Seismic wave interferometry using surface or sea bottom distributed acoustic sensing

Abstract

A method for performing one or more capture utilization and sequestration (CCUS) operations. The method includes obtaining, from a vibration sensor, ambient noise data associated with a subsurface of a resource site. The method also includes generating, from the ambient noise data, passive virtual shot data indicating temporal properties or interactions present in the ambient noise data. The method also includes determining, using the passive virtual shot data, shot analysis data. The method also includes generating, using the shot analysis data, a model representing changes in past CCUS operations for the resource site. The method also includes initiating, based on the model, one or more additional CCUS operations for the resource site.

Description

SEISMIC WAVE INTERFEROMETRY USING SURFACE OR SEA BOTTOM DISTRIBUTED ACOUSTIC SENSINGCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 606,436, filed December 5, 2023, the entirety of which is hereby incorporated by reference.BACKGROUND

[0002] 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. There is, therefore, a desire to develop monitoring mechanisms that monitor or otherwise hack fluid storage structures to: effectively manage the aforementioned uncertainties; comply with fluid storage specifications from regulatory bodies; and also accurately report carbon storage parameters to stakeholders including said regulatory bodies.

[0003] Moreover, conformance specifications for fluid storage systems may be based on time-lapse (4D) image data. The time-lapse image data may be acquired using streamers or less commonly seabed nodes. The costs associated with the approach are very high, and also include redundancies with regard to the monitoring of fluid plume migration activity e.g., carbon dioxide plume migration). The monitoring of fluid plume migration activity may be based on developing the timelapse image data for interpretation.SUMMARY

[0004] One or more embodiments provide for a method for performing one or more capture utilization and sequestration (CCUS) operations. The method includes obtaining, from a vibration sensor, ambient noise data associated with a subsurface ofa resource site. The method also includes generating, from the ambient noise data, passive virtual shot data indicating temporal properties or interactions present in the ambient noise data. The method also includes determining, using the passive virtual shot data, shot analysis data. The method also includes generating, using the shot analysis data, a model representing changes in past CCUS operations for the resource site. The method also includes initiating, based on the model, one or more additional CCUS operations for the resource site.

[0005] One or more embodiments also provide for a system. The system includes a processor and a data repository storing computer usable program code which, when executed by the processor, performs a computer-implemented method for performing one or more capture utilization and sequestration (CCUS) operations. The computer- implemented method includes receiving or recording, from a vibration sensor, ambient noise data associated with a subsurface of a resource site. The computer- implemented method also includes generating, from the ambient noise data, passive virtual shot data indicating temporal properties or interactions present in the ambient noise data. The computer-implemented method also includes determining, using the passive virtual shot data, shot analysis data. The computer-implemented method also includes generating, using the shot analysis data, a model representing changes in past CCUS operations for the resource site. The computer-implemented method also includes initiating, based on the model, one or more additional CCUS operations for the resource site.

[0006] One or more embodiments also provide for a non-transitory computer readable storage medium storing program code which, when executed by a processor, performs a computer-implemented method for performing one or more capture utilization and sequestration (CCUS) operations. The computer-implemented method includes receiving or recording, from a vibration sensor, ambient noise data associated with a subsurface of a resource site. The computer-implemented method also includes generating, from the ambient noise data, passive virtual shot dataindicating temporal properties or interactions present in the ambient noise data. The computer-implemented method also includes determining, using the passive virtual shot data, shot analysis data. The computer-implemented method also includes generating, using the shot analysis data, a model representing changes in past CCUS operations for the resource site. The computer-implemented method also includes initiating, based on the model, one or more additional CCUS operations for the resource site.

[0007] Other aspects of one or more embodiments will be apparent from the following description and the appended claims.BRIEF DESCRIPTION OF DRAWINGS

[0008] The disclosure is illustrated by way of example, and not by way of limitation of the figures in the accompanying drawings in which like reference numerals are used to refer to similar elements. It is emphasized 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.

[0009] FIG. 1A shows a schematic depiction of the steps of a carbon capture and storage (CCS) project lifecycle and impacts of using seismic data during the decision process within each stage of the CCS project lifecycle, in accordance with one or more embodiments.

[0010] FIG. IB shows seismic data changes associated with carbon or fluid storage operations according to some embodiments, in accordance with one or more embodiments.

[0011] FIG. 1C shows a characterization and assessment overview of the potential storage complex and surrounding area, in accordance with one or more embodiments.

[0012] FIG. ID shows an example of the uplift in the static image that can be achieved when reprocessing legacy seismic data with signal processing and imaging workflows, in accordance with one or more embodiments.

[0013] FIG. IE provides a summary of monitoring techniques, summarized under monitoring objectives, in accordance with one or more embodiments.

[0014] FIG. IF shows a selected experiment procedure output derived from a three-dimensional (3D) fiber optic cable distributed acoustic sensing (S-DAS) field experiment, in accordance with one or more embodiments.

[0015] FIG. 1G provides a comparison between the predicted plume extent and the true or model-based plume extent, in accordance with one or more embodiments.

[0016] FIG. 1H shows a high-level workflow associated with building monitoring scenarios for fluid storage and containment operations at a resource site, in accordance with one or more embodiments.

[0017] FIG. 2 shows a cross-sectional view of a resource site for which the process of FIG. 1H may be executed, in accordance with one or more embodiments.

[0018] FIG. 3 shows a networked system illustrating a communicative coupling of devices or systems associated with the resource site of FIG. 2, in accordance with one or more embodiments.

[0019] FIG. 4 shows a comparison of analysis data used to direct the choice of metrics used for optimally detecting fluid storage events at a resource site, in accordance with one or more embodiments.

[0020] FIG. 5 shows a fluid detection map, in accordance with one or more embodiments.

[0021] FIG. 6 shows a detailed workflow for optimizing fluid or gas storage (GS) operations at a resource site, in accordance with one or more embodiments.

[0022] FIG. 7 shows a flowchart showing a departure from discrete acquisition of data, in accordance with one or more embodiments.

[0023] FIG. 8 shows a workflow for implementing the disclosed adaptive monitoring regime, in accordance with one or more embodiments.

[0024] FIG. 9 shows an implementation with a spoke receiver geometry that has a single source in the center, in accordance with one or more embodiments.

[0025] FIG. 10 includes a configuration with a central source with a spiral cable layout, in accordance with one or more embodiments.

[0026] FIG. 11 depicts a parallel receiver geometry with a single source at the center, in accordance with one or more embodiments.

[0027] FIG. 12 shows an implementation of a detection coverage based on a central shot with reflection and diving waves being recorded along the spokes with increasing offset, in accordance with one or more embodiments.

[0028] FIG. 13 includes a spoke receiver geometry with additional line of shots around the outside of the spoke pattern, in accordance with one or more embodiments.

[0029] FIG. 14 depicts an example of detection coverage from the line of shots acquired around the edge of the outer circumference of the spoke geometry, in accordance with one or more embodiments.

[0030] 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 and PS coverage, in accordance with one or more embodiments.

[0031] FIG. 16 includes a spoke geometry with additional spiral shots, in accordance with one or more embodiments.

[0032] FIG. 17 shows a small spiral shot line that may be adequate for the early stages of plume migration, in accordance with one or more embodiments.

[0033] FIG. 18 shows the subsurface coverage for PP and PS seismic analysis when the spiral shot line extends to the out circumference of the receiver array, in accordance with one or more embodiments.

[0034] FIG. 19 depicts a representation of how this design could be achieved using hardware for interrogation of the fiber optic cable, in accordance with one or more embodiments.

[0035] FIG. 20 provides a comparison of the duration of time taken to acquire the shots within each design, in accordance with one or more embodiments.

[0036] FIG. 21, FIG. 22, FIG. 23, FIG. 24, FIG. 25, FIG. 26, FIG. 27, FIG. 28, FIG. 29, FIG. 30, FIG. 31, FIG. 32, and FIG. 33 provide an example of injected plume edge detection, in accordance with one or more embodiments.

[0037] FIG. 34A shows a passive recording of ambient noise using vibration sensors, in accordance with one or more embodiments.

[0038] FIG. 34B depict virtual shots derived from FIG. 34 A, in accordance with one or more embodiments.

[0039] FIG. 35 shows a dispersion curve generated using virtual shots or analysis data as herein, in accordance with one or more embodiments.

[0040] FIG. 36 provides a detailed workflow for methods, systems, and computer programs that facilitate generating subsurface characterization data using ambient noise data captured by a deployed vibration sensor, in accordance with one or more embodiments.

[0041] FIG. 37A, FIG. 37B, FIG. 37C, FIG. 37D, FIG. 37E, and FIG. 37F show an example of seismic wave interferometry using surface or sea bottom distributed acoustic sensing according to a passive, virtual shot interferometric method, in accordance with one or more embodiments.

[0042] Like elements in the various figures are denoted by like reference numerals for consistency.DETAILED DESCRIPTION

[0043] One or more embodiments are directed to seismic wave interferometry using surface or sea-bottom horizontal distributed acoustic sensing (DAS). One or more embodiments may be used for measurement, monitoring, and verification (MMV) for carbon capture, utilization, and storage (CCUS), as well as for infrastructure monitoring.

[0044] One or more embodiments may minimize 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. The effective monitoring campaigns help ensure 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 help ensure optimal reservoir production operations that quantify and improve fluid storage models with associated predictability data.

[0045] According to some embodiments, monitoring of a fluid storage site may continue even after injection operations end (e.g, fluid injection operations). Continuance of monitoring may increases operational bottlenecks (e.g. , costs) compared with other hydrocarbon exploratory activities. Additionally, when 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 has a high level of predictability to support the long-term responsibility for monitoring the stored fluid within the storage complex.

[0046] One or more embodiments support the design of fluid capture and storage monitoring campaigns by quantifying site-specific uncertainties across multiple scenarios and determining or predicting monitoring methods that detect specific risk events. Additionally, the workflows presented herein may be automatic, semi-automatic, or a combination thereof. One or more embodiments also may be based on feedback from real-time or near real-time sensor measurements or from historic sensor measurements at the fluid storage site. The feedback may 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.

[0047] The disclosed systems and methods may be accomplished using interconnected devices and systems that obtain data associated with various parameters of interest at a resource site. The workflows and flowcharts described herein implicate a new computer processing approach (e.g. , hardware, special purpose processors, and specially programmed general-purpose processors) because such analyses cannot be performed manually. Thus, the described systems and methods are directed to tangible implementations or solutions to specific technological problems in developing natural resources such as oil, gas, water well industries, and other mineral exploration operations. More specifically, the systems and methods presently disclosed may be applicable to operations associated with fluid storage at a resource site (e.g., oil field, saline aquifers, etc.)

[0048] 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 the resource site via feedback loops executed by one or more computing device processors or through other control devices or mechanisms that make determinations regarding whether a given action, template, or resource data, etc., is sufficiently accurate.

[0049] Regulations associated with carbon capture and storage operations, in some cases, may provide that monitoring is undertaken to demonstrate to regulators that agiven carbon storage activity is safe and proceeding as planned. According to one embodiment, determining that a given carbon storage operation is optimal includes a process such as measuring relevant carbon storage parameters, monitoring said parameters, and verifying that said parameters are performing as intended. The process may be referred to as a measure, monitor, verify (MMV) plan.

[0050] The MMV plan may be driven by two objectives. The first objective includes adherence to planned guidelines associated with a given MMV plan. The first objective uses time-lapse measurements to be undertaken to confirm that a stored fluid (e.g., carbon dioxide, methane, hydrogen) plume migration is conforming to a generated model (e.g., surface or subsurface) associated with the MMV plan. The secondary objective includes containment operations and includes measurements to be undertaken to verify the absence of effects or factors external to the storage complex.

[0051] The MMV plan may blend together a number of discreet measurements acquired using a variety of tools. For example, the MMV plan can account for several domains including an atmosphere domain, 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 preinjection phase, an injection phase, and a post-injection and closure phase. Furthermore, operating costs associated with the MMV plan can be large and span several decades. As such, there is a desire to develop MMV plans or strategies that reduce these costs and achieve regulatory objectives in a cost-effective maimer.

[0052] Thus, one or more embodiments includes adaptive monitoring mechanisms associated with using deployed fiber optic cable e.g., S-distributed acoustic sensing (DAS), or S-DAS) sensors to record seismic data. In addition, the disclosed approach addresses associated with the acquisition geometry data used to generate S-DAS data for use as part of adaptive monitoring operations associated with a given MMV plan.The adaptive monitoring operations may be dependently or independently of adaptive monitoring associated with an MMV plan.

[0053] A relevant consideration involved in designing and implementing a fluid capture and storage campaign is the 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., use of sensors to capture real-time or near real-time data or historic sensor data) for detecting site-specific risks in the subsurface domain may be optimized for longevity as well as maintaining low operational costs.

[0054] For the fluid capture and storage operations disclosed herein for designing and implementing fluid storage campaigns, a number of different monitoring configurations may be compared against site-specific conditions captured by sensors in real-time 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 monitoring systems. When the monitoring objective is to confirm conformance to an existing subsurface model, a positive outcome may include confirming the accuracy of the generated fluid or gas storage (GS) models using acquired data from the monitoring systems. If the acquired data confirms that the GS model is inaccurate or has a substantially high degree of uncertainty, then additional data may be acquired to verify and reduce the uncertainty in the GS model.

[0055] Given, for example, a monitoring timeframe of about 20 to 50 years, or more, the process of confirming that the GS model is accurate may use automation or semi-automation operations (e.g., simulations). In other embodiments, 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 GS model may be a manual process initiatedby 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 or simulation icons) on a graphical user interface device.

[0056] 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 refocused attention on carbon capture and storage (CCS). In the International Energy Agency (TEA) Sustainable Development Scenario in which global fluid (e.g., carbon dioxide) 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, the International Energy Agency (IEA) estimates that between 5700 megatons (Mt) of carbon dioxide (CARBON DIOXIDE) should be sequestrated or stored by 2025 to achieve the net zero goal.

[0057] The net zero goal is a challenge because of the rapid increase in the scale of both carbon capture process and the storage of said carbon. The Global CCS Institute indicates that, in 2022, 61 new CCS facilities were announced. The facilities resulted in 196 CCS projects in development as of September 2022. The number of CCS projects is a 44% year-on-year increase in capture capacity, but the projects in development will store 244 Mt per year of fluid (e g., carbon dioxide). For example, the IEA indicates that carbon dioxide capture plants take between three and five years to build, while the assessment and development processes for carbon dioxide storage can take many times longer. Thus, the task ahead of the energy transition industry includes that of scaling up CCS or fluid storage operations.

[0058] Attention is now turned to the figures. The task of scaling up CCS or fluid storage operations may be better understood by reviewing the various phases of the CCS project lifecycle, as depicted in FIG. 1A. In particular, FIG. 1A shows a schematic depiction of the steps of the CCS project lifecycle and impacts of using seismic data during the decision process within each stage of the CCS project lifecycle. The CCS project lifecycle can include a multifaceted and complex processrequiring undesirable investment and technical expertise at each of the steps depicted in FIG. 1A.

[0059] One or more embodiments leverage seismic data (e.g, seismic data acquired at the surface) for CCS or fluid storage operations. The development of carbon capture facilities may be based on largest capital expenditure (CAPEX) within the CCS lifecycle process. However, a proportion of risk lies within the carbon storage (CS) process itself.

[0060] CCS lifecycle project decisions may be underpinned by the subsurface screening and characterization work undertaken ahead of the final CCS development operations based in part on the accuracy of predicted injectivity rates associated with the fluid to be stored. The CCS development operations also may be based on capacity of the storage volume used to store the fluid and operational expenditure (OPEX) associated with monitoring measurement and verification (MMV) strategies stipulated by a regulatory framework. Each of the stages may be based on geophysics and on particular seismic data.

[0061] The balance of cost control and technical excellence is a task within the seismic industry. However, the described aspects are within the monitoring domain, and in particular the post injection and long-term monitoring stages of CCS project. Geophysics, including the acquisition and analysis of seismic data, plays a role in the exploration of hydrocarbon and mineral resources.

[0062] For example, seismic data may be used as an exploration tool to provide information on migration pathways, trapping mechanisms, accurate structural information in the subsurface, etc. 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.

[0063] FIG. IB shows seismic data changes associated with carbon or fluid storage operations according to some embodiments. In the regional reconnaissance stage, 2- dimensional (2D) or 3-dimensional (3D) volume data may be used. During a license round application or during the focused screening stage, the 3D volume data may be merged and reprocessed to provide a contiguous data set. In the final site appraisal stage, 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.

[0064] In the early stages of understanding gross storage volumes, available data may be used to help with reconnaissance at a regional or basin scale. The challenges of these data sets are numerous, including irregular coverage; limited bandwidth; residual noise and multiple energy; inaccurate imaging; and variable illumination. However, the use of legacy data, combined with fast, targeted reprocessing may be used to meet the aspects of the objective of assessing storage potential at a regional scale. In the case of data rich basins, once an area has been identified as a potential storage site, available 2D or 3D seismic data are 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 provide for structural interpretation across the area. In the site appraisal stage, the area of interest may be reduced to allow for more costly processing sequences to be applied to the data to improve upon the above-mentioned data quality issues of the vintage data.

[0065] The acquisition of new seismic data may depend on a number of factors. In the data rich basin scenario, the new seismic data is 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 the proximity data associated with areas havingexisting or planned third-party activity, such as dual land use considerations like wind farms. Within the principal projects being executed in Northern Europe, for example, a preference to acquire new, purpose designed seismic data ahead of injection is growing. This data has the dual use of characterizing the fluid storage volume and overburden and a more suitable seismic baseline data set to match the geometry and acquisition parameters that will be deployed during the MMV program.

[0066] Furthermore, in the case where the proximity to the emitter is related 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 performed early in the site screening and feasibility process. This data, according to some implementations, is designed and acquired with its role in the future MMV plan fully considered.

[0067] The process of geological play-based screening, using available seismic and well data, is described by (Bradshaw, 2005, European Communities 2011 and Ringrose, 2020) and is captured in the first two columns of FIG. 1C. Specifically, FIG. 1C shows a characterization and assessment overview of the potential storage complex and surrounding area, based on Annex I of CCS-Directive (European Communities, 2011). It is appreciated that the processes of container focused seismic interpretation, geomechanical modelling of the storage site, petrophysical assessment of the storage site, and CARBON DIOXIDE storage capacity estimations may be combined to provide a geological site summary and ranking. This may be further leveraged together with other non-geological criteria (e.g, proximity to an emitter or source of CARBON DIOXIDE, presence and condition of existing wells in the area and perhaps most importantly, the acceptance of CS operations. This is 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.

[0068] It has been shown that the application of contemporary seismic data- processing and imaging techniques to heritage seismic datasets associated with the Southern North Sea (SNS) can provide improvements for both Bunter and sub- Zechstein storage targets. Recent examples include the reprocessing of heritage datasets over the Cavendish area as depicted in FIG. ID (Ramani et al., 2021) and Viking CCS targets. In particular, FIG. ID shows an example of the uplift in the static image that can be achieved when reprocessing legacy seismic data with contemporary signal processing and imaging workflows. In addition to the application of contemporaiy workflows, there are a number of seismic data- processing and imaging technologies which may reduce interpretation uncertainty and improve the integrity of subsurface characterization. Least-squares migration in the image domain (LSMi) and depth-domain inversion (DDI) provide an illumination-corrected reflectivity estimate improving resolution and amplitude fidelity. DDI takes this concept further to provide more rock properties, providing crucial information for geomechanics and flow simulations. Full-waveform inversion (FWI) is now used in high fidelity velocity model building and options exist 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 the model associated with the limited information provided by legacy seismic datasets. The siting of CS sites in shallow water also highlights the usefulness of adequately removing 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 (IWM) technique. For example, in very shallow water the limitations of towed streamer geometries may mean that small angle primaiy reflections are not 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 characterization.

[0069] The disclosed subject-mater automates 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 allowing users to provide high-level objectives with the 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 TM with relevant information associated with the storage site being available for parameterization to conform to relevant monitoring objectives. The resulting interactive report or model generated from said modeling may provide stakeholders with an understanding of GS storage considerations at the storage site together with the option to drill-down into specific simulation 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, or safety operations at the resource site.

[0070] Furthermore, regulation may impose the monitoring of carbon storage systems to demonstrate to regulatory bodies that the activities and structures surrounding said carbon storage systems are 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 include conformance operations with associated time-lapse measurements undertaken to confirm that fluid migration activity (e.g., CARBON DIOXIDE plume migration) conforms to a developed subsurface model. Furthermore, the MMV plan also includes containment operations including obtaining measurements that verify the stability of the developed subsurface model or robustness of an implemented storage complex based on the subsurface model. The MMV plan could be built or developed to blendstogether a number of discreet measurements acquired from the 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. Furthermore, the MMV plan can also account for the different phases of the injection (e.g., fluid injection) including pre-injection operations, injection operations, post-injection operations, and closure operations. The costs associated with the MMV plan can be large and span several decades. The disclosed subject-matter provides methods, systems, and computer programs that reduce these costs and also satisfy regulatory rules in an optimal and cost-effective fashion. According to one embodiment the disclosed techniques 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.

[0071] According to one embodiment, the disclosed approach develops 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 provides for the verification of whether a stored fluid (e.g. , carbon dioxide, CH4, or some other gas) is in place or not. Thus, this approach may avoid the creation of an image of the subsurface, but to simply verifies whether a stored fluid is present within a given sector of the storage complex (e.g, subsurface storage complex). According to one embodiment, the disclosed technology links the sampled data to a subsurface model on which the storage complex is based. Specifically, measurement(s) including sampling subsurface data provides adequate information both to confirm the presence of the stored fluid plume (e.g., gas plume) (or not) and also feed back into the subsurface model and thereby refine and adjust the subsurface model throughout the lifecycle of the storage complex. In some implementations, the disclosed technology includes:designing geometry parameters; selecting appropriate sensors to track or measure the designed geometry parameters; developing a mechanism to update the properties of the subsurface model based on the tracking; executing history matching operations on the subsurface model using the mechanism; executing update or optimization operations on the subsurface model based on the history matching operations; and executing verification operations on the updated subsurface model to ensure that the subsurface model is within a specified tolerance threshold value. If the subsurface model is not within the tolerance threshold value, the measurements from sensors may not be accurately matched to the subsurface model. This mismatch can be triggered or flagged to initiate a corrective process that leverages additional data acquisition operations or efforts that drive the optimization and design of the subsurface model or the storage facility based on same. According to one embodiment, the sensors used for sampling or tracking 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 specifications associated with the subsurface model update operations. In addition, the disclosed approach also leverages real-time or near real-time data associated with 4D full waveform inversion (FWI) operations or from 4D Machine Learning associated with plume property prediction from pre-stack 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 model, which is fungible, organic, or otherwise elastic or tweakable to ensure convergence on an optimal subsurface 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 number of verification operations may be executed on the optimal subsurface model as further discussed elsewhere herein.

[0072] Characterization.

[0073] According to some embodiments, the steps specified to adequately characterize a site for fluid or carbon storage includes the creation of a static geological model, upon which dynamic modelling coupled with geomechanical simulations can be undertaken, with sensitivity analysis, in order to identify and quantify the risks relating to the integrity of the site and storage complex. Dynamic simulators may be used to provide answers to questions such as storage capacity, injectivity and containment. In one embodiment, these simulators consider different storage scenarios 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., carbon dioxide) as well as simulate over long timescales and integrate or couple the simulators with geomechanical analysis. Geomechanical simulations by the simulators may be used to evaluate the strength of the rock given pore pressure changes for different injection scenarios as well as mitigate residual risks associated with failure or fault reactivation. As discussed, 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 form the bridge between the simulator and seismic domains and may therefore include an area of ongoing research.

[0074] Monitoring measurement and verification.

[0075] According to some implementations, the monitoring strategy may be incorporated into the design phase (see FIG. 1 A). When considering the design of the MMV strategy, objectives of the measurements being captured may be reviewed. FIG. IE describes some of the technologies applied within a carbon or fluid or gas storage MMV and groups them under multiple objectives. In particular, FIG. IE provides a summary of monitoring techniques, summarized under monitoring objectives (e.g., assurance or verification), area of investigation e.g., surface,borehole, near borehole or reservoir) and, phase of project (e.g., baseline, injection, post injection and long term). The first objective relates to assurance which may include assuring stakeholders that the carbon or fluid storage related activity is not having any unwanted outcome to the environment. In this way a link to containment of the fluid or carbon may be established. The second objective may include to verify the carbon or fluid storage related activity is progressing as planned based on acquired measurements confirm that the carbon or fluid storage activity is conformable to the predicted activity and that the subsurface model is therefore sufficiently accurate. Under these two objectives are listed seven measurement types that can be combined to verify that these objectives have been achieved and therefore address the regulatory rales. For the EU, this would be, providing a comparison between the actual and modelled behavior of stored fluid (e.g, carbon dioxide) relative to a water formation or a storage site as well as detecting irregularities, detecting migration of fluid, detecting leakage of stored fluid, detecting adverse effects for the surrounding environment, assessing the effectiveness of any corrective measures, and updating the assessment of the safety and integrity of the storage complex in the short and long term.

[0076] It is appreciated that some techniques may not be valid for some sites, and the design process, according to some embodiments, may be designed using a riskbased approach whereby the site-specific risks are identified, and the appropriate blend of technique and data type is derived to measure, monitor, and verify them. The area of the subsurface sampled by each measurement may be considered and accounted for. The timeline / phase of the injection cycle also may be considered in terms of the specified resolution, the areas sampled, and the value acquisition will bring. For example, in the early stages of injecting the fluid (e.g, carbon dioxide), the fluid or gas plume may be localized around a well, and so the monitoring data may be used to calibrate the initial dynamic models and can also be used to optimize injection rates. In the latter stages of injection, or in the post injection phase, thesubsurface model may be calibrated and further analyzed for insights. Therefore, the optimization of injection rates may be less impactful, and the fluid or gas plume may likely extend over an area that is far outside of that sampled by borehole measurements.

[0077] The role of seismic data within this holistic monitoring regime may be to assist with the 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 model and as the fluid (e.g, carbon dioxide) and pressure plume expand outside of the area which can be sampled by borehole seismic, a reversion to surface seismic data may be used to provide information that verify the migration of the fluid or gas plume and the absence of leakage.

[0078] While seismic data (e.g., borehole data and surface data) has been useful in monitoring fluid e.g., carbon dioxide) injection operations and has been proposed as part of the MMV plans for the 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 the CCS 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.

[0079] A full feasibility design study may be undertaken, according to some embodiments, to identify the expected time-lapse response to fluid (e.g., carbon dioxide) injection. This can identify, in a phased manner, which methods can detect the migration of fluid (e.g., carbon dioxide) within the storage unit 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 be effective at identifying fluid in place data. The viability of the seismic technique is dependent on the in-situ conditions and may bevalidated 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 the fluid into a depleted hydrocarbon field. While the case of injection into a saline aquifer may present a 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 suggest careful consideration in the acquisition and interpretation stages. This process may be driven by a forward modelling approach and non-seismic techniques 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 a fluid leak can be detected, for any given depth. The multiphase nature of fluid (e.g., carbon dioxide) may be analyzed for insights that may be incorporated into the analysis.

[0080] Additionally, while seismic methods may be sensitive to fluid (e.g., carbon dioxide) presence, the fluid (e.g., carbon dioxide) saturation quantification using seismic techniques may be less reliable than EM or gravity methods according to some embodiments. This may be considered when addressing monitoring objective(s). According to one embodiment, this disclosure provides mechanisms for monitoring movement within the storage unit. In addition, the disclosed techniques enable verifying saturation levels within the storage unit. Furthermore, the disclosed approach enables detecting leakage outside of the storage unit.

[0081] Given that the MMV strategy is in place to assure the safety of the operations and verify that they are proceeding as predicted, there is a desire to deploy methodologies and technologies that have been proven to be effective. 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 operation, there is opportunity to innovate and re-evaluate the cost-effectiveness of seismic monitoring, especially in the post-injection phase of the carbon storage or fluid storage project.

[0082] Conceptually, when the use of time-lapse seismic data for the monitoring of hydrocarbon production are compared, some distinctions between said time-lapse seismic data may lead to opportunities. In the first instance, a storage unit based on, for example, screening and MMV criteria may be chosen. This should limit, according to some embodiments, the complexity of the storage area. Additionally, the objective includes both seeking out and understanding increasing levels of complexity (e.g., as production rates drop) or identifying bypassed fluids in a storage complex, but to also compartmentalized subsurface structures such as reservoirs. In addition, the objective also includes confirming the migration of the fluid plume (e.g, carbon dioxide) plume, the absence of leakage and the conformance to the subsurface model relative to the storage facility within the subsurface. A third consideration may include managing the evolution of the monitoring design. In a hydrocarbon production scenario, operations that increase the level of monitoring such as the production rate decreases and the desire to better describe the subsurface to extract more value out of the CAPEX already invested is justified may be executed. Conversely, within fluid or carbon storage operations, the monitoring plan may be an upfront specification. 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 (i.e., injection, post-injection, closure) via automation and integration may be adopted. Given the upfront investment in baseline data and a geological environment lacking in complexity (e.g, relative to newly discovered hydrocarbon sites), the disclosed techniques enables the leveraging of artificial intelligence or machine learning techniques to assist in the processing and interpretation of time-lapse data and the accompanying evolution of time-lapse acquisition geometry from dense to sparse data points within the subsurface. Thedisclosed techniques also focuses on OPEX cost together with the predicted scale-up of sites requiring monitoring resulting in the investigation and development of new acquisition sensors and technology.

[0083] According to one embodiment, distributed acoustic sensing (DAS) technologies are used for the disclosed techniques. 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 may be deployed to capture surface seismic data for seismic monitoring operations. The use of surface deployed DAS (S- DAS) based on the disclosed methods shows potential as a sensor with which to acquire seismic data to monitor fluid or gas plume movement.

[0084] 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 techniques. Neural networks may be leveraged to predict the plume extent directly from a pre-stack seismic data. Initial trials on realistic synthetic data can indicate predicted plume extent relative to the true model and that 4D noise related to mis-positioning and 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 a 4D outcome that can be used to determine if further additional processing or / and acquisition is used as part of a semi-automated, adaptive monitoring workflow. In addition, using the machine learning techniques can decrease the sampling of the monitor survey in some cases. The neural network, for example, can be trained using a baseline data set based on early monitoring surveys and the knowledge or insights obtained from the training can be used to reconstruct subsequent monitoring surveys that are acquired with a geometry that has a lowersource and receiver effort. Initial results indicate that the drop in accuracy of the subsequent prediction may be within acceptable limits for the delineation of fluid (e.g., carbon dioxide) plume bodies and can also be used to quantify a relationship between sample density and the subsurface model update. Machine learning has several other opportunities to increase efficiency, reduce costs, and assist in the scale up of fluid or other CCS project development. These approaches may include the automation of seismic interpretation, fault interpretation, capturing subsurface structure and reservoir property uncertainty, and, the screening of legacy wells at a basin scale to assess risk and build an understanding of well integrity as part of the initial site selection criteria.

[0085] FIG. IF shows a selected experiment procedure output derived from a 3D S-DAS field experiment. In addition, FIG. 1G provides a comparison between the predicted plume extent and the true (e.g., model-based) plume extent.

[0086] As can be seen in FIG. 1H, the workflows for GS campaign design and execution as disclosed may involve modeling operations that define / build specific scenarios associated with a given resource site. The GS campaign design and execution may also include, parameterizing a GS model using a computation engine e.g., the model factory) and executing the GS model in one or more simulators, following which analysis operations may be executed on the modeling results to generate one or more reports or models, or automatically or semi-automatically configure equipment associated with GS operations at a given resource site. In particular, the disclosed technology allows the testing or simulating, in parallel, multiple physical properties associated with a given resource site across a number of geological realizations. The results from these tests may be fed into one or more analysis engines that condenses the vast simulation results into actionable insights or configuration settings, or safety data that may be used to optimize the configuration or operation of GS equipment at a given resource site. This may hone in or otherwise pinpoint precise monitoring techniques to deploy at each stage included in the GScampaign, thereby mitigating against GS project risks and operational costs. Using the results from the simulations (e.g., insights from the simulations), adaptive monitoring or safety strategies may be seamlessly implemented in conjunction with GS operations at a given resource site.

[0087] Resource Site.

[0088] 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. 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 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 or reservoir) including geophysical or chemical information. For example, the chemical information may include chemical information associated with the subsurface or chemical information associated with the surface / above ground areas of the resource site (200). 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 of FIG. 1H. In other embodiments, the techniques disclosed herein may be applied to surface seismic monitoring applications, surface gravity applications, surface electromagnetic applications, surface ground heave applications, and surface measurement by induced seismicity applications. According to some implementations, the disclosed techniques may be applied to remote sensing applications e.g., satellite-based measurements), subsea applications associated with permanent sensors, temporary sensor applications, applications associated with remotely operated vehicles, and applications associated with aerial-based measurements (e.g., performed from planes, helicopters, or drones).Such measurements may include Synthetic Aperture Radar (S AR) data, atmospheric concentration data associated with molecules such as carbon dioxide, CH4, or fluid concentration data associated with fluids or gases within the seabed.

[0089] At least some of the resource site (200) may be on land, on water, or below water. In addition, while a resource site (200) is depicted, the technology described herein may be used with any combination of one or more resource sites (e.g., multiple oil fields or multiple well sites, one or more saline aquifers, one or more depleted oil / gas fields, etc.), one or more processing facilities, etc. As can be seen in FIG. 2, the resource site (200) 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 number 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 may extend through the shale layer (206a) and the carbonate layer (206)b. The data acquisition tools, for example, may be adapted to take measurements and detect geophysical or chemical characteristics of the various formations shown.

[0090] 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 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 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 or analysis. The data collected from various sources at the resource site (200) may be processed orevaluated or used as training data, and / or used to generate high resolution result sets for characterizing a resource at the resource site, 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.

[0091] Data acquisition tool (202a) is illustrated as a measurement truck, which may include 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 Chr istmas 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, or other parameters of operations as further discussed below.

[0092] Sensors may be positioned throughout 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 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, and sensors that can be used for acquiring data regarding the formation, wellbore, formation fluid / gas, wellbore fluid, gas / oil / water included in the formation / wellbore fluid, or any other suitable sensor. For example, the sensors may include accelerometers, flow rate sensors, pressure transducers, electromagnetic sensors,acoustic sensors, temperature sensors, chemical agent detection sensors, nuclear sensors, or any additional suitable sensor. In one embodiment, the data captured by the one or more 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 suggest 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.

[0093] Evaluation sensors may be featured in downhole tools such as tools (202b)- (202d) and may include for instance electromagnetic, acoustic, nuclear, and optic sensors. Examples of tools, including evaluation sensors that can be used in the framework of the current method include electromagnetic tools, including imaging sensors, such as FMITM or QuantaGeo™ (mark of Schlumberger); induction sensors, such as Rt Scanner™ (mark of Schlumberger); multifrequency dielectric dispersion sensors, such as Dielectric Scanner™ (mark of Schlumberger); acoustic tools including sonic sensors, such as Sonic Scanner™ (mark of Schlumberger); or ultrasonic sensors, such as pulse-echo sensors, as in UBI™ or PowerEcho™ (marks of Schlumberger); or flexural sensors PowerFlex™ (mark of Schlumberger); nuclear sensors, such as Litho Scanner™ (mark of Schlumberger); or nuclear magnetic resonance sensors; fluid sampling tools, including fluid analysis sensors, such as InSitu Fluid Analyzer™ (mark of Schlumberger); distributed sensors, including fiber optic. Such evaluation sensors may be used in particular for evaluating the foimation in which the well is formed (i.e., determining petrophysical or geological properties of the formation), for verifying the integrity of the well (such as casing or cement properties) or analyzing the produced fluid (flow, type of fluid, etc.).

[0094] As shown, data acquisition tools (202a)-(202d) may generate data plots or measurements (208a)-(208d), respectively. These data plots are depicted within theresource site (200) to demonstrate that data generated by some of the operations executed at the resource site (200).

[0095] 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 that may be generated and updated in real-time. These measurements may be analyzed to better define properties of the formation(s), or determine the accuracy of the measurements, or check for and compensate for measurement errors. The plots of each of the respective measurements may be aligned or scaled for comparison and verification purposes. In some embodiments, base data associated with the plots may be incorporated into site planning, or used for modeling a test at the resource site (200). The respective measurements that can be taken may be any of the above.

[0096] Other data may also be collected, such as historical data of the resource site (200) or sites similar to the resource site (200), user inputs, information (e.g., economic information) associated with the resource site (200) or sites similar to the resource site (200), or other measurement data, and other parameters of interest. Similar measurements may also be used to measure changes in formation aspects over time.

[0097] 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) or at remote locations. A surface unit (e.g., one or more terminals (320)) may be used to communicate with the onsite tools or offsite operations, as well as with other surface or downhole sensors. The surface unit may be capable of sending commands to the oil field equipment / systems, and receiving data therefrom. 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.

[0098] The data collected by sensors may be used alone or in combination with other data. The data may be collected in one or more databases or transmitted on- oroff-site. The data 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 oil field (200). In one embodiment, the data is stored in separate databases, or combined into a single database.

[0099] High-Level Networked System.

[0100] FIG. 3 shows a high-level networked system diagram illustrating a communicative coupling of devices or systems associated with the resource site (200). The system 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 locations and may be scalable based on the specifications 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 the cloud-computing platform (310) may include a private network or portions of public networks. In some cases, a cloud-computingplatform (310) may include remote storage or other application processing capabilities.

[0101] The system 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 the user to receive, view, and transmit infoimation. In one embodiment, the user terminals (314a) and (314b) are computing systems having interfaces and devices including keyboards, touchscreens, display screens, speakers, microphones, a mouse, styluses, etc. The user terminals (314) may be communicatively coupled to the one or more servers of the cloud-computing platform (310). The user terminals (314) may be client terminals or expert terminals, enabling collaboration between clients and experts through the system of FIG. 3.

[0102] The system of FIG. 3 may also include at least one or more oil fields (200) having, 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). The resource site (200) may also have one or more 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) 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 more resource 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), or displayed on user interfaces associated with the set of servers of the cloud computing platform (310), 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 thecloud-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 or remotely from the resource site (200) and also send statuses / updates to other terminals such as the user terminals (314).

[0103] The system 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), or to the user terminals (314a) and (314b), or to the set of terminals (320) at the resource site (200) or to sensors at the oil field, or to other equipment at the resource site (200).

[0104] A processor, as discussed with reference to the system 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.

[0105] 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 or across multiple internal or external enclosures of a computing system 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 refers to the medium itself (z.e., tangible, not a signal) and not data storage persistency (e.g., RAM vs. ROM).

[0106] Note that instructions can be provided on one computer-readable or machine-readable storage medium, or alternatively, can be provided on multiple computer-readable or machine -readable storage media distributed in a large system having possible plural nodes or non-transitory storage means. Such computer-readable or machine -readable storage medium or media is 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.

[0107] It is appreciated that the described system of FIG. 3 is an example that may have more or fewer components than shown, may combine additional components, or may have a different configuration or arrangement of the components. The various components shown may be implemented in hardware, software, or a combination of both, hardware and software, including one or more signal processing or application specific integrated circuits.

[0108] 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 of FIG. 3. For example, the flowchart of FIG. 1H, as well as the flowcharts below, 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 be. The various modules of FIG. 3, combinations of these modules, 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 processorsmay be associated with the cloud-based 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).

[0109] In some embodiments, a computing system is provided that includes at least one processor, at least one memoiy, and one or more programs stored in the at least one memory, such that the programs include instructions, which when executed by the at least one processor, are configured to perform any method disclosed herein.

[0110] In some embodiments, a computer readable storage medium 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.[OHl] Embodiments.

[0112] Two major concerns facing fluid storage projects are development constraints (e.g, financial viability) and reservoir confinement issues. Operators strive to maintain injected fluid within a given storage site (e.g, reseivoir or aquifer) and prove to regulatory bodies that any fluid leakages are detectable or reportable. After a baseline for operational safety is established, the operator may verify injection performance (e.g., fluid injection performance) to secure that the fluid storage project is viable. Furthermore, other challenges associated with fluid storage include reliance or dependence on practices from other activities (e.g, hydrocarbon activities) which may not directly or indirectly align with fluid storage settings or configurations of the fluid storage equipment. According to some implementations, such activities may include abandonment of legacy wells without having a properseal (e.g., tight fluid seal) in place to prevent fluid leakage or having an incorrect type of seal (e.g., cement seal) or workflow in place for plugging fluid emissions. Other activities include use of chemicals during hydrocarbon extraction that restrict the flow of fluid or result in reactions that harm future fluid injection performance or improper over-pressuring or under-pres suring of the reservoir resulting in structural damage to the storage complex. In addition, there is an expected increase in fluid storage projects worldwide which will likely overtake the available fluid storage resources (e.g., human and non-human storage resources) which can greatly be improved using the techniques disclosed herein. Furthermore, government agencies may periodically assure or reassure that any fluid storage model being implemented for a given resource site is predictable and well controlled to comply with safety specifications for phases of a project (e.g., injection or post-injection phase of fluid storage and management) associated with a GS campaign for the resource site.

[0113] Under a given definition / characterization (e.g., digital quantification, uncertainty parameter quantification, etc.) of subsurface uncertainty, FIG. 4 shows that a measurement (e.g., exemplified with pressure) conducted in one well (e.g., injection well) may not be able to distinguish or detect an event (e.g, fluid leakage), whereas the same measurement conducted in a different well (e.g., monitor well #1”) may distinguish over or detect an event. As such, the example shown in FIG. 4 illustrates a spatial deployment (e.g., preferential spatial deployment) of a measurement with respect to monitorability of an event (e.g., fluid leakage). In some embodiments, the visualization shown in FIG. 4 indicates a number of different measurements (e.g., formation conductivity data or formation pressure data) that highlight which of the number of different measurements are more likely to detect the event. It is appreciated that the visualization shown in FIG. 4 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 techniques disclosed herein, and which facilitate generation ofvisualizations such as those shown in FIG. 4, enable the selection of measurement technology or spatial deployment of said technology in order to increase the likelihood that an event may be detected. In some embodiments, selection of the measurement technology is based on a definition, or characterization or modeling, of one or more subsurface uncertainties which determine expected distributions of measurement responses associated with events (e.g., fluid leakage). Once a first deployment of measurement technology has been made and 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, control gas monitoring equipment, or control containment infrastructure (e.g., valves, pumps, etc.) associated with the GS model(s). According to some implementations, the disclosed technology provides 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 specified 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.

[0114] According to some embodiments, the disclosed technology provides a monitoring design tool for subsurface applications associated with GS operations at a resource site. Furthermore, the disclosed technology provides useful monitoring mechanisms that are applicable both to GS operations and campaigns associated with geothermal solutions, hydrocarbon operations, offshore wind site operations,groundwater exploration activities, and other subsurface monitoring and optimization projects.

[0115] 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 to automate comparisons of said monitoring strategy with optimal baseline methods. Moreover, the disclosed technology may incorporate tools such as Agile TM Reservoir Modelling applications to leverage cloud computing resources and thereby accelerate uncertainty assessments associated with GS operations at a resource site. In particular, the systems and methods disclosed leverage in a number of simulators executing tests in parallel in order to enhance or improve a GS model being implemented at a given resource site to accelerate GS project execution times, as well as provide timely insights 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 or practices included in a given GS campaign for a resource site, and which span leakage pathways and monitoring methods for said GS campaign. Moreover, the disclosed technology may allow users to customize or otherwise 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 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 platform, users can adapt their simulators to securely ran on the sameplatform. As such, the disclosed technology is capable of evolving to become an “app repository” for simulators associated with GS storage operations.

[0116] Workflow For Optimizing Fluid Storage (GS) Operations.

[0117] At a high level, a user may model the storage complex / site for a given GS operation using a subsurface modelling application (e.g., Petrel TM) to generate a GS model. The storage complex or storage site may include areas below ground where fluid is stored, or a volume of space above a reservoir where fluid is stored, 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 already captured fluid in which case the systems and workflows disclosed are 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. The GS model may be released or otherwise liberated into another testing or simulation tool, or application (e.g., a Delfi TM digital platform or an Open Subsurface Data Universe (OSDU TM)) 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 reviewed and additional contextual information may be added to same. 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 number of desiredsimulation configurations that test the scenarios for a number of time periods (days, weeks, months, or years). The simulation configurations may be matched to specific simulators which receive said configurations for testing the GS model. Once the simulators complete testing the GS model using a number 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 number 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 a previously established baseline simulation data. In addition, information about the storage site or storage complex structure (e.g, reservoir extent, layer geometry, 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.

[0118] The fluid detection map depicted in FIG. 5, for example, indicates a carbon dioxide distribution data (e.g, probabilistic carbon dioxide distribution data) of a change in relative acoustic impedance between a carbon dioxide pre-injection baseline and a current state after several years of injection. The fluid detection map may be generated using an ensemble of simulation results from the number of simulations or testing of the GS model which outlines 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 the probability of change associated with 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 where themovement 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 structuring in order to optimize fluid detectability at the resource site. In addition, the detection map may include a number of colorings 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 detection map may include a red coloring that indicates a highest likelihood of fluid leakage for a given location at the resource site, a yellow color to indicate a medium likelihood of fluid leakage for other locations at the resource site, and a green coloring indicating a low likelihood of fluid leakage for some locations at the resource or fluid storage site. It is appreciated that the coloring on the detection map may be a spectrum of colors with red at the extreme end of the spectrum indicating a high likelihood of fluid leakage events, and green at the low end of the spectrum indicating a low likelihood of fluid leakage events.[01191 A benefit of the disclosed approach is the drilling-down into individual simulators or workflows to understand fluid detection thresholds based on a number of site-specific scenarios or simulation conditions. As fluid storage sites become operational, so does the actual monitoring data associated with said sites. Previously, baseline monitoring data was available for configuring simulation parameters of the GS model. The additional real-time or near real-time data, or historic data, captured at the 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 for the user’s attention 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 on the GS model. Such detection or flagging of data abnormalities mayadaptively enable the GS model to be updated or otherwise parametrically revised in order to facilitate accurate detection of fluid events at the storage site.

[0120] The disclosed technology is directed to methods and systems for optimizing fluid 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. At block (602), the data processing engine may facilitate generating a GS model associated with the resource site such that the GS model includes 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. At block (604), the data processing engine enables determining risk thresholds 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 at the resource site. 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 real-time or near real-time data associated with the resource site that has been captured by one or more sensors deployed around the one or more locations at the resource site. 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 number of fluid leakage events based on the one or moreparameters of the GS model over multiple time periods, a number of fluid monitoring plans that track the number of fluid leakage events across a number of geological realizations, and a number of dependent or independent simulations or tests that inform the impact of the fluid monitoring plans over the multiple time periods. Turning to block (610) of FIG. 6, the data processing engine is used to execute the simulation plan across multiple simulators in parallel, a defined uncertainty space derived from the uncertainty data, the multiple time periods, and the number of geological realizations. 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 locations at the resource site, and configuration data associated with configuring one or more monitoring systems at the resource site. 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.[0121 J These and other implementations may each optionally include one or more of the following features. The resource site may include 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. 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 included in the risk profile data based onemission constraints imposed by regulatory bodies. In addition, the one or more parameters of the GS model may include one or more of raw or processed data captured at the resource site, onshore or offshore geological data associated with the resource site including seismic data and the temporal or spatial distribution data of subsurface geological structures of the resource site, well characteristics data including the well log data, structural data derived from the onshore or offshore geological data, faults data, and interpreted geo-layering data, geophysical data indicating one or more of seismic information, gravity information, electromagnetic information, and nuclear information associated with the resource site, and configuration data associated with the one or more monitoring systems at the resource site. The configuration data, according to some embodiments, includes at least one of line spacing between sensors at the resource site, and frequency configurations used to tune electromagnetic sensors at the resource site. Moreover, the workflow data associated with the resource site includes data indicating the frequency of executing the simulation plan, data indicating the frequency of updating the simulation plan, data indicating time-lapse measurements included in the multiple time periods, and dynamic post-processing operations data. The dynamic postprocessing operations data according to some embodiments includes at least one of noise removal operations from the captured real-time 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. It is appreciated that the risk profile data includes 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. 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.

[0122] According to some embodiments, executing the simulation plan includes executing a number of simulation streams that test multiple geo-physical properties associated with the one or more locations at the resource site in parallel and concurrently across the number of geological realizations. The number of geological realizations may include a number of GS sub-models of the subsurface associated with the resource site such that the number of GS sub-models indicate at least one of fault characteristics data associated with the resource site, transmissibility data associated with the resource site, or distribution data indicating statistical characterizations of the subsurface of the resource site. Furthermore, the analysis data may be used to generate a report or model based on analyzing the simulation results across the defined uncertainty space in different time steps and across different geological realizations included in the number of geological realizations.

[0123] The report or model may include a number of items. For example, the report or model may include one or more of a visualization indicating the fluid detection map, a matrix indicating efficacy level data for using a number of different GS operations at the resource site based on the simulations to determine an optimal GS campaign for the resource site, a number 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.

[0124] 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. In addition, the multiple simulators referenced above may include one or more of flow-based simulator(s) that depend on pressure measurements within the subsurface of the resource site, electromagnetic 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 thesubsurface at the resource site, including but not limited to pulsed-neutron methods simulator(s) or nuclear magnetic resonance (NMR) simulator(s). 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, or depleted oil or fluid reservoir) associated with the resource site. 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.

[0125] In addition, the multiple simulators may include acoustic simulator(s) that depend on acoustic properties of the subsurface at the resource site. It is appreciated that simulations associated with the multiple simulators may include a number 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.

[0126] This provides a direct value of a quantity, for example, in the subsurface of the resource site. However, when using other geophysical methods that 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. 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. 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.

[0127] According to some implementations, the one or more parameters of the GS model characterize one or more of surface seismic data, surface gravity data, surface electromagnetic field data, surface ground heave data, and surface measurement data indicating induced seismicity. In addition, the data processing engine referenced in FIG. 6 may facilitate generating, using the analysis data, a report or model including one or more of a number 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. In some embodiments, parameterizing the one or more parameters of the GS model includes updating grid resolution data for at least one parameter included in the one or more parameters of the GS model.

[0128] 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. 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. 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.

[0129] Workflow For Monitoring Stored Fluid.

[0130] FIG. 7 shows a flowchart showing a departure from discrete acquisition of data such as vertical seismic profiling (VSP) data, high density surface seismic (HD) data acquired prior to simulation which includes baseline data, and HD monitoring data. In particular, this departure may also include moving away from discrete updates of the subsurface model to a system of sparse detection and automated or semi-automated or efficient updates of the subsurface model. It is appreciated that theleft axis of FIG. 7 shows implementation effects that result in lower cost while the right axis of FIG. 7 shows the improved digital integration impacts from implementing the disclosed techniques.

[0131] In one embodiment, the disclosed approach streamlines 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 approach 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 techniques enable the efficiencies in history matching operations associated with the subsurface 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 is particularly beneficial as sparse data is used instead of a full spectrum of subsurface or surface data.

[0132] According to one embodiment, the disclosed technology includes the following processing stages associated with MMV operations. A first processing stage includes generating a baseline data (e.g., high resolution baseline data) enabling usage of a Synthetic Seismic Volume Generation Software (Sim2Seis) workflow or simulation tool. A second processing stage includes executing a detailed simulation of an injection scenario based on parameterizing a subsurface model based on the baseline data.

[0133] A third processing stage includes designing a detection campaign based on the simulation, including detecting that a seismic data set aimed at monitoring subsurface changes due to fluid plume changes, such that a quantitative flag (e.g., 1 or 0) or a qualitative flag (e.g., yes / no) is raised with respect to the presence or absence of fluid in the subsurface (e.g., ground). The detection operation may be adapted based on an expected signal-to-noise ratio and a response of the Sim2Seis simulation using the subsurface model relative to expected fluid plume changes dueto 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 any given time set It is appreciated that this detection approach is cost-effective and is more rapid than full 4D imaging operations or efforts.

[0134] A fourth processing stage includes executing of parameter detection during injection operations included in the simulation. A fifth processing stage includes executing compliance operations with pre-drill scenario checkups, including evaluating fluid presence with respect to an injection plan to verify or trigger quality assurance flags when simulation and field observation data are in agreement. A sixth processing stage includes executing operations associated with reassurance of the next survey, including analyzing and generating regulatory specifications associated with the subsurface model of the next survey. A seventh processing stage includes designing and conforming the subsurface model associated with the next survey to comply with said regulatory specifications.

[0135] An eighth processing stage includes a non-compliance scenario. In the case of deviation from Sim2Seis simulation operations e.g., the plume location is not in agreement with flow simulation), the eighth stage includes executing one or more of the following steps. The step may be updating the subsurface model based on back projection of detection information using a fluid flow simulation. The step may be executing an adaptive monitoring operation including. The step may be 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. Thus, the subsurface model is updated, regulatory compliance is preserved, subsurface uncertainty is reduced and safe operation is enabled. Additionally, an adaptive monitoring strategy is redesigned.

[0136] Adaptive Monitoring.

[0137] 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 approach shifts from a 4D seismic reservoir monitoring solution relying on full reservoir production and monitoring to a plume centric approach which tracks the progress of the fluid (e.g., carbon dioxide) plume through a focused 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., carbon dioxide) 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 to confirm adherence to the subsurface model.

[0138] In some cases, the disclosed approach facilitates the identification of irregularities associated with the subsurface model. For example, the irregularities may include data which includes data that is not predicted by the subsurface model during the simulations. Examples for such irregularities can be seismic amplitude data changes in places within the subsurface that are not predicted by the flow simulations or the subsurface model and which arrive at different times relative to expected different spatial distribution data associated with the subsurface model. These irregularities can trigger additional targeted acquisition. Prioritizing the link back to the subsurface simulation model may enable this process to leverage opportunities and thereby optimize the link between automation or semi-automation of the 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 or quantification of the match or mismatch through uncertainty analysis. These aspects are further depicted in the workflow of FIG. 8 which shows a workflow for implementing the disclosed adaptive monitoring regime.

[0139] Fluid capture and storage or carbon capture and storage (CCS) operations have been identified as an enabler to meet net zero targets. The process of identifying, characterizing, and a 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. Within this, seismic data has a role to play through the lifecycle of the carbon or fluid storage project. Legacy seismic data can enable regional screening of fluid storage capacities. Newly reprocessed seismic data can support the characterization of the priority storage areas and other design considerations while, new acquisition of seismic data can help minimize some outstanding uncertainty or risk within the reservoir or overburden (e.g, overburden indicating sedimentary column data relative to a subsurface structure), while also forming an ideal baseline for future monitoring. Carbon or fluid storage regulations have highlighted the goal for understanding the subsurface before implementation activities are executed. To do this, the utilization of the available seismic data may be used prior to the commencement of fluid (e.g., carbon dioxide injection) operations.

[0140] Fluid or CS MMV is a broad, cross discipline undertaking and uses a riskbased approach. The technologies deployed provided in this disclosure can depend on identified risks to safe storage and containment and can consider site geology, volume of fluid (e.g., carbon dioxide), and the regulatory framework applied to the storage facility. Within this, seismic data (e.g., borehole seismic data or surface seismic data) may be used in characterizing the near-well area, for initial calibration of the subsurface model, and verifying fluid (e.g, carbon dioxide) plume and pressure movement and detecting leakage. The viability of the seismic technique may be dependent on the in-situ conditions within the subsurface and may be validated through modelling as part of the design process (e.g., deployment to monitor injection into aquifers versus deployment to monitor injection into depleted fields). Where the seismic technique is shown to give a recordable signal that can be directlylinked back to subsurface changes, opportunities exist to improve the costeffectiveness of seismic data within the monitoring system. The disclosed approach also includes the use of fiber optic cable deployed horizontally at the surface of the subsurface within which the fluid storage facility is located to record active or passive seismic data as well as use machine learning techniques to accelerate the time-lapse interpretation for use in monitoring fluid (e.g., carbon dioxide) plume movement and leakage as well as optimize and streamline the seismic history matching process. These developments combine to provide an integrated digital solution that enables a cost-efficient, adaptive monitoring system to verify safe carbon storage operations.

[0141] According to one embodiment, model data including one or more of geophysical data, gravity data, electromagnetic field data, borehole data, reservoir data, or well log data may be used to generate a subsurface 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, the generation of the subsurface model may be based on using one or more of the aforementioned data to generate a stratigraphic framework included in the subsurface model. The stratigraphic framework may have static or dynamic properties 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 or geostatistics data 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 or machine learning or artificial intelligence operations; porosity or permeability parameters indicating relationships based on rock physics or laboratory tests on subsurface samples.

[0142] In one embodiment, the created subsurface model may be subjected to one or more simulations or tests based on the above parameters. The simulations, forexample, provide response data based on dynamic values associated with or ascribed to the various parameters during the simulation. 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 simulation relative to established thresholds for the subsurface model. In particular, the deviations data can trigger update operations to the subsurface model based on updating one or more of the aforementioned parameters to maintain, keep, or transition the subsurface model in a stable state.

[0143] In one or more embodiments, 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 be done following the description above where seismic data 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 is 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 based on the fluid injection rate data or fluid pressure data associated with storing fluid in the subsurface to predict the seismic response by one or more of the following:

[0144] (I) Applying saturation data, or pressure data, or temperature data to vary one or more parameters of the subsurface model. (II) Estimating effective stress changes associated with geomechanics interactions of one or more parameters of the model. (Ill) Estimating the fluid (e.g, gas) compressibility of the subsurface model using an equation of state (EOS) data or a National Institute of Standards and Technology NIST dataset (e.g, compliance data). (IV) Applying calibrated rockphysics indicated by one or more parameters of the subsurface model to predict an elastic response of the subsurface given the simulated fluid (e.g. , carbon dioxide) 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 included 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 included in the subsurface.

[0145] According to one embodiment, spatial or temporal fluid distribution data (e.g., 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, fluid anisotropy data may also be generated in response to executing the simulation for any given time period.

[0146] The ability to simulate the seismic response of the subsurface based on flow or subsurface parameters provide for the following capabilities. First, generating quality assurance data to optimize the fluid storage operations at the resource site based on the seismic response data when seismic predictions are in conformance with the simulations.

[0147] Second, flagging fluid irregularities with which to trigger two processes. The first process is updating flow simulation parameters of the subsurface model based on new spatial or temporal patterns by back-projection of the location data of the fluid plume. The second process is developing optimal survey design and modeling operations or workflows where source and receiver configuration is selected to best mitigate against potential issues which were not considered before the observations of said deviations.

[0148] 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; (II) directly or dynamically updating parameters associated with the subsurface model based on the simulation; (III) commissioning additional geophysics surveys where mitigation operations are used for the subsurface storage facility without incurring additional costs; (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.

[0149] Example Implementations for Measure, Monitor, and Verify (MMV) Plans.

[0150] In some implementations, conformance objectives may be met by capturing time-lapse (4D) seismic data. This can be acquired using 3-dimensional 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 a 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 includes 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 repeatedly collecting dense 3-dimensional seismic surveys, processing said surveys to obtain a seismic image, and developing a 4-dimensional image based on same for interpretation by subtracting the 3- dimensional images for any given time step. As further discussed below, the disclosed approach does not suffer from these challenges.

[0151] According to some embodiments, the disclosed solution includes 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 disclosedapproach includes 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 create an image of the subsurface, but to verify 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 subsurface model. More specifically, the measurement(s) involved in this process provide adequate information both to confirm the presence or absence of a fluid or gas plume, and to feed back into the model e.g., subsurface model) such that the model can be refined or otherwise optimized or adjusted or updated throughout the life of the fluid storage project. The receiver or data aggregation efforts are designed to cover the anticipated extent of the plume through the life of the injection program (with an error margin) and the source or data detection efforts are adaptive to reflect the monitoring objective at any given time or phase of the injection program.[01521 According to some implementations, geometry data associated with a detected fluid is depicted in FIG. 9. In particular, FIG. 9 shows an implementation with a spoke receiver geometry that has a single source in the center. This geometry, for example, covers an area over which a 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 uncertainly 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 or appropriate probability maps of fluid saturation data or fluid-in-place data, which may be created by running one or more flow simulation scenarios.

[0153] The primary geometry, according to one embodiment, includes 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 atthe injection well head). Coverage plots of PP and PS seismic energy are shown in this figure. For a single central source, the coverage of the PS seismic energy increases (relative to the PP 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 SDAS solution due to the improved PS response of SDAS.

[0154] FIG. 9 also indicates the extent of the plume size for vaiying 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 give a dense initial coverage during the first 0-1 year or 1-3 years of injection. The geometry then becomes gradually more sparse 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 spoke design. The length of each spoke 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).

[0155] 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 with a central source with a spiral cable layout. In particular, FIG. 10 shows a spiral receiver geometry with a single source in the center.

[0156] The second includes a configuration 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 with a single source at the center. Each of these plot show an associated PP and PS coverage.

[0157] According to one embodiment, operations associated with the source effort may be independent of receiver layout and can adapt to identified risks or stage of injection or results of a previous survey. The basic form includes one or more central sources (e.g., a rig-based source). These are repeated to build signal and overcomenoise. The indicative subsurface coverage of this central source is provided in FIG. 12. In particular, FIG. 12 shows an implementation of detection coverage based on a central shot with reflection and diving waves being recorded along the spokes with increasing offset. As injection continues, it may be useful to acquire additional shots or detections as identified in the MMV plan. The proposed geometries are adaptive to accommodate this. The first level of additional shots can include 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 with an 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, according to some embodiments, may be recorded into an array to improve subsurface coverage in several ways. The improvement includes 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). 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 from the line of shots acquired around the edge of the outer circumference of the spoke geometry. 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 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 and PS coverage.

[0158] An additional level of shot acquisition effort is shown in FIG. 16. More specifically, FIG. 16 includes a spoke geometry with additional spiral shots. 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 extend to the outer circumference and beyond, according to some embodiments. 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 be prescribed at any given time step as part of the original MMV plan, or it may be triggered in response to irregularities between the predicted and measured extent of the plume. FIG. 17 shows a small spiral shot line that may be adequate for the early stages of plume migration. In particular, FIG. 17 shows an example of the reflection or imaging coverage from a small spiral line of shots acquired from the center of the spoke design and extends 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 (e.g., 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)).

[0159] As the injection project matures and the plume extent increases, the shot acquisition effort can be increased adaptively. FIG. 18 shows the subsurfacecoverage for PP and PS seismic analysis where 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 from a large spiral line of shots acquired from the center of the spoke design and extending out to cover the full receiver spoke which, in this case, includes 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 includes two objectives for when monitoring fluid injection. This geometry can be adapted with longer spokes should monitoring the pressure changes over a larger area be desired.

[0160] 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 specifications of the fiber optic interrogator. Specifically, FIG. 19 depicts a representation of how this design could be achieved using hardware for interrogation of the 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 is about less than 50 km, but each interrogator can monitor 2 fiber optic cables. This can lead to visualizing the number of spokes that can be achieved with an associated number of interrogators. It is anticipated that hardware capacity and capabilities will evolve quickly and the design will adjust accordingly. This image is for representation and does not limit the disclosed approach to just the figure in question. Taking an indicative representation of the available hardware and its cun ent specifications (e.g., each being able to interrogate at least 2 fiber optic cables over 50 km per cable), tradeoffs between the number of intenogators used and the density of spokes in the geometry design are depicted in this figure. The proposed solution is robust to this, and the appropriate design is assessed as part of the survey design and MMV plan considering what therisks are that are to be monitored for the given amount of fluid injection planned. The proposed solution is also robust to advances in interrogator hardware, with increases in the length of fiber optic cable that can be interrogated at any given time.

[0161] FIG. 20 provides impact data associated with using the above-discussed geometries as part of an adaptive monitoring strategy relative to a time and motion study as well as cost savings. In particular, FIG. 20 provides a comparison of the duration of time taken to acquire the shots within each design. For example, the proposed SDAS 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 when compared 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 primarily 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 specifies.

[0162] Using surface DAS to record time shifts for detection and monitoring of storage sites.

[0163] Again, carbon storage is regulated. The regulations may specify that monitoring is undertaken to demonstrate to the regulator that the activity is safe and proceeding as planned. This is widely termed MMV (measure, monitor, verify). The MMV plan divides into two. The primary objective is conformance. Confoimance involves time-lapse measurements to be undertaken to confirm the carbon dioxide (or other injected substances) plume migration is conforming to the subsurface model. The secondary objective is containment. Containment involves measurements to be undertaken to verify the absence of effects outside of the storage complex. The carbon storage operator builds a MMV plan that blends together a number of discreetmeasurements acquired using a variety of tools. Well-formed MMV plans account for several domains (e.g., atmosphere, biosphere, hydrosphere, and geosphere), and also account for the different phases of the injection project (e.g., pre-inj ection, injection, post-injection, and closure). The operating cost associated with the MMV plan can be large and span several decades. The industry goal is to reduce these costs and achieve the regulators objectives in a most cost-effective manner.

[0164] As discussed elsewhere in this application, this disclosure provides the concept of adaptive monitoring and the use of surface deployed fiber optic cable (S- DAS) as a sensor to record seismic data, and have also addressed the acquisition geometry used to acquire S-DAS data for use as part of adaptive monitoring or independently of adaptive monitoring. In this part of the application, embodiments to achieve the 4D detection objective are provided (i.e., detect the presence of carbon dioxide and delineate its spatial extent).

[0165] To enable a cost-efficient way to meet the conformance objective within the MMV plan specified by carbon storage regulation, embodiments are disclosed herein of a cost-efficient way to detect and verify the edge of a carbon dioxide plume using time shifts in seismic recordings, i.e., time shifts between a baseline recording, acquired before carbon dioxide injection) and a monitor recording at some point in the future. The exact timing of this monitor survey will be defined in the MMV plan and may be updated based on the analysis of the previous monitor or other suitable criteria. These time shifts can be recorded in many types of seismic wave. For the example shown here we refer to seismic refractions, but the methodology is equally applicable to time shifts in body waves, diving rays etc. In some embodiments, the disclosed techniques can also be used to record variations in time shifts associated with reflections.

[0166] One or more embodiments are illustrated in FIGS. 21-31. In this example we reference time shifts associated with seismic refraction energy generated by a source shooting from the outside of the carbon dioxide plume at a distance largerthan the offset from the edge of the carbon dioxide plume into a surface receiver array going through the carbon dioxide plume. However, in varying embodiments, time shifts in seismic wave types other than refraction may be used to implement the embodiment.

[0167] In the illustrated example, the method capitalizes on well-known characteristics of the ray path of refraction energy and the fact that the presence of the carbon dioxide will change the timing of the refractions originating from the boundary below where the carbon dioxide plume is trapped. It comes in two steps: detection and positioning.

[0168] Detection: According to Snell’s law, refraction energy travels primarily horizontally at the velocity of the layer below the boundary where they are generated, and they can be recorded at any offsets larger than the offset. In this example, we use this characteristic combined with a layout where the source and the receiver array are positioned on a line going both through the source and the injector well where the carbon dioxide plume originates. In this specific example, the source will be located outside of the carbon dioxide plume at a distance larger than the offset (Xc) from the edge of the plume. When refraction energy encounters the carbon dioxide plume on the horizontal part of its ray path, it travels slower than when brine was in place (this will be the case for carbon dioxide replacing brine) and therefore creates a timing difference compared to the previous measurement done at the same location when the carbon dioxide plume had not yet reached the area. Note that the method described also records unperturbed refraction energy, this will be recorded on the part of the receiver array located between the source and the plume edge and for allowing those recordings. To record both perturbed and unperturbed refraction ray paths on enough receivers, positioning the source at a distance of at least 1.5 times the offset from the plume edge or more can be effective. The comparison of the timing of the refractions in a baseline (recorded before injection begins) shot gather and a monitor (after plume extension) shot gather will allow a scan with minimum source effort for anychange in velocity below the boundary where the carbon dioxide plume is being trapped and detect a change.

[0169] Positioning: It is also possible to characterize the ray path of the refraction along the exiting upgoing leg, and to predict at which point the refraction has stopped travelling horizontally and has started going toward the surface (this is again based on knowledge of the angle derived from Snell’s law, where the refracted ray becomes parallel to the boundary between two mediums). We use this second fact to predict the location of the change in the subsurface associated to the onset of the time shift. This should be located at half an offset (Xc / 2) on the line going from the receiver to the source. In some example embodiments, the method also leverages the presence of a dense receiver layout to allow accurate identification of the time shift onset as shown on FIGS. 22-23. Note that the benefit of having a seismic source located outside (z'.e., located at a position that is not directly above) the carbon dioxide 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 perturbed refraction. The method is, however, also applicable to the scenario when the seismic source is located at a position directly above the carbon dioxide plume and both the entry and exit leg of the refraction is perturbed.

[0170] Techniques in this disclosure may also be used to identify and process time shifts in body waves, diving waves or any first break energy. The methodology can also be extended to time shifts or amplitude changes associated with reflections. This has the potential to provide information above the storage unit and uses less a-priori information from the flow simulation model. The advantage to this is that it could provide information in the area / zone less than the offset from the injector well if used in conjunction with a rig-based source, and it could provide information about carbon dioxide in the overburden which would contribute to the containment objectives of the MMV plan.

[0171] FIG. 21 outlines the principle of seismic refractions. FIG. 22 shows an example of the impact that an anomalous velocity would have on the timing of a given refraction and which receivers would detect this variation in timing or time shift (relative to the baseline which was recorded prior to injection). FIG. 23, highlights the advantage of using fiber optic cable, with the increase spatial sampling along the cable relative to the spatial sampling that could be achieved economically with existing seismic sensors (e.g, ocean bottom nodes, or geophones in the land case).

[0172] Once we identify the onset of a time shift (relative to the baseline recording) we can locate the edge of the velocity anomaly (i.e., carbon dioxide plume) that is causing the time shift. FIG. 24 shows the relationship to the offset, which is related to the angle of reflection (where the refracted ray becomes parallel to the boundary between two mediums) for a given velocity contrast. In this example, the method used to pick the time shifts in FIGS. 25-30 is a cross-correlation calculated in a window based on the expected arrival time of the refraction. The method may use limited prior information of the subsurface: the depth of the boundary (H) at the location of the carbon dioxide plume, an estimate of the interval velocity above the boundary (VI), and an estimate of the interval velocity below the boundary (V2) below which the plume is observed. This knowledge is used for two purposes: first to have estimate of the arrival time of the refraction, and secondly to position the source at a distance larger than the offset (Xc) from the edge of the plume location. In theoiy there is no limit of how far the source is to be from the edge of the carbon dioxide plume and therefore the source may be sufficiently far from the injector well (in one modelling experiment, the offset is estimated to be around 4km at the plume level and the left shot is located around 10 km from the well).

[0173] Using this prior information, the cross-correlation between co-located traces (repeated source and receiver positions) of a baseline shot gather and a monitorshot gather is calculated in a window around the expected arrival time of the refraction from the top aquifer. A straight ray assumption in a layer cake medium is sufficient to estimate the refraction arrival time. The refraction arrival time increases linearly with offset (this is based on Snell’s law and Pythagorean identities and is essentially equivalent to applying a linear move-out (LMO) to flatten the refractions in the shot gather). To increase detection, one may calculate the cross-correlation in several windows derived from this reference window and shifted relative to it, to identify a time shift that would appear slightly earlier or later in the seismic trace. This allows inaccuracy to be taken into account in our estimate of the arrival time of the refraction and ensure that we are picking the time shifts related to the presence of the plume. After collecting the results from the windows and filtering the crosscorrelation showing a low correlation value (less than 0.6 in our case), we sort the time shift by increasing offsets and pick the location of the first time shift (first time shift larger than 0.25ms in our case). From this time shift, we extract the position of the receiver that based on the straight ray assumption is beyond the edge of the carbon dioxide plume of half an offset (Xc = 4km in our case and therefore Xc / 2 = 2km) and use its position minus Xc / 2 in the direction of the source to define this point as the edge of the plume. The same method is used with a baseline and monitor shot located on the other side of the carbon dioxide plume allowing to map both edges of the carbon dioxide plume.

[0174] One or more embodiments may be varied. Indeed, in some embodiments, the disclosed techniques may be used to relate the onset of the recorded time shift to the lateral extent of the velocity anomaly (carbon dioxide plume), and will be adaptive based on the geological setting and available a-priori information.

[0175] Thus, the foregoing example method, when combined with the improved spatial sampling of the S-DAS technique and the S-DAS geometries (FIG. 33), allows detection of the lateral extent of the carbon dioxide plume within a known subsurface model. That is, in an area where we have a good understanding of thecurrent seismic velocity structure within the subsurface, and the predicted velocity variations that would occur as a function of carbon dioxide injection, derived from time-lapse rock physics analysis and flow simulation analysis, which is commonly available within a carbon dioxide geological storage project.

[0176] By deploying the fiber optic cable in a spoke geometry (or similar variation) in conjunction with active sources located outside of the receiver array (off the end of the spoke), spiral shot deployment across the receiver array, or by using passive ambient noise to create ‘pseudo shots’, one may detect the anomalous velocity zone within the subsurface which relates to the area in which the carbon dioxide plume has displaced brine, in the case of the saline aquifer storage scenario (or other fluid in the case of a depleted oil or gas reservoir storage scenario). This is achieved through the recording and analysis of seismic time shifts. That is variations in the arrival of a given seismic wave from a given baseline dataset (recorded before injection begins). By mapping the contact with the anomalous velocity zone around the injection site we can build the 3D spatial extent of the plume. This map of the plume extent can therefore be corroborated against the predicted carbon dioxide plume, derived from the flow simulation, and an assessment be made on whether the operations are in conformance with the predictions, as provided by the CCS regulations. By using techniques in accordance with the disclosed methods, conformance assessment can be made with significantly less effort than a conventional time-lapse seismic survey and greater spatial coverage than can be achieved with methodologies using sensors deployed within injection or monitoring wells.

[0177] Use of Noise Data for Seismic Modeling.

[0178] The disclosed approach is directed to using deployed vibration sensors (e.g., fiber optics sensors horizontally deployed) at a site (e.g., resource or nonresource sites) to capture ambient noise associated with a subsurface of the site and thereby generate subsurface information of the site using said ambient noise. Thisprocess, according to one embodiment, may be regarded as a type of seismic interferometry.

[0179] According to one embodiment, the disclosed approach uses deployed fiber optic cables to record (e.g., passively record) noise data associated with a subsurface (e.g, geological formations, ocean-bottoms, geological surfaces, etc.) to generate subsurface models or information continuously in time and dynamically or adaptively trigger operations associated with monitoring, measuring, and verifying (MMV) objectives for carbon capture, usage, and storage (CCUS) and other subsurface monitoring purposes.

[0180] In one or more embodiments, the disclosed approach includes one or more of the following features: noise analysis and classification operations using noise data captured (e.g, passively captured) by vibration sensors; generation of passive virtual shots using the captured noise data and based on noise characteristics continuously in time (e.g., per 1 hour or 1 day, or 1 week, or 2 weeks, or 1 months, or 1 year, etc.).

[0181] Furthermore, the disclosed solution involves analyzing the passive virtual shots based on one or more of: changes in virtual shots along the fiber optics with respect to time; change in virtual shots driven attributes with respect to time (e.g, surface wave dispersion, etc.), and time-lapse inversion of virtual shots using, for example, full-waveform inversion (FWI) or surface / Scholte wave inversion techniques. In one embodiment an MMV operation may be triggered or otherwise activated for a one or more assets or sites being monitored such that changes associated with passive virtual shots are classified and flagged with attendant actions or subsequent operations (e.g, MMV or CCUS operations) being sanctioned or authorized for execution based on changes observed in the subsurface.

[0182] FIG. 34A depicts the use of noise data to generate the virtual shots depicted in FIG. 34B, according to some embodiments, based on interferometric principles. It is appreciated that the disclosed approach facilitates the use of these virtual shots generated from ambient noise recorded on fiber optics interrogator to developsubsurface models or multidimensional characterizations of a subsurface. The noise data, when properly processed and converted to virtual shots, may be used to monitor subsurface changes through time and thereby trigger or activate actions associated with CCUS and other MMV objectives.

[0183] In some embodiments, the disclosed approach uses one or more inversion techniques including one or more of the following. (I) Noise analysis and classification where the ambient noise included in the noise data is recorded continuously in time and analyzed with respect to one or more dominant frequencies relative to noise patterns or sources. It is appreciated that low frequencies are first analyzed prior to analyzing high frequencies included in the noise data.(II) Generation of passive virtual shots based on noise characteristics continuously for specific time durations (e.g., per 1 hour, 1 day, 1 week, 2 weeks, 1 month, etc ). In some embodiments, seismic interferometric principles may be leveraged to compute (e.g., automatically compute) virtual shots continuously in time for selected intervals. These virtual shots may be generated by carefully processing the ambient noise patterns included in the captured noise data to enhance surface wave and body waves interferometry data included therein. The virtual shots may be further computed from the continuous recording by a seismic data acquisition system (SDAS) and are stored and used for various monitoring objectives. (Ill) Analysis of the passive virtual shots using analysis data derived from the virtual shots and which indicate shot behavior through time which can be used to augment, enhance, or otherwise optimize a monitoring framework. In one embodiment, the following properties of the virtual shots are used to generate the analysis data: changes in virtual shots along the fiber optics with respect to space and time; change in virtual shots driven attributes with respect to time (e.g., surface wave dispersion curves, (e.g, FIG. 34B); and time-lapse inversion of virtual shots using FWI and surface / Scholte wave inversion. The result of this type of analysis is that quantitative or qualitative estimation of location of subsurface property changes and theirmagnitudes with their associated uncertainties can be obtained to generate the analysis data. (IV) Use of the generated analysis data to activate or otherwise enable initiation of one or more MMV triggers for CCUS / other monitoring operations for assets or sites under consideration such that the indicated changes included in the analysis data are classified and flagged, and actions are sanctioned based on changes observed in the subsurface based on the analysis data.

[0184] In one embodiment, a geological software tool (e.g., Sim2Seis™ framework) may be used to analyze the relative information included in the noise data in space and time. In particular, the information derived or synthesized in step 3 above may be used to generate actionable data that can affect MMV objectives and can include for example: commissioning of active sources efforts; and verifying compliance and containment of gas plumes (e.g, carbon dioxide plumes) for carbon capture and utilization scenarios. FIG. 35 shows a dispersion curve generated using virtual shots or analysis data as discussed above.

[0185] The disclosed approach beneficially facilitates the generation of continuous, on demand, real-time subsurface models, data, and geological interactions and statuses at low cost based on passively detected noise data of a subsurface under consideration. Moreover, the disclosed solution uses ambient noise included in the captured noise data to analyze subsurface changes continuously in time and tagger MMV alerts. It is appreciated that fiber optic sensors or other vibration sensors can be used as cheap detection systems that can be customized or otherwise adapted for passive interferometry operations and thereby enable the reduction of data acquisition and analysis costs.

[0186] Moreover, this disclosure provides cost-effective implementations for CCUS where MMV objectives are specified. A use case includes continuously monitoring changes in Scholte wave velocity from shallow seabed fiber optics over a CCS site (e.g, the North Sea) and use the of passive virtual shots to monitor changes in the subsurface velocity. This information is contrasted with the Sim2Seisworkflow where conformant and confinement CCS objectives are being demonstrated to operators and regulators. Actions such as additional measurements (e.g., using active sources) or feedback to CCS engineers are commissioned based on this analysis.

[0187] According to one embodiment, the disclosed solution is incorporated into a geological software system that is optimized for scenarios where data capture repeatability is high for fixed receiver locations (e.g., locations where the fiber cables are fixed).

[0188] FIG. 36 provides an example of a detailed workflow (3600) for methods, systems, and computer programs that facilitate generating subsurface characterization data using ambient noise data captured by a deployed vibration sensor. It is appreciated that a signal processing engine stored in a memory device (e.g., transitory or non-transitory memory) may cause a computer processor to execute the various processing stages of the workflows discussed herein. For example, the disclosed techniques may be implemented as a signal processing engine within a geological software tool such that the signal processing engine enables, supports, or facilitates arranging two or more seismic sources to separate wavefields generated therefrom based on the processes outlined herein.

[0189] At block (3602), the signal processing engine may receive or record, using a vibration sensor, ambient noise data associated with a subsurface of a resource site. Following this, the signal processing engine may generate at block (3604), using the ambient noise data, passive virtual shot data indicating temporal (e.g., 1 hour / 1 day / 1 week / 1 month) properties or temporal interactions of noise data included in the passive virtual shot data relative to: one or more positions along the vibration sensor; noise attribute data at particular time points; and time-lapse inversion data associated with the virtual shots associated with full-wave inversion (FWI) or Surface / Scholte wave inversion operations. According to one embodiment, the time-lapse inversion of passively generated virtual shots (VS) may be generated based on FWI techniquesor other elastic full-wave inversion (eFWI) technology for shallow water or a combination of FWI and Scholte wave inversion technology.

[0190] Referring again to block (3604), a virtual shot may be generated as follows. The term “shot” refers to a source pulse directed into or beneath the ground (e.g., beneath the seafloor). A “virtual shot” is a shot that is simulated using background noise in the vibrations in or on the ground, rather than an artificially induced shot. A “gather” is data gathered regarding the shot.

[0191] Thus, in seismic interferometry, virtual shot gathers are synthesized seismic records that simulate the response of a hypothetical source placed at a specific receiver location. The technique leverages cross-correlation between seismic signals recorded by an array of sensors. The cross-correlation process makes simulates a source being placed at one of the receiver locations. In other words, the source is virtual. By generating a virtual source, virtual shot gathers may be generated without physically deploying sources at those locations.

[0192] At block (3606), the signal processing engine determines, using the passive virtual shot data, shot analysis data indicating one or more of: data transitions or data changes at the one or more positions along the vibration sensor; data transitions or data changes within the noise attribute data for a number of time points including the particular time points; and the time-lapse inversion data. For example, the captured or sampled noise data includes elastic properties of the subsurface of the site or resource site. When virtual shots are constructed using, for example, interferometric techniques from the noise data, the virtual shots included therein can represent a seismic response of the earth. As this seismic response is observed over time, seismic velocity (VS) data may be constructed from the noise data continuously for specific time durations (e.g., per Ihr, 1 week, or even 1 month, etc.). It is appreciated that the selected timeframe is arbitrary as there is continuous generation of shots in a number of time steps at the vibration sensor locations. If there is a change in the subsurface (e.g., there is a change in elastic properties due to carbon dioxide injection), then thischange will be detected or reflected by the VS data over time. These changes when detected can provide valuable subsurface information that help monitor, for example, a gas (e.g., carbon dioxide) plume for CCS scenarios. In addition, the data changes associated with the one or more positions along the vibration sensor may include changes related to: seismic travel time data changes; velocity data changes (e.g, direct arrivals of seismic waves, body waves, or surface wave velocities); seismic phase data changes and wave amplitude data changes along the vibration sensor as observed by the virtual shots. It is appreciated that the time-lapse inversion data may indicate inverting the data acquired by the vibration sensors at different time steps, timestamps, or time-points to highlight the subsurface property changes through time.

[0193] Turning to block (3610), the signal processing engine may classify, using the shot analysis data, MMV trigger data associated with the shot analysis data to generate a report or model indicating CCUS operations for the resource site. The signal processing engine may be further used to initiate, at block (3610), using the report or model, one or more capture utilization and sequestration (CCUS) operations for the resource site. In one embodiment, the report or model (e.g, seismic analysis report or model) indicates recorded or analyzed geological information or material interactions (e.g., fluid or gas interactions) with the geological formation in the subsurface which can be used to model the subsurface at the resource site. In one embodiment, the reports or models may be used to qualitatively or quantitatively characterize or describe subsurface property changes at the resource site to foster energy development operations. In the case of CCS monitoring, the report or model can be used to track or otherwise monitor changes in location of a fluid or gas plume (e.g, carbon dioxide plume), and changes in the state of stress / pressure within the subsurface relative to the fluid or gas plume. In one embodiment, the report or model enables estimation (quantitative or qualitative estimation) of fluid saturation changes within the subsurface based on seismic petrophysical inversion techniques applied during the generation of the analysis data.

[0194] FIG. 37 A, FIG. 37B, FIG. 37C, FIG. 37D, FIG. 37E, and FIG. 37F show an example of seismic wave interferometry using surface or sea bottom distributed acoustic sensing according to a passive, virtual shot interferometric method, in accordance with one or more embodiments. Thus, the method of FIG. 36 may be used to perform the steps represented by FIG. 37A through FIG. 37F.

[0195] FIG. 37A shows noise data taken on 7 consecutive days in July of 2022. The noise data is taken by acoustic sensors disposed on the seabed in the North Sea. However, the noise data was taken once per minute on each of the 7 days. Note that there is considerable variation in the noise data. Slow (less than 15 meters per second) seabed surface waves consistently occurred in selected periods over the recording times.

[0196] FIG. 37B shows the effect of performing the virtual shot technique described with respect to FIG. 36 on 60 different noise signals taken over the course of one hour. The resulting virtual shot plot is shown in FIG. 37B.

[0197] FIG. 37C shows the effect of performing the virtual shot technique described with respect to FIG. 36 on about 25,920 different noise signals taken over the course of eighteen days (with seabed surface noise measurements recorded each minute of the eighteen days). The resulting virtual shot plot is shown in FIG. 37C. As can be seen in FIG. 37C, a pattern noticeable with difficulty in the virtual shot plot FIG. 37B has become more easily discernable in the virtual shot plot of FIG. 37C. The crease-like object shown in the virtual shot plot of FIG. 37C represents the presence of a subsurface feature (e.g, a plume of a liquid that includes carbon dioxide that has been injected into the Earth).

[0198] FIG. 37D shows a frequency-wavenumber (FK) spectrum plot generated using the virtual shot plot of FIG. 37C. An FK spectrum refers to a two-dimensional Fourier transformed used in seismology, where the "f ' represents frequency and "k" represents wavenumber. Thus, the FK spectrum plot of FIG. 37D shows the distribution of seismic wave energy across different frequencies and spatialwavelengths within the seismic data set represented by FIG. 37C. The FK spectrum shown in FIG. 37D is a tool for analyzing the direction and apparent velocity of seismic waves arriving at an array of sensors, allowing the identification of specific wave types and to separate the specific wave types from background noise. In turn, the detected specific wave types may allow the generation of a subsurface model of subsurface features within the ground.

[0199] FIG. 37E shows another example. Specifically, FIG. 37E shows a dispersion analysis performed using the passive data generated and processed as shown in FIG. 37A, FIG. 37B, FIG. 37C, and FIG. 37D, for 1 hour of measurements (at one measurement per minute), ten hours of measurements (at one measurement per minute), and 24 hours of measurements (at one measurement per minute). As can be seen in the three virtual shot plots of FIG. 37E, the passive background noise measurements in the vibrations of the seabed floor, over time, may be used to generate plots of subsurface features. In turn, the virtual shot plots may be used to generate the a dispersion plot similar to the dispersion plot of FIG. 37D. Over still longer times, the dispersion plots may vary over time. In this manner, the movement subsurface features may be monitored. Accordingly, when carbon is injected into the Earth in a variety of forms, the resulting subsurface features generated by the injected carbon substances may be tracked over time.

[0200] Based on the traced position over time, one or more capture utilization and sequestration (CCUS) operations may be initiated. For example, one or more wellbores may be drilled into the earth in order to relieve or, by injection of a liquid, increase subsurface pressure distributions of the subsurface plume in order to alter, slow, or speed the course of the plume. Other CCUS operations, as described elsewhere herein, also may be undertaken.

[0201] FIG. 37F shows inversion plots generated using the 18 days of data taken as described with respect to FIG. 37A through FIG. 37E. As can be seen, subsurface features at a depth of over two kilometers may be tracked using the virtual shot datadescribed above. Specifically, time-lapse inversion data associated with the virtual shots may be tracked. The time-lapse inversion data is based on full-wave inversion or surface-Scholte wave inversion operations.

[0202] One or more embodiments provide for a method for generating subsurface characterization data using ambient noise data captured by a deployed vibration sensor. The method includes receiving or recording, using a computer processor and the vibration sensor, ambient noise data associated with a subsurface of a resource site. The method also includes generating, using the computer processor and the ambient noise data, passive virtual shot data indicating temporal (1 hour / 1 day / 1 week / 1 month) properties or interactions of noise data included in the passive virtual shot data relative to: one or more positions along the vibration sensor, noise attribute data at particular time points, and time-lapse inversion data associated with the virtual shots based on full- wave inversion or Surface / Scholte wave inversion operations. The method also includes determining, using the computer processor and the passive virtual shot data, shot analysis data indicating one or more of: data transitions or data changes at the one or more positions along the vibration sensor, data transitions or data changes within the noise attribute data for a number of time points including the particular time points, and the time-lapse inversion data. The method also includes classifying, using the computer processor and the shot analysis data, MMV trigger data associated with the shot analysis data to generate a report or model indicating CCUS operations for the resource site. The method also includes initiating, using the computer processor and the report or model, one or more capture utilization and sequestration (CCUS) operations for the resource site.

[0203] The foregoing description, for explanation purposes, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit one or more embodiments 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 theprinciples of one or more embodiments and any practical applications thereof, to thereby enable others skilled in the art to use the one or more embodiments with various modifications as are suited to the particular use contemplated. 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.

[0204] One or more embodiments may be implemented on a computing system specifically designed to achieve an improved technological result When implemented in a computing system, the features and elements of the disclosure provide a technological advancement over computing systems that do not implement the features and elements of the disclosure. Any combination of mobile, desktop, server, router, switch, embedded device, or other types of hardware may be improved by including the features and elements described in the disclosure.

[0205] For example, as shown in FIG. 38A, the computing system (3800) may include one or more computer processor(s) (3802), non-persistent storage device(s) (3804), persistent storage device(s) (3806), a communication interface (3808) (e.g., Bluetooth interface, infrared interface, network interface, optical interface, etc.), and numerous other elements and functionalities that implement the features and elements of the disclosure. The computer processor(s) (3802) may be an integrated circuit for processing instructions. The computer processor(s) (3802) may be one or more cores, or micro-cores, of a processor. The computer processor(s) (3802) includes one or more processors. The computer processor(s) (3802) may include a central processing unit (CPU), a graphics processing unit (GPU), a tensor processing unit (TPU), combinations thereof, etc.

[0206] The input device(s) (3810) may include a touchscreen, keyboard, mouse, microphone, touchpad, electronic pen, or any other type of input device. The input device(s) (3810) may receive inputs from a user that are responsive to data and messages presented by the output device(s) (3812). The inputs may include textinput, audio input, video input, etc., which may be processed and transmitted by the computing system (3800) in accordance with one or more embodiments. The communication interface (3808) may include an integrated circuit for connecting the computing system (3800) to a network (not shown) (e.g., a local area network (LAN), a wide area network (WAN) such as the Internet, mobile network, or any other type of network) or to another device, such as another computing device, and combinations thereof.

[0207] Further, the output device(s) (3812) may include a display device, a printer, external storage, or any other output device. One or more of the output device(s) (3812) may be the same or different from the input device(s) (3810). The input device(s) (3810) and output device(s) (3812) may be locally or remotely connected to the computer processor(s) (3802). Many different types of computing systems exist, and the aforementioned input device(s) (3810) and output device(s) (3812) may take other forms. The output device(s) (3812) may display data and messages that are transmitted and received by the computing system (3800). The data and messages may include text, audio, video, etc., and include the data and messages described above in the other figures of the disclosure.

[0208] Software instructions in the form of computer readable program code to perform embodiments may be stored, in whole or in part, temporarily or permanently, on a non-transitory computer readable medium such as a solid state drive (SSD), compact disk (CD), digital video disk (DVD), storage device, a diskette, a tape, flash memory, physical memory, or any other computer readable storage medium. Specifically, the software instructions may correspond to computer readable program code that, when executed by the computer processor(s) (3802), is configured to perform one or more embodiments, which may include transmitting, receiving, presenting, and displaying data and messages described in the other figures of the disclosure.

[0209] The computing system (3800) in FIG. 38A may be connected to, or be a part of, a network. For example, as shown in FIG. 38B, the network (3820) may include multiple nodes (e.g., node X (3822) and node Y (3824), as well as extant intervening nodes between node X (3822) and node Y (3824)). Each node may correspond to a computing system, such as the computing system (3800) shown in FIG. 38A, or a group of nodes combined may correspond to the computing system (3800) shown in FIG. 38 A. By way of an example, embodiments may be implemented on a node of a distributed system that is connected to other nodes. By way of another example, embodiments may be implemented on a distributed computing system having multiple nodes, where each portion may be located on a different node within the distributed computing system. Further, one or more elements of the aforementioned computing system (3800) may be located at a remote location and connected to the other elements over a network.

[0210] The nodes e.g., node X (3822) and node Y (3824)) in the network (3820) may be configured to provide services for a client device (3826). The services may include receiving requests and transmitting responses to the client device (3826). For example, the nodes may be part of a cloud computing system. The client device (3826) may be a computing system, such as the computing system (3800) shown in FIG. 38 A. Further, the client device (3826) may include or perform at least a portion of one or more embodiments.

[0211] The computing system of FIG. 38A may include functionality to present data (including raw data, processed data, and combinations thereof) such as results of comparisons and other processing. For example, presenting data may be accomplished through various presenting methods. Specifically, data may be presented by being displayed in a user interface, transmitted to a different computing system, and stored. The user interface may include a graphical user interface (GUI) that displays information on a display device. The GUI may include various GUI widgets that organize what data is shown, as well as how data is presented to a user.Furthermore, the GUI may present data directly to the user (e.g., data presented as actual data values through text), or rendered by the computing device into a visual representation of the data, such as through visualizing a data model.

[0212] As used herein, the term “connected to” contemplates multiple meanings. A connection may be direct or indirect (e.g., through another component or network). A connection may be wired or wireless. A connection may be a temporaiy, permanent, or a semi-permanent communication channel between two entities.

[0213] The various descriptions of the figures may be combined and may include, or be included within, the features described in the other figures of the application. The various elements, systems, components, and steps shown in the figures may be omitted, repeated, combined, or altered as shown in the figures. Accordingly, the scope of the present disclosure should not be considered limited to the specific arrangements shown in the figures.

[0214] In the application, ordinal numbers ( .g., first, second, third, etc.) may be used as an adjective for an element (z.e., any noun in the application). The use of ordinal numbers is not to imply or create any particular ordering of the elements, nor to limit any element to being a single element unless expressly disclosed, such as by the use of the terms “before,” “after,” “single,” and other such terminology. Rather, ordinal numbers distinguish between the elements. By way of an example, a first element is distinct from a second element, and the first element may encompass more than one element and succeed (or precede) the second element in an ordering of elements.

[0215] Further, unless expressly stated otherwise, the conjunction “or” is an inclusive “or” and, as such, automatically includes the conjunction “and,” unless expressly stated otherwise. Further, items joined by the conjunction “or” may include any combination of the items with any number of each item, unless expressly stated otherwise.

[0216] In the above description, numerous specific details are set forth in order to provide a more thorough understanding of the disclosure. However, it will be apparent to one of ordinary skill in the art that the technology may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description. Further, other embodiments not explicitly described above can be devised which do not depart from the scope of the claims as disclosed herein. Accordingly, the scope should be limited only by the attached claims.

Claims

CLAIMSWhat is claimed is:

1. A method for performing one or more capture utilization and sequestration (CCUS) operations, the method comprising: obtaining, from a vibration sensor, ambient noise data associated with a subsurface of a resource site; generating, from the ambient noise data, passive virtual shot data indicating temporal properties or interactions present in the ambient noise data; determining, using the passive virtual shot data, shot analysis data; generating, using the shot analysis data, a model representing changes in past CCUS operations for the resource site; and initiating, based on the model, one or more additional CCUS operations for the resource site.

2. The method of claim 1, wherein the temporal properties or interactions present in the ambient noise data relate to at least one of: one or more positions along the vibration sensor; noise attribute data at particular time points; and time-lapse inversion data associated with the virtual shots based on full-wave inversion or surface-Scholte wave inversion operations.

3. The method of claim 1, wherein the shot analysis data indicates at least one of: data transitions at the one or more positions along the vibration sensor; data transitions within noise attribute data for a plurality of time points; and time-lapse inversion data associated with the virtual shots based on full-wave inversion or surface-Scholte wave inversion operations.

4. The method of claim 1, further comprising: generating subsurface characterization data using the ambient noise data.

5. The method of claim 1, wherein the vibration sensor comprises a fiber optic sensor.

6. The method of claim 1, wherein the fiber optic sensor is deployed to perfoim horizontal distributed acoustic sensing.

7. The method of claim 1, wherein obtaining the ambient noise data is performed periodically at a first time interval for a monitoring period comprising a second time interval, wherein the second time interval is longer than the first time interval.

8. A system comprising: a processor; a data repository storing computer usable program code which, when executed by the processor, performs a computer-implemented method for performing one or more capture utilization and sequestration (CCUS) operations, the computer-implemented method comprising: receiving or recording, from a vibration sensor, ambient noise data associated with a subsurface of a resource site; generating, from the ambient noise data, passive virtual shot data indicating temporal properties or interactions present in the ambient noise data; determining, using the passive virtual shot data, shot analysis data; generating, using the shot analysis data, a model representing changes in past CCUS operations for the resource site; and initiating, based on the model, one or more additional CCUS operations for the resource site.

9. The system of claim 1, wherein the temporal properties or interactions present in the ambient noise data relate to at least one of: one or more positions along the vibration sensor; noise attribute data at particular time points; andtime-lapse inversion data associated with the virtual shots based on full-wave inversion or surface-Scholte wave inversion operations.

10. The system of claim 1, wherein the shot analysis data indicates at least one of: data transitions at the one or more positions along the vibration sensor; data transitions within noise attribute data for a plurality of time points; and time-lapse inversion data associated with the virtual shots based on full-wave inversion or surface-Scholte wave inversion operations.

11. The system of claim 1, wherein the computer-implemented method further comprises: generating subsurface characterization data using the ambient noise data.

12. The system of claim 1, wherein the vibration sensor comprises a fiber optic sensor.

13. The system of claim 1, wherein the fiber optic sensor is deployed to perform horizontal distributed acoustic sensing.

14. The system of claim 1, wherein, for the computer-implemented method, receiving or recording of the ambient noise data is performed periodically at a first time interval for a monitoring period comprising a second time interval, wherein the second time interval is longer than the first time interval.

15. A non-transitory computer readable storage medium storing program code which, when executed by a processor, performs a computer-implemented method for performing one or more capture utilization and sequestration (CCUS) operations, the computer- implemented method comprising: receiving or recording, from a vibration sensor, ambient noise data associated with a subsurface of a resource site; generating, from the ambient noise data, passive virtual shot data indicating temporal properties or interactions present in the ambient noise data; determining, using the passive virtual shot data, shot analysis data; generating, using the shot analysis data, a model representing changes in past CCUS operations for the resource site; and initiating, based on the model, one or more additional CCUS operations for the resource site.

16. The non-transitory computer readable storage medium of claim 1, wherein the temporal properties or interactions present in the ambient noise data relate to at least one of: one or more positions along the vibration sensor; noise attribute data at particular time points; and time-lapse inversion data associated with the virtual shots based on full-wave inversion or surface-Scholte wave inversion operations.

17. The non-transitory computer readable storage medium of claim 1, wherein the shot analysis data indicates at least one of: data transitions at the one or more positions along the vibration sensor; data transitions within noise attribute data for a plurality of time points; and time-lapse inversion data associated with the virtual shots based on full-wave inversion or surface-Scholte wave inversion operations.

18. The non-transitory computer readable storage medium of claim 1, wherein the computer-implemented method further comprises: generating subsurface characterization data using the ambient noise data.

19. The non-transitory computer readable storage medium of claim 1, wherein the vibration sensor comprises a fiber optic sensor.

20. The non-transitory computer readable storage medium of claim 1, wherein the fiber optic sensor is deployed to perform horizontal distributed acoustic sensing.

Images