Waveform labelling for carbon capture, utilization, and storage monitoring

Abstract

A method for performing waveform labelling for subsurface monitoring of carbon capture, utilization, and storage (CCUS) monitoring is disclosed. The method includes: receiving a baseline model of a subsurface of a carbon monitoring site; identifying one or more zones within the baseline model that are candidates for containing carbon dioxide (CO2); assigning different labels to the one or more zones; generating masks for each of the one or more zones using the assigned labels; receiving a baseline survey and / or a monitoring survey; and applying the masks to the baseline survey and / or the monitoring survey to produce a masked baseline survey and / or a masked monitoring survey.

Description

PATENT Atorney Docket No.: IS24.1429-US-NPWAVEFORM LABELLING FOR CARBON CAPTURE, UTILIZATION, ANDSTORAGE MONITORINGCross-Reference to Related Applications

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 730,688, filed on December 11, 2024, which is incorporated by reference.Background

[0002] Each geological CO2 storage site involves an MMV (measurement, monitoring and verification) plan. The plan can be split into two objectives. The first part of the plan is conformance (e.g., tracking of the CO2 plume and the pressure envelope). The second part of the plan is containment (e.g., monitoring to demonstrate the absence of identified risks such as caprock leaks, leakage along faults, and so on).

[0003] One technology to address 3D spatial conformance is time-lapse (4D) seismic (e.g., using borehole and surface measurements). The costs associated with the acquisition, processing, and interpretation of time-lapse seismic data are high. However, the objectives of CO2 monitoring may be different from those of conventional reservoir monitoring. For example, while the objective of a conventional 4D study may be to infer changes to the rock and fluid properties of the subsurface (e.g., seismic velocities, density, fluid saturation, porosity, permeability, etc.), the objective of 4D monitoring of a CO2 site may be detection and / or localization of the CO2 plume. There is likely to be data redundancy when using conventional full -coverage, high-density time-lapse seismic for CO2 storage site monitoring.Summary

[0004] The present disclosure includes a method for performing waveform labelling for subsurface monitoring of carbon capture, utilization, and storage (CCUS) monitoring. The method may include receiving a baseline model of a subsurface of a carbon monitoring site. The method may also include identifying one or more zones within the baseline model that are candidates for containing carbon dioxide (CO2). The method may further include assigning different labels to the one or more zones. In addition, the method may include generating masks for each of the one or more zones using the assigned labels. The method may include receiving a baseline survey and / orPATENT Atorney Docket No.: IS24.1429-US-NP a monitoring survey. Further, the method may include applying the masks to the baseline survey and / or the monitoring survey to produce a masked baseline survey and / or a masked monitoring survey.

[0005] The present disclosure also includes a computing system. The computing system may include one or more processors. The computing system may also include a memory system that may include one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations. The operations may include receiving a baseline model of a subsurface of a carbon monitoring site. The operations may also include identifying one or more zones within the baseline model that are candidates for containing carbon dioxide (CO2). The operations may further include assigning different labels to the one or more identified zones. In addition, the operations may include generating masks for each of the one or more zones using the assigned labels. Further, the operations may include receiving a baseline survey and / or a monitoring survey from one or more of a sensor, a data storage device, or a wireless and / or wired network. The operations may include determining, based on the baseline survey and / or the monitoring survey, one or more sources and one or more receivers that allow energy recorded during the baseline survey and / or the monitoring survey to identify the zones. The operations may also include selecting the sources and receivers that contain a highest proportion of energy from each zone as a monitoring survey geometry, where the highest proportion comprises values within a predetermined percentage of a maximum, the maximum being the highest energy for each zone, or where the highest proportion comprises a predetermined number of sources and the receivers with a highest proportion of energy from a particular zone. The operations may further include applying the masks to the baseline survey and / or to the monitoring survey to produce a masked baseline survey and / or a masked monitoring survey.

[0006] The present disclosure also includes a non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations. The operations may include receiving a baseline model of a subsurface of a carbon monitoring site, where the baseline model may include P-wave velocity, S-wave velocity, density, acoustic impedance, shear impedance, anisotropy, attenuation, or a combination thereof, and the baseline model may include a smooth velocity model that generates diving waves. The operations may also include identifying one or more zones within thePATENT Atorney Docket No.: IS24.1429-US-NP baseline model that are configured to be candidates containing carbon dioxide (CO2), where each of the one or more zones includes a region of interest where the CO2 is able to migrate. The operations may further include assigning different labels to the one or more identified zones, where the labels may include colors, the labels may correspond to different changes in the subsurface with different risk levels, and the different risk levels may include one or more of the CO2 being contained, a migration of the CO2 being within a predetermined risk level, and / or the migration of the CO2 exceeding the predetermined risk level. The operations may include receiving a baseline survey and a monitoring survey from one or more of a sensor, a data storage device, or a wired and / or wireless network. The operations may also include generating masks for each of the zones using the assigned labels, where the masks may be generated using waveform labelling to isolate energy in the baseline survey and / or the monitoring survey associated with each zone in the baseline model, and the masks may be generated by differencing an output of two or more waveform labelling experiments. The operations may further include determining, based on the baseline survey and / or the monitoring survey, one or more sources and one or more receivers that allow energy recorded during the baseline survey and / or the monitoring survey to identify the zones, where the recorded energy includes the diving waves. In addition, the operations may include selecting the sources and receivers that contain a highest proportion of energy from each zone as a monitoring survey geometry, where the sources may be selected based at least in part on one or more of sensitivity analysis, accessibility of source location, and / or location of sources in a permanent system, where the highest proportion may include values within a predetermined percentage of a maximum, the maximum being the highest energy for each zone, or where the highest proportion may include a predetermined number of sources and the receivers with a highest proportion of energy from a particular zone. Further, the operations may include applying the masks to the baseline survey and / or to the monitoring survey to produce a masked baseline survey and / or a masked monitoring survey. The operations may include comparing the masked baseline survey and the masked monitoring survey, using the selected sources and receivers, to detect whether changes have occurred to the zones, where comparing may include comparing a plurality of 4D metrics computed directly from the masked baseline survey and the masked monitoring survey, and the plurality of 4D metrics may include two or more of normalized root mean square (NRMS), correlation coefficients, energy levels, and predictability. The operations may include displaying results of the comparison. The operations may also include performing an action basedPATENT Atorney Docket No.: IS24.1429-US-NP upon or in response to the results of the comparison, where the action may include acquiring a new monitoring survey to allow a targeted update of the baseline model or the action comprises generating or transmitting a signal that recommends, instructs, or causes a physical action to occur, the physical action may include making an intervention at the carbon monitoring site, and the intervention may occur in response to a CO2 leakage and comprises physically addressing a cause for the CO2 leakage.

[0007] It will be appreciated that this summary is intended merely to introduce some aspects of the present methods, systems, and media, which are more fully described and / or claimed below. Accordingly, this summary is not intended to be limiting.Brief Description of the Drawings

[0008] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present teachings and together with the description, serve to explain the principles of the present teachings. In the figures:

[0009] Figure 1 illustrates an example of a system that includes various management components to manage various aspects of a geologic environment, according to an embodiment.

[0010] Figure 2 illustrates a schematic view of a method for performing CCUS monitoring, according to an embodiment.

[0011] Figure 3 illustrates an image showing how the different regions may be defined within a model, according to an embodiment.

[0012] Figures 4A-4D illustrate an example of these different regions, according to an embodiment.

[0013] Figures 5A-5C illustrate a gather modelled using the baseline model with (e.g., green, blue and red) colored waveforms overlaid, and Figures 5D-5F illustrate the (e.g., red, blue and green) masks derived from the colored waveforms, according to an embodiment.

[0014] Figures 6A-6D illustrate perturbed versions of the model, with 4D perturbations in the green zone (Figure 6A), the blue zone (Figure 6B), the red zone (Figure 6C), and outside of the four zones (Figure 6D), according to an embodiment.

[0015] Figures 7A-7L illustrate how the different perturbations impact the masked residuals, according to an embodiment.PATENT Atorney Docket No.: IS24.1429-US-NP

[0016] Figure 8 illustrates the NRMS difference between the baseline and monitor data for each of these configurations, according to an embodiment.

[0017] Figure 9A illustrates the Marmousi 2 velocity model, and Figures 9B-9D illustrate the three regions of interest that may be considered, according to an embodiment.

[0018] Figures 10A-10C illustrate three different perturbations that occur at different depth ranges representing different 4D perturbations, according to an embodiment.

[0019] Figures 11 A-l 1C illustrate RMS differences between the unperturbed and perturbed data for the perturbation in the green region (Figure 11 A), the perturbation in the blue region (Figure 1 IB), and the perturbation in the red region (Figure 11C).

[0020] Figure 12 illustrates a flowchart of a method for performing waveform coloring for carbon capture, utilization, and storage (CCUS) monitoring, according to an embodiment.

[0021] Figure 13 illustrates a schematic view of a computing system for performing at least a portion of the method(s) described herein, according to an embodiment.Detailed Description

[0022] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be apparent to one of ordinary skill in the art that the present disclosure may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0023] It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the present disclosure. The first object or step, and the second object or step, are both, objects or steps, respectively, but they are not to be considered the same object or step.

[0024] The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used in this description and the appendedPATENT Atorney Docket No.: IS24.1429-US-NP claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and / or” as used herein refers to and encompasses any possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Further, as used herein, the term “if’ may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.

[0025] Attention is now directed to processing procedures, methods, techniques, and workflows that are in accordance with some embodiments. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined and / or the order of some operations may be changed.System Overview

[0026] Figure 1 illustrates an example of a system 100 that includes various management components 110 to manage various aspects of a geologic environment 150 (e.g., an environment that includes a sedimentary basin, a reservoir 151, one or more faults 153-1, one or more geobodies 153-2, etc.). For example, the management components 110 may allow for direct or indirect management of sensing, drilling, injecting, extracting, etc., with respect to the geologic environment 150. In turn, further information about the geologic environment 150 may become available as feedback 160 (e.g., optionally as input to one or more of the management components 110).

[0027] In the example of Figure 1, the management components 110 include a seismic data component 112, an additional information component 114 (e.g., well / logging data), a processing component 116, a simulation component 120, an attribute component 130, an analysis / visualization component 142 and a workflow component 144. In operation, seismic data and other information provided per the components 112 and 114 may be input to the simulation component 120.

[0028] In an example embodiment, the simulation component 120 may rely on entities 122. Entities 122 may include earth entities or geological objects such as wells, surfaces, bodies,PATENT Atorney Docket No.: IS24.1429-US-NP reservoirs, etc. In the system 100, the entities 122 can include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data 112 and other information 114). An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.

[0029] In an example embodiment, the simulation component 120 may operate in conjunction with a software framework such as an object-based framework. In such a framework, entities may include entities based on pre-defined classes to facilitate modeling and simulation. A commercially available example of an object-based framework is the MICROSOFT® .NET® framework (Redmond, Washington), which provides a set of extensible object classes. In the .NET® framework, an object class encapsulates a module of reusable code and associated data structures. Object classes can be used to instantiate object instances for use in by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data.

[0030] In the example of Figure 1, the simulation component 120 may process information to conform to one or more attributes specified by the attribute component 130, which may include a library of attributes. Such processing may occur prior to input to the simulation component 120 (e.g., consider the processing component 116). As an example, the simulation component 120 may perform operations on input information based on one or more attributes specified by the attribute component 130. In an example embodiment, the simulation component 120 may construct one or more models of the geologic environment 150, which may be relied on to simulate behavior of the geologic environment 150 (e.g., responsive to one or more acts, whether natural or artificial). In the example of Figure 1, the analysis / visualization component 142 may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component 120 may be input to one or more other workflows, as indicated by a workflow component 144.

[0031] As an example, the simulation component 120 may include one or more features of a simulator such as the ECLIPSE™ reservoir simulator (SLB, Houston Texas), the INTERSECT™ reservoir simulator (SLB, Houston Texas), etc. As an example, a simulation component, a simulator, etc. may include features to implement one or more meshless techniques (e.g., to solve one or more equations, etc.). As an example, a reservoir or reservoirs may be simulated withPATENT Atorney Docket No.: IS24.1429-US-NP respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as SAGD, etc ).

[0032] In an example embodiment, the management components 110 may include features of a commercially available framework such as the PETREL® seismic to simulation software framework (SLB, Houston, Texas). The PETREL® framework provides components that allow for optimization of exploration and development operations. The PETREL® framework includes seismic to simulation software components that can output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) can develop collaborative workflows and integrate operations to streamline processes. Such a framework may be considered an application and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

[0033] In an example embodiment, various aspects of the management components 110 may include add-ons or plug-ins that operate according to specifications of a framework environment. For example, a commercially available framework environment marketed as the OCEAN® framework environment (SLB, Houston, Texas) allows for integration of add-ons (or plug-ins) into a PETREL® framework workflow. The OCEAN® framework environment leverages .NET® tools (Microsoft Corporation, Redmond, Washington) and offers stable, user-friendly interfaces for efficient development. In an example embodiment, various components may be implemented as add-ons (or plug-ins) that conform to and operate according to specifications of a framework environment (e.g., according to application programming interface (API) specifications, etc.).

[0034] Figure 1 also shows an example of a framework 170 that includes a model simulation layer 180 along with a framework services layer 190, a framework core layer 195 and a modules layer 175. The framework 170 may include the commercially available OCEAN® framework where the model simulation layer 180 is the commercially available PETREL® model-centric software package that hosts OCEAN® framework applications. In an example embodiment, the PETREL® software may be considered a data-driven application. The PETREL® software can include a framework for model building and visualization.

[0035] As an example, a framework may include features for implementing one or more mesh generation techniques. For example, a framework may include an input component for receipt of information from interpretation of seismic data, one or more attributes based at least in part onPATENT Atorney Docket No.: IS24.1429-US-NP seismic data, log data, image data, etc. Such a framework may include a mesh generation component that processes input information, optionally in conjunction with other information, to generate a mesh.

[0036] In the example of Figure 1, the model simulation layer 180 may provide domain objects 182, act as a data source 184, provide for rendering 186 and provide for various user interfaces 188. Rendering 186 may provide a graphical environment in which applications can display their data while the user interfaces 188 may provide a common look and feel for application user interface components.

[0037] As an example, the domain objects 182 can include entity objects, property objects and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, bodies, reservoirs, etc., while property objects may be used to provide property values as well as data versions and display parameters. For example, an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).

[0038] In the example of Figure 1, data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks. The model simulation layer 180 may be configured to model projects. As such, a particular project may be stored where stored project information may include inputs, models, results and cases. Thus, upon completion of a modeling session, a user may store a project. At a later time, the project can be accessed and restored using the model simulation layer 180, which can recreate instances of the relevant domain objects.

[0039] In the example of Figure 1, the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and one or more other features such as the fault 153-1, the geobody 153-2, etc. As an example, the geologic environment 150 may be outfitted with any of a variety of sensors, detectors, actuators, etc. For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a well site and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more satellites may be provided forPATENT Atorney Docket No.: IS24.1429-US-NP purposes of communications, data acquisition, etc. For example, Figure 1 shows a satellite in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or instead include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

[0040] Figure 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment 157 and / or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.

[0041] As mentioned, the system 100 may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of a workflow. In such an example, the workflow editor may provide for selection of one or more predefined worksteps, one or more customized worksteps, etc. As an example, a workflow may be a workflow implementable in the PETREL® software, for example, that operates on seismic data, seismic attribute(s), etc. As an example, a workflow may be a process implementable in the OCEAN® framework. As an example, a workflow may include one or more worksteps that access a module such as a plug-in (e.g., external executable code, etc.).Waveforms Coloring for Carbon Capture, Utilization, and Storage (CCUS) Monitoring

[0042] The present disclosure introduces a cost-effective 4D monitoring workflow that may be based on understanding the relationship between localized changes in the subsurface and the associated changes in the monitor data versus a baseline. These localized changes can be monitoring objectives (e.g., to confirm that the behavior of the plume conforms to the MMV plan,PATENT Atorney Docket No.: IS24.1429-US-NP or to demonstrate the absence of risks identified in the plan). While the present disclosure discusses CO2 monitoring, other subsurface changes may also be monitored according to this disclosure.

[0043] It may be assumed that as part of the MMV planning process, a subsurface dynamic model is available, and that subsurface simulations (e.g., reservoir modelling, rock physics) have been carried out to predict the expected behavior of the plume through the lifecycle of the CO2 storage site, as well as potential containment risk events that may occur. It may also be assumed that, at the onset of a CCUS project, a high-quality baseline subsurface model has been generated (e.g., from a full-waveform inversion (FWI)).

[0044] With the known subsurface model, and the changes in the subsurface that are to be detected, it may then be possible to predict where the seismic monitor data should change in response to those potential changes in the subsurface. This can either be a prediction of which measurement locations are most important (e.g., to design a sparse monitoring survey), or a mask may be generated that indicates where, within each gather in the monitoring dataset, the data is expected to change in response to the different subsurface changes. The masked data may then be used to determine whether an associated subsurface change has occurred, either through computing 4D metrics in the data domain, or by using the masks to drive focused imaging of the 4D data (e.g., through FWI).

[0045] Making the connection between regions in the subsurface (e.g., model domain) and the data domain is not a straightforward task. When using full waveform modelling, multiple events may be present in the data at once, waveforms may be complicated, and it may be difficult to identify isolated events in the resulting seismograms. A method to indirectly solve this problem using a model probing approach assesses the ability of a FWI to illuminate and / or resolve a change in the subsurface. By probing the model with a range of acquisition geometries, the method may determine the most cost-effective acquisition geometry that meets the desired illumination / resolution. The analysis of the 4D detectability may be performed in the model domain, and like FWI, can be sensitive to cycle skipping (e.g., if subsurface changes are large).

[0046] While the above-mentioned approach may be well suited to the FWI case, the sensitivity to cycle skipping, and the desire to work in the model domain means that this approach may not be well-suited to provide a more general connection between the subsurface and data. The method described herein proposes to make the connection between model changes and data changes using a new method of “waveform coloring.” Waveform coloring uses full waveform modelling toPATENT Atorney Docket No.: IS24.1429-US-NP identify events or regions of interest in the model domain, and to track (e.g., by coloring the waveforms) their progression over time. This allows the location (e.g., in time and space) where the events are detected by the receivers to be identified, providing a direct connection between a region in the subsurface model and the recorded data.

[0047] Below details how waveform coloring may be used as part of a sparse / cost-effective CCUS monitoring workflow. Figure 2 illustrates a schematic view of a method for performing CCUS monitoring, according to an embodiment. Beginning with a baseline model, zones in the model may be identified that correspond to different changes in the subsurface with different risk levels. Waveform coloring may be performed for each of these identified regions to connect subsurface changes to corresponding changes in the data domain. The colored waveforms may be used to generate masks for each colored zone. These masks may be applied to the baseline and monitor data (or some combination of) which may then allow a measure of the probability of a 4D subsurface change having occurred within each region.

[0048] Thus, the method may involve• Having a baseline subsurface model (e.g., P-wave velocity (Vp), S-wave velocity (Vs), density (Rho), acoustic impedance, shear impedance, anisotropy, attenuation, and the like) of a carbon monitoring site.• Identifying regions of interest in that model, where CO2 may be contained (e.g., conformance), or where CCh may migrate (e.g., leakage risks). These regions may have a different color or label assigned.• Using waveform coloring for each region of interest to determine where the data may change in response to a change in each region of the model.• Using the colored waveforms to generate masks to be applied to the data to isolate regions that may correspond to those different changes.• Comparing masked data between baseline and monitor surveys to detect whether changes have occurred in the different regions of interest.

[0049] The comparison of the masked data may be performed for a small number of sources in the data domain, for example, by comparing different 4D metrics computed directly from the data, or the masked data may be used for targeted model updates (e.g., as input for a sparse FWI approach).PATENT Atorney Docket No.: IS24.1429-US-NP

[0050] The coloring approach described above may also be used to select which survey locations are most important to detect the 4D changes. For example, in a synthetic coloring experiment, the colored waveforms may be generated for densely sampled sources and / or receivers, and the source and / or receivers with the largest colored regions may be selected to provide a sparse monitoring configuration.

[0051] Figure 3 illustrates an image showing how the different regions may be defined within a model, according to an embodiment. A first color or hatching pattern (e.g., green) represents the base case (e.g., where the CO2 is in conformance). A second color or hatching pattern (e.g., blue) represents acceptable migration of CO2. A third color or hatching pattern (e.g., red) represents a high-risk unacceptable migration of CO2. These different regions may be used in a synthetic waveform coloring approach to map regions of the sub-surface to the data domain.

[0052] More particularly, at the start of a CCUS monitoring project, it may be assumed that a dense baseline seismic survey has been conducted, and that an accurate subsurface model is available. For example, diving wave FWI may have been used to generate a baseline model.

[0053] Having found the baseline model, the method then defines regions of the subsurface that represent different risks (Figure 3). This can include a region where the CO2 is in conformance with the base case (e.g., colored green), a region where CO2 has migrated within a predetermined risk level (that is, an acceptable region (e.g., colored blue)), and / or a region where the CO2 has exceeded the predetermined risk level and migrated into regions that would be considered a high risk (e.g., colored red).

[0054] A first application of waveform coloring for CCUS monitoring may be to determine the sources and receivers that allow the diving waves to illuminate the three selected regions. Since waveform coloring involves simulating synthetic seismic data, it can be applied using many sources and receivers and selecting those sources and receivers that contain the highest proportion of each of the three colors as the monitoring survey geometry. That is, the simulated data, based on the baseline model, can be used to select the source and receiver geometry to be used in a real- world monitor survey. The highest proportion may be based on a maximum. For example, find the source location that has the highest energy for each zone and select values within a percentage of that maximum. The percentage may be predetermined. Non-limiting examples of the percentage include 1%, 5%, 10%, 20%, 50%, and the like. Alternatively, the highest proportion may be based on the number of sources and receivers to use. For example, find a number of sources and receiversPATENT Atorney Docket No.: IS24.1429-US-NP with the highest proportion of energy for that zone. The number may be predetermined and may be based on available budget for the monitoring survey.

[0055] Once the monitoring survey geometry has been selected, one or more (e.g., three) passes of waveform coloring may be run. In the first, the three regions may be identified as the regions of interest, and coloring may be used to determine where in the monitor data the events passing through those regions are detected. In the second, the blue and the green regions may be used, and in the third, the green region may be used. The coloring provides three different masks:(1) For red, blue and green regions.(2) For blue and green regions.(3) For the green region.

[0056] Differencing (3) and (2) may isolate events that have passed through the blue region, and differencing (2) and (1) may isolate events that have passed through the red region. Three different regions are used for illustration, and the same workflow would apply to any combination of regions. The differencing step relies on the zones being set up as in Figure 3, where the regions are connected and the differencing allows separating out, say, the outer zone from the inner zones.

[0057] Figures 4A-4D illustrate an example of these different regions, according to an embodiment. More particularly, Figure 4A illustrates a baseline velocity model used in the synthetic study, Figure 4B illustrates the green region of interest, Figure 4C illustrates the blue region of interest, and Figure 4D illustrates the red region of interest. Said another way, a smooth velocity model that generates diving waves is used as the baseline model (Figure 4A). A smoothed model refers to a model that has had sharper features like interface in the model removed. Those interfaces may generate reflected or scattered waves. Diving waves may be sensitive to the overall velocity changes (and not the sharper interfaces). So by smoothing the model, the simulated waveform may contain less reflected and scattered energy, but retain the diving waves. For example, a model can be smoothed by convolving the input model with a smoothing operator such as a Gaussian function. Three regions of interest are defined representing the three scenarios described above (green, blue and red in Figures 4B, 4C, and 4D, respectively).

[0058] Figures 5A-5C illustrate a gather modelled using the baseline model with the green, blue and red colored waveforms, respectively, overlaid. Figures 5D-5F illustrate the green, blue, and red masks, respectively, derived from the colored waveforms, according to an embodiment. The waveform coloring for each region may be computed for a single source gather located at thePATENT Atorney Docket No.: IS24.1429-US-NP surface in the center of the model. This source gather is shown in grayscale in Figure 5A with the green colored waveforms overlaid, in Figure 5B with the blue colored waveforms overlaid, and in Figure 5C with the red colored waveforms overlaid. The differencing of the colored regions to give the three detection masks are shown in Figures 5D-5F.

[0059] Figures 6A-6D illustrate perturbed versions of the model, with 4D perturbations in the green zone (Figure 6A), the blue zone (Figure 6B), the red zone (Figure 6C), and outside of the three zones (Figure 6D), according to an embodiment. The baseline model may now be perturbed in different ways to represent a 4D change in the subsurface. The first change occurs within the green region (Figure 6A), a second change in the blue region (Figure 6B), a third change in the red region (Figure 6C) and a fourth change outside of the three regions (Figure 6D).

[0060] For each of these perturbations, a gather equivalent to the baseline source gather creates the monitor dataset. While the waveform coloring and generation of the masks may be performed in a simulated model, the masks are applied to data recorded during physical baseline and monitor surveys. To illustrate this, data may be simulated for the different perturbations to represent the physical monitor data. The monitor and baseline data may then be differenced to create the 4D residual, and the three masks in Figures 5D-5F may be applied to the data. This data screening allows inspection of which of the colored regions the 4D change has occurred in.

[0061] Figures 7A-7L illustrate how the different perturbations impact the masked residuals, according to an embodiment. More particularly, Figures 7A-7L illustrate 4D residuals after the application of the three masks (e.g., green, blue and red respectively) for the perturbation in the green zone (Figures 7A-7C), the perturbation in the blue zone (Figures 7D-7F), the perturbation in the red zone (Figures 7G-7I), and the perturbation outside of the three zones (Figures 7J-7L).

[0062] Starting with the perturbation in the green zone (Figure 6A), the residuals within green, blue and red masks are shown in Figures 7A-7C respectively. Here, it may be seen that, as expected, there is energy within the green mask, with no obvious energy within the blue or red mask. This may be repeated for the perturbation in the blue region in Figures 7D-7F, where it may be seen that there is energy in both the green and blue masks, and no obvious energy in the red mask. Even though the perturbation is in the blue region, it may also be expected that the data in the green region may change, since some of the waveforms have passed through the blue region to reach the green region.PATENT Atorney Docket No.: IS24.1429-US-NP

[0063] The results with the perturbation in the red region are shown in Figure 7G-7I, where now the residual has energy across each mask as expected. There may be an increase in the red masked residual compared to the perturbation in blue, and again there may be energy in the green and blue masked regions as the waveform has passed through the red perturbation to reach the corresponding regions of interest.

[0064] Finally Figures 7J-7L show the results where the perturbation is outside of the three regions. Here, there may be seen a strong 4D residual across the three zones, again because the waveforms in each of the three zones interact with the perturbation, even though it is outside of these zones.

[0065] The above observations can be captured by computing different 4D metrics for each of the perturbations (e.g., green, blue, red and outside perturbations) for each of the colored regions. Possible 4D metrics include normalized root mean square (NRMS), correlation coefficients, energy levels, predictability, and the like. Figure 8 illustrates the NRMS difference between the baseline data and the monitor data for each of these configurations, according to an embodiment. More particularly, Figure 8 shows the NRMS difference measured between the baseline data and the monitor data within each of the zones, where the perturbation is in the green zone, the blue zone, red zone, and outside of the zones. There is a clear trend in these statistics:For the perturbation in green, changes may be within the green region.For perturbation in blue, there may be changes in the blue and green regions.For perturbations in red and outside, there may be changes across the three regions.

[0066] Thus, with a known baseline model, known regions of interest, and a waveform coloring approach, the data may be screened to determine in which region a subsurface change has occurred. The NRMS difference was used as an example attribute here, but many other attributes may be used and combined to provide more robust 4D screening metrics. This may include (but is not limited to) RMS amplitude ratios, time delays, and predictability metrics.Marmousi 2 Example

[0067] A second example may use or include more localized 4D changes in the 2D Marmousi 2 model. Whereas the smooth ID model in Figures 4A-4D predominantly generates diving wave energy, the Marmousi 2 model contains detailed structure, with both reflected and diving waves present in the data. As this model has changes along the horizontal direction, and to show thePATENT Atorney Docket No.: IS24.1429-US-NP robustness of the approach, the impact of waveform coloring and the perturbations on multiple source locations may be considered.

[0068] Figure 9A illustrates the Marmousi 2 velocity model, and Figures 9B-9D illustrate the three regions of interest (green, blue, and red, respectively) that may be considered, according to an embodiment. These regions are like those in Figures 4A-4D and may allow the method to differentiate between 4D changes across different depth ranges in the model.

[0069] Figures 10A-10C illustrate three different perturbations that occur at different depth ranges representing different 4D perturbations, according to an embodiment. More particularly, Figure 10A shows a perturbation in the green region, Figure 10B in the blue region, and Figure 10C in the red region. These changes may be considered to correspond to CO2 migrating vertically within the subsurface.

[0070] Figures 11 A-l 1C illustrate RMS differences between the unperturbed and perturbed data for the perturbation in the green region (Figure 11 A), the perturbation in the blue region (Figure 11B), and the perturbation in the red region (Figure 11C), according to an embodiment. Colors correspond to the RMS values computed for each of the waveform coloring masks corresponding to the three regions of interest. The process of generating the waveform coloring masks for each of the three regions and then assessing the changes within those regions for each of the three perturbations, may be carried out for seven sources spaced at 2500 m intervals along the surface of the model. For each mask and each perturbation, the RMS difference between the unperturbed and perturbed data are shown in Figures 11 A-l 1C.

[0071] Starting with the perturbation in the green region, Figure 11 A shows RMS values within the green region for the sources (e.g., reducing to the right as distance from the perturbation increases). As expected, with the perturbation in green, there is minimal change observed in the blue and red masked regions.

[0072] Figure 1 IB shows the same for the perturbation in blue, with RMS changes within green and blue, and little change in red. The blue RMS value for the source at 5000 m drops close to zero, and this is likely due to the waveforms from that source “undershooting” the perturbation. In this example, the masks were filtered to focus on the diving waves, and had reflections also been included this would have looked different.

[0073] Finally, Figure 11C shows the RMS values when the perturbation is in the red region, and here we see changes in the four regions. Thus, like in the earlier ID example, the method mayPATENT Atorney Docket No.: IS24.1429-US-NP differentiate between 4D perturbations occurring in different regions of interest using a waveform screening approach based on waveform coloring.

[0074] The above description assumes that the 4D monitor survey has sources at locations that were also used in the baseline data. In this case, the 4D residual can be computed as the observed monitor data minus the observed predicted data. If the 4D monitor survey locations do not match baseline survey locations, the 4D residual can instead be computed as the synthetic baseline data (e.g., generated in the baseline model) minus the observed monitor data.

[0075] The above example considers the case where the data screening is performed in the data domain. This can be a cost-effective monitoring scheme, because (with correct selection of sources) it involves a few sparse monitoring sources. The above approach can also be combined with the FWI based approach, where the masks for the different zones can be used to select the part of the monitor data to be used to compute the gradient. In this way, FWI can be used to update the baseline velocity model individually in each zone, to provide a model domain assessment of whether the plume is in conformance, or if any of the identified subsurface risks have materialized. The update may be a targeted update. In some cases, the updated baseline model may become a new baseline model for the next monitoring experiment.Exemplary Method

[0076] Figure 12 illustrates a flowchart of a method for performing waveform coloring for carbon capture, utilization, and storage (CCUS) monitoring, according to an embodiment. An illustrative order of the method 1200 is provided below; however, one or more portions of the method 1200 may be performed in a different order, simultaneously, repeated, or omitted. At least a portion of the method 1200 may be performed with a computing system (described below).

[0077] The method 1200 may include receiving a baseline model of a subsurface of a carbon monitoring site, as at 1205. The baseline model may be or include P-wave velocity, S-wave velocity, density, acoustic impedance, shear impedance, anisotropy, attenuation, or a combination thereof.

[0078] The method 1200 may also include identifying zones within the baseline model that are configured to be candidates containing carbon dioxide (CO2) or permitting migration of the CO2, as at 1210.PATENT Atorney Docket No.: IS24.1429-US-NP

[0079] The method 1200 may also include assigning different indicators to the zones, as at 1215. The indicators may be or include colors, hatching patterns, and the like.

[0080] The method 1200 may also include generating masks for each of the zones, as at 1220.

[0081] The method 1200 may also include receiving a baseline survey and a monitoring survey, as at 1225. The baseline survey and the monitoring survey may be received from one or more of a sensor, a data storage device, or a wired and / or wireless network.

[0082] The method 1200 may also include applying the masks to the baseline survey and to the monitoring survey to produce a masked baseline survey and a masked monitoring survey, as at 1230.

[0083] The method 1200 may also include comparing the masked baseline survey and the masked monitoring survey to detect whether changes over occurred to the zones, as at 1235.

[0084] The method 1200 may also include displaying results of the comparison, as at 1240.

[0085] The method 1200 may also include performing an action based upon or in response to the comparison, as at 1245. The action may be or include generating and / or transmitting a signal (e.g., using a computing system) that instructs or causes a physical action to occur at the carbon monitoring site. The action may also or instead include performing the physical action at the carbon monitoring site. The action may include acquiring a new monitoring survey to allow a targeted model update, generating or transmitting a signal that recommends, instructs, or causes a physical action to occur. The physical action may include an intervention at a carbon monitoring site. The intervention may occur in response to a CO2 leakage and may include physically addressing a cause for the CO2 leakage.Exemplary Computing System

[0086] In some embodiments, the methods of the present disclosure may be executed by a computing system. Figure 13 illustrates an example of such a computing system 1300, in accordance with some embodiments. The computing system 1300 may include a computer or computer system 1301A, which may be an individual computer system 1301A or an arrangement of distributed computer systems. The computer system 1301A includes one or more analysis modules 1302 that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module 1302 executes independently, or in coordination with, one or more processors 1304, which is (orPATENT Atorney Docket No.: IS24.1429-US-NP are) connected to one or more storage media 1306. The processor(s) 1304 is (or are) also connected to a network interface 1307 to allow the computer system 1301A to communicate over a data network 1309 with one or more additional computer systems and / or computing systems, such as 1301B, 1301C, and / or 1301D (note that computer systems 1301B, 1301C and / or 1301D may or may not share the same architecture as computer system 1301A, and may be located in different physical locations, e.g., computer systems 1301A and 1301B may be located in a processing facility, while in communication with one or more computer systems such as 1301C and / or 1301D that are located in one or more data centers, and / or located in varying countries on different continents).

[0087] A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

[0088] The storage media 1306 may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of Figure 13 storage media 1306 is depicted as within computer system 1301A, in some embodiments, storage media 1306 may be distributed within and / or across multiple internal and / or external enclosures of computing system 1301 A and / or additional computing systems. Storage media 1306 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), BLURA Y '!disks, or other types of optical storage, or other types of storage devices. Note that the instructions discussed above may be provided on one computer-readable or machine-readable storage medium, or may be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). An article or article of manufacture may refer to any manufactured single component or multiple components. The storage medium or media may be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.PATENT Atorney Docket No.: IS24.1429-US-NP

[0089] In some embodiments, computing system 1300 contains one or more method execution module(s) 1308. In the example of computing system 1300, computer system 1301A includes the method execution module 1308. In some embodiments, a single method execution module may be used to perform some aspects of one or more embodiments of the methods disclosed herein. In other embodiments, a plurality of method execution modules may be used to perform some aspects of methods herein.

[0090] It should be appreciated that computing system 1300 is merely one example of a computing system, and that computing system 1300 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of Figure 13, and / or computing system 1300 may have a different configuration or arrangement of the components depicted in Figure 13. The various components shown in Figure 13 may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and / or application specific integrated circuits.

[0091] Further, the steps in the processing methods described herein may be implemented by running one or more functional modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and / or their combination with general hardware are included within the scope of the present disclosure.

[0092] Computational interpretations, models, and / or other interpretation aids may be refined in an iterative fashion; this concept is applicable to the methods discussed herein. This may include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system 1300, Figure 13), and / or through manual control by a user who may make determinations regarding whether a given step, action, template, model, or set of curves has become sufficiently accurate for the evaluation of the subsurface three-dimensional geologic formation under consideration.

[0093] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or limiting to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods described herein are illustrated and described may be re-arranged, and / or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explainPATENT Atorney Docket No.: IS24.1429-US-NP the principles of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosed embodiments and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

PATENT Atorney Docket No.: IS24.1429-US-NPCLAIMSWhat is claimed is:

1. A method for performing waveform labelling for subsurface monitoring of carbon capture, utilization, and storage (CCUS) monitoring, the method comprising: receiving a baseline model of a subsurface of a carbon monitoring site; identifying one or more zones within the baseline model that are candidates for containing carbon dioxide (CO2); assigning different labels to the one or more zones; generating masks for each of the one or more zones using the assigned labels; receiving a baseline survey and / or a monitoring survey; and applying the masks to the baseline survey and / or the monitoring survey to produce a masked baseline survey and / or a masked monitoring survey.

2. The method of claim 1, wherein the baseline model comprises P-wave velocity, S-wave velocity, density, acoustic impedance, shear impedance, anisotropy, attenuation, or a combination thereof.

3. The method of claim 1, further comprising: determining, based on the baseline survey and / or the monitoring survey, one or more sources and one or more receivers that allow energy recorded during the baseline survey and / or the monitoring survey to identify the one or more zones; selecting the sources and receivers that contain a highest proportion of energy from each zone as a monitoring survey geometry, wherein the highest proportion comprises values within a predetermined percentage of a maximum, the maximum being the highest energy for each zone, or wherein the highest proportion comprises a predetermined number of sources and the receivers with a highest proportion of energy from a particular zone; and comparing the masked baseline survey and the masked monitoring survey, using the selected sources and receivers, to detect whether changes have occurred to the zones.

4. The method of claim 3, further comprising:PATENT Atorney Docket No.: IS24.1429-US-NP displaying results of the comparison; and performing an action based upon or in response to the results of the comparison.

5. The method of claim 4, wherein: the action comprises acquiring a new monitoring survey to allow a targeted update of the baseline model, or the action comprises generating or transmitting a signal that recommends, instructs, or causes a physical action to occur, the physical action comprises making an intervention at the carbon monitoring site, and the intervention occurs in response to a CO2 leakage and comprises physically addressing a cause for the CO2 leakage.

6. The method of claim 3, wherein the sources are selected based at least in part on one or more of sensitivity analysis, accessibility of source location, and / or location of sources in a permanent system.

7. The method of claim 3, wherein: comparing comprises comparing a plurality of 4D metrics computed directly from the masked baseline survey and the masked monitoring survey, and the plurality of 4D metrics comprises two or more of normalized root mean square (NRMS), correlation coefficients, energy levels, and predictability.

8. The method of claim 3, wherein: the baseline model comprises a smooth velocity model, the smooth velocity model generates diving waves, and the energy recorded comprises the diving waves.

9. The method of claim 1, wherein each of the one or more zones comprises a region of interest where the CO2 is able to migrate.PATENT Atorney Docket No.: IS24.1429-US-NP10. The method of claim 1 , wherein the labels correspond to different changes in the subsurface with different risk levels.

11. The method of claim 10, wherein the different risk levels include one or more of the CO2 being contained, a migration of the CO2 being within a predetermined risk level, and / or the migration of the CO2 exceeding the predetermined risk level.

12. The method of claim 1, wherein the masks are generated using waveform labelling to isolate energy in the baseline survey and / or the monitoring survey associated with each zone in the baseline model.

13. The method of claim 1, wherein the masks are generated by differencing an output of two or more waveform labelling experiments.

14. A computing system, comprising: one or more processors; and a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising: receiving a baseline model of a subsurface of a carbon monitoring site; identifying one or more zones within the baseline model that are candidates for containing carbon dioxide (CO2); assigning different labels to the one or more identified zones; generating masks for each of the one or more zones using the assigned labels; receiving a baseline survey and / or a monitoring survey from one or more of a sensor, a data storage device, or a wireless and / or wired network; determining, based on the baseline survey and / or the monitoring survey, one or more sources and one or more receivers that allow energy recorded during the baseline survey and / or the monitoring survey to identify the zones; selecting the sources and receivers that contain a highest proportion of energy from each zone as a monitoring survey geometry, wherein the highest proportion comprisesPATENT Atorney Docket No.: IS24.1429-US-NP values within a predetermined percentage of a maximum, the maximum being the highest energy for each zone, or wherein the highest proportion comprises a predetermined number of sources and the receivers with a highest proportion of energy from a particular zone; and applying the masks to the baseline survey and / or to the monitoring survey to produce a masked baseline survey and / or a masked monitoring survey.

15. The computing system of claim 14, further comprising: comparing the masked baseline survey and the masked monitoring survey, using the selected sources and receivers, to detect whether changes have occurred to the zones; displaying results of the comparison; and performing an action based upon or in response to the results of the comparison.

16. The computing system of claim 15, wherein: comparing comprises comparing a plurality of 4D metrics computed directly from the masked baseline survey and the masked monitoring survey, and the plurality of 4D metrics comprises two or more of normalized root mean square (NRMS), correlation coefficients, energy levels, and predictability.

17. The computing system of claim 14, wherein: the masks are generated using waveform labelling to isolate energy in the baseline survey and / or the monitoring survey associated with each zone in the baseline model, and / or the masks are generated by differencing an output of two or more waveform labelling experiments.

18. The computing system of claim 14, wherein the sources are selected based at least in part on one or more of sensitivity analysis, accessibility of source location, and / or location of sources in a permanent system.

19. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising:PATENT Atorney Docket No.: IS24.1429-US-NP receiving a baseline model of a subsurface of a carbon monitoring site, wherein: the baseline model comprises P-wave velocity, S-wave velocity, density, acoustic impedance, shear impedance, anisotropy, attenuation, or a combination thereof, and the baseline model comprises a smooth velocity model that generates diving waves; identifying one or more zones within the baseline model that are configured to be candidates containing carbon dioxide (CO2), wherein each of the one or more zones comprises a region of interest where the CO2 is able to migrate; assigning different labels to the one or more identified zones, wherein: the labels comprise colors, the labels correspond to different changes in the subsurface with different risk levels, and the different risk levels include one or more of the CChbeing contained, a migration of the CO2 being within a predetermined risk level, and / or the migration of the CO2 exceeding the predetermined risk level; receiving a baseline survey and a monitoring survey from one or more of a sensor, a data storage device, or a wired and / or wireless network; generating masks for each of the zones using the assigned labels, wherein: the masks are generated using waveform labelling to isolate energy in the baseline survey and / or the monitoring survey associated with each zone in the baseline model, and the masks are generated by differencing an output of two or more waveform labelling experiments; determining, based on the baseline survey and / or the monitoring survey, one or more sources and one or more receivers that allow energy recorded during the baseline survey and / or the monitoring survey to identify the zones, wherein the recorded energy comprises the diving waves; selecting the sources and receivers that contain a highest proportion of energy from each zone as a monitoring survey geometry, wherein the sources are selected based at least in part on one or more of sensitivity analysis, accessibility of source location, and / or location of sources in a permanent system, wherein the highest proportion comprises values within a predetermined percentage of a maximum, the maximum being the highest energy for each zone, or wherein thePATENT Atorney Docket No.: IS24.1429-US-NP highest proportion comprises a predetermined number of sources and the receivers with a highest proportion of energy from a particular zone; applying the masks to the baseline survey and / or to the monitoring survey to produce a masked baseline survey and / or a masked monitoring survey; comparing the masked baseline survey and the masked monitoring survey, using the selected sources and receivers, to detect whether changes have occurred to the zones, wherein: comparing comprises comparing a plurality of 4D metrics computed directly from the masked baseline survey and the masked monitoring survey, and the plurality of 4D metrics comprises two or more of normalized root mean square (NRMS), correlation coefficients, energy levels, and predictability; displaying results of the comparison; and performing an action based upon or in response to the results of the comparison, wherein: the action comprises acquiring a new monitoring survey to allow a targeted update of the baseline model or the action comprises generating or transmitting a signal that recommends, instructs, or causes a physical action to occur, the physical action comprises making an intervention at the carbon monitoring site, and the intervention occurs in response to a CO2 leakage and comprises physically addressing a cause for the CO2 leakage.

20. The non-transitory computer-readable medium of claim 19, wherein the monitoring survey further comprises the selected sources and receivers from the one or more of the sensor, the data storage device, or the wired and / or the wireless network.

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