique and data type is derived to measure, monitor, and verify said technique. The area of the subsurface sampled by each measurement can be considered and accounted for. The timeline / phase of the fluid injection cycle also can be considered in terms of the resolution required, the areas sampled, and the value said data acquisition will bring. For example, in the early stages of injecting the fluid (e.g., CO2) into the storage complex, the fluid or gas plume may be localised around a well, and so the monitoring data may be used to calibrate the initial subsurface computing models and can also be used to optimise injection rates of the fluid. In the latter stages of injection, or in the post injection phase, the subsurface computing model(s) may be calibrated and further analyzed for insights. Therefore, the optimisation of injection rates may be less critical, and the fluid or gas plume may likely extend over an area that is far outside of that sampled by borehole measurements.

[0082] The role of seismic data within this holistic monitoring regime may be to assist with verification or conformance objective of CCS operations. The various MMV techniques in this disclosure can be grouped into three broad categories: borehole techniques; near-hole techniques; and field wide techniques. In the initial injection phase, borehole and surface-to- borehole seismic data may be used to provide a first calibration of the subsurface computing model and as the fluid (e.g., CO2) and pressure plume expand outside of the area / volume of the storage complex which can be sampled by sensors (e.g., seismic sensors), a reversion to surface seismic data may be used to provide information that verify the migration of the fluid or gas plume as well as confirm the absence or presence of fluid leakage.

[0083] While seismic data (e.g., borehole data and surface data) has been useful in monitoring fluid (e.g., CO2) injection operations and has been proposed as part of the MMV plans for certain CCS projects, these techniques should not be used in isolation. OPEX costs remain a consideration to the viability of each CCS project and the overall scalability of theCCS contribution to the net zero objective. The use of non-seismic methods, such as microgravity may be considered in order to reduce the frequency at which the seismic monitor survey is deployed.

[0084] A full feasibility design study may be undertaken, according to some embodiments, to identify the expected time-lapse response to fluid (e.g., CO2) injection. This can identify, in a phased manner, which methods can detect the migration of fluid (e.g., CO2) within the storage complex as well as which methods are best able to detect leakage outside of the storage unit. It is appreciated that the seismic technique may not always be effective at identifying fluid in place data. The viability of the seismic technique is dependent on in-situ conditions and needs to be validated through modelling as part of the design process. For example, the seismic response generated when injecting fluid into a saline aquifer may be very different to the seismic response generated when injecting fluid into a depleted hydrocarbon field or reservoir. While the case of injection into a saline aquifer may present a significant change in the seismic response, the equivalent case with a depleted oil reservoir scenario may present a much more suitable seismic response. This might either limit the viability of the seismic technique or require careful consideration in the acquisition and interpretation stages. This process may be driven by a forward modelling approach and a non-seismic technique should be included in such a process. Importantly, as part of the forward modelling, sensitivity analysis may be undertaken to determine a threshold at which fluid leak can be detected, for any given depth. The multiphase nature of fluid (e.g., CO2) may be analyzed for insights that may be incorporated into the analysis.

[0085] Additionally, while seismic methods may be sensitive to fluid (e.g., CO2) presence, the fluid (e.g., CO2) saturation quantification using seismic techniques may be less reliable than EM or gravity methods according to some embodiments. This needs to be considered when addressing monitoring objective(s).

[0086] According to one embodiment, this disclosure provides mechanisms for monitoring movement within the storage complex (e.g., fluid storage complex). In addition, the disclosed methods and systems enable verifying fluid saturation levels within the storage complex. Furthermore, the disclosed approach enables detecting leakage outside of the storage complex.

[0087] Given that the MMV strategy is in place to assure the safety of the fluid storage operations and verify that they are proceeding as predicted, there is a need to deploy methodologies and technologies that are effective such as the disclosed methods and systems. Given the differences between the objectives of time-lapse monitoring for hydrocarbon production and the objectives of time-lapse monitoring for fluid storage or carbon storage operations, there is an opportunity to innovate and re-evaluate the cost effectiveness of seismic monitoring, especially in the post-injection phase of a carbon storage or fluid storage project.

[0088] Conceptually, when time-lapse seismic data for monitoring hydrocarbon production is compared, some distinctions between said time-lapse seismic data may lead to certain optimization opportunities. In the first instance, a storage complex based on, for example, screening and MMV criteria may be chosen. This should limit, according to some embodiments, the complexity of the storage area or volume of the storage complex. Additionally, the objective may be not to only seek out and understand increasing levels of complexity (e.g., as production rates drop) or identify bypassed fluids in a storage complex, but to also compartmentalised subsurface structures such as reservoirs for efficient fluid storage. In addition, the objective may also include confirming the migration of the fluid (e.g., CO2) plume, the absence of leakage and the conformance to the subsurface computing model relative to the storage facility within the subsurface. A third consideration may comprise managing the evolution of the monitoring design.

[0089] In a hydrocarbon production scenario, operations that increase the level of monitoring such as monitoring the production rate decreases and the need to better describe the subsurface to extract more value out of the CAPEX already invested may be implemented. Conversely, within fluid or carbon storage operations, the monitoring plan may be an upfront requirement. This provides a desirable scenario where planning of a monitoring strategy in place for the entirety of the project may be implemented. As a result, an adaptive cost-effective monitoring solution based on the phase of the fluid or carbon storage operations (e.g., injection operations, post-injection operations, and closure operations) via automation and integration may be adopted. Given the upfront investment in baseline data and a geological environment lacking in significant complexity (e.g., relative to newly discovered hydrocarbon sites), the disclosed techniques enable leveraging of artificial intelligence or machine learning techniques to assist in processing and interpreting time-lapse data and the accompanying evolution of time-lapse acquisition geometry from dense to sparse data points within the subsurface. The disclosed methods and systems also focus on OPEX costs together with the predicted scale-up of sites requiring monitoring resulting in the investigation and development of new acquisition sensors and technology.

[0090] According to one embodiment, distributed acoustic sensing (DAS) technologies are used for the disclosed methods and systems. In particular, the DAS technologies may be optimized to provide an integrated borehole monitoring solution, providing multi-purpose measurements across different domains and objectives within the fluid storage or carbon storage associated with the MMV program. Furthermore, the DAS technologies disclosed may be deployed to capture surface seismic data for seismic monitoring operations. The use of S-DAS technologies based on the disclosed methods shows significant potential as a sensor with which to acquire seismic data to monitor fluid or gas plume movement.

[0091] According to some embodiments, having a purposed designed, high-trace density baseline seismic survey ahead of injection can provide a training data set for machine learning. Neural networks may be leveraged in conjunction with the machine learning to predict the plume extent directly from pre-stack seismic data. Initial trials on realistic synthetic data can indicate predicted plume extent relative to the true model such that 4D noise related to mis-positioning of sensors and / or acquisition variations adequately suppressed by applying a non-repeatable noise suppression algorithm thereby training the dataset in areas outside of anticipated data changes. While this is unlikely to provide a level of accuracy comparable to traditional time-lapse processing methodologies, it does provide the potential to deliver 4D outcome data that can be used to determine if further additional processing and / or acquisition processes is required as part of a semi-automated, adaptive monitoring workflow. In addition, using the machine learning techniques can decrease sampling of the monitor survey data in some cases. The neural network associated with the subsurface computing model, for example, can be trained using a baseline data set based on early monitoring surveys and knowledge or insights obtained from the training can be used to reconstruct subsequent monitoring surveys that are acquired with a geometry that has a lower source and receiver effort. Initial results indicate that the drop in accuracy of the subsequent prediction may be within acceptable limits for delineating fluid (e.g., CO2) plume bodies and can also be used to quantify a relationship between sample density and the subsurface computing model update. Machine learning hasseveral other key opportunities to increase efficiency, reduce costs, and assist in the scale up of fluid or other CCS project development, according to some embodiments. These approaches may include automating seismic interpretation, fault interpretation, capturing subsurface structure data and reservoir property uncertainty data, and screening legacy wells at a basin scale to assess risk and build an understanding of well integrity as part of the initial site selection criteria.

[0092] FIG. IF shows an exemplary depiction lOOf of various systems including sensor systems for generating output data derived from a 3D S-DAS field experiment based on the disclosed methods and systems.

[0093] FIG. 1G provides an exemplary depiction 100g of a comparison between a predicted plume extent 114a and a true (e.g., model-based) plume extent 114b. Also illustrated in this figure input data 115a to a neural network data structure of the subsurface computing model and output data 115b from the neural network data structure of the subsurface computing model.

[0094] As can be seen in FIG. 1H, the workflows lOOh for GS campaign design and execution as disclosed may involve modeling computing operations that define and / or build specific scenarios 116a associated with a given resource site (e.g., a storage complex at a resource site). The GS campaign design and execution may also include, parameterizing a GS computing model using a computation engine (e.g., the model factory 116b) and executing the GS model in one or more simulators 116c following which analysis operations may be executed (e.g., executed using one or more analysis engines 116d) on the modeling results to generate one or more reports and / or automatically or semi-automatically configure equipment associated with GS operations at a given resource site. In particular, the disclosed technology allows testing and / or simulating, in parallel or in tandem, multiple physical properties associated with a given storage complex at a resource site across a plurality of geological realizations. The results from these tests may be fed into one or more analysis engines 116d that condenses the vast simulation results into actionable insights and / or configuration settings, and / or safety data that may be used to optimize the configuration and / or operation of GS equipment at a given resource site. This may home in or otherwise pinpoint precise monitoring techniques to deploy at each stage comprised in the GS campaign thereby mitigating against GS project risks and operational costs. Using the results from the simulations (e.g., insights from the simulations),adaptive monitoring and / or safety strategies may be seamlessly implemented in conjunction with GS operations at a given resource site.Resource Site

[0095] FIG. 2 shows a cross-sectional view of a resource site 200 for which the process of FIG. 1H may be executed. While the illustrated resource site 200 represents a subterranean formation, the resource site, according to some embodiments, may be below water bodies such as oceans, seas, lakes, ponds, wetlands, rivers, etc.

[0096] According to one embodiment, various measurement tools capable of sensing one or more parameters such as seismic two-way travel time, density, resistivity, production rate, etc., of a subterranean formation and / or geological formations may be provided at the resource site. As an example, wireline tools may be used to obtain measurement information related to geological attributes (e.g., geological attributes of a wellbore and / or reservoir) including geophysical and / or chemical information. For example, the chemical information may include chemical information associated with the subsurface and / or chemical information associated with the surface / above ground areas of the resource site 200.

[0097] In some embodiments, various sensors may be located at various locations around the resource site 200 to monitor and collect data for executing the process outlined in at least FIG. 1H. In other embodiments, the techniques disclosed herein may be applied to surface seismic monitoring applications, surface gravity applications, surface seismic applications, surface ground heave applications, and surface measurement of induced seismicity applications. According to some embodiments, the disclosed methods and systems may be applied to remote sensing applications (e.g., satellite-based measurements), subsea applications associated with permanent sensors, temporary sensor applications, remotely operated vehicle applications, and aerial -based measurement (e.g., performed from planes, helicopters, and / or drones) applications. The aerial-based measurements may include Synthetic Aperture Radar data measurements, atmospheric concentration data measurements associated with molecules such as CO2, CH4, and / or fluid concentration data measurements associated with fluids or gases within the seabed.

[0098] Part, or all, of the resource site 200 may be on land, on water, or below water. In addition, while a resource site 200 is depicted, the disclosed methods and systems may beused with any combination of one or more resource sites (e.g., multiple oil fields or multiple wellsites, one or more saline aquifers, one or more depleted oil / gas fields, etc.), one or more processing facilities, etc.

[0099] Furthermore, the resource site 200 of FIG. 2 may have data acquisition tools 202a, 202b, 202c, and 202d positioned at various locations within the resource site 200. The subterranean structure 204 may have a plurality of geological formations 206a-206d. As shown, this structure may have several formations or layers, including a shale layer 206a, a carbonate layer 206b, a shale layer 206c, and a sand layer 206d. A fault 207 within the subterranean formation of the resource site 200 may extend through the shale layer 206a and the carbonate layer 206b. In addition, the data acquisition tools, for example, may be adapted to take measurements and detect geophysical and / or chemical characteristics of the various formations shown.

[0100] While a specific subterranean formation with specific geological structures is depicted, it is appreciated that the oil field 200 may contain a variety of geological structures and / or formations, sometimes having extreme complexity. In some locations of a given geological structure, for example below a water line (e.g., aquifer) relative to the given geological structure, fluid may occupy pore spaces of the formations. Each of the measurement devices may be used to measure properties of the formations and / or other geological features. While each data acquisition tool is shown as being in specific locations in FIG. 2, it is appreciated that one or more types of measurement may be taken at one or more locations across one or more sources of the resource site 200 or other locations for comparison and / or analysis.

[0101] The data collected from various sources at the resource site 200 may be processed and / or evaluated and / or used as training data, and or used to generate high resolution result sets for characterizing a resource at the resource site, and / or used for generating resource models, etc. In one embodiment, the data collected by one or more sensors at the resource site may include data associated with the number of wells of a first reservoir or second reservoir at the resource site, data associated with the number of grid cells of the first or second reservoir, data associated with the average permeability of the first or second reservoir, data associated with the production duration history (e.g., number of years of production) of the first reservoir or second, etc.

[0102] Turning back to FIG. 2, data acquisition tool 202a is illustrated as a measurement truck, which may comprise devices or sensors that take measurements of the subsurface through sound vibrations such as, but not limited to, seismic measurements. Drilling tool 202b may include a downhole sensor adapted to perform logging while drilling (LWD) data collection. The wireline tool 202c may include a downhole sensor deployed in a wellbore or borehole. Production tool 202d may be deployed from a production unit or Christmas tree into a completed wellbore. Examples of parameters that may be measured include weight on bit, torque on bit, subterranean pressures (e.g., underground fluid pressure), temperatures, flow rates, compositions, rotary speed, particle count, voltages, currents, and / or other parameters of operations as further discussed below.

[0103] In some cases, sensors may be positioned about the storage complex to collect data relating to various storage complex operations, such as sensors deployed by the data acquisition tools 202. The sensors may include any type of sensor such as a metrology sensor (e.g., temperature, humidity), an automation enabling sensor, an operational sensor (e.g., pressure sensor, H2S sensor, thermometer, depth, tension), evaluation sensors, that can be used for acquiring data regarding a subsurface formation, wellbore, formation fluid / gas, wellbore fluid, gas / oil / water comprised in the formation / wellbore fluid, or any other suitable sensor. For example, the sensors may include accelerometers, flow rate sensors, pressure transducers, seismic sensors, acoustic sensors, temperature sensors, chemical agent detection sensors, nuclear sensor, and / or any additional suitable sensors.

[0104] In one embodiment, the data captured by the one or sensors may be used to characterize, or otherwise generate one or more parameter values for a high resolution result set used to, for example, generate a resource model or a GS model as the case may require. In other embodiments, test data or synthetic data may also be used in developing the resource model or the GS model via one or more simulations such as those discussed in association with the workflows presented herein.

[0105] Evaluation sensors may be featured in downhole tools such as tools 202b-202d and may include for instance seismic sensors, acoustic sensors, nuclear sensors, and optic sensors. Examples of tools including evaluation sensors that can be used in the framework of the current method include seismic tools such as imaging sensors. In one embodiment, the imaging sensors comprise one or more of: FMI™ or QuantaGeo™ (mark of SLB, Houston TX)sensors; induction sensors including Rt Scanner™ (mark of SLB, Houston TX) sensors, multifrequency dielectric dispersion sensors including Dielectric Scanner™ (mark of SLB, Houston TX); acoustic tools including sonic sensors such as Sonic Scanner™ (mark of SLB, Houston TX) or ultrasonic sensors including pulse-echo sensors or UBI™ or PowerEcho™ (marks of SLB, Houston TX) sensors or flexural sensors PowerFlex™ (mark of SLB, Houston TX) sensors; nuclear sensors such as Litho Scanner™ (mark of SLB, Houston TX) sensors or nuclear magnetic resonance sensors; fluid sampling tools including fluid analysis sensors such as InSitu Fluid Analyzer ™ (mark of SLB, Houston TX) sensors; and distributed sensors including fiber optic sensors. According to some embodiments, the disclosed evaluation sensors are used for: evaluating or determining formation data associated with a well or reservoir at the resource site (e.g., determining petrophysical or geological properties of a subsurface formation); verifying or determining integrity data for the well (e.g., integrity data such as casing data or cement properties data); and / or analyzing determined fluid data associated with produced or stored fluid at the resource site. It is appreciated that the fluid data can comprise flowrate data, fluid type data (e.g., whether the fluid is liquid or gaseous or a combination thereof).

[0106] As shown, data acquisition tools 202a-202d may generate data plots or measurements 208a-208d, respectively. These data plots are depicted within the resource site 200 to demonstrate or indicate data generated by some of the operations executed at the resource site 200. Data plots 208a-208c are examples of static data plots that may be generated by data acquisition tools 202a-202c, respectively. However, it is herein contemplated that data plots 208a-208c may also be data plots generated and updated in real time. These measurements may be analyzed to better define properties of the formation(s) and / or determine the accuracy of the measurements and / or check for and compensate for measurement errors. The plots of each of the respective measurements may be aligned and / or scaled for comparison and verification purposes. In some embodiments, base data associated with the plots may be incorporated into site planning, modeling a test at the resource site 200. The respective measurements that can be taken may be any of the above.

[0107] Other data may also be collected, such as historical data of the resource site 200 and / or sites similar to the resource site 200, user inputs, information (e.g. , economic information) associated with the resource site 200 and / or sites similar to the resource site 200, and / or othermeasurement data and other parameters of interest. Similar measurements may also be used to measure changes in formation aspects over time.

[0108] In one embodiment, computer facilities such as those discussed in association with FIG. 3 may be positioned at various locations about the resource site 200 (e.g., a surface unit) and / or at remote locations. A surface unit (e.g., one or more terminals 320) may be used to communicate with the onsite tools and / or offsite operations, as well as with other surface or subsurface / downhole sensors. The surface unit may be capable of sending commands to the resource site (e.g., oil / gas field) equipment / sy stems, and / or receiving data therefrom. In some cases, the surface unit may also collect data generated during production operations and can produce output data, which may be stored or transmitted for further processing at the resource site or to an offsite location relative to the resource site.

[0109] It is appreciated that the data collected by sensors associated with the resource site may be used alone or in combination with other data for CCS operations. It is further appreciated that the data aggregated using the sensors may be collected in one or more databases and / or transmitted to one or more computing systems at the resource site or to the offsite location. In one embodiment, the data associated with the sensors may be historical data, real time data, or combinations thereof. The real time data may be used in real time or stored for later use. The data may also be combined with historical data or other inputs for further analysis or for modeling purposes to optimize production processes at the resource site 200. In one embodiment, the data is stored in separate databases, or combined into a single database.High-Level Network System

[0110] FIG. 3 shows a high-level network system 300 illustrating a communicative coupling of devices or systems associated with the resource site 200 of FIG. 2. The system 300 shown in the figure may include a set of processors 302a, 302b, and 302c for executing one or more processes discussed herein. The set of processors 302 may be electrically coupled to one or more servers (e.g., computing systems) including memory 306a, 306b, and 306c that may store for example, program data, databases, and other forms of data. Each server of the one or more servers may also include one or more communication devices 308a, 308b, and 308c. The set of servers may provide a cloud-computing platform 310. In one embodiment, the set of servers includes different computing devices that are situated in different locationsand may be scalable based on the needs and workflows associated with the oil field 200. The communication devices of each server may enable the servers to communicate with each other through a local or global network such as an Internet network. In some embodiments, the servers may be arranged as a town 312, which may provide a private or local cloud service for users. A town may be advantageous in remote locations with poor connectivity. Additionally, a town may be beneficial in scenarios with large networks where security may be of concern. A town in such large network embodiments can facilitate implementation of a private network within such large networks. The town may interface with other towns or a larger cloud network, which may also communicate over public communication links. Note that cloud-computing platform 310 may include a private network and / or portions of public networks. In some cases, a cloud-computing platform 310 may include remote storage and / or other application processing capabilities.

[0111] In some embodiments, the system 300 of FIG. 3 may also include one or more user terminals 314a and 314b each including at least a processor to execute programs, a memory (e.g., 316a and 316b) for storing data, a communication device and one or more user interfaces and devices that enable a user to receive, view, and transmit information. In one embodiment, the user terminals 314a and 314b is a computing system having interfaces and devices including keyboards, touchscreens, display screens, speakers, microphones, a mouse, styluses, etc. Furthermore, the user terminals 314 may be communicatively coupled to the one or more servers of the cloud-computing platform 310. In particular, the user terminals 314 may be client terminals or expert terminals, enabling collaboration between clients and experts through the system 300 of FIG. 3.

[0112] The system 300 of FIG. 3 may also include computing systems associated with at least one or more resource sites 200, such that the computing systems may have, for example, a set of terminals 320, each including at least a processor, a memory, and a communication device for communicating with other devices communicatively coupled to the cloud-computing platform 310. In addition, the at least one or more resource sites 200 may also have associated sets of sensors (e.g., one or more sensors described in association with FIG. 2) or sensor interfaces 322a and 322b communicatively coupled to the set of terminals 320 and / or directly coupled to the cloud-computing platform 310. In some embodiments, data collected by the one or more sensors / sensor interfaces 322a and 322b may be processed to generate a one or moreresource models (e.g., reservoir models) or one or more resolved data sets used to generate the resource model which may be displayed on a user interface associated with the set of terminals 320, and / or displayed on user interfaces associated with the set of servers of the cloud computing platform 310, and / or displayed on user interfaces of the user terminals 314. Furthermore, various equipment / devices discussed in association with the resource site 200 may also be communicatively coupled to the set of terminals 320 and or communicatively coupled directly to the cloud-computing platform 310. The equipment and sensors may also include one or more communication device(s) that may communicate with the set of terminals 320 to receive orders / instructions locally and / or remotely from the resource site 200 and also send statuses / updates to other terminals such as the user terminals 314.

[0113] The system 300 of FIG. 3 may also include one or more client servers 324 including a processor, memory and communication device. For communication purposes, the client servers 324 may be communicatively coupled to the cloud-computing platform 310, and / or to the user terminals 314a and 314b, and / or to the set of terminals 320 at the resource site 200 and / or to sensors at the oil field, and / or to other equipment at the resource site 200.

[0114] A processor, as discussed with reference to the system 300 of FIG. 3, may include a microprocessor, a graphical processing unit (GPU), a microcontroller, a processor module or subsystem, a programmable integrated circuit, a programmable gate array, or another control or computing device.

[0115] The memory / storage media discussed above in association with FIG. 3 can be implemented as one or more computer-readable or machine-readable storage media that are non-transitory. In some embodiments, storage media may be distributed within and / or across multiple internal and / or external enclosures of a computing system and / or additional computing systems. Storage media may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories; magnetic disks such as fixed, floppy and removable disks; other magnetic media including tape; optical media such as compact disks (CDs) or digital video disks (DVDs), BluRays or any other type of optical media; or other types of storage devices. “Non-transitory” computer readable medium refersto the medium itself (i.e., tangible, not a signal) and not data storage persistency (e.g., RAM vs. ROM).

[0116] Note that instructions can be provided on one or more computer-readable or machine-readable storage media, or alternatively, can be provided on multiple computer- readable or machine-readable storage media distributed in a large system having possibly plural nodes and / or non-transitory storage means. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). The storage medium or media can be located either in a computer system running the machine- readable instructions, or located at a remote site from which machine-readable instructions can be downloaded over a network for execution.

[0117] It is appreciated that the described system 300 of FIG. 3 is an example that: may have more or fewer components than shown; may combine additional components; and / or may have a different configuration or arrangement of the components than those shown in FIG. 3. The various components shown may be implemented in hardware, software, or a combination of both, hardware and software, including one or more signal processing engines and / or application specific integrated circuits.

[0118] Further, the steps in the flowcharts described below may be implemented by running one or more functional modules in an information processing apparatus such as general-purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, GPUs or other appropriate devices associated with the system 300 of FIG. 3. For example, the disclosed workflows may be executed using a signal processing engine stored in memory 306a, 306b, or 306c such that the signal processing engine includes instructions that are executed by the one or more processors such as processors 302a, 302b, or 302c as the case may require. The various modules of the system 300 of FIG. 3, combinations of these modules, and / or their combination with general hardware are included within the scope of protection of the disclosure. While one or more computing processors (e.g., processors 302a, 302b, or 302c) may be described as executing steps associated with one or more of the flowcharts described in this disclosure, the one or more computing device processors may be associated with the cloudbased computing platform 310 and may be located at one location or distributed across multiple locations. In one embodiment, the one or more computing device processors may also be associated with other systems of FIG. 3 other than the cloud-computing platform 310.

[0119] In some embodiments, a computing system associated with the system 300 is provided that includes at least one processor, at least one memory, and one or more programs stored in the at least one memory, such that the programs comprise instructions, which when executed by the at least one processor, are configured to perform any method disclosed herein.

[0120] In some embodiments, a computer readable storage medium associated with the system 300 is provided, which has stored therein one or more programs, the one or more programs including instructions, which when executed by a processor, cause the processor to perform any method disclosed herein. In some embodiments, a computing system is provided that includes at least one processor, at least one memory, and one or more programs stored in the at least one memory for performing any method disclosed herein. In some embodiments, an information processing apparatus for use in a computing system is provided for performing any method disclosed herein.Embodiments

[0121] Two major concerns facing fluid storage projects are development constraints (e.g, financial viability) and reservoir confinement issues. Operators need to ensure that injected fluid stays within a given storage complex or site (e.g., reservoir or aquifer storage complex) and prove to regulatory bodies that any fluid leakages are detectable and / or reportable and / or are resolvable. After a baseline for operational safety is established, the operator (e.g., a digital tracking system) may monitor injection performance (e.g., fluid injection performance) to ensure that the fluid storage project is viable. Furthermore, other challenges associated with fluid storage may include reliance or dependence on practices from other activities (e.g., hydrocarbon exploratory or nonexploratory activities) which may not directly or indirectly align with fluid storage settings or configurations of fluid storage equipment associated with . According to some implementations, such activities may include abandonment of legacy wells without having a proper seal (e.g., without a tight fluid seal) in place to prevent fluid leakage or having an incorrect type of seal (e.g, cement seal) and / or workflows and / or mechanisms in place for plugging fluid emissions. Other exemplary activities include use of chemicals during hydrocarbon extraction that restrict the flow of fluid or result in reactions that harm future fluid inj ection performance or significant over-pressuring or under-pressuring of a reservoir resulting in structural damage to the storage complex.

[0122] In some cases, there may be an expected increase in fluid storage projects worldwide which will likely overtake the available fluid storage resources (e.g., human and nonhuman storage resources) and which can be greatly improved using the techniques disclosed herein. Furthermore, government agencies may need to be assured or periodically reassured that any fluid storage models being implemented for a given resource site (e.g., storage complex at the resource site) are predictable and well controlled to comply with safety requirements for all phases (e.g., injection or post-injection phase of fluid storage and management) associated with a GS campaign for the resource site.

[0123] FIGS. 4A and 4B show exemplary comparisons between leakage monitoring in the injection well of FIG. 4A versus the monitor well of FIG. 4B. It is appreciated that the data in FIGS. 4A and 4B were tracked under a given defmition / characterization (e.g., digital quantification, uncertainty parameter quantification, etc.) for a given subsurface uncertainty and comprise data measurements (e.g., exemplified with pressure) conducted in one well (e.g., the injection well) which may not be able to distinguish or detect a critical event (e.g., fluid leakage), whereas the same measurement conducted in a different well (e.g., the monitor well #1”) may distinguish over, or detect a critical event. As such, the examples shown in FIGS. 4A and 4B are associated with a spatial deployment (e.g., preferential spatial deployment) of a measurement with respect to monitorability of a critical event (e.g., fluid leakage).

[0124] In some embodiments, the visualization shown in FIGS. 4A and 4B indicate a plurality of different measurements (e.g., formation conductivity data or formation pressure data) that highlight which of the plurality of different measurements are more likely to detect a critical event (e.g., a fluid leakage event). It is appreciated that the visualization shown in FIGS. 4A and 4B may enable establishing distinctions between scenarios where there is substantial fluid leakage relative to base scenarios where there is little to no fluid leakage. It is further appreciated that the disclosed methods and systems facilitate generation of visualizations such as those shown in FIGS. 4A and 4B enable the selection of measurement technology and / or spatial deployment of said technology in order to increase the likelihood that a critical event may be detected. In some embodiments, selection of the measurement technology is based on a definition and / or characterization and / or modeling of one or more subsurface uncertainties which determine expected distributions of measurement responses associated with critical events (e.g. , fluid leakage). Once a first deployment of measurement technology has been madeand some measurements have been obtained, said measurements can be used to refine or otherwise optimize fluid or gas storage (GS) model(s) (e.g., carbon storage (CS) model(s)) and thereby minimize uncertainties associated with said models. In turn, the GS model (s) (e.g., refined GS model(s)) can be used to improve a monitoring plan, and / or control gas monitoring equipment, and / or control containment infrastructure (e.g., valves, pumps, etc.) associated with the GS model(s). According to some implementations, the disclosed methods and systems provide an efficient way for operators to analyze and communicate risk associated with GS operations at a resource site, both internally and externally. This significantly decreases the time of approval from designing a given GS campaign to its execution. The automation of major parts of the disclosed workflows allow operators to run multiple projects using the same resources, allowing the scaling up of the fluid storage activities such as carbon capture, utilization, and storage (CCUS) activities to required volumes. By performing in a continuous loop of feeding measurements from the monitoring GS systems back into generated computing GS model(s) in the operating phase, predictability of such GS model(s) are greatly improved for the next iteration in the testing or simulation phase.

[0125] According to some embodiments, the disclosed methods and systems provide a monitoring design tool for subsurface applications associated with GS operations at a resource site. Furthermore, the disclosed methods and systems provide useful monitoring mechanisms that are not only applicable to GS operations but can also be applied to campaigns associated with geothermal solutions, hydrocarbon operations, offshore wind site operations, groundwater exploration activities, and other subsurface monitoring and optimization projects.

[0126] According to some embodiments, the disclosed technology builds upon a number of technologies and workflows. For example, the disclosed techniques enhance uncertainty workflows to account for fluid storage use-case of measurement detectability to define a monitoring strategy and to automate comparisons of said monitoring strategy with optimal baseline methods and models. Moreover, the disclosed methods and systems may incorporate tools such as Agile™ Reservoir Modelling applications and thereby leverage cloud computing resources to accelerate uncertainty assessments associated with GS operations at a resource site. In particular, the systems and methods disclosed leverage a plurality of simulators executing tests in parallel in order to enhance and / or improve a GS model being implemented at a given resource site to accelerate GS project execution times as well as provide timelyinsights for such projects and reporting same to regulatory agencies. Multiple simulators (and therefore multi-physics techniques) may be combined into similar or dissimilar workflows to determine the most probable measurement detection scenarios that define optimal configurations and / or practices comprised in a given GS campaign for a resource site and which span all leakage pathways and monitoring methods for said GS campaign. Moreover, the disclosed methods and systems may allow users to customize or design end-to-end monitoring strategies for GS projects — from fluid injection phases to project handover to other domain experts. In addition, since the software architecture disclosed herein is built around the support of many diverse simulators, the disclosed systems and methods effectively become a high- performance platform for executing simulations or tests associated with a GS monitoring campaign. The disclosed architecture defines unified software interfaces that ensure compatibility for the data shared by multiple testing tools or simulators. By openly sharing interface definitions and cultivating an ecosystem around the disclosed platform, users can adapt their simulators to securely run on the same platform. As such, the disclosed methods and systems are capable of evolving to become an “app repository” for simulators associated with GS storage operations.Workflow For Optimizing Fluid Storage (GS) Operations

[0127] At a high level, a user may model a storage complex or site for a given GS operation using a subsurface modelling application (e.g., Petrel ™) to generate a GS model. The storage complex or storage site may include areas above or below ground where fluid is stored and / or a volume of space above a reservoir where fluid is stored and / or an area (e.g., an area adjacent to a resource site) associated with fluid storage operations without reference to an origin of the fluid being stored. The GS model, according to some embodiments, is associated with captured fluid in which case the systems and workflows disclosed is directed to storage and monitoring of captured fluids or gases such as carbon dioxide, methane, and hydrogen. Furthermore, the GS model may have associated geological uncertainties that may be quantified during the model generation stage based on synthetic data, non-synthetic data such as real-time data or near real-time data captured at the resource site or historical data captured at the resource site, or a combination of synthetic and non-synthetic data. These aspects are further discussed below.

[0128] The GS model may be released or otherwise transmitted to another testing or simulation tool or application (e.g., a Delfi ™ digital platform or an Open Subsurface Data Universe (OSDU™) platform). According to some embodiments, the testing tool may detect project information such as offshore / onshore data, well characteristics data, structural information, etc. associated with a resource site to construct a simulation plan for testing the GS model for the resource site. The simulation plan may be analyzed and additional contextual information may be added to it based on, or without, the analysis. For example, the additional contextual information may include risk profile data for legacy wells, high-risk zone data for seal leakage scenarios, or monitoring configuration data for controlling monitoring equipment associated with the GS campaign for the resource site. Based on defined scenarios from the previous steps, the testing tool may generate a plurality of required simulation configurations that test the scenarios for a plurality of time periods (e.g., days, weeks, months, or years). The simulation configuration data may be matched to specific simulators which receive said configuration data for testing the GS model. Once the simulators complete testing the GS model using a plurality of scenarios in parallel, the results from such testing are post-processed to determine detectability of fluid concentrations throughout the storage site or storage complex based on each testing method employed by the simulators. Fluid detectability may be computed using computer generated statistical data derived from a plurality of stochastic geological realizations associated with the storage site. For example, the statistical data may include a value indicating a probability of fluid detections such that the value is a number between zero and one. The value, according to some embodiments may be compared to previously established baseline simulation data. In addition, information about the storage site or storage complex (e.g., reservoir extent information, layer geometry information, etc.) may be used to automatically determine a relative importance of fluid detectability at each location (e.g., fluid detection outside a reservoir may be ranked higher than fluid detections within a reservoir) such that the likelihood of such fluid detection events may be derived from the aforementioned stochastic data. In some cases, a fluid detection map may be generated based on data measurements captured about the storage complex.

[0129] Depicted in FIG. 5, for example, is a fluid detection map 500 which indicates fluid distribution data (e.g., CO2 probabilistic distribution data) based on a change in relative acoustic impedance between a fluid (e.g., CO2) pre-inj ection baseline and a current fluid stateafter several years of injection. The fluid detection map 500 may be generated using an ensemble of simulation results from the plurality of simulations or tests of the GS model and which outline uncertainty data and fluid detection data associated with one or more sections of the fluid storage site or fluid storage complex combined with an effective medium model for acoustic impedance. The fluid detection map may also show a probability of change associated with the acoustic impedance (e.g., caused by injected fluid) being greater than a detectability threshold value, thus indicating regions of the subsurface of the storage site or storage complex where the movement of fluid can likely be detected using, for example, seismic techniques. Moreover, the fluid detection map may include scaling or ranking data that may be directly fed into site-specific design configurations or structures in order to optimize fluid detectability at the resource site. In addition, the fluid detection map 500 may include a plurality of color shades that indicate varying degrees of intensity of fluid detection data at multiple locations at the storage site or storage complex at the resource site. For example, the fluid detection map may include a shades of grey colorings that indicate a highest likelihood of fluid leakage for a given location at the resource site, a medium likelihood of fluid leakage for other locations at the resource site, and a low likelihood of fluid leakage for some locations at the resource or fluid storage site. It is appreciated that the colorings on the fluid detection map 500 may comprise a spectrum of colors with coded to reflect an extreme end of the spectrum indicating a high likelihood of fluid leakage events to a low end of the spectrum indicating a low likelihood of fluid leakage events.

[0130] A benefit of the disclosed methods and systems is drilling down into individual simulators and / or workflows to understand fluid detection thresholds based on a plurality of site-specific scenarios and / or simulation conditions. As fluid storage sites become operational, so does the actual monitoring data associated with said sites. In some cases, base line monitoring data may be available for configuring simulation parameters of the GS model. The additional real-time or near-real-time data or historic data captured at a fluid storage site may be used to improve, enhance, or otherwise optimize the GS model and thereby strengthen the accuracy of the simulation results during subsequent iterations of executing tests or simulations using the GS model based on actual fluid storage site conditions. Anomalies or data abnormalities may be flagged and compared with the GS model parameters (simply referred to as parameters elsewhere herein) before, during, or after execution of the one or more tests orsimulations on the GS model. Such detection or flagging of data abnormalities may adaptively enable the GS model to be updated or otherwise parametrically revised in order to facilitate accurate detection of fluid events at the storage site.Flowchart For Optimizing Fluid Storage

[0131] The disclosed technology is directed to methods and systems for optimizing fluid storage or gas storage (GS) operations at a resource site as exemplified in the flowchart of FIG. 6. It is appreciated that a data processing engine stored in a memory device may cause a computer processor to execute the various processing stages of FIG. 6.

[0132] At block 602, the data processing engine may facilitate generating a GS model (e.g., a GS computing model) associated with the resource site such that the GS model comprises one or more parameters that characterize at least one of: temporal or spatial distribution data of subsurface geological structures associated with the resource site; uncertainty data indicating varying degrees of uncertainty ascribed to the temporal or spatial distribution data; well log data associated with the resource site; risk profile data associated with the resource site; zone leakage data associated with the resource site; temporal or spatial distribution data of a surface or a subsea terrain associated with the resource site; temporal or spatial distribution data of an atmospheric condition associated with the resource site; or workflow data associated with the resource site.

[0133] At block 604, the data processing engine enables determining risk thresholds (e.g., risk threshold data) for the GS operations based on the risk profile data associated with the resource site. In one embodiment, the risk thresholds indicate a tolerance level for fluid leakage at one or more locations (e.g., storage complexes) at the resource site.

[0134] The data processing engine may also facilitate parameterizing, based on the risk thresholds, the one or more parameters of the GS model at block 606 using one or more of: synthetic data based on domain-specific information associated with the resource site; or realtime or near-real-time data associated with the resource site that have been captured by one or more sensors deployed around the one or more locations at the resource site.

[0135] At block 608, the data processing engine is used to generate, using the parameterized GS model, a simulation plan for the GS operations at the resource site. The simulation plan may indicate at least one of: a plurality of fluid leakage events based on the oneor more parameters of the GS model over multiple time periods; a plurality of fluid monitoring plans that track the plurality of fluid leakage events across a plurality of geological realizations; and a plurality of dependent or independent simulations or tests that inform an impact of the fluid monitoring plans over the multiple time periods.

[0136] Turning to block 610 of FIG. 6, the data processing engine may execute or initiate executing the simulation plan across: multiple simulators in parallel; a defined uncertainty space derived from the uncertainty data; the multiple time periods; and the plurality of geological realizations.

[0137] In some embodiments, the data processing engine may facilitate aggregating, at block 612, analysis data generated from executing the simulation plan. The analysis data may indicate one or more of: fluid concentration data across the one or more locations at the resource site; fluid leakage data across the one or more location at the resource site; and configuration data associated with configuring one or more monitoring systems at the resource site.

[0138] At block 614, the data processing engine is used to initiate, using the analysis data, generation of a fluid detection map that indicates fluid distribution data associated with the one or more locations at the resource site. The data processing engine may also enable configuring, using the analysis data, the one or more monitoring systems (e.g., carbon dioxide, hydrogen, and methane monitoring systems) at the resource site.

[0139] These and other implementations may each optionally include one or more of the following features.

[0140] The resource site may comprise a fluid storage site at the resource site including one or more of: an aquifer, a saline aquifer, an oil reservoir, a depleted oil reservoir, a fluid reservoir, or a depleted fluid reservoir.

[0141] Furthermore, the risk thresholds may quantify one or more of: a minimum amount of fluid leakage that is allowed at the resource site; a specific amount of fluid that is allowed to leak from a primary aquifer into a secondary aquifer; a specific amount of fluid that is allowed to leak into legacy wells; a specific amount of fluid that is allowed to leak from one well into a neighboring well; resolution data of the one or more monitoring systems at the resource site; or regulatory data comprised in the risk profile data based on emission constraints imposed by regulatory bodies.

[0142] In addition, the one or more parameters of the GS model may include one or more of: raw data or processed data captured at the resource site; onshore or offshore geological data associated with the resource site comprising seismic data; the temporal or spatial distribution data of subsurface geological structures of the resource site; well characteristics data comprising the well log data; structural data derived from the onshore or offshore geological data; geological faults data; interpreted geo-layering data; geophysical data indicating one or more of seismic information, gravity information, seismic information, and nuclear information associated with the resource site; and configuration data associated with the one or more monitoring systems at the resource site.

[0143] The configuration data, according to some embodiments, includes at least one of: line spacing data measured between sensors (e.g., a first seismic sensor and a second seismic sensor) at the resource site; and frequency configurations data used to tune seismic sensors at the resource site.

[0144] Moreover, the workflow data associated with the resource site comprises: data indicating frequency of executing the simulation plan; data indicating frequency of updating the simulation plan; data indicating time lapse measurements comprised in the multiple time periods; and dynamic post-processing operations data.

[0145] The dynamic post-processing operations data according to some embodiments includes at least one of: noise removal operations that remove noise data from the captured realtime or near-real-time data associated with the resource site; or inference operations data associated with aggregating the analysis data to provide inferences that indicate the impact of the fluid monitoring plans over the multiple time periods.

[0146] It is appreciated that the risk profile data comprises: a quantitative measure of stake holder risk tolerance levels based on domain (e.g., reservoir domain, wellbore domain, etc.) expert data; and data associated with subsurface analysis operations.

[0147] Additionally, the zone leakage data may indicate one or more paths of fluid leakage across the one or more locations at the resource site including: leakages across a reservoir at the resource site; and leakages across legacy wells including abandoned wells at the resource site.

[0148] According to some embodiments, executing the simulation plan comprises executing a plurality of simulation streams that test multiple geo-physical properties associatedwith the one or more locations at the resource site in parallel and concurrently across the plurality of geological realizations.

[0149] The plurality of geological realizations may comprise a plurality of GS submodels of the subsurface associated with the resource site such that the plurality of GS submodels indicate at least one of fault characteristics data associated with the resource site; transmissibility data associated with the resource site; or fluid distribution data indicating statistical characterizations of fluid detections within the subsurface of the resource site.

[0150] Furthermore, the analysis data may be used to generate a report based on analyzing the simulation results across the defined uncertainty space in different time steps and across different geological realizations comprised in the plurality of geological realizations.

[0151] The report may include, for example, one or more of: a visualization indicating the fluid detection map; a matrix indicating efficacy level data for using a plurality of different GS operations at the resource site based on the simulations to determine an optimal GS campaign for the resource site; a plurality of tabular data; a two-dimensional visualization indicating a first monitoring design for optimally monitoring fluid stored at the one or more locations (e. , storage complexes) at the resource site; or a three-dimensional visualization indicating a second monitoring design for optimally monitoring fluid stored at the one or more locations at the resource site.

[0152] The report may also include risk threshold data associated with: the one or more monitoring systems at the resource site; or one or more GS operations at the resource site.

[0153] In addition, the multiple simulators referenced above may comprise one or more of: flow-based simulator(s) that depend on pressure measurements within the subsurface of the resource site; seismic simulator(s) that depend on formation resistivity data within the subsurface at the resource site; and nuclear simulator(s) that predict responses of certain nuclear measurement(s) within the subsurface at the resource site, including but not limited to pulsed- neutron methods simulator(s) and / or nuclear magnetic resonance (NMR) simulator(s).

[0154] In some embodiments, the multiple simulators include geomechanical simulator(s) that predict subsurface responses to changes in mechanical conditions at the resource site, including simulators that simulate compaction or expansion of a reservoir (e.g., oil or fluid reservoir, depleted oil or fluid reservoir) associated with the resource site.

[0155] The multiple simulators may also include electric simulator(s) that predict direct or alternating current responses to changes in subsurface conditions, including changes to electric conductivity within reservoir layers associated with the resource site.

[0156] In addition, the multiple simulators may include acoustic simulator(s) that depend or rely on acoustic properties of the subsurface at the resource site.

[0157] It is appreciated that simulations associated with the multiple simulators may comprise a plurality of simulation methods that can either be executed in an in-situ simulation state or in an inverse state. In the in-situ state, for example, a parameter associated with the GS model may be directly calculated using computational methods and based on previous simulator results. This provides a direct value of a quantity, for example, in the subsurface of the resource site. However, when using other geophysical methods to measure properties such as a magnetic response, an averaging operation may be executed over a large number of data points associated with GS locations at the resource site since the magnetic wave travels through a given volume before arriving at the area of interest (e.g., from the surface to a reservoir or from a borehole to a specific layer) associated with the resource site.

[0158] To recover the parameter of interest, the measured response may be subjected to a process such as a geophysical inversion process which uses a series of models / relations to translate the measured response to the parameter of interest.

[0159] According to one embodiment, results from executing one or more of the multiple simulators can be used in their raw form or may be subjected to post-processing operations such as a geophysical inversion workflow.

[0160] According to some embodiments, the one or more parameters of the GS model characterize one or more of surface seismic data, surface gravity data, surface seismic field data, surface ground heave data, and surface measurement data indicating induced seismicity.

[0161] In addition, the data processing engine referenced in FIG. 6 may facilitate generating, using the analysis data, a report including one or more of a plurality of tabular data; a two dimensional visualization indicating a first monitoring design for optimally monitoring fluid stored at the one or more locations at the resource site; or a three-dimensional visualization indicating a second monitoring design for optimally monitoring fluid stored at the one or more locations at the resource site.

[0162] In some embodiments, parameterizing the one or more parameters of the GS model comprises updating grid resolution data for at least one parameter comprised in the one or more parameters of the GS model.

[0163] In some implementations, the fluid storage operations are associated with storing fluid including at least one of carbon dioxide gas, hydrogen gas, and methane gas.

[0164] Furthermore, a phase of the stored fluid may be based on one or more of: a depth within a subsurface (e.g, fluid storage complex) of the resource site within which the fluid is stored; and pressure within the subsurface of the resource site within which the fluid is stored.

[0165] It is appreciated that leakages of fluid from the storage complex may result in the leaked fluid changing phase due to pressure differentials between the pressure within the storage complex relative to pressures within the leakage zones around the storage complex.Dataflow For Monitoring Stored Fluid

[0166] FIG. 7 shows an exemplary dataflow showing a departure from discrete acquisition of data such as vertical seismic profiling (VSP) and high density surface seismic (HD seismic data acquisition prior to simulation. This dataflow contrasts baseline data with HD monitoring data. In particular, this data flow establishes a moving away from discrete data updates of the subsurface model to a system of sparse detection and automated and / or semiautomated and / or efficient update of the subsurface model. It is appreciated that the left axis 702 of FIG. 7 shows implementation effects that result in lower cost while the right axis 704 of FIG. 7 shows an improved digital integration impacts from implementing the disclosed methods and systems.

[0167] In one embodiment, the disclosed methods and systems streamline measure- monitor-verify (MMV) operations for fluid storage. In particular, the MMV operation objectives including maintenance and compliance operations are further enhanced by the disclosed methods and systems to ensure that fluid storage operations are proceeding as planned and that fluid levels are at projected levels based on the subsurface model. In particular, the disclosed methods and systems enable efficiencies in history matching operations associated with the subsurface model (e.g, subsurface computing model) by leveraging new and sparse data measurements associated with the subsurface model thereby impacting the cost of data acquisition and hence the overall operating costs of the MMV fluid operations. This isparticularly beneficial as sparse data is used instead of a full spectrum of subsurface and / or surface data thereby speeding up the monitoring process.

[0168] According to one embodiment, the disclosed methods and systems include the following processing stages associated with MMV operations:• generating baseline data (e.g., high resolution baseline data) enabling usage of a Synthetic Seismic Volume Generation Software (Sim2Seis) workflow or simulation tool;• executing a detailed simulation of an injection scenario based on parameterizing a subsurface model based on the baseline data;• designing a detection campaign based on the simulation including: detecting that a seismic data set aimed at monitoring subsurface changes due to fluid plum changes, such that a flag ( .g., quantitative (e.g., 1 or 0) or a qualitative flag (e.g, yes / no) is raised or indicated with respect to the presence and / or absence of fluid in the subsurface (e.g. , ground); the detection operation may be adapted or configured based on an expected signal-to-noise ratio and a response of the Sim2Seis simulation using the subsurface model relative to expected fluid plum changes due to fluid injection into the subsurface; the output of a detection campaign may provide an indicator of the presence of fluid at given multidimensional locations for a given time set; this detection approach is cost-effective and is more rapid than full 4D imaging operations or efforts;• executing parameter detection during injection operations comprised in the simulation;• executing compliance operations with pre-drill scenario checkup including:• evaluating fluid presence with respect to an injection plan to verify and / or trigger quality assurance flags when simulation and field observation data are in agreement;• executing operations associated with reassurance of the next survey, including: analyzing and generating regulatory requirements associated with the subsurface model of the next survey; and designing and conforming the subsurface model associated with the next survey to comply with said regulatory requirements;Non-compliance scenario:In case of deviating from Sim2Seis simulation operations (e.g., the plume location is not in agreement with flow simulation), execute one or more of:• updating the subsurface model based on back projection of detection information using a fluid flow simulation;• executing an adaptive monitoring operation including: a survey design operation adapted to the latest information and aimed at addressing the subsurface uncertainty associated with the subsurface model and thereby maximize assurance and compliance, such that: the subsurface model is updated; regulatory compliance is preserved; subsurface uncertainty is reduced, and safe operation is enabled; and adaptive monitoring strategy is redesigned.Adaptive Monitoring

[0169] The disclosed adaptive monitoring offers a shift in the monitoring approach for fluid or carbon storage sites or facilities (e.g., subsurface facilities). For example, the disclosed methods and systems shift from a 4D seismic reservoir monitoring system relying on full reservoir production and monitoring data to a plume-centric approach which tracks the progress of the fluid e.g., CO2) plume through a focused and / or targeted detection scheme or localized area within the subsurface (e.g., subsurface fluid storage facility). According to one embodiment, a subsurface model, built and maintained to enable a comparison between an actual and modeled behavior of stored fluid (e.g., CO2) relative to subsurface structures such as water or fluid formations within the subsurface (e.g., fluid storage facility in the subsurface). Through a process of risk analysis and survey design, an adaptive data acquisition strategy can be established, moving from an accurate high-density baseline and first monitor surveys to calibrate the subsurface model (in tandem with well-based measurements) towards sparser, targeted, multi-domain measurements that confirm adherence to the subsurface model.

[0170] In some cases, the disclosed approach facilitates identification of significant irregularities associated with the subsurface model. For example, the irregularities may comprise data that is not predicted by the subsurface model during the simulations. Examples of such irregularities can be seismic amplitude data changes in places or locations within the subsurface that are not predicted by the flow simulations or the subsurface model and whicharrive at different times relative to expected different spatial distribution data associated with the subsurface model. These irregularities can trigger additional targeted acquisition.

[0171] Prioritizing the link back to the subsurface simulation model may enable the disclosed process to leverage opportunities and thereby optimize the link between automation or semi-automation of fluid processes within the subsurface including identification of the 4D change / plume extent, integration of the production data and seismic data to improve the history match, and / or quantification of the match or mismatch through uncertainty analysis. These aspects are further depicted in the dataflow of FIG. 8 directed to the disclosed adaptive monitoring regime.

[0172] Fluid capture and storage or carbon capture and storage (CCS) operations can enable meeting net zero emission targets. The process of identifying and characterising fluid monitoring storage facility (e.g. fluid storage facilities within a subsurface) may be reliant on cross domain geological and geophysical technologies, workflows, and expertise. According to one embodiment, seismic data has a role to play through the lifecycle of carbon or fluid storage operations. For example, legacy seismic data can enable regional screening of fluid storage capacities. Newly reprocessed seismic data can support characterizing priority storage areas and other design considerations while, new acquisition of seismic data can help minimise some outstanding uncertainty or risk within the reservoir or overburden (e.g., overburden indicating sedimentary column data relative to a subsurface structure such as a reservoir all the way to the surface), while also forming an ideal baseline for future monitoring. Carbon or fluid storage regulations highlight the requirement for understanding the subsurface before implementation fluid storage activities. To that end, available seismic data may be leveraged prior to the commencement of fluid ( .g., CO2 injection) storage operations.

[0173] Fluid or CS MMV can comprise broad cross discipline undertakings and requires a risk-based approach for implementation. The methods and systems provided in this disclosure can depend on identified risks to safe storage and containment and can consider site geology, volume of fluid (e.g., CO2) and a regulatory framework applied to the storage facility. Seismic data (e.g., borehole seismic data and / or surface seismic data) may be used in characterizing near-well areas, for initial calibration of the subsurface model, and verifying fluid (e.g., CO2) plume and pressure movement to detect fluid leakage. The viability of the seismic technique may be dependent on in-situ conditions within the subsurface and needs tobe validated through modelling as part of the fluid storage design process (e.g., equipment deployment to monitor fluid injection into aquifers versus equipment deployment to monitor fluid injection into depleted fields). Where the seismic technique is shown to give a recordable signal that can be directly linked back to subsurface changes, significant opportunities exist to improve the cost effectiveness of seismic data within the monitoring system.

[0174] The disclosed methods and systems also include the use of fiber optic cables deployed horizontally at the surface of the subsurface within which the fluid storage facility or storage complex is located to record active and / or passive seismic data as well as use machine learning techniques to accelerate the time-lapse interpretation for use in monitoring fluid (e.g., CO2) plume movement and leakage and thereby optimize and streamline the seismic history matching process. These developments are combined to provide an integrated digital solution that enables a cost-efficient, adaptive monitoring system that verifies safe fluid or carbon storage operations.

[0175] According to one embodiment, model data including one or more of geophysical data, gravity data, seismic field data, borehole data, reservoir data, or well log data may be used to generate a subsurface model (e.g., a subsurface computing model). The various data, according to one embodiment may be captured by one or more sensors deployed at a resource site associated with fluid storage operations. Furthermore, generation of the subsurface model may be based on using one or more of the aforementioned data to generate a stratigraphic framework comprised in the subsurface model. The stratigraphic framework of the subsurface model may have static and / or dynamic properties or parameters including: horizon parameters derived from seismic data; fault parameters (e.g., fault transmissivity parameters) derived from seismic interpretation operations (automatic or otherwise); lithography parameters derived from well log data and / or geostatic data and / or seismic data obtained from seismic inversion operations; porosity parameters derived from well log data and mapped in space based on seismic inversion or geostatic operations and / or machine learning or artificial intelligence operations; porosity or permeability parameters indicating geological relationships based on rock physics or laboratory tests on subsurface samples.

[0176] In one embodiment, the created subsurface model may be subjected to one or more simulations or tests based on the above parameters. The simulations, for example, can provide response data based on dynamic values associated with, or ascribed to the variousparameters during the simulations. The response data, for example, may indicate deviation information or irregularities data based on predictions or forecasting operations made by the subsurface model during the simulations relative to established thresholds for the subsurface model. In particular, deviations data can trigger update operations to the subsurface model based on updating or revising one or more of the aforementioned parameters to maintain, keep, or transition the subsurface model in a stable state.

[0177] According to exemplary implementations, baseline survey data may be used to initialize the subsurface model prior to executing simulations based on same. In particular, the initialization of the subsurface model may done following the description above where seismic data and / or well data together with calibrated geo-physics (e.g, rock physics, sand physics, fluid physics data associated with a fluid storage facility) relationship data can be used to configure or calibrate the parameters of the subsurface model to indicate flow simulation and estimation of the elastic properties of the subsurface model based on the simulations. Based on the flow simulation, a survey may be designed (e.g., via subsequent simulations) to detect changes in the subsurface properties or parameters of the subsurface model during the simulations. During the simulation associated with the survey, one or more parameters of the subsurface model may be adapted or configured or calibrated based on fluid injection rate data and / or fluid pressure data associated with storing fluid in the subsurface to predict the seismic response based on, or by implementing one or more of:(i) applying saturation data, and / or pressure data, and / or temperature data to vary or change one or more parameters of the subsurface model;(ii) estimating effective stress data changes associated with geomechanics interactions of one or more parameters of the model;(iii) estimating the fluid (e.g, gas) compressibility of the subsurface model using or based on an equation of state (EOS) data and / or a National Institute of Standards and Technology NIST dataset (e.g., compliance data);(iv) applying calibrated rock physics indicated by one or more parameters of the subsurface model to predict an elastic response of the subsurface given the simulated fluid (e.g., CO2) saturation pressure data, fluid stress data, and temperature data of the subsurface model in space relative to the flow simulation;(v) varying one or more seismic modeling tests comprised in the simulation including varying convolutional amplitude-variation-with-angle (AV A) modeling operations using one or more of Zoeppritz techniques, ray tracing operations, and finite difference elastic operations to predict the seismic response given different geometries comprised in the subsurface.

[0178] According to one embodiment, spatial and / or temporal fluid distribution data fluid pressure data, fluid temperature data, fluid saturation data, etc.) may be generated in response to executing the simulation using the subsurface model. In addition, elastic properties of the subsurface model including seismic wave velocity data (e.g., compressional-wave velocity data Vp and shear-wave velocity data Vs) as well as fluid density data and / or fluid anisotropy data may also be generated in response to executing the simulation for a given time period.

[0179] The ability to simulate the seismic response of the subsurface based on flow or subsurface parameters enable:(i) generating quality assurance data to optimize fluid storage operations at the resource site based on the seismic response data when seismic predictions are in conformance with the simulations;(ii) flagging fluid irregularities which trigger: a. updating flow simulation parameters of the subsurface model based on new spatial and / or temporal patterns by back-projection of the location data of the fluid plume b. developing optimal survey design and modeling operations or workflows where source and receiver configurations are selected to best mitigate against potential issues which were not considered before the observations of said deviations.

[0180] Benefits from executing the one or more tests or simulations on the model include:(i) reducing cost in monitoring program for the subsurface fluid storage facility for which the subsurface was developed over the life of the resource site (e.g., storage complex at the resource site);(ii) directly and / or dynamically updating parameters associated with the subsurface model based on the simulation;(iii) commissioning additional geophysics survey where mitigation operations are needed for the subsurface storage facility without incurring additional costs; and(iv) providing a better handle over volumetric and performance data of the fluid or carbon capture and storage site for future decisions, economic or otherwise, associated with the fluid storage site.Implementations for Measure, Monitor, and Verify (MMV) Plans

[0181] In some implementations, conformance objectives may be met by capturing time-lapse (4D) seismic data. This can be acquired using 3-dimensional (3D) vertical seismic profile (VSP) data in the early stages of fluid / gas injection and plume migration and can be extended to towed streamer seismic systems once the plume exceeds the subsurface coverage of the 3-dimensional (VSP) data. For example, the plume can exceed the subsurface coverage of the 3 -dimensional data spanning about 1-2 km radius relative to a given well or less commonly, relative to an ocean bottom node seismic system. The costs associated with this approach can be very high and the captured data can include significant redundancies with regard to the monitoring objectives of a fluid (e.g., carbon dioxide, methane, hydrogen) plume migration. In other words, these techniques rely on collecting repeatedly dense 3D seismic surveys, processing said surveys to obtain a seismic image, and developing a 4D image based on same for interpretation by subtracting the 3-dimensional images for a given time step. As further discussed below, the disclosed methods and systems does not suffer from these challenges.

[0182] According to some embodiments, the disclosed methods and systems comprise a monitoring system that re-designs 4D seismic data acquisition by moving away from a 4D imaging objective to a 4D detection objective. In particular, the disclosed methods and systems comprise a sparse acquisition geometry system that samples the subsurface and can verify whether a given fluid or gas is in place or not. In this way the focus is no longer to merely creating an image of the subsurface, but to verifying whether fluid or gas is present within a given sector of the storage complex (e.g., subsurface storage complex). According to one embodiment, the verification of fluid in place is linked or otherwise tied back to the subsurfacemodel. More specifically, the measurement(s) involved in this process provide adequate information to not only confirm the presence or absence of a fluid or gas plume but also, feed back into the model (e.g., subsurface computing model) such that the model can be refined or otherwise optimized or adjusted or updated throughout the life of the fluid storage project. In addition, the receiver or data aggregation efforts can be designed to cover the anticipated extent of the plume through the life of the injection program (e.g., with an error margin) and the source or data detection efforts can be adapted or configured to reflect the monitoring objective at any given time or phase of the injection program.

[0183] According to one embodiment, geometry data 900 associated with detected fluid is depicted in FIG. 9. In particular, FIG. 9 shows an implementation with a spoke receiver geometry 902 that has a single source in the center. This geometry, for example, covers an area over which fluid (e.g., carbon dioxide, methane, hydrogen) is expected to migrate over the life or period of the injection project, with a degree of safety to allow for uncertainty in the model prediction of the plume migration. In one embodiment, the layout of the designed detection and monitoring system is guided by flow simulations and / or appropriate probability maps of fluid saturation data and / or fluid-in-place data which may be created by running one or more flow simulation scenarios.

[0184] The primary geometry, according to one embodiment, comprises a spoke layout structure. In FIG. 9, the layout of sensor systems, for example, can be implemented using fiber optic cables or other seismic receiver sensors arranged in a spoke configuration originating from a central point (e.g., a central point located at the injection well head). Coverage plots of PP 904 and PS 906 seismic energy are shown in this figure. For a single central source, the coverage of the PS 906 seismic energy increases (relative to the PP 904 coverage) due to the asymmetrical ray path, the reflection point moving towards the receiver (relative to the PP reflection point). This is beneficial to the S-DAS system due to the improved PS response of S-DAS.

[0185] FIG. 9 also indicates the extent of the plume size for varying periods of time (e.g., 3 months, 6 months, 1 year, 3 years, up to 20 years). In one embodiment, the geometry or configuration is designed to provide a dense initial coverage during the first 0-1 years and / or 1-3 years of injection. The geometry then becomes gradually sparser in years 3-5, 5-10 and 10 - 20 and beyond. The receivers (e.g., the fiber optic cable) may be laid out in a radial spokedesign. The length of each spoke, the number of spokes, and the angle between each spoke may be a function of the survey design (e.g., completed as part of the initial feasibility study) and relates to expected plume migration and identified risks in the overburden (e.g., these elements are identified as part of a wider risk based analysis conducted when defining the carbon storage MMV plan and can be part of, or independent of the survey design relating to cable layout).

[0186] While FIG. 9 shows a rig based source with a spoke cable layout and spiral shots, two other configurations are provided. The first, which is shown in FIG. 10 includes a configuration 1000 with a central source having a spiral cable layout (e.g., layouts 1002, 1004, or 1006). In particular, FIG. 10 shows a spiral receiver geometry with a single source in the center.

[0187] The second includes a configuration 1100 with a central source having a linear cable layout as shown in FIG. 11. It is appreciated that the configuration of FIG. 11 depicts a parallel receiver geometry (e.g., layouts 1102, 1104, 1106) with a single source at the center. Each of these plot show an associated PP and PS coverage.

[0188] According to one embodiment, operations associated with the source effort may be independent of receiver layout and can adapt or change to identified risks and / or stages of injection and / or results of a previous survey. The fundamental form comprises one or more central sources (e.g., a rig based source). In one embodiment, the one or more central source are repeated to build signal and overcome noise. The indicative subsurface coverage of the one or more central sources is provided in FIG. 12. In particular, FIG. 12 shows an exemplary implementation of a detection coverage 1200 based on a central shot with reflection and diving waves being recorded along the spokes with increasing offset. As injection continues, it may be necessary to acquire additional shots or detections as identified in the MMV plan. The proposed geometries shown in visualizations 1202, 1204, 1206, and 1208 may be adapted to accommodate fluid plume changes. The first level of additional shots can comprise a shot line that extends around the circumference of the fiber optic cable layout as shown in FIG. 13. In particular, FIG. 13 includes a spoke receiver geometry 1300 with additional line of shots around the outside of the spoke pattern. This could be achieved with a source system, or a group of source systems being deployed behind a source boat in the offshore case or by an appropriate vibrator or impulse source in the onshore case. These shots (with attendant PP coverage 1304and PS coverage 1306) according to some embodiments, may be recorded into an array to improve subsurface coverage in several ways. The improvement comprises improving the coverage in the area of, or between the spokes since the shots are located around the circumference of the receiver array (e.g., the right hand side of FIG. 13).

[0189] Additionally, and as shown in FIG. 14, the maximum offset record may be doubled for a single line as recordings are made across the full width of the array. In particular, FIG. 14 depicts an example of detection coverage 1400 from the line of shots (e. ., exemplified in visualizations 1402, 1404, 1406, 1408, 1410, and 1412) acquired around the edge of the outer circumference of the spoke geometry of FIG. 13. Reflection and diving waves may be recorded along the spokes with increasing offset. The maximum offset, according to one embodiment, is dictated by the diameter of the receiver coverage. Additionally, a circle of high quality subsurface coverage may be created around the outer extent of the receiver array which can be used to verify, through 4D imaging, that the plume has not migrated outside of the monitoring area of FIG. 15. In particular, FIG. 15 shows an example of the reflection or imaging coverage from the line of shots acquired around the circumference of the receiver geometry for both PP coverage 1502 and PS coverage 1504.

[0190] An additional level of shot acquisition effort is shown in FIG. 16. More specifically, FIG. 16 comprises a spoke geometry 1600 with additional spiral shots 1602 with attendant PP coverage 1604 and PS coverage 1606. In the illustrated embodiment, shot coverage can be adapted for phase of injection and anticipated size of plume. For example, in the early stages, a reduced number of shots can be acquired to reduce the acquisition time. As the plume increases in extent, the spiral shot line can be increased. In this scenario, data is acquired using a spiral line of shots that originate in the center of the receiver array and extends to the outer circumference and beyond, according to some embodiments.

[0191] According to one embodiment, the width of the spiral determines the trace density and may be specified as part of the survey design for a given site and the risks identified as part of the MMV plan. This shot configuration may lead to continuous coverage within the array for PP realizations or data and near continuous coverage within the array for PS realizations or data. This may allow the acquisition of offsets up to and beyond 15 km at a trace density suitable for imaging. Furthermore, this level of source acquisition effort may beprescribed at a given time step as part of the original MMV plan, or it may be triggered in response to significant irregularities between the predicted and measured extent of the plume.

[0192] FIG. 17 shows a spiral shot line (e.g., small spiral shot line) 1700 that may be adequate for the early stages of plum migration. In particular, FIG. 17 shows an example of the reflection or imaging coverage (e.g., PP coverage 1702 and PS coverage 1704) from a small spiral line of shots acquired from the center of the spoke design and extend out to approximately a 2.6 km radius. This can be used in the early phases of the injection program while the plume extent is still small. In the depicted implementation of FIG. 17, the monitored radius may be approximately 4 km. This is more than double the subsurface coverage that would be achieved by a 3D VSP in this scenario. In general, a 3D VSP would provide 1-2 km radius of subsurface coverage from a well at the common depth for fluid (e.g., carbon dioxide, methane, or hydrogen storage).

[0193] As the injection project matures and the plume extent increases, the shot acquisition effort can be increased adaptively. FIG. 18 shows the subsurface coverage 1800 for PP seismic analysis and PS seismic analysis when the spiral shot line extends to the out circumference of the receiver array. In particular, the FIG. 18 includes an example of the reflection or imaging coverage (e.g., PP coverage 1802 and PS coverage 1804) from a large spiral line of shots acquired from the center of the spoke design and extend out to cover the full receiver spoke which, in this case, comprises a radius of 10 km from the center. This can be used in the latter phases of the injection program once the plume has developed in size. Good PP and PS coverage may be desirable so that there can be mapping in both saturation changes and pressure changes which comprise two objectives for monitoring fluid injection. This geometry can be adapted or configured with longer spokes should the pressure envelope be required to be monitored over a larger area.

[0194] While the disclosed approach provides a marine or aquatic implementation, the disclosed geometries are applicable for land-based or onshore scenarios. FIG. 19 considers the translation of the disclosed design into an acquisition scenario that considers requirements of the fiber optic interrogator. Specifically, FIG. 19 depicts a representation 1900 of how this design could be achieved using hardware for interrogating a fiber optic cable. Such hardware, according to one embodiment, has a current limitation on the total length of the fiber optic cable that can be acquired. In this example, it is assumed that the total length must not exceed 50 km,but each interrogator can monitor 2 fiber optic cables. This can lead to visualizing the number of spokes (e.g., 24 spokes 1902 versus 16 spokes 1904 versus 8 spokes 1906) that can be achieved with an associated number of interrogators. It is appreciated that hardware capacity and capabilities will evolve quickly, and the design will adjust accordingly. This image is for representation only and does not limit the disclosed approach to just the figure in question. Taking an indicative representation of the available hardware and its current specifications (e.g., each being able to interrogate at least 2 fiber optic cables over 50 km per cable), tradeoffs between the number of interrogators required and the density of spokes in the geometry design are also depicted in FIG. 19.

[0195] The proposed methods and systems is robust, and the appropriate design is assessed as part of the survey design and MMV plan considering what the risks are that need to be monitored for the given amount of fluid injection planned. The proposed methods and systems is also robust to advances in interrogator hardware, with increases in the length of fiber optic cable that can be interrogated at a given time.

[0196] FIG. 20 provides impact data associated with using the above-discussed geometries as part of an adaptive monitoring strategy of stored fluid in a storage complex relative to a time and motion study as well as cost savings. In particular, FIG. 20 provides a comparison visualization 2000 of the duration of time taken to acquire the shots within each of the disclosed design. For example, the proposed S-DAS geometries have the potential to reduce the acquisition time by up to 91% or 92% when comparing the time taken to acquire the shots in the large spiral shot line relative to the equivalent time it would take to acquire a towed streamer seismic survey over the same area. It is appreciated that the duration of usage is focused on acquisition time, where it can be shown that the large spiral shot acquisition shows a 92% reduction in acquisition time relative to the equivalent towed streamer seismic acquisition. This facilitates much more regular monitoring cycles and is in addition to the central source monitoring that can be acquired at far more frequent time steps (e.g., monthly, weekly, or daily as the MMV plan requires.Using surface DAS to record time-shifts for detecting and monitoring of storage sites

[0197] Regulations require that monitoring carbon storage is undertaken to demonstrate to regulatory bodies that a given carbon or gas storage activity is not only safe but is alsoproceeding as planned. According to one embodiment, the process of monitoring carbon storage may comprise generating a measure, monitor, and / or verify (MMV) plan. For example, the MMV plan can comprise a digital document that is configured to have two objectives: a primary objective and a secondary objective. The primary objective may include conformance data associated with established fluid storage parameters and can require time lapse measurements that confirm that fluid plume (e.g., CO2 or other injected fluid plume) data is conformant relative to a generated subsurface model of a storage complex under consideration. The secondary objective on the other hand can include determining containment data and can require taking measurements associated with the storage complex that verify the absence of effects (e.g., fluid leakage effects) outside of the storage complex.

[0198] According to one embodiment, an MMV plan may be generated or otherwise developed to blend together a number of discreet datapoints or data measurements acquired using a variety of computational tools. Well-formed MMV plans can account for several domains including an atmosphere domain, a biosphere domain, a hydrosphere domain, and a geosphere domain. In addition, well-formed or optimal MMV plans can account for different phases (e.g., pre-injection, injection, post-injection) associated with injecting fluid into a fluid storage complex. The operating cost associated with the MMV plan can be large and span several decades. The goal is to reduce these costs and achieve regulatory objectives in an optimal or efficient manner.

[0199] As discussed elsewhere herein, this disclosure provides methods and systems for adaptive monitoring and use of Seismic Distributed Acoustic Sensing (S-DAS) sensors that record seismic data. Furthermore, the disclosed S-DAS sensors may be arranged or otherwise configured to address one or more acquisition geometries required to optimally acquire S-DAS data for use as part of adaptively monitoring or independently monitoring fluid stored in a storage complex.

[0200] According to one embodiment, this disclosure includes methods and systems that facilitate generating 4D detection objectives data associated with fluid (e.g., CO2) storage and thereby determine or delineate spatial extent data for said fluid storage. To enable optimally satisfying compliance and / or conformance objectives associated with a given MMV plan, the disclosed methods and systems beneficially detect and verify fluid zone data, or fluid section data, or fluid edge data of a fluid (e.g., CO2, CF , etc.) plume using time-shift data in seismicrecordings (e.g., time-shift data between a baseline recording acquired before fluid injection) and a monitor recording at some point in the future. In some cases, the exact timing of a monitor survey is defined in the MMV plan and may be updated based on the analysis of a previous monitor or other suitable criteria. The time-shift data, according to one embodiment, can be recorded in many types of seismic waves. For example, the time-shift data can comprise or be associated with seismic refractions data, time-shift data included in body waves, diving rays, time-shift reflections data, etc.

[0201] FIGS. 21-31 show time-shift data associated with seismic refraction energy generated by a signal source associated with an S-DAS sensor (e.g., a seismic transmitterreceiver system deployed densely) shooting seismic waves from an outside or exterior portion of a fluid plume at a distance larger than a critical offset parameter relative to the edge of the fluid plume into a surface receiver array of sensors going through the fluid plume. In some embodiments, time-shift data in seismic wave types other than the seismic refractions data referenced above may be used to implement various embodiments of the disclosed subjectmatter.

[0202] In some cases, the disclosed methods and systems capitalize on characteristics of the ray path of refraction energy and the fact that the presence or absence of fluid can change the timing of the refractions originating from a boundary below which the fluid plume is trapped. Two aspects of the foregoing fluid monitoring methods includes fluid detection and ray path positioning which are discussed below.

[0203] Fluid Detection: According to Snell’s law, refraction energy travels primarily horizontally at the velocity of a layer below the boundary where the energy is generated, such that the energy can be recorded at offsets larger than the critical offset. In some cases, this characteristic can be combined with a layout where the source and the receiver array are positioned on a line or trajectory going both through the source and the injector well where the fluid plume originates. Furthermore, the source sensor system associated with the S-DAS sensor may be located outside of the fluid plume at a distance larger than the critical offset (e.g., represented as Xc) from the edge of the fluid plume. When refraction energy encounters the fluid plume on the horizontal part of its ray path, it travels slower than when brine is in place and therefore creates a timing difference compared to previous measurements done at the samelocation when the fluid plume has not yet reached the area. This is the case for fluid such as CO2 that can replace or occupy space associated with brine.

[0204] Note that the disclosed methods and systems can record unperturbed refraction energy which can be recorded on the part of the receiver array located between the source and the plume edge. To record both perturbed and unperturbed refraction ray paths on enough receivers, positioning the source sensor at a distance of at least 1.5 times the critical offset from the plume edge or more is effective according to some embodiments. The comparison of the timing of the refractions in a baseline (e.g., recorded before injection begins) shot gather and a monitor (e.g., after plume extension) shot gather allows scanning with minimum source effort for any change in velocity below the boundary where the fluid (e.g., CO2, CH4, etc.) plume is being trapped to detect a change.

[0205] Positioning: It is also possible to characterize the ray path of the refraction along exiting an upgoing ray path, and to predict at which point the refraction has stopped the ray from travelling horizontally and has started going toward the surface. This, according to one embodiment, can be based on knowledge of the critical angle derived from Snell’s law. Furthermore, this fact can be used to predict the location of the change in the subsurface associated with the onset of the time-shift: this can be located at half a critical offset (Xc / 2) on the line going from the receiver to the source. In some embodiments, the disclosed methods and systems also leverage the presence of a dense receiver layout to allow accurate identification of the time-shift onset. Note that the benefit of having a seismic source located outside (e.g., located at a position that is not directly above) the fluid plume is that the ray path of the refraction is perturbed on the exit leg of the refraction, thus ensuring a good differentiation between the unperturbed refraction and the perturbed refraction. The disclosed methods and systems are also applicable to a scenario where the seismic source is located at a position directly above the fluid plume and both the entry and exit path or leg of the refraction energy is perturbed.

[0206] The disclosed methods and systems may also be used to identify and process time-shift data in body waves, diving waves, or first break energy. Furthermore, the disclosed methods and systems can also be extended to time-shifts or amplitude changes or variations associated with energy reflections associated with the disclosed S-DAS system. This has the potential to provide information above a fluid storage complex and can require less a-prioriinformation from a flow simulation model. The advantage to this is that the disclosed approach can provide information in an area or zone proximal to the fluid storage complex that is less than the critical offset from, for example, the injector well, if used in conjunction with a rigbased source that can provide information about fluid (e.g., CO2, CH4, etc.) in the overburden which would contribute to the containment objectives of a developed MMV plan.

[0207] FIG. 21 is a visualization 2100 indicating seismic refractions according to some embodiments of this disclosure. In particular, this figure indicates that after the critical angle of reflection, refractions (e.g., wave refractions) can travel along an interface between an upper layer with velocity VI, and a lower layer with a velocity V2 such that Vl< V2.

[0208] FIG. 22 shows an exemplary visualization 2200 indicating the impact that an anomalous velocity would have on the timing of a given refraction such that a receiver can detect this variation in timing or time-shift e.g., relative to the baseline which is recorded prior to fluid injection). In instances where the velocity V2 changes over time (e.g., the fluid content of the rock alters such as CO2 replacing brine), this will result in a variation in the total time taken for the refraction (e.g., refraction of transmitted seismic wave) to travel from the source to the receiver. In other words, a time shift in the arrival time of the refraction may be observed at the receiver based on the velocity V2 changing over time. Note that the exit on the horizontal section of the ray path travelling in a fluid (e.g., CO2) plume can be affected based on the changes in the velocity V2 such that receivers at smaller offsets can detect unperturbed ray paths (e.g., seismic wave paths).

[0209] FIG. 23 shows a visualization 2300 that highlights the advantage of using fiber optic cables, with an increase in spatial sampling of seismic waves along the cable relative to the spatial sampling that can be achieved using some seismic sensors (e.g., ocean bottom nodes, or geophones in the land case). In particular, the use of S-DAS systems which provide significantly greater inline sampling improves picking or detecting the aforementioned timeshifts as well as accurately locating the point or location of the edge anomalies. Once the onset of a time-shift (e.g., relative to a baseline recording) is determined, the edge of the velocity anomaly (e.g., indicative of a fluid (e.g., CO2 plume) can be located based on the time-shift.

[0210] FIG. 24 shows visualization 2400 indicating critical offset relationships associated with the critical angle of reflection for a given velocity contrast. In this example, the time shifts data shown in, for example, FIGS. 25-30 can be cross-correlated using a windowfunction based on the expected arrival time of the refraction (e.g., refraction of a transmitted seismic wave). This approach can leverage limited prior data of the subsurface including: depth of the boundary (H) data at the location of the fluid plume; an estimate of interval velocity data above the boundary associated with velocity VI; and an estimate of the interval velocity below the boundary associated with velocity V2 below which the fluid plume is observed. This prior data can be used for: estimating an arrival time of the refraction; and positioning a source at a distance larger than the critical offset Xc from the edge of the fluid plume location. According to one embodiment, there is no limit to how far a source sensor system (e.g., an S-DAS source sensor system) needs to be placed relative to the edge of the fluid plume and therefore the source can be sufficiently far from an injector well during, for example, a modelling experiment. In such cases, the critical offset can be approximately 4 km or at least 4 km relative to the fluid plume level while sensor shot location can be at least 10 km from a well under consideration. According to one embodiment, the critical offset Xc can be estimated based on a knowledge of the seismic velocity and depth of an interface being traversed by seismic waves. In addition, the refraction time-shift can be used to characterize an underground blowout event associated with fluid storage. Moreover, by understanding the velocity of seismic waves traversing a subsurface, the critical offset Xc can be derived for a given depth (e.g., a depth of the fluid seal or a predicted depth of the fluid plume via flow simulation analysis). By identifying a first receiver location that detects time-shift data associated with seismic waves traversing the subsurface, the edge of the plume may be determined based on this time-shift. In the illustrated example, the edge of the fluid plume may be determined by identifying the edge of the fluid front as being half the value of the critical offset Xc away from the receiver in a direction the energy or seismic waves traveled.

[0211] Using this prior information, the cross-correlation between co-located traces (e.g. , co-located traces repeated at source and receiver positions) of a baseline shot gather (e.g. , received seismic signal) and a monitor shot gather is calculated or otherwise determined in a window (e.g., temporal window or spatial window) around the expected arrival time of the refraction from a subsurface structure such as a top aquifer. A straight ray assumption in a layer cake medium may be sufficient to estimate a refraction arrival time such that the refraction arrival time increases linearly or nonlinearly with offset data based on Snell’s law and / orPythagorean identities. In one embodiment, this approach mimics applying a linear move-out (LMO) signal processing process to flatten refractions in a shot gather.

[0212] To increase signal detection, cross-correlation data associated with several windows (e. , temporal or spatial windows) derived from a reference window and shifted relative to the reference window may be used to identify a time-shift that appears slightly earlier or later in a captured seismic trace. This can take into account inaccuracy data in the estimate of the arrival time of a refraction (e.g, a refracted signal) and thereby ensure that time-shifts related to the presence of a fluid plume is picked. After collecting results from all windows and filtering cross-correlated data showing a low correlation value (e.g, a correlation value less than 0.6), time-shift data may be sorted by increasing offset data to pick or select one or more locations of a first significant time-shift (e.g. , first time shift larger than 0.25 milliseconds (ms)). From this time-shift, the position of a receiver system can be extracted based on a straight ray data beyond the edge of the fluid e.g., CO2) plume of half a critical offset (e.g., critical offset values of Xc = 4 km or Xc / 2 = 2 km) such that the extracted position minus Xc / 2 in the direction of the source defines the edge of the fluid plume. This method may be used with baseline data to monitor shots (e.g., captured seismic data) located on the other side of the fluid (e.g., CO2) plume allowing the mapping of both edges of the fluid plume.

[0213] In FIG. 25, time-shift data may be determined or otherwise calculated between two shots (e.g., seismic shots) at different times. The first shot in this example may be recorded prior to fluid injection to determine baseline data. The second shot may also be recorded after a given amount of time (e.g., 10 years) after fluid injection. The time-shift data calculated via a cross-correlation between the two shot records can be used to generate the plot 2500 indicated in FIG. 25. Also shown in this figure is an onset of the time-shift or delay caused by the replacement or displacement of brine by a fluid (e.g., CO2) in a storage complex of interest. The plots 2600 of FIG. 26 provide a 2D aspect where the onset of the time-shift is selected to estimate where the edge of the fluid plume is for an offset value of Xc / 2 at a specific depth. Plots 2700 of FIG. 27 show an implementation where offset data is determined in a reverse direction relative to the direction of FIG. 26. In FIG. 28, the plots 2800 indicate the actual extent of the fluid plume derived from a simulation and can be compared with a derived extent of the fluid plume using the disclosed methods and systems. FIG. 29 shows plots 2900 associated with recording a horizontal component of velocity displacement (Vx) correspondingto measurements derived using the disclosed S-DAS techniques. In FIG. 30, plots 3000 are associated with deriving a lateral extent of fluid plume within a velocity structure suing the disclosed S-DAS techniques.

[0214] Those with skill in the art will appreciate that embodiments of the disclosed methods and systems are not solely dependent on the examples presented herein. In particular, the disclosed methods and systems may be used to relate the onset of a recorded time-shift comprised in a captured seismic wave to a lateral extent of a velocity anomaly associated fluid plume in a subsurface, and may be adaptive based on the geological setting and available a- priori information. Thus, the foregoing exemplary methods and systems, when combined with the improved spatial sampling of the disclosed S-DAS technique and the disclosed S-DAS geometries indicated in FIGS. 31-33, allow detecting the lateral extent of fluid plume within a known subsurface model. That is, in an area where there is good understanding of a current seismic velocity structure within the subsurface, predicted velocity variations of a transmitted seismic wave that occur as a function of fluid (e.g., CO2) injection can be derived from time lapse rock physics analysis and flow simulation analysis. It is appreciated that visualization 3100 of FIG. 31 leverages the disclosed 3D approach to identify or otherwise determine the spatial extent of fluid plume as stored fluid migrates away from an injection well. The visualization 3200 of FIG. 32 is associated with a source effort of at least 24 source locations in combination with a 3D spoke array. The time-shifts associated with the spoke configuration can be picked on 24 separate linear receiver arrays of 20 km length extracted from the 3D spoke array. For each of the arrays, 2 source locations may be used (e.g., one ascending in the receiver array in and one descending in the receiver array). All picks (e.g., larger than 3 milliseconds (ms) in this 3D case) can be connected into an outline which is then used to estimate the location of the edge of the plume (e.g., using half a critical offset of Xc / 2= 2 km). The resulting predicted plume edge is shown in this figure which compares favorably to the actual plume edge despite using a limited prior knowledge of the subsurface (e.g., with a constant H, VI and V2).

[0215] By deploying one or more fiber optic cables in a spoke geometry (e.g., of the S- DAS source sensor array) or a similar variation in conjunction with active sources (e.g., signal sources of sensors) located outside of the receiver (e.g., receiver sensors) array (e.g., off the end of the spoke), spiral shot deployment across the receiver array, or by using passive ambientnoise to create "pseudo shots" or "virtual shots," an anomalous velocity zone within the subsurface may be detected (e.g., using an interferometric process) which relates to, or indicates an area in which the fluid (e.g., CO2) plume has displaced substances such as brine, in the case of the saline aquifer storage scenarios (or other fluid in the case of a depleted oil or gas reservoir storage scenario). This can be achieved by recording and analyzing seismic time-shifts. That is to say that variations in the arrival of a given seismic wave can be determined from a given baseline dataset (e.g, recorded before injection begins). By mapping seismic wave contact with the anomalous velocity zone around the injection site, a 3-dimensional (3D) spatial extent of the fluid plume can be determined. This map of the plume extent can therefore be corroborated against the predicted fluid plume, derived from one or more flow simulations and an assessment can be made on whether the operations are in conformance with predictions, required by CCS regulations. By using the disclosed methods and systems, conformance assessment can be made with significantly less effort relative to conventional time-lapse seismic surveys and greater spatial coverage can be achieved than using sensors deployed within injection or monitoring wells.

[0216] Visualization 3300 of FIG. 33 provides additional context on the disclosed S- DAS geometries and their adaptive source effort. In one embodiment, the signal source associated with the disclosed S-DAS sensor is suited to record refraction time-shift data because of available offset data across the sensor array of the S-DAS sensor used. A central source can be used to record or determine refraction time-shift data in an immediate vicinity around an injection well (e. ., when the plume extent is less than the critical offset of reflection away from the injection well), and therefore the location of the source. This spiral source can be used for recording both time-shift data that originate from refractions as well as time-shift data that originate from reflection as well as body waves (e.g, diving rays).Additional Implementations

[0217] According to some embodiments, the disclosed methods and systems leverage a dual process of analyzing refractional time-shift data such that the dual process includes: picking or selecting onset data (e.g., a location associated with the onset of the time-shift) associated with a seismic signal; and positioning fluid plume relative to the picked or selected onset data. While onset data selection can be done based on threshold value data of a first time-shift that is larger than a value being selected at the onset location, an alternative implementation employs splitting the time-shift versus offset plot (e.g, time-shift versus offset graph or visualization) into: flat data points before the onset; and slope data points that increase linearly with the offset and thereby fit a line in the slope of the slope data points. This fitted line may then be used to determine the offset at which the time-shift is zero (e.g., the intercept point) as indicated in FIGS. 34A and 34B.

[0218] In particular, FIGS. 34A and 34B show plots where the time-shifts are plotted as a function of offsets for each spokes of the disclosed S-DAS system. It is appreciated that a clustering computing process (e.g., K-means computing process) can be used to select a group of points 3402 located in the “slope” part. A line 3404 may then be fitted using linear regression as shown in FIG. 34B. In particular, the data being analyzed may be simplified into clusters (e.g., indicated as red dots in FIG. 34A) using a clustering computing process (e.g., a K-means computing process) in order to facilitate isolating a cluster that meets some characteristics or criteria following which the line 3404 may be fitted to the points located in that cluster using linear regression. According to one embodiment, the characteristics or criteria include instances where the gradient presents expected polarity data since time-shifts can increase when a source- to-receiver distance increases.

[0219] In some implementations, additional checks may be performed on the result of the linear regression. These checks can include verifying whether the gradient presents an expected polarity since a time-shift can increase when the source-to-receiver distance increases. If the resulting line satisfies the additional checks, the offset at which the line intercepts the zero offset time-shift can be designated as the "onset" location. The receiver on the spoke with the closest offset equivalent to the zero time-shift may then be pulled. This then becomes less and less true when there is a difference (e.g., substantial difference) between the azimuth of the source-receiver and the azimuth of the spoke. However, since the shots aligned with the spokes and the shots adjacent to the shots aligned with the spokes can be used in the disclosed embodiments, the disclosed methods generate accurate results.

[0220] Furthermore, another improvement to the disclosed method and systems comprises positioning the predicted fluid plume edges from the picked onset location since a 3D approach is being used to back-propagate along the ray paths of the refraction recorded at the onset receiver where said refraction is recorded relative to the top aquifer where therefraction emerges as indicated in FIG. 35. In particular, FIG. 35 shows a visualization including a 2D ray tracing 3501 used to back propagate a ray path of the refraction from the location picked at the onset detection point of the surface relative to the plume edge at the top of a reservoir. A 3D implementation 3502 is shown to the right of the 2D ray tracing approach.

[0221] FIG. 36 shows a plurality of plots indicating shots aligned with spokes. In particular, this figure shows the placing of adjacent shots relative to the shots aligned with the spokes such that the adjacent shots and the shots aligned with the spokes show similar responses. Specifically, one of the observed benefits of using the 3D ray tracing is to improve the positioning of a fluid plume edge relative to 2D ray tracing implementations. It is appreciated that while 2D ray tracing approaches assume a ray subsurface path within a plane of a given spoke, when a given source location and the spoke are both in the same plane, the addition of adjacent source locations benefit from the disclosed 3D ray tracing technique to predict an out — of-plane ray path.

[0222] In an exemplary implementation, 6 shots are used for each of the spokes (e.g., 3 at each end) such that an overall minimum source acquisition effort of twenty 24 shots (e.g., see the 24 spokes of FIG. 37) is generated thereby yielding the visualization shown in FIG. 37. In particular, FIG. 37 shows results including adjacent shots for a 10-year injection period. The left part 3701 of this figure shows a boundary of the 3D model used for the modelling relative to the location of the fiber optic cables used for recording the refraction and the shot locations 3701a. . ,3701n shown in the right part 3702 of this figure. In addition, the right part 3702 of this figure comprises resultant raw picks that can be displayed or otherwise superimposed on top of the plume thickness map. FIG. 38 shows an implementation 800 of the S-DAS layout (e.g., a reciprocal layout) of FIG. 37 with source-receiver pairs implemented using hydrophone sensors. It is appreciated that the disclosed time-shift detection methods and systems can be implemented by deploying hydrophone sensor systems and / or co-located additional geophone sensor systems (e.g., at least 2 or 4 co-located geophone sensor systems) in, for example, an ocean bottom node receiver system in a marine or in land environment. In one embodiment, the hydrophone sensor systems or the geophone sensor systems (e.g., indicated as disks 3801a. . .380 In) correspond to location data of signal sources with the S-DAS receiver locations being replaced by locations of a seismic source depicted in right part 3702 of this figure. In thecase of this source-receiver reciprocal layout, at least 24 seismic receiver systems may be used to implement the source-receiver reciprocal layout.Exemplary Workflow for Detecting Fluid Plume Edge Data

[0223] FIGS. 39A and 39B provide exemplary detailed workflows 3900a and 3900b for methods, systems, and computer programs for detecting or monitoring fluid plume in a subsurface space of interest. It is appreciated that a data managing module or a data engine stored in a memory device may cause a computer processor to execute the various processing stages of the workflows 3900a and 3900b. For example, the disclosed techniques may be implemented as a data manager or signal processing engine within a geological software tool such that the data manager or signal processing engine enables the modeling of geological structures and / or modeling of fluid storage complexes to facilitate monitoring of a stored subsurface fluid.

[0224] At block 3902, the data engine determines a subsurface space of interest for storing fluid.

[0225] At block 3904, the data engine arranges a source sensor array of a Seismic Distributed Acoustic Sensing (S-DAS) sensor relative to a receiver sensor array of the S-DAS sensor on a trajectory going through the source sensor array and an injector well associated with the subsurface space of interest. The injector, according to one embodiment, is configured to inject fluid into the subsurface space of interest.

[0226] Prior to fluid injection into the subsurface space of interest, the data engine may coordinate transmitting, from the source sensor array of the S-DAS sensor, a first set of seismic signals indicating baseline measurements that reflect an absence of fluid in the subsurface space of interest as indicated at block 3906.

[0227] Turning to block 3908, the data engine may coordinate receiving, using the receiver sensor array of the S-DAS sensor, the baseline measurements and thereby generate baseline data for the subsurface space of interest.

[0228] At block 3910, the data engine may track or otherwise coordinate injecting fluid into the subsurface space of interest via the injector well.

[0229] Turning to block 3912, the data engine may coordinate or otherwise initiate transmitting, using the source sensor array of the S-DAS sensor, a second set of seismic signalsindicating fluid plume measurements that reflect the presence fluid plume associated with the subsurface space of interest.

[0230] At block 3914, the data engine may receive, using the receiver sensor array of the S-DAS sensor, the fluid plume measurements and thereby generate fluid plume data for the subsurface space of interest.

[0231] At block 3916, the data engine determines, based on the baseline data and the fluid plume data, time-shift data between the baseline data and the fluid plume data and thereby generate fluid plume extent data.

[0232] Turning to block 3918, the data engine formats the fluid plume extent data into a first set of data values that characterize a first extent or rate of spread of the fluid plume relative to the subsurface space of interest.

[0233] At block 3920, the data engine visualizes the first extent or rate of spread of the fluid plume relative to the subsurface space of interest on a graphical display device.

[0234] In other embodiments, a system and a computer program can include or execute the method described above. These and other implementations may each optionally include one or more of the following features.

[0235] The above method further comprises: analyzing the time-shift data to select or pick onset datapoints of one or more locations where a time-shift associated with at least the second set of seismic signals originates; and positioning plume datapoints associated with the fluid plume extent data based on the onset datapoints to indicate at least the first extent or rate of spread of the fluid plume relative to the subsurface space of interest.

[0236] In some embodiments, the fluid plume extent data is used to configure one or more of: fluid injection rate of equipment injecting fluid into the subsurface space of interest; and sensitivity settings or resolution settings associated with the source sensor array of the S- DAS sensor or the receiver sensor array of the S-DAS sensor.

[0237] Furthermore, the source sensor array may be located outside the fluid plume at a distance greater than a critical offset value, such that the critical offset value indicates a distance between a first location of the fluid plume relative to a second location of the source sensor array. In some embodiments, the source sensor array is located outside the fluid plume at a distance less than a critical offset value, such that the critical offset value indicates adistance between a first location of the fluid plume relative to a second location of the source sensor array.

[0238] According to one embodiment, the above method further comprises: generating, based on the critical offset value and at least the fluid plume extent data, a computing model indicating or characterizing a spatio-temporal multi-dimensional path or trajectory of the fluid plume associated with the subsurface space of interest; adapting or varying data values associated with at least the critical offset value and the fluid plume data or the time-shift data between a first time and a second time in a plume extent computing simulation; determining, based on the plume extent computing simulation, a projection of the fluid plume extent data from the first time to the second time and thereby generate an updated fluid plume extent data; formatting the updated fluid plume extent data into a second set of data values that characterize a second extent or rate of spread of the fluid plume relative to the subsurface space of interest between the first time and the second time; and visualizing the second extent or rate of spread of the fluid plume relative to the subsurface space of interest on a graphical display device.

[0239] In some embodiments, the updated fluid plume extent data is used to configure or determine one or more: fluid injection rate of equipment injecting fluid into the subsurface space of interest; and sensitivity settings or resolution settings associated with source sensor array of the S-DAS sensor or the receiver sensor array of the S-DAS sensor.

[0240] According to one embodiment, detecting or monitoring fluid is based on a measure, monitor, and verify (MMV) plan, such that the MMV plan comprises a digital document indicating one or more of: conformance data configured to establish fluid storage parameters associated with time lapse data measurements that confirm that fluid plume data including fluid plume edge data associated with the subsurface space of interest is conformant relative to a subsurface model; and containment data configured to indicate fluid measurements associated with the subsurface space of interest to verify the presence or absence of fluid leakage of fluid stored in the subsurface space of interest relative to a surface or subsurface environment surrounding the subsurface space of interest.

[0241] In some cases, the MMV plan accounts for: an atmosphere domain associated with fluid storage within the subsurface space of interest; a biosphere domain associated with the fluid storage within the subsurface space of interest; a hydrosphere domain associated withthe fluid storage within the subsurface space of interest; and a geosphere domain associated with the fluid storage with thin the subsurface space of interest.

[0242] Furthermore, the MMV plan, according to some embodiments, accounts for a plurality of phases associated with injecting fluid into the subsurface space of interest, the plurality of phases including: a pre-injection phase; an injection phases; and a post-injection phase.

[0243] In addition, the source sensor array of the S-DAS sensor can be arranged to have a geometry comprising at least one of: a spiral geometry; a spoke geometry; or a linear geometry.

[0244] It is appreciated that the fluid is CO2.

[0245] It is further appreciated that the subsurface space of interest comprises a depleted fluid reservoir comprising one or more of a depleted oil reservoir or a depleted aquifer.

[0246] While any discussion of or citation to related art in this disclosure may or may not include some prior art references, there is no concession or acquiescence to the position that any given reference is prior art or analogous prior art.

[0247] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit this disclosure to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to explain the principles of this disclosure and its practical applications, to thereby enable others skilled in the art to use the disclosed methods and systems and various embodiments with various modifications as are suited to the particular use contemplated.

[0248] It is appreciated that the term optimize / optimal and its variants (e.g. , efficient or optimally) may simply indicate improving, rather than the ultimate form of 'perfection' or the like.

[0249] It is further appreciated that, although the terms first, second, etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of this disclosure. The first object or step, and the second object or step, are both objects or steps, respectively, but they are not to be considered the same object or step.

[0250] The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used in the description of the disclosed embodiments and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and / or” as used herein refers to and encompasses any possible combination of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0251] As used herein, the term “if’ may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.

[0252] Those with skill in the art will appreciate that while some terms in this disclosure may refer to absolutes, e.g., all source receiver traces, each of a plurality of objects, etc., the methods and techniques disclosed herein may also be performed on fewer than all of a given thing, e.g., performed on one or more components and / or performed on one or more source receiver traces. Accordingly, in instances in the disclosure where an absolute is used, the disclosure may also be interpreted to be referring to a subset.

Claims

CLAIMSWhat is claimed is:

1. A method for detecting or monitoring fluid plume in a subsurface space of interest, the method comprising: determining a subsurface space of interest for storing fluid; arranging a source sensor array of a Surface Distributed Acoustic Sensing (S-DAS) sensor relative to a receiver sensor array of the S-DAS sensor on a trajectory going through the source sensor array and an injector well associated with the subsurface space of interest, the injector well being configured to inject fluid into the subsurface space of interest; prior to fluid injection into the subsurface space of interest, transmitting, from the source sensor array of the S-DAS sensor, a first set of seismic signals indicating baseline measurements that reflect an absence of fluid in the subsurface space of interest; receiving, using the receiver sensor array of the S-DAS sensor, the baseline measurements and thereby generate baseline data for the subsurface space of interest; injecting fluid into the subsurface space of interest via the injector well; transmitting, using the source sensor array of the S-DAS sensor, a second set of seismic signals indicating fluid plume measurements that reflect the presence fluid plume associated with the subsurface space of interest; receiving, using the receiver sensor array of the S-DAS sensor, the fluid plume measurements and thereby generate fluid plume data for the subsurface space of interest; determining, based on the baseline data and the fluid plume data, time-shift data between the baseline data and the fluid plume data and thereby generate fluid plume extent data; formatting the fluid plume extent data into a first set of data values that characterize a first extent or rate of spread of the fluid plume relative to the subsurface space of interest; and visualizing the first extent or rate of spread of the fluid plume relative to the subsurface space of interest on a graphical display device.

2. The method of Claim 1, further comprising:analyzing the time-shift data to select or pick onset datapoints of one or more locations where a time-shift associated with at least the second set of seismic signals originates; and positioning plume datapoints associated with the fluid plume extent data based on the onset datapoints to indicate at least the first extent or rate of spread of the fluid plume relative to the subsurface space of interest.

3. The method of Claim 1 , wherein the fluid plume extent data is used to configure one or more of fluid injection rate of equipment injecting fluid into the subsurface space of interest, and sensitivity settings or resolution settings associated with source sensor array of the S- DAS sensor or the receiver sensor array of the S-DAS sensor.

4. The method of Claim 1, wherein: the source sensor array is located outside the fluid plume at a distance greater than a critical offset value, the critical offset value indicating a distance between a first location of the fluid plume relative to a second location of the source sensor array, or the source sensor array is located outside the fluid plume at a distance less than a critical offset value, the critical offset value indicating a distance between a first location of the fluid plume relative to a second location of the source sensor array.

5. The method of Claim 4, further comprising: generating, based on the critical offset value and at least the fluid plume extent data, a computing model indicating or characterizing a spatio-temporal multi-dimensional path or trajectory of the fluid plume associated with the subsurface space of interest; adapting or varying data values associated with at least the critical offset value and the fluid plume data or the time-shift data between a first time and a second time in a plume extent computing simulation; determining, based on the plume extent computing simulation, a projection of the fluid plume extent data from the first time to the second time and thereby generate an updated fluid plume extent data;'llformatting the updated fluid plume extent data into a second set of data values that characterize a second extent or rate of spread of the fluid plume relative to the subsurface space of interest between the first time and the second time; and visualizing the second extent or rate of spread of the fluid plume relative to the subsurface space of interest on a graphical display device.

6. The method of Claim 5, wherein the updated fluid plume extent data is used to configure or determine one or more: fluid injection rate of equipment injecting fluid into the subsurface space of interest, and sensitivity settings or resolution settings associated with source sensor array of the S- DAS sensor or the receiver sensor array of the S-DAS sensor.

7. The method of Claim 1, wherein the detecting or monitoring is based on a measure, monitor, and verify (MMV) plan, the MMV plan comprising a digital document indicating one or more of: conformance data configured to establish fluid storage parameters associated with time lapse data measurements that confirm that fluid plume data including fluid plume edge data associated with the subsurface space of interest is conformant relative to a subsurface model, and containment data configured to indicate fluid measurements associated with the subsurface space of interest to verify the presence or absence of fluid leakage of fluid stored in the subsurface space of interest relative to a surface or subsurface environment surrounding the subsurface space of interest.

8. The method of Claim 7, wherein the MMV plan accounts for: an atmosphere domain associated with fluid storage within the subsurface space of interest, a biosphere domain associated with the fluid storage within the subsurface space of interest, a hydrosphere domain associated with the fluid storage within the subsurface space of interest, anda geosphere domain associated with the fluid storage with thin the subsurface space of interest.

9. The method of Claim 7, wherein the MMV plan accounts for a plurality of phases associated with injecting fluid into the subsurface space of interest, the plurality of phases including: a pre-inj ection phase, an injection phases, and a post-injection phase.

10. The method of Claim 1, wherein the source sensor array of the S-DAS sensor is arranged to have a geometry comprising at least one of: a spiral geometry, a spoke geometry, or a linear geometry.

11. A system for detecting or monitoring fluid plume in a subsurface space of interest, the system comprising: a computer processor, and memory storing a data processing engine that comprises instructions which are executable by the computer processor to: determine a subsurface space of interest for storing fluid; arrange a source sensor array of a Surface Distributed Acoustic Sensing (S-DAS) sensor relative to a receiver sensor array of the S-DAS sensor on a trajectory going through the source sensor array and an injector well associated with the subsurface space of interest, the injector well being configured to inject fluid into the subsurface space of interest; prior to fluid injection into the subsurface space of interest, transmit, from the source sensor array of the S-DAS sensor, a first set of seismic signals indicating baseline measurements that reflect an absence of fluid in the subsurface space of interest;receive, using the receiver sensor array of the S-DAS sensor, the baseline measurements and thereby generate baseline data for the subsurface space of interest; inject fluid into the subsurface space of interest via the injector well; transmit, using the source sensor array of the S-DAS sensor, a second set of seismic signals indicating fluid plume measurements that reflect the presence fluid plume associated with the subsurface space of interest; receive, using the receiver sensor array of the S-DAS sensor, the fluid plume measurements and thereby generate fluid plume data for the subsurface space of interest; determine, based on the baseline data and the fluid plume data, time-shift data between the baseline data and the fluid plume data and thereby generate fluid plume extent data; format the fluid plume extent data into a first set of data values that characterize a first extent or rate of spread of the fluid plume relative to the subsurface space of interest; and visualize the first extent or rate of spread of the fluid plume relative to the subsurface space of interest on a graphical display device.

12. The system of Claim 11, wherein the instructions are executable by the computer processor to: analyze the time-shift data to select or pick onset datapoints of one or more locations where a time-shift associated with at least the second set of seismic signals originates; and position plume datapoints associated with the fluid plume extent data based on the onset datapoints to indicate at least the first extent or rate of spread of the fluid plume relative to the subsurface space of interest.

13. The system of Claim 11 , wherein the fluid plume extent data is used to configure one or more of: fluid injection rate of equipment injecting fluid into the subsurface space of interest, andsensitivity settings or resolution settings associated with the source sensor array of the S-DAS sensor or the receiver sensor array of the S-DAS sensor.

14. The system of Claim 11, wherein: the source sensor array is located outside the fluid plume at a distance greater than a critical offset value, the critical offset value indicating a distance between a first location of the fluid plume relative to a second location of the source sensor array, or the source sensor array is located outside the fluid plume at a distance less than a critical offset value, the critical offset value indicating a distance between a first location of the fluid plume relative to a second location of the source sensor array.

15. The system of Claim 14, wherein the instructions are executable by the computer processor to: generate, based on the critical offset value and at least the fluid plume extent data, a computing model indicating or characterizing a spatio-temporal multi-dimensional path or trajectory of the fluid plume associated with the subsurface space of interest; adapt or vary data values associated with at least the critical offset value and the fluid plume data or the time-shift data between a first time and a second time in a plume extent computing simulation; determine, based on the plume extent computing simulation, a projection of the fluid plume extent data from the first time to the second time and thereby generate an updated fluid plume extent data; format the updated fluid plume extent data into a second set of data values that characterize a second extent or rate of spread of the fluid plume relative to the subsurface space of interest between the first time and the second time; and visualize the second extent or rate of spread of the fluid plume relative to the subsurface space of interest on a graphical display device.

16. The system of Claim 15, wherein the updated fluid plume extent data is used to configure or determine one or more: fluid injection rate of equipment injecting fluid into the subsurface space of interest, andsensitivity settings or resolution settings associated with the source sensor array of the S-DAS sensor or the receiver sensor array of the S-DAS sensor.

17. The system of Claim 11, wherein the detecting or monitoring is based on a measure, monitor, and verify (MMV) plan, the MMV plan comprising a digital document indicating one or more of: conformance data configured to establish fluid storage parameters associated with time lapse data measurements that confirm that fluid plume data including fluid plume edge data associated with the subsurface space of interest is conformant relative to a subsurface model, and containment data configured to indicate fluid measurements associated with the subsurface space of interest to verify the presence or absence of fluid leakage of fluid stored in the subsurface space of interest relative to a surface or subsurface environment surrounding the subsurface space of interest.

18. A computer program for detecting or monitoring fluid plume in a subsurface space of interest, the computer program comprising instructions stored in a non-transitory computer readable medium such that the instructions are executable by a computer processor to: determine a subsurface space of interest for storing fluid; arrange a source sensor array of a Surface Distributed Acoustic Sensing (S-DAS) sensor relative to a receiver sensor array of the S-DAS sensor on a trajectory going through the source sensor array and an injector well associated with the subsurface space of interest, the injector well being configured to inject fluid into the subsurface space of interest; prior to fluid injection into the subsurface space of interest, transmit, from the source sensor array of the S-DAS sensor, a first set of seismic signals indicating baseline measurements that reflect an absence of fluid in the subsurface space of interest; receive, using the receiver sensor array of the S-DAS sensor, the baseline measurements and thereby generate baseline data for the subsurface space of interest; inject fluid into the subsurface space of interest via the injector well;transmit, using the source sensor array of the S-DAS sensor, a second set of seismic signals indicating fluid plume measurements that reflect the presence fluid plume associated with the subsurface space of interest; receive, using the receiver sensor array of the S-DAS sensor, the fluid plume measurements and thereby generate fluid plume data for the subsurface space of interest; determine, based on the baseline data and the fluid plume data, time-shift data between the baseline data and the fluid plume data and thereby generate fluid plume extent data; format the fluid plume extent data into a first set of data values that characterize a first extent or rate of spread of the fluid plume relative to the subsurface space of interest; and visualize the first extent or rate of spread of the fluid plume relative to the subsurface space of interest on a graphical display device.

19. The computer program of Claim 18, wherein the instructions are executable by the computer processor to: analyze the time-shift data to select or pick onset datapoints of one or more locations where a time-shift associated with at least the second set of seismic signals originates; and position plume datapoints associated with the fluid plume extent data based on the onset datapoints to indicate at least the first extent or rate of spread of the fluid plume relative to the subsurface space of interest.

20. The computer program of Claim 18, wherein the fluid plume extent data is used to configure one or more of: fluid injection rate of equipment injecting fluid into the subsurface space of interest, and sensitivity settings or resolution settings associated with the source sensor array of the S-DAS sensor or the receiver sensor array of the S-DAS sensor.

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