WO2024248798A1

WO2024248798A1 - Automating gas storage monitoring design by quantifying subsurface uncertainty

Info

Publication number: WO2024248798A1
Application number: PCT/US2023/023854
Authority: WO
Inventors: Christian BRÆDSTRUP; Mike BRANSTON; Morten Kristensen; Miriam Lucy LEE
Original assignee: Schlumberger Canada Ltd; Services Petroliers Schlumberger SA; Geoquest Systems BV; Schlumberger Technology Corp
Current assignee: Schlumberger Canada Ltd; Services Petroliers Schlumberger SA; Geoquest Systems BV; Schlumberger Technology Corp
Priority date: 2023-05-30
Filing date: 2023-05-30
Publication date: 2024-12-05
Anticipated expiration: 2025-11-30

Abstract

The disclosed technology is directed to methods and systems for optimizing gas storage (GS) operations. The methods comprise generating a GS model associated with a resource site such that the GS model comprises one or more parameters; determining risk thresholds for the GS operations based on risk profile data associated with the resource site; parameterizing, based on the risk thresholds, the one or more parameters; generating, using the parameterized GS model, a simulation plan for the GS operations at the resource site; executing the simulation plan across: multiple simulators in parallel, a defined uncertainty space derived from uncertainty data, multiple time periods, and the plurality of geological realizations. In some embodiments, the methods include aggregating analysis data generated from executing the simulation plan. The analysis data may indicate: gas concentration data; gas leakage data; and configuration data associated with configuring a monitoring system at the resource site.

Description

AUTOMATING GAS STORAGE MONITORING DESIGN BY QUANTIFYING

SUBSURFACE UNCERTAINTY

BACKGROUND

[0001] Safe subsurface storage of gases such as hydrogen, methane, and carbon dioxide, for example, is a major challenge facing the energy industry. In particular, subsurface gas storage has uncertainties that place both development (e.g., financial feasibility) and environmental safety constraints on gas storage projects thereby negatively impacting project execution and completion timelines for gas storage.

SUMMARY

[0002] The disclosed technology, according to some embodiments minimizes uncertainties associated with development and safety constraints for gas storage operations, including but not limited to storage of methane, hydrogen and carbon dioxide. For example, actively preparing for high-risk scenarios associated with gas storage operations may be accomplished via the design and implementation of effective monitoring campaigns. This ensures efficient gas containment as gas leakage events can jeopardize project viability and license acquisition to operate workflows and mechanisms for gas storage. Moreover, conforming field measurements with well-designed and implemented gas storage monitoring campaigns can ensure optimal reservoir production operations that enable quantifying and improving gas storage models with associated predictability data.

[0003] According to some embodiments, monitoring of a gas storage site must continue even after injection operations end (e.g., gas injection operations) which significantly increases operational bottlenecks (e.g., costs) compared with other hydrocarbon exploratory activities. Additionally, when, for example, an injection operation into a storage complex at a resource site ends, the subsurface model generated (e.g., gas storage model) for the storage complex needs to have a high level of predictability to support the long-term responsibility for monitoring the stored gas within the storage complex. The disclosed technology innovatively supports the design of gas capture and storage monitoring campaigns by quantifying sitespecific uncertainties across multiple scenarios and determining or predicting monitoring methods that detect specific risk events. Additionally, the workflows discussed may be automatic, semi-automatic, or a combination thereof and may be based on feedback from realtime or near real-time sensor measurements and/or from historic sensor measurements at the gas storage site in order to improve the predictability of the gas storage model, enhance storage site integrity in the long term, and iteratively optimize gas storage and monitoring campaigns at the gas storage site.

BRIEF DESCRIPTION OF THE DRAWINGS

[0004] The disclosure is illustrated by way of example, and not by way of limitation in the figures of the accompanying drawings in which like reference numerals are used to refer to similar elements. It is 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. [0005] FIG. 1 shows an exemplary high-level workflow associated with building monitoring scenarios for gas storage and containment operations at a resource site.

[0006] FIG. 2 shows a cross-sectional view of a resource site for which the process of

FIG. 1 may be executed.

[0007] FIG. 3 shows a networked system illustrating a communicative coupling of devices or systems associated with the resource site of FIG. 2.

[0008] FIG. 4 shows an exemplary comparison of analysis data used to direct the choice of metrics needed for optimally detecting gas storage events at a resource site.

[0009] FIG. 5 shows an exemplary gas detection map according to some embodiments of this disclosure.

[0010] FIG. 6 shows a detailed workflow for optimizing gas storage (GS) operations at a resource site.

DETAILED DESCRIPTION

[0011] 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 invention. However, it will be apparent to one of ordinary skill in the art that the invention 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.

[0012] The disclosed systems and methods may be accomplished using interconnected devices and systems that obtain a plurality of data associated with various parameters of interest at a resource site. The workfl ows/flowcharts described in this disclosure, according to the some embodiments, implicate a new processing approach (e.g., hardware, special purpose processors, and specially programmed general -purpose processors) because such analyses are too complex and cannot be done by a person in the time available or at all. Thus, the described systems and methods are directed to tangible implementations or solutions to specific technological problems in developing 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 gas storage at a resource site (e.g., oil field, saline aquifers, etc.).

[0013] 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 and/or through other control devices/mechanisms that make determinations regarding whether a given action, template, or resource data, etc., is sufficiently accurate.

[0014] According to some embodiments of this disclosure, a relevant consideration involved in designing and implementing a gas capture and storage campaign is the likelihood that method A associated with the gas capture and storage campaign will detect event B within a range of subsurface uncertainties. Monitoring techniques (e.g., use of sensors to capture realtime and/or near-real time data and/or historic sensor data) for detecting important site-specific risks in the subsurface domain may be optimized for longevity as well as maintaining low operational costs. For the gas capture and storage operations disclosed herein for designing and implementing gas storage campaigns, a plurality 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 from this may include confirming the accuracy of the generated gas storage (GS) models using acquired data from the monitoring systems. If the acquired data confirms that the GS model is inaccurate or for example, has a substantially high degree of uncertainty, then additional data may need to be acquired to verify and reduce the uncertainty in the GS model. Given, for example, a monitoring timeframe of about 20 to 50+ years, the process of confirming that the GS model is accurate lends itself to automation or semi-automation operations (e.g., simulations) as the case may require. 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 initiated by a user. Initiating tests or simulations using a configured or parameterized GS model, for example, may involve a user activating one or more visual indicators (e.g., test/simulation icon) on a graphical user interface device.

[0015] The disclosed subject-matter automates the design process for 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 ™ with relevant information associated with the storage site being available for parameterization to conform to relevant monitoring objectives. The resulting interactive report generated from said modeling may provide stakeholders with an understanding of GS storage considerations at the storage site together with the option to drill-down into specific simulation and/or testing scenarios. By designing open simulator interfaces that can aggregate and or configure GS models for a given resource site, cross-domain factors across multiple domains associated with a given resource site may be factored in the GS campaigns for a given resource site. The cross-domain factors may include interactions between drilling, extracting, and/or safety operations at the resource site. [0016] As can be seen in FIG. 1, 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 and/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 plurality 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 and/or configuration settings, and/or safety data that may be used to optimize the configuration and/or operation of GS equipment at a given resource site. This may hone in or otherwise pinpoint precise monitoring techniques to deploy at each stage comprised in the GS campaign thereby mitigating against GS project risks and operational costs. Using the results from the simulations (e.g., insights from the simulations), adaptive monitoring and/or safety strategies may be seamlessly implemented in conjunction with GS operations at a given resource site.

[0017] Resource Site

[0018] FIG. 2 shows a cross-sectional view of a resource site 200 for which the process of FIG. 1 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 and/or geological formations may be provided at the resource site. As an example, wireline tools may be used to obtain measurement information related to geological attributes (e.g., geological attributes of a wellbore and/or reservoir) including geophysical and/or chemical information. For example, the chemical information may include chemical information associated with the subsurface and/or chemical information associated with the surface/above ground areas of the resource site 200. 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. 1. 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 of 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, and/or drones). Such measurements may include Synthetic Aperture Radar data, atmospheric concentration data associated with molecules such as CO2, CH4, and/or gas concentration data associated with gases within the seabed.

[0019] Part, or all, of the resource site 200 may be on land, on water, or below water. In addition, while a resource site 200 is depicted, the technology described herein may be used with any combination of one or more resource sites (e.g., multiple oil fields or multiple wellsites, one or more saline aquifers, one or more depleted oil/gas fields, etc.), one or more processing facilities, etc. 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 plurality of geological formations 206a-206d. As shown, this structure may have several formations or layers, including a shale layer 206a, a carbonate layer 206b, a shale layer 206c, and a sand layer 206d. A fault 207 may extend through the shale layer 206a and the carbonate layer 206b. The data acquisition tools, for example, may be adapted to take measurements and detect geophysical and/or chemical characteristics of the various formations shown.

[0020] While a specific subterranean formation with specific geological structures is depicted, it is appreciated that the oil field 200 may contain a variety of geological structures and/or formations, sometimes having extreme complexity. In some locations of a given geological structure, for example below a water line (e.g., aquifer) relative to the given geological structure, fluid may occupy pore spaces of the formations. Each of the measurement devices may be used to measure properties of the formations and/or other geological features. While each data acquisition tool is shown as being in specific locations in FIG. 2, it is appreciated that one or more types of measurement may be taken at one or more locations across one or more sources of the resource site 200 or other locations for comparison and/or analysis. The data collected from various sources at the resource site 200 may be processed and/or evaluated and/or used as training data, and or used to generate high resolution result sets for characterizing a resource at the resource site, and/or used for generating resource models, etc. In one embodiment, the data collected by one or more sensors at the resource site may include data associated with the number of wells of a first reservoir or second reservoir at the resource site, data associated with the number of grid cells of the first or second reservoir, data associated with the average permeability of the first or second reservoir, data associated with the production duration history (e.g., number of years of production) of the first reservoir or second, etc.

[0021] Data acquisition tool 202a is illustrated as a measurement truck, which may comprise devices or sensors that take measurements of the subsurface through sound vibrations such as, but not limited to, seismic measurements. Drilling tool 202b may include a downhole sensor adapted to perform logging while drilling (LWD) data collection. The wireline tool 202c may include a downhole sensor deployed in a wellbore or borehole. Production tool 202d may be deployed from a production unit or Christmas tree into a completed wellbore. Examples of parameters that may be measured include weight on bit, torque on bit, subterranean pressures (e.g., underground fluid pressure), temperatures, flow rates, compositions, rotary speed, particle count, voltages, currents, and/or other parameters of operations as further discussed below.

[0022] Sensors may be positioned about the storage complex to collect data relating to various storage complex operations, such as sensors deployed by the data acquisition tools 202. The sensor may include any type of sensor such as a metrology sensor (e.g., temperature, humidity), an automation enabling sensor, an operational sensor (e.g., pressure sensor, H2S sensor, thermometer, depth, tension), evaluation sensors, that can be used for acquiring data regarding the formation, wellbore, formation fluid/gas, wellbore fluid, gas/oil/water comprised in the formation/wellbore fluid, or any other suitable sensor. For example, the sensors may include accelerometers, flow rate sensors, pressure transducers, electromagnetic sensors, acoustic sensors, temperature sensors, chemical agent detection sensors, nuclear sensor, and/or any additional suitable sensors. In one embodiment, the data captured by the one or sensors may be used to characterize, or otherwise generate one or more parameter values for a high resolution result set used to, for example, generate a resource model or a GS model as the case may require. In other embodiments, test data or synthetic data may also be used in developing the resource model or the GS model via one or more simulations such as those discussed in association with the workflows presented herein.

[0023] 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 FMI™ or QuantaGeo™ (mark of Schlumberger); induction sensors such as Rt Scanner™ (mark of Schlumberger), multifrequency dielectric dispersion sensor 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 sensor 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 formation in which the well is formed (/.<?., determining petrophysical or geological properties of the formation), for verifying the integrity of the well (such as casing or cement properties) and/or analyzing the produced fluid (flow, type of fluid, etc.).

[0024] As shown, data acquisition tools 202a-202d may generate data plots or measurements 208a-208d, respectively. These data plots are depicted within the resource site 200 to demonstrate that data generated by some of the operations executed at the resource site 200.

[0025] 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) and/or determine the accuracy of the measurements and/or check for and compensate for measurement errors. The plots of each of the respective measurements may be aligned and/or scaled for comparison and verification purposes. In some embodiments, base data associated with the plots may be incorporated into site planning, modeling a test at the resource site 200. The respective measurements that can be taken may be any of the above. [0026] Other data may also be collected, such as historical data of the resource site 200 and/or sites similar to the resource site 200, user inputs, information (e.g., economic information) associated with the resource site 200 and/or sites similar to the resource site 200, and/or other measurement data and other parameters of interest. Similar measurements may also be used to measure changes in formation aspects over time.

[0027] Computer facilities such as those discussed in association with FIG. 3 may be positioned at various locations about the resource site 200 (e.g., a surface unit) and/or at remote locations. A surface unit (e.g., one or more terminals 320) may be used to communicate with the onsite tools and/or offsite operations, as well as with other surface or 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.

[0028] 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 and/or transmitted on or offsite. 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.

[0029] High-Level Networked System

[0030] 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 needs and workflows associated with the oil field 200. The communication devices of each server may enable the servers to communicate with each other through a local or global network such as an Internet network. In some embodiments, the servers may be arranged as a town 312, which may provide a private or local cloud service for users. A town may be advantageous in remote locations with poor connectivity. Additionally, a town may be beneficial in scenarios with large networks where security may be of concern. A town in such large network embodiments can facilitate implementation of a private network within such large networks. The town may interface with other towns or a larger cloud network, which may also communicate over public communication links. Note that cloud-computing platform 310 may include a private network and/or portions of public networks. In some cases, a cloud-computing platform 310 may include remote storage and/or other application processing capabilities.

[0031] 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 information. In one embodiment, the user terminals 314a and 314b is a computing system having interfaces and devices including keyboards, touchscreens, display screens, speakers, microphones, a mouse, styluses, etc. The user terminals 314 may be communicatively coupled to the one or more servers of the cloudcomputing 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.

[0032] 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 and/or directly coupled to the cloudcomputing 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, and/or displayed on user interfaces associated with the set of servers of the cloud computing platform 310, and/or displayed on user interfaces of the user terminals 314. Furthermore, various equipment/devices discussed in association with the resource site 200 may also be communicatively coupled to the set of terminals 320 and or communicatively coupled directly to the cloud-computing platform 310. The equipment and sensors may also include one or more communication device(s) that may communicate with the set of terminals 320 to receive orders/instructions locally and/or remotely from the resource site 200 and also send statuses/updates to other terminals such as the user terminals 314.

[0033] 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, and/or to the user terminals 314a and 314b, and/or to the set of terminals 320 at the resource site 200 and/or to sensors at the oil field, and/or to other equipment at the resource site 200.

[0034] 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.

[0035] The memory/storage media discussed above in association with FIG. 3 can be implemented as one or more computer-readable or machine-readable storage media that are non-transitory. In some embodiments, storage media may be distributed within and/or across multiple internal and/or external enclosures of a computing system and/or additional computing systems. Storage media may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories; magnetic disks such as fixed, floppy and removable disks; other magnetic media including tape; optical media such as compact disks (CDs) or digital video disks (DVDs), BluRays or any other type of optical media; or other types of storage devices. “Non-transitory” computer readable medium refers to the medium itself (i.e., tangible, not a signal) and not data storage persistency (e.g., RAM vs. ROM).

[0036] 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 possibly plural nodes and/or non-transitory storage means. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). The storage medium or media can be located either in a computer system running the machine- readable instructions, or located at a remote site from which machine-readable instructions can be downloaded over a network for execution.

[0037] 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, and/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 and/or application specific integrated circuits.

[0038] 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. 1 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, and/or their combination with general hardware are included within the scope of protection of the disclosure. While one or more computing processors (e.g., processors 302a, 302b, or 302c) may be described as executing steps associated with one or more of the flowcharts described in this disclosure, the one or more computing device processors may be associated with the cloudbased computing platform 310 and may be located at one location or distributed across multiple locations. In one embodiment, the one or more computing device processors may also be associated with other systems of FIG. 3 other than the cloud-computing platform 310.

[0039] 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, such that the programs comprise instructions, which when executed by the at least one processor, are configured to perform any method disclosed herein. [0040] 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. [0041] Embodiments

[0042] Two major concerns facing all gas storage projects are development constraints

(e.g., financial viability) and reservoir confinement issues. Operators must ensure that injected gas stays within a given storage site (e.g., reservoir or aquifer) and prove to regulatory bodies that any gas leakages are detectable and/or reportable. After a baseline for operational safety is established, the operator must ensure injection performance (e.g., gas injection performance) to secure that the gas storage project is viable. Furthermore, other challenges associated with gas storage include reliance or dependence on practices from other activities (e.g., hydrocarbon activities) which may not directly or indirectly align with gas storage settings or configurations of the gas storage equipment. According to some implementations, such activities may include abandonment of legacy wells without having a proper seal (e.g., tight gas seal) in place to prevent gas leakage or having an incorrect type of seal (e.g., cement seal) and/or workflow in place for plugging gas emissions. Other exemplary activities include use of chemicals during hydrocarbon extraction that restrict the flow of gas or result in reactions that harm future gas injection performance or significant over-pressuring or under-pressuring of the reservoir resulting in structural damage to the storage complex. In addition, there is an expected increase in gas storage projects worldwide which will likely overtake the available gas storage resources (e.g., human and nonhuman storage resources) and which can be greatly improved using the techniques disclosed herein. Furthermore, government agencies need to be assured or periodically reassured that any gas storage models being implemented for a given resource site are predictable and well controlled to comply with safety requirements for all phases (e.g., injection or post-injection phase of gas storage and management) associated with a GS campaign for the resource site. [0043] 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 a critical event (e.g., gas leakage), whereas the same measurement conducted in a different well (e.g., monitor well #1”) may distinguish over or detect a critical 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 a critical event (e.g., gas leakage). In some embodiments, the visualization shown in FIG. 4 indicates a plurality of different measurements (e.g., formation conductivity data or formation pressure data) that highlight which of the plurality of different measurements are more likely to detect the critical event. It is appreciated that the visualization shown in FIG. 4 may enable establishing distinctions between scenarios where there is substantial gas leakage relative to base scenarios where there is little to no gas leakage. It is further appreciated that the techniques disclosed herein and which facilitate generation of visualizations such as those shown in FIG. 4 enable the selection of measurement technology and/or spatial deployment of said technology in order to increase the likelihood that a critical event may be detected. In some embodiments, selection of the measurement technology is based on a definition and/or characterization and/or modeling of one or more subsurface uncertainties which determine expected distributions of measurement responses associated with critical events (e.g., gas 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 gas storage (GS) model(s) (e.g., carbon storage (CS) model(s)) and thereby minimize uncertainties associated with said models. In turn, the GS model(s) (e.g., refined GS model(s)) can be used to improve a monitoring plan, and/or control gas monitoring equipment, and/or control containment infrastructure (e.g., valves, pumps, etc.) associated with the GS model(s). According to some implementations, the disclosed 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 gas storage activities such as carbon capture, utilization, and storage (CCUS) activities to required volumes. By performing in a continuous loop of feeding measurements from the monitoring GS systems back into generated computing GS model(s) in the operating phase, predictability of such GS model(s) are greatly improved for the next iteration in the testing or simulation phase.

[0044] 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 not only applicable to GS operations but can also be applied to campaigns associated with geothermal solutions, hydrocarbon operations, offshore wind site operations, groundwater exploration activities, and other subsurface monitoring and optimization projects.

[0045] 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 gas storage use-case of measurement detectability to define a monitoring strategy and to automate comparisons of said monitoring strategy with optimal baseline methods. Moreover, the disclosed technology may incorporate tools such as Agile ™ 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 a plurality of simulators executing tests in parallel in order to enhance and/or improve a GS model being implemented at a given resource site to accelerate GS project execution times as well as provide timely insights for such projects and reporting same to regulatory agencies. Multiple simulators (and therefore multiphysics techniques) may be combined into similar or dissimilar workflows to determine the most probable measurement detection scenarios that define optimal configurations and/or practices comprised in a given GS campaign for a resource site and which span all leakage pathways and monitoring methods for said GS campaign. Moreover, the disclosed technology may allow users to customize or otherwise design end-to-end monitoring strategies for GS projects — from gas 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 run on the same platform. As such, the disclosed technology is capable of evolving to become an “app repository” for simulators associated with GS storage operations.

[0046] Workflows

[0047] 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 ™) to generate a GS model. The storage complex or storage site may include areas below ground where gas is stored and/or a volume of space above a reservoir where gas is stored and/or an area (e.g., an area adjacent to a resource site) associated with gas storage operations without reference to an origin of the gas being stored. The GS model, according to some embodiments, is associated with already captured gas in which case the systems and workflows disclosed is directed to storage and monitoring of captured 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 ^1M digital platform or an Open Subsurface Data Universe (OSDU ^1M) 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 plurality of required simulation configurations that test the scenarios for a plurality 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 plurality of scenarios in parallel, the results from such testing are post-processed to determine detectability of gas concentrations throughout the storage site or storage complex based on each testing method employed by the simulators. Gas detectability may be computed using computer generated statistical data derived from a plurality of stochastic geological realizations associated with the storage site. For example, the statistical data may include a value indicating a probability of gas 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 gas detectability at each location (e.g. gas detection outside a reservoir may be ranked higher than gas detections within a reservoir) such that the likelihood of such gas detection events may be derived from the aforementioned stochastic data.

[0048] The gas detection map depicted in FIG. 5, for example, indicates a CCh distribution data (e.g., probabilistic CO2 distribution data) of a change in relative acoustic impedance between a CCbpre-inj ection baseline and a current state after several years of injection. The gas detection map may be generated using an ensemble of simulation results from the plurality of simulations or testing of the GS model and which outlines uncertainty data and gas detection data associated with one or more sections of the gas storage site or gas storage complex combined with an effective medium model for acoustic impedance. The gas detection map may also show the probability of change associated with acoustic impedance (e.g., caused by injected gas) being greater than a detectability threshold value, thus indicating regions of the subsurface of the storage site where the movement of gas can likely be detected using, for example, seismic techniques. Moreover, the gas detection map may include scaling or ranking data that may be directly fed into site-specific design configurations or structurings in order to optimize gas detectability at the resource site. In addition, the detection map may include a plurality of colorings that indicate varying degrees of intensity of gas 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 gas leakage for a given location at the resource site, a yellow color to indicate a medium likelihood of gas leakage for other locations at the resource site, and a green coloring indicating a low likelihood of gas leakage for some locations at the resource or gas 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 gas leakage events and green at the low end of the spectrum indicating a low likelihood of gas leakage events.

[0049] A benefit of the disclosed approach is the drilling-down into individual simulators and/or workflows to understand gas detection thresholds based on a plurality of sitespecific scenarios and/or simulation conditions. As gas storage sites become operational so does the actual monitoring data associated with said sites. Previously, only base line monitoring data is available for configuring simulation parameters of the GS model. The additional realtime or near-real-time data or historic data captured at the gas 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 gas 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 may adaptively enable the GS model to be updated or otherwise parametrically revised in order to facilitate accurate detection of gas events at the storage site.

[0050] The disclosed technology is directed to methods and systems for optimizing gas storage (GS) operations at a resource site as exemplified in the flowchart of FIG. 6. It is appreciated that a data processing engine stored in a memory device may cause a computer processor to execute the various processing stages of FIG. 6. At block 602, the data processing engine may facilitate generating a GS model associated with the resource site such that the GS model comprises one or more parameters that characterize at least one of: temporal or spatial distribution data of subsurface geological structures associated with the resource site, uncertainty data indicating varying degrees of uncertainty ascribed to the temporal or spatial distribution data, well log data associated with the resource site, risk profile data associated with the resource site, zone leakage data associated with the resource site, temporal or spatial distribution data of a surface or a subsea terrain associated with the resource site, temporal or spatial distribution data of an atmospheric condition associated with the resource site, or workflow data associated with the resource site. 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 gas 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 have 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 plurality of gas leakage events based on the one or more parameters of the GS model over multiple time periods, a plurality of gas monitoring plans that track the plurality of gas leakage events across a plurality of geological realizations, and a plurality of dependent or independent simulations or tests that inform an impact of the gas 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 plurality 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: gas concentration data across the one or more locations at the resource site, gas leakage data across the one or more location at the resource site, and configuration data associated with configuring one or more monitoring systems at the resource site. At block 614, the data processing engine is used to initiate, using the analysis data, generation of a gas detection map that indicates gas 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.

[0051] These and other implementations may each optionally include one or more of the following features. The resource site may comprise a gas storage site at the resource site including one or more of: an aquifer, a saline aquifer, an oil reservoir, a depleted oil reservoir, a gas reservoir, or a depleted gas reservoir. Furthermore, the risk thresholds may quantify one or more of: a minimum amount of gas leakage that is allowed at the resource site, a specific amount of gas that is allowed to leak from a primary aquifer into a secondary aquifer, a specific amount of gas that is allowed to leak into legacy wells, a specific amount of gas that is allowed to leak from one well into a neighboring well, resolution data of the one or more monitoring systems at the resource site, or regulatory data comprised in the risk profile data based on emission constraints imposed by regulatory bodies. In addition, the one or more parameters of the GS model may include one or more of: raw data or processed data captured at the resource site, onshore or offshore geological data associated with the resource site comprising seismic data and the temporal or spatial distribution data of subsurface geological structures of the resource site, well characteristics data comprising the well log data, structural data derived from the onshore or offshore geological data, 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 comprises: data indicating frequency of executing the simulation plan, data indicating frequency of updating the simulation plan, data indicating time lapse measurements comprised in the multiple time periods, 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 gas monitoring plans over the multiple time periods. It is appreciated that the risk profile data comprises a quantitative measure of stake holder risk tolerance levels based on domain (e.g., reservoir domain, wellbore domain, etc.) expert data and data associated with subsurface analysis operations. Additionally, the zone leakage data may indicate one or more paths of gas 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.

[0052] According to some embodiments, executing the simulation plan comprises executing a plurality 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 plurality of geological realizations. The plurality of geological realizations may comprise a plurality of GS sub-models of the subsurface associated with the resource site such that the plurality 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 based on analyzing the simulation results across the defined uncertainty space in different time steps and across different geological realizations comprised in the plurality of geological realizations. The report may include, for example, one or more of: a visualization indicating the gas detection map, a matrix indicating efficacy level data for using a plurality of different GS operations at the resource site based on the simulations to determine an optimal GS campaign for the resource site, a plurality of tabular data, a two- dimensional visualization indicating a first monitoring design for optimally monitoring gas stored at the one or more locations at the resource site, or a three-dimensional visualization indicating a second monitoring design for optimally monitoring gas stored at the one or more locations at the resource site. 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 comprise 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 the subsurface at the resource site, including but not limited to pulsed- neutron methods simulator(s) and/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 gas reservoir, depleted oil or gas 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. 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 comprise a plurality of simulation methods that can either be executed in an in-situ simulation state or in an inverse state. In the in-situ state, for example, a parameter associated with the GS model may be directly calculated using computational methods and based on previous simulator results. This provides a direct value of a quantity, for example, in the subsurface of the resource site. However, when using other geophysical methods 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.

[0053] 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 including one or more of: a plurality of tabular data, a two dimensional visualization indicating a first monitoring design for optimally monitoring gas stored at the one or more locations at the resource site, or a three-dimensional visualization indicating a second monitoring design for optimally monitoring gas stored at the one or more locations at the resource site. In some embodiments, parameterizing the one or more parameters of the GS model comprises updating grid resolution data for at least one parameter comprised in the one or more parameters of the GS model.

[0054] In some implementations, the gas storage operations are associated with storing gas including at least one of: carbon dioxide gas, hydrogen gas, and methane gas. Furthermore, a phase of the stored gas may be based on one or more of: a depth within a subsurface (e.g., gas storage complex) of the resource site within which the gas is stored, and pressure within the subsurface of the resource site within which the gas is stored. It is appreciated that leakages of gas from the storage complex may result in the leaked gas changing phase due to pressure differentials between the pressure within the storage complex relative to pressures within the leakage zones around the storage complex.

[0055] While any discussion of or citation to related art in this disclosure may or may not include some prior art references, Applicant neither concedes nor acquiesces to the position that any given reference is prior art or analogous prior art.

[0056] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to use the invention and various 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.

[0057] 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 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 invention. 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.

[0058] The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used in the description of the invention and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any possible combination of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

[0059] 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.

[0060] Those with skill in the art will appreciate that while some terms in this disclosure may refer to absolutes, e.g., all source receiver traces, each of a plurality of objects, etc., the methods and techniques disclosed herein may also be performed on fewer than all of a given thing, e.g., performed on one or more components and/or performed on one or more source receiver traces. Accordingly, in instances in the disclosure where an absolute is used, the disclosure may also be interpreted to be referring to a subset.

Claims

What is claimed is:

1. A method for optimizing gas storage (GS) operations at a resource site, the method comprising: generating, using one or more computer processors, a GS model associated with the resource site, the GS model comprising 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; determining, using the one or more computer processors, risk thresholds for the GS operations based on the risk profile data associated with the resource site, the risk thresholds indicating a tolerance level for gas leakage at one or more locations at the resource site; parameterizing, using the one or more computer processors and based on the risk thresholds, the one or more parameters of the GS model 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 have been captured by one or more sensors deployed around the one or more locations at the resource site; generating, using the one or more computer processors and the parameterized GS model, a simulation plan for the GS operations at the resource site, the simulation plan indicating at least one of: a plurality of gas leakage events based on the parameters of the GS model over multiple time periods, a plurality of gas monitoring plans that track the plurality of gas leakage events across a plurality of geological realizations, and a plurality of dependent or independent simulations or tests that inform an impact of the gas monitoring plans over the multiple time periods; executing, using the one or more computer processors, the simulation plan across: multiple simulators in parallel, a defined uncertainty space derived from the uncertainty data, the multiple time periods, and the plurality of geological realizations; aggregating, using the one or more computer processors, analysis data generated from executing the simulation plan, the analysis data indicating one or more of: gas concentration data across the one or more locations at the resource site, gas leakage data across the one or more location at the resource site, and configuration data associated with configuring one or more monitoring systems at the resource site; and initiating, using the one or more computer processors and the analysis data, generation of a gas detection map that indicates gas distribution data associated with the one or more locations at the resource site, and configuring the one or more monitoring systems at the resource site.

2. The method of claim 1, wherein the resource site comprises a gas storage site at the resource site including one or more of: an aquifer, a saline aquifer, an oil reservoir, a depleted oil reservoir, a gas reservoir, or a depleted gas reservoir.

3. The method of claim 1, wherein the risk thresholds quantify one or more of: a minimum amount of gas leakage that is allowed at the resource site, a specific amount of gas that is allowed to leak from a primary aquifer into a secondary aquifer, a specific amount of gas that is allowed to leak into legacy wells, a specific amount of gas that is allowed to leak from one well into a neighboring well, resolution data of the one or more monitoring system at the resource site, or regulatory data comprised in the risk profile data based on emission constraints imposed by regulatory bodies.

4. The method of claim 1, wherein the one or more parameters of the GS model include one or more of: raw data or processed data captured at the resource site, onshore or offshore geological data associated with the resource site comprising seismic data and the temporal or spatial distribution data of subsurface geological structures of the resource site, well characteristics data comprising the well log data, structural data derived from the onshore or offshore geological data, 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, configuration data associated with the one or more monitoring systems at the resource site including: line spacing between sensors at the resource site, and frequency configurations used to tune electromagnetic sensors at the resource site.

5. The method of claim 1 , wherein the workflow data associated with the resource site comprises: data indicating frequency of executing the simulation plan, data indicating frequency of updating the simulation plan, data indicating time lapse measurements comprised in the multiple time periods, dynamic post-processing operations data including: 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 gas monitoring plans over the multiple time periods.

6. The method of claim 1, wherein the risk profile data comprises a quantitative measure of stake holder risk tolerance levels based on domain expert data and data associated with subsurface analysis operations.

7. The method of claim 1, wherein the zone leakage data indicates one or more paths of gas 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.

8. The method of claim 1, wherein executing the simulation plan comprises executing a plurality 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 plurality of geological realizations, the plurality of geological realizations comprising a plurality of GS sub-models of the subsurface associated with the resource site such that the plurality of GS submodels indicate: 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.

9. The method of claim 1, wherein the analysis data is used to generate a report based on analyzing the simulation results across the defined uncertainty space in different time steps and across different geological realizations comprised in the plurality of geological realizations, such that the report includes one or more of: a visualization indicating the gas detection map, a matrix indicating efficacy level data for using a plurality of different GS operations at the resource site based on the simulations to determine an optimal GS campaign for the resource site, a plurality of tabular data, a two-dimensional visualization indicating a first monitoring design for optimally monitoring gas stored at the one or more locations at the resource site, or a three-dimensional visualization indicating a second monitoring design for optimally monitoring gas stored at the one or more locations at the resource site. 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.

10. The method of claim 1, wherein the multiple simulators comprise one or more of: flow-based simulators that depend on pressure measurements within the subsurface of the resource site, electromagnetic simulators that depend on formation resistivity data within the subsurface at the resource site, nuclear simulators that predict responses of certain nuclear measurements within the subsurface at the resource site, geomechanical simulators that predict subsurface responses to changes in mechanical conditions at the resource site, electric simulators 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, or acoustic simulators that depend on acoustic properties of the subsurface at the resource site.

11. The method of claim 1, wherein 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.

12. The method of claim 1, further comprising generating, using the one or more computer processors and the analysis data, a report including one or more of: a plurality of tabular data, a two dimensional visualization indicating a first monitoring design for optimally monitoring gas stored at the one or more locations at the resource site, or a three-dimensional visualization indicating a second monitoring design for optimally monitoring gas stored at the one or more locations at the resource site.

13. The method of claim 1, wherein parameterizing the one or more parameters comprises updating grid resolution data for at least one parameter comprised in the one or more parameters.

14. A system for optimizing gas storage (GS) operations at a resource site, the system comprising: a computer processor, and memory storing a data processing engine that comprises instructions that are executable by the computer processor to: generate a GS model associated with the resource site, the GS model comprising 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; determine risk thresholds for the GS operations based on the risk profile data associated with the resource site, the risk thresholds indicating a tolerance level for gas leakage at one or more locations at the resource site; parameterize, based on the risk thresholds, the one or more parameters of the GS model 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 have been captured by one or more sensors deployed around the one or more locations at the resource site; generate, using the parameterized GS model, a simulation plan for the GS operations at the resource site, the simulation plan indicating at least one of: a plurality of gas leakage events based on the one or more parameters of the GS model over multiple time periods, a plurality of gas monitoring plans that track the plurality of gas leakage events across a plurality of geological realizations, and a plurality of dependent or independent simulations or tests that inform an impact of the gas monitoring plans over the multiple time periods; execute the simulation plan across: multiple simulators in parallel, a defined uncertainty space derived from the uncertainty data, the multiple time periods, and the plurality of geological realizations; aggregate analysis data generated from executing the simulation plan, the analysis data indicating one or more of: gas concentration data across the one or more locations at the resource site, gas leakage data across the one or more location at the resource site, and configuration data associated with configuring one or more monitoring systems at the resource site; and initiate, using the analysis data, generation of a gas detection map that indicates gas distribution data associated with the one or more locations at the resource site, and configuring the one or more monitoring systems at the resource site.

15. The system of claim 14, wherein the resource site comprises a gas storage site at the resource site including one or more of: an aquifer, a saline aquifer, an oil reservoir, a depleted oil reservoir, a gas reservoir, or a depleted gas reservoir.

16. The system of claim 14, wherein the one or more parameters of the GS model include one or more of: raw data or processed data captured at the resource site, onshore or offshore geological data associated with the resource site comprising seismic data and the temporal or spatial distribution data of subsurface geological structures of the resource site, well characteristics data comprising the well log data, structural data derived from the onshore or offshore geological data, 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, configuration data associated with the one or more monitoring systems at the resource site including: line spacing between sensors at the resource site, and frequency configurations used to tune electromagnetic sensors at the resource site.

17. The system of claim 14, wherein executing the simulation plan comprises executing a plurality 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 plurality of geological realizations, the plurality of geological realizations comprising a plurality of GS sub-models of the subsurface associated with the resource site such that the plurality of GS submodels indicate: 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.

18. The system of claim 14, wherein 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.

19. A computer program for optimizing gas storage (GS) operations at a resource site, the computer program comprising instructions, that when executed by a computer processor of a computing device, causes the computing device to: generate a GS model associated with the resource site, the GS model comprising 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; determine risk thresholds for the GS operations based on the risk profile data associated with the resource site, the risk thresholds indicating a tolerance level for gas leakage at one or more locations at the resource site; parameterize, based on the risk thresholds, the one or more parameters of the GS model 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 have been captured by one or more sensors deployed around the one or more locations at the resource site; generate, using the parameterized GS model, a simulation plan for the GS operations at the resource site, the simulation plan indicating at least one of: a plurality of gas leakage events based on the one or more parameters of the GS model over multiple time periods, a plurality of gas monitoring plans that track the plurality of gas leakage events across a plurality of geological realizations, and a plurality of dependent or independent simulations or tests that inform an impact of the gas monitoring plans over the multiple time periods; execute the simulation plan across: multiple simulators in parallel, a defined uncertainty space derived from the uncertainty data, the multiple time periods, and the plurality of geological realizations; aggregate analysis data generated from executing the simulation plan, the analysis data indicating one or more of: gas concentration data across the one or more locations at the resource site, gas leakage data across the one or more location at the resource site, and configuration data associated with configuring one or more monitoring systems at the resource site; and initiate, using the analysis data, generation of a gas detection map that indicates gas distribution data associated with the one or more locations at the resource site, and configuring the one or more monitoring systems at the resource site.

20. The computer program of claim 19, wherein the resource site comprises a gas storage site at the resource site including one or more of: an aquifer, a saline aquifer, an oil reservoir, a depleted oil reservoir, a gas reservoir, or a depleted gas reservoir.

21. A method for optimizing gas storage (GS) operations at a resource site, the method comprising: generating, using one or more computer processors, a GS model associated with the resource site; determining, using the one or more computer processors, risk thresholds for the GS operations based on risk profile data associated with the resource site, the risk thresholds indicating a tolerance level for gas leakage at one or more locations at the resource site; parameterizing, using the one or more computer processors and based on the risk thresholds, one or more parameters of the GS model; generating, using the one or more computer processors and the parameterized GS model, a simulation plan for the GS operations at the resource site; executing, using the one or more computer processors, the simulation plan; aggregating, using the one or more computer processors, analysis data generated from executing the simulation plan; and initiating, using the one or more computer processors and the analysis data, generation of a gas detection map that indicates gas distribution data associated with the one or more locations at the resource site.

22. The method of claim 21, wherein the one or more parameters of the GS model 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, the 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.

23. The method of claim 21, wherein the simulation plan indicates at least one of: a plurality of gas leakage events based on the one or more parameters of the GS model over the multiple time periods, a plurality of gas monitoring plans that track the plurality of gas leakage events across a plurality of geological realizations, and a plurality of dependent or independent simulations or tests that inform an impact of the gas monitoring plans over the multiple time periods.

24. The method of claim 21, wherein the GS operations are associated with storing gas including at least one of: carbon dioxide gas, hydrogen gas, and methane gas.

25. The method of claim 24, wherein a phase of the gas is based on one or more of: a depth within a subsurface of the resource site within which the gas is stored, and pressure within the subsurface of the resource site within which the gas is stored.

26. The method of claim 21, wherein the one or more parameters of the GS model are parameterized 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 have been captured by one or more sensors deployed around the one or more locations at the resource site.

27. The method of claim 21, wherein the simulation plan is executed across: multiple simulators in parallel, a defined uncertainty space derived from uncertainty data, multiple time periods, and a plurality of geological realizations.

28. The method of claim 21, wherein the analysis data indicates one or more of: gas concentration data across the one or more locations at the resource site, gas leakage data across the one or more location at the resource site, and configuration data associated with configuring one or more monitoring systems at the resource site.

29. The method of claim 21, wherein the analysis data is used to initiate configuring one or more monitoring systems at the resource site.