Real-time system to detect the metrics for reservoir capacity and insights in CCUS operations

Abstract

A computer-implemented method includes training an artificial intelligence / machine learning (AI / ML) algorithm to generate a reservoir model representative of a storage reservoir and generating the reservoir model. a computer containing the trained reservoir model receives a set of real-time reservoir data generated at the storage reservoir, the set of real-time reservoir data being representative of measured reservoir operations and analyzes the real-time reservoir data using the trained reservoir model. The computer generates at least one recommended alteration to reservoir operations based on an output of the reservoir model.

Description

71 CC S-511079-WO-2 BHI0589PCTREAL-TIME SYSTEM TO DETECT THE METRICS FOR RESERVOIR CAPACITY AND INSIGHTS IN CCUS OPERATIONSCROSS REFERENCE TO RELATED APPLICATIONSThis application claims the benefit of Italian Application Serial No.102024000030222 filed on December 31, 2024, which is incorporated herein by reference in its entirety.BACKGROUND

[0001] Various embodiments supported by aspects of the present disclosure relate to automated risk management with alert detection in association with resource recovery and fluid sequestration industries, and more particularly, to carbon capture, utilization and storage (CCUS) industries.

[0002] In the resource recovery and fluid sequestration industries, some systems are configured to provide storage of one or more materials (e.g. storage of CO2 in a carbon capture system) at a particular site. Storage sites include a maximum capacity beyond which the storage site may not fully contain the stored materials.

[0003] Currently most storage sites rely on manual and periodic measurement technologies to estimate the available remaining capacity of the storage site. Such methods typically require active measurement through physical processes and may be insufficiently accurate, time consuming, expensive, can provide untimely, or otherwise incorrect data regarding the storage capacity of the storage site.

[0004] Lack of current up to date metrics in real-time can hamper the ability of a system operator to make informed operational decisions and can potentially result in overestimations or underestimations of available storge capacity of the storage site. The over and underestimations, in turn, result in inefficient uses of storage resources and increased operational costs.

[0005] As such, it is desirable to provide a system able to analyze storage site capacity in real time using measurements that account for current storage parameters.SUMMARY

[0006] Embodiments of the present disclosure are directed to a computer-implemented method that includes training an artificial intelligence / machine learning (AI / ML) algorithm to generate a reservoir model representative of a storage reservoir and71 CC S-511079-WO-2 BHI0589PCTgenerating the reservoir model wherein a computer containing the trained reservoir model receives a set of real-time reservoir data generated at the storage reservoir, the set of real-time reservoir data being representative of measured reservoir operations and analyzes the realtime reservoir data using the trained reservoir model. The computer generates at least one recommended alteration to reservoir operations based on an output of the reservoir model.

[0007] Embodiments of the present disclosure are also directed to a system including analysis equipment comprising a processor and a memory, wherein the memory comprises instructions stored thereon that, when executed by the processor, cause the processor to perform operations including: training an artificial intelligence / machine learning (AI / ML) algorithm to generate a reservoir model representative of a storage reservoir and generating the reservoir model, receiving, at a computer containing the trained reservoir model, a set of real-time reservoir data generated at the storage reservoir, the set of real-time reservoir data being representative of measured reservoir operations, and analyzing the real-time reservoir data using the trained reservoir model, and generating at least one recommended alteration to reservoir operations based on an output of the reservoir model.

[0008] Further aspects supported by the present disclosure and features of example embodiments are illustrated in the accompanying drawings and / or described in the following description.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The following descriptions should not be considered limiting in any way. With reference to the accompanying drawings, like elements are numbered alike:

[0010] Figure 1 is a diagram illustrating an example embodiment of a system for real¬ time metrics assessment and advisory alerts associated with the reservoir capacity in accordance with aspects of the present disclosure.

[0011] Figure 2 illustrates an example workflow in accordance with one or more embodiments of the present disclosure.

[0012] Figure 3 illustrates an example flowchart of a method in accordance with one or more embodiments of the present disclosure.DETAILED DESCRIPTION

[0013] A detailed description of one or more embodiments of the disclosed apparatus and method are presented herein by way of exemplification and not limitation with reference to the Figures.71 CC S-511079-WO-2 BHI0589PCT

[0014] FIG. 1 is a diagram illustrating an example embodiment of a system 100 for automated risk management with alert detection in association with energy industry operations in accordance with aspects of the present disclosure.

[0015] The system 100 is configured to perform in any suitable energy industry operation, such as, for example, a drilling operation, a stimulation operation, a measurement operation and / or a production operation. In some embodiments, the energy industry operations may include carbon capture, utilization and storage (CCUS) operations. However, example aspects of the techniques for real-time storage capacity monitoring as supported by the system 100 and described herein are not limited to energy industry operations and an associated downhole environment.

[0016] According to one or more embodiments of the present disclosure, the systems and techniques described herein support digital solutions for providing real-time monitoring and analysis of carbon storage sites. The systems and techniques described herein support real-time analysis with increased accuracy compared to some other approaches.

[0017] The system 100 includes a borehole 135 in a subsurface formation 130. A borehole string 140 (also referred to herein as a drill string) is disposed in the borehole 135 that penetrates the formation 130. The borehole 135 may be an open hole, a cased hole or a partially cased hole. In one embodiment, the borehole string 140 is a stimulation or injection string that includes a tubular, such as a coiled tubing, pipe (e.g., multiple pipe segments) or wired pipe, that extends from a wellhead at a surface location (e.g., at a drill site or offshore stimulation vessel).

[0018] As described herein, a “string” refers to any structure or carrier suitable for lowering a tool or other component through a borehole or connecting a drill bit to the surface and is not limited to the structure and configuration described herein. The term "carrier" as used herein means any device, device component, combination of devices, media and / or member that may be used to convey, house, support or otherwise facilitate the use of another device, device component, combination of devices, media and / or member. Example nonlimiting carriers include casing pipes, wirelines, wireline sondes, slickline sondes, drop shots, downhole subs, BHAs and drill strings.

[0019] In one embodiment, the system 100 is configured as a hydraulic stimulation system. As described herein, “hydraulic stimulation” includes any injection of a fluid into a formation. A fluid may be any flowable substance such as a liquid or a gas, a flowable solid such as sand, and / or a mixture thereof.71 CC S-511079-WO-2 BHI0589PCT

[0020] In this embodiment, the borehole string 140 includes a stimulation assembly that includes one or more tools 150 or components to facilitate stimulation of the formation 130. Non-limiting examples of the tools 150 included in the borehole string 140 include a fracturing assembly (e.g., a fracture or “frac” sleeve device), a perforation assembly (e.g., shaped charges, torches, projectiles and other devices for perforating the borehole wall and / or casing), and isolation or packer subs.

[0021] The tools 150 may support various processes including formation drilling, geosteering, and formation evaluation (FE) for measuring versus depth and / or time one or more physical quantities in or around a borehole 135. The tools 150 may be included in or embodied as a BHA, drillstring component, or other suitable carrier. A “carrier” as described herein means any device, device component, combination of devices, media and / or member that may be used to convey, house, support or otherwise facilitate the use of another device, device component, combination of devices, media and / or member. Example non-limiting carriers include drill strings of the coiled tubing type, of the jointed pipe type and any combination or portion thereof. Other carriers include, but are not limited to, casing pipes, wirelines, wireline sondes, slickline sondes, drop shots, downhole subs, bottom-hole assemblies, and drill strings.

[0022] One or more of the tools 150 may include suitable electronics or processors configured to communicate with a surface processing unit (e.g., a computing device 105) and / or control the respective tool 150 or assembly.

[0023] The system 100 includes surface equipment 110 for performing various energy industry operations. For example, the surface equipment 110 is configured for injection of fluids into the borehole 135 in order to, e.g., fracture the formation 130. In one or more embodiments, the surface equipment 110 includes an injection device such as a high pressure pump 115 in fluid communication with a fluid tank 120, mixing unit or other fluid source or combination of fluid sources. The pump 115 injects fluid into the borehole string 140 or the borehole 135 to introduce fluid into the formation 130, for example, to stimulate and / or fracture the formation 130. The pump 115 may be located downhole or at a surface location.

[0024] One or more flow rate and / or pressure sensors 125 may be disposed in fluid communication with the pump 115 and the borehole string 140 for measurement of fluid characteristics. The sensors 125 may be positioned at any suitable location, such as proximate to (e.g., at the discharge output) or within the pump 115, at or near the wellhead, or at any other location along the borehole string 140 or the borehole 135. The sensors described herein are exemplary, as various types of sensors may be used to measure various parameters.71 CC S-511079-WO-2 BHI0589PCT

[0025] A computing device 105 (e.g., computing device 105-a) may be disposed in operable communication with components such as sensors 125 located above the surface, the pump 115, and / or downhole components. For example, the computing device 105 may be in operable communication with sensors (e.g., pressure sensors, temperature sensors, vibration sensors, gas sensors, and the like) located below the surface and / or in the borehole string 140. In some examples, the computing device 105-a may be in operable communication with a tool 150 (or multiple tools).

[0026] The system 100 supports communication between the computing device 105 and other devices of the system 100 via wired communication protocols, wireless communication protocols (e.g., electromagnetic (EM) signals, WiFi, Bluetooth™, ZigBee™, Ubiquiti™, 3G, 4G, LIE, and the like), and / or combinations including one or more of the foregoing.

[0027] The system 100 supports telemetry techniques capable of transmitting data from components located downhole to the surface and / or surface equipment 110. Non¬ limiting examples of the telemetry techniques include acoustic telemetry or mud pulse (MP) telemetry supportive of transmitting information by generating vibrations in fluid in the borehole 135, electromagnetic (EM) telemetry supportive of transmitting information by way of signals that propagate at least in part through the earth (e.g., through formations 130). Other non-limiting examples of telemetry techniques supported by aspects of the present disclosure include the use of hardwired drill pipe, fibre optic cable, or drill collar acoustic telemetry to carry data to the surface and / or surface equipment 110.

[0028] The system 100 may include one or more access nodes 170 supportive of communicating data along the borehole string 140 (e.g., up or down the borehole string 140). In one or more embodiments, the access nodes 170 may be implemented in the borehole 135 or a communication borehole (not illustrated) separate from the borehole 135. In some examples, the one or more access nodes 170 may provide functionality as wireless access nodes for relaying data from a tool 150 to the surface (e.g., to a computing device 105).

[0029] In one or more embodiments, the system 100 may include a chain of access nodes 170 spaced apart along the borehole string 140, and the chain of access nodes 170 may support repeating of data in a unidirectional (e.g. downhole to surface or surface to downhole) or bidirectional manner. For example, an access node 170 (or chain of access nodes 170) may support the communication of data between a computing device 105, a tool 150, and the like.71 CC S-511079-WO-2 BHI0589PCT

[0030] Accordingly, for example, the communication protocols and telemetry techniques supported by the system 100 enable communication between computing devices 105 (e.g., computing device 105-a, computing device 105-b, and the like) and downhole components.

[0031] The computing device 105 is configured to receive, store and / or transmit data generated from components (e.g., pump 115, fluid tank 120, sensors 122, sensors 125, sensors 155, sensors 160, and the like) included in the surface equipment 110 and / or downhole components (e.g., a tool 150, downhole sensors 155, and the like). The computing device 105 includes processing components configured to analyze received data (e.g., data received from the pump 115, fluid tank 120, sensors 125, a tool 150, and the like). The computing device 105 includes processing components configured to provide data (and / or control signals to other components of the system 100. The computing device 105 includes any number of suitable components, such as processors, memory, communication devices and power sources.

[0032] The sensors 155 may be configured to measure various parameters of the formation 130 and / or borehole 135. For example, the sensors 155 may include formation evaluation sensors (e.g., resistivity, dielectric constant, water saturation, porosity, density and permeability), sensors for measuring borehole parameters (e.g., borehole size, borehole inclination and azimuth, and borehole roughness), sensors for measuring geophysical parameters (e.g., acoustic velocity, acoustic travel time, electrical resistivity), sensors for measuring borehole fluid parameters (e.g., viscosity, density, clarity, rheology, pH level, and gas, oil and water contents), boundary condition sensors, and sensors for measuring physical and chemical properties of the borehole fluid.

[0033] The system 100 includes sensors 160 for measuring force, operational and / or environmental parameters related to bending or other static and / or dynamic deformation of one or more downhole components. The sensors 160 are described collectively herein as “deformation sensors” and encompass any sensors, located at the surface and / or downhole, that provide measurements relating to bending or other deformation, static or dynamic, of a downhole component. Examples of deformation include deflection, rotation, strain, torsion and bending. Such sensors 160 provide data that is related to forces on the component (e.g., strain sensors, WOB sensors, TOB sensors) and are used to measure deformation orbending that could result in a change in position, alignment and / or orientation of one or more other sensors 160 or one or more sensors 155. In some non-limiting embodiments, the sensors 160 may include one or more of: (i) a strain gauge, (ii) a transmitter oriented at a non-X, non-Z71 CC S-511079-WO-2 BHI0589PCTangle, (iii) a receiver oriented at a non-X, non-Z angle, (iv) a differential magnetometer, (v) a differential accelerometer, (vi) an optical sensor, and (vii) an optical fiber sensor.

[0034] For example, the system 100 may include a distributed sensor system (DSS) disposed at the borehole string 140 and tool 150 (e.g., a BHA) and including a plurality of sensors 160. The sensors 160 may perform measurements associated with forces on the borehole string 140 that may result in deformation, and can thereby result in misalignment of one or more sensors 155. Non-limiting example of measurements performed by the sensors 160 include accelerations, velocities, distances, angles, forces, moments, and pressures. Sensors 160 may also be configured to measure environmental parameters such as temperature and pressure. In a non-limiting example, the sensors 160 may be distributed throughout the borehole string 140 and / or at a tool 150 (e.g., a drill bit) at the distal end of borehole string 140. In other embodiments, the sensors 160 may be configured to measure directional characteristics at various locations along the borehole 135. Examples of such directional characteristics include inclination and azimuth, curvature, strain, and bending moment.

[0035] In some examples, the system 100 may include sensors 160 coupled to a downhole component, such as, for example, a drill pipe section or a tool 150 (e.g., a BHA). In some examples, the sensors 160 may be deformation sensors (e.g., strain gauges configured to measure strain).

[0036] A software system 101 may include machine learning model(s) 107 which may be trained and / or updated using historical training data provided or accessed by any devices or systems described herein, alongside real-time data generated during operation of certain embodiments disclosed herein and added to the training data. The machine learning model(s) 107 may be built and updated by a computing device 105 (e.g., computing device 105-a, computing device 105-b, or the like) based on the training data (also referred to herein as training data and feedback).

[0037] The machine learning model(s) 107 may be provided in any number of formats or forms. In one or more embodiments, the operations described herein may implement machine learning and / or rule-based systems to generate a mixing instruction. In one or more embodiments, the software system 101 may include (e.g., for theoretical and empirical processes) rule-based systems using predefined rules to make decisions or perform tasks, which may operate based on if-then statements.

[0038] In one or more embodiments, the software system 101 may include natural language processing (NLP) techniques supportive of the interaction between computers and71 CC S-511079-WO-2 BHI0589PCThuman language, enabling machines to understand, interpret, and generate human language (e.g., risk descriptions, risk remediation plans, risk mitigation plans, alerts, and the like).

[0039] In one or more embodiments, the software system 101 may include computer vision techniques supportive of image processing, object recognition, and image segmentation. In an example, the computer vision techniques may support the integration of sensor data (e.g., from sensors 155, sensors 160, or the like).

[0040] In one or more embodiments, the software system 101 may include data mining techniques supportive of discovering patterns and relationships in large datasets (e.g., from database 180, data sources 202 later described herein with reference to FIG. 2, other data later described herein with reference to FIG. 2, and the like) and extract information.

[0041] In one or more embodiments, the software system 101 may include or implement genetic algorithms supportive of determining approximate solutions to optimization and search problems, expert systems (computer systems) configured to emulate the decision-making ability of a human expert in a specific domain, fuzzy logic techniques supportive of modeling uncertainty and imprecision in data, simulation and modeling, regression analysis, clustering techniques, and dimensionality reduction (e.g., principal component analysis (PCA) or t-SNE).

[0042] Example aspects of the machine learning model(s) 107, such as generating (e.g., building, training) and applying the machine learning model(s) 107, are described with reference to the figure descriptions herein.

[0043] Aspects of the software system 101 described herein support reducing operator workload and enhancing the efficiency of analyzing real-time data 157 using an analysis system 109 and a reservoir model 108 representing the storage site. In addition, aspects of the software system 101 described herein provide a recommendation display 187 based on the analysis provided by the analysis system 109 and can implement user inputs 190 received by a user who has reviewed the recommendations.

[0044] Example aspects of methods, procedures, and processes supported by the software system 101 are described herein. The real-time analysis supported by software system 101 entails a comprehensive software application design that enables an automated workflow, fostering increased user confidence.

[0045] The software system 101 provides a user interface (e.g., at a computing device 105) that enables operators (e.g., field engineers) with the ability to review real-time data 157 and recommendations 187 regarding the capacity of a storage site. Via the user interface, the software system 101 supports user controller and automated responses to the71 CC S-511079-WO-2 BHI0589PCTrecommendations 187. The responses can include scheduling a decrease in flow rate into the storage site, scheduling an increase in flow rate into the storage site, stopping flow into a storage site, maintaining a same flow rate into a storage site, or any similar response. In some cases, the user input 190 may be utilized to control future automated responses. In other cases, the user input 190 may be utilized to provide in the moment responses to alerts or conditions identified using the analysis 109 of the real-time data 157.

[0046] In one or more embodiments, the software system 101 integrates a machine learning algorithm (implemented using one or more trained machine learning models 107) to at least partial determine the reservoir model 108 and, based on the reservoir model 108 and the real-time data 157, generate analysis 109. The analysis 109 provides a recommendation display 187 to operators (e.g., user 226, illustrated in FIG. 2) who can then use the recommendation display 187 to provide inputs 190 affecting the efficiency and efficacy of capture operations.

[0047] In one or more embodiments, aspects of the automation solution may be implemented at computing device 105 (e.g., computing device 105-a), another computing device 105 (e.g., computing device 105-b) in electronic communication with the computing device 105 via a wired and / or wireless communication protocol, surface equipment 110, and / or processing circuitry included in the surface equipment 110. In the example of FIG. 1, computing device 105-a may be located at the drill site, and computing device 105-b may be located at the drill site or at a remote site. In some cases, computing device 105-a and / or computing device 105-b may be a server. In some cases, additional computing devices beyond computing device 105-a and computing device 105-b may be incorporated and function similarly to computing device 105-b.

[0048] According to one or more embodiments of the present disclosure described herein, aspects of the system 100 provide real-time risk analysis and recommendations. The system 100 supports automated association of risks to alerts generated by an industrial system. The terms “alerts” and “alarms” may be used interchangeably herein and refer broadly to any notifications provided to a user 226 (illustrated in FIG. 2) responsive to one or more conditions or combinations of conditions.

[0049] As will be described herein, the software system 101 provides a hardware and software platform supportive of end-to-end monitoring of the system 100. For example, the software system 101 supports end-to-end monitoring of conditions associated with surface equipment 110, formation 130, borehole 135, borehole string 140, tools 150, and the like). For example, the software system 101 is capable of generating using a reservoir model 108 in71 CC S-511079-WO-2 BHI0589PCTconjunction with the machine learning model(s) 107 the generate an analysis 109 of the data 157 provided by the sensors (e.g., from sensors 155, sensors 160, or the like).

[0050] In one example, the analysis 109 may provide a digital real-time inventory information identifying a remaining capacity of a carbon reservoir based on the real time date 157, and provide analysis 109 using an analysis algorithm which provides updated information on the status of the carbon reservoir. The example efficiently integrates with reservoir monitoring infrastructure (e.g. sensors 155, sensors 160, or the like) using the reservoir model 108 in conjunction with the machine learning model(s) 107. The recommendation display 187 provides a means for a user 226 (see FIG. 2) to see the real-time reservoir capacity metrics when the carbon injection is completed as while as while the injection is happening.

[0051] The software system 101 is scalable and reliably able to support multi-tenancy systems where a particular user 226 is simultaneously utilizing multiple distinct reservoirs. In addition, the recommendation display may provide alarms and / or automatically implement preventative actions and corrections. By way of example, when the software system 101 determines that a current flow rate of carbon into a reservoir will exceed a capacity of the reservoir before manual review of the user 226, the software system 101 may automatically interface with the system 100 and cause the system 100 to slow or stop a flow until the user 226 provides a contrary input at the inputs 190.

[0052] The software system 101, exemplified by the workflow 200 of FIG. 2 provides more accurate and up to date reservoir metrics (e.g. reservoir pressure, volume, temperature, geophysical surveys, and injection rates). The up to date reservoir metrics reduces the occurrence of anomalies where the actual parameters at the reservoir differ from estimated parameters based on out of date predictions. The software system 101 can be further integrated with an alert system, providing for real-time alerts based on the real time data and the real time data analysis. The real time alerts further allows for the software system 101 to identify and understand where anomalies arise. The machine learning system(s) 107 incorporated into the reservoir model 108 can include feedback learning, allowing the reservoir model 108 to be updated when new anomalies occur increasing the future accuracy of the reservoir model 108 and preventing similar anomalies from occurring in the future.

[0053] In some examples, the software system 101 is data source agnostic and can be interconnected with any industrial system and / or multiple distinct industrial systems having different sensor configurations. In such a case, the reservoir model 108 is constructed based71 CC S-511079-WO-2 BHI0589PCTon the available data sources using the machine learning system(s) 107. The data source agnostic aspect of the software system 101 allows the system to be scalable to any size and / or number of reservoirs.

[0054] In addition, the software system 101 includes multiple cross functional data source integrations which highlight the real-time sensor data (e.g. reservoir pressure, volume, temperature, geophysical surveys, and injection rates). The data sources (data 157) combined with the simulation model (reservoir model 108) provide an understanding of the current capacity of the reservoir on a demand basis with minimal latency.

[0055] With continued reference to the system 100, and software system 101, of FIG.1, FIG. 2 illustrates a workflow 200 for monitoring data sources 202 at an industrial operation such as a carbon capture reservoir (reservoir). The workflow 200 generally includes a reservoir model portion 208 and a real-time data analysis portion 209. Arrows connecting features of the workflow 200 indicate a general direction of data flow, and are not strictly limiting on a flow of data between features.

[0056] Within the model reservoir portion 208, a simulation and modeling feature 210 uses the machine learning system(s) 107 to simulate operation of the reservoir based on a set of available input data sources 212 (e.g. rock data, log analysis, seismic traces, fluid data, etc). The input data sources 212 are all data sources included in the real-time data sources 202, as well as any contextual data regarding the reservoir site. In one example the contextual data can include, at a minimum seismo-stratigraphic information, petrophysical datasets, geological and basin modeling inputs, rock mechanics, geomechanical data, and well and production related information in case of depleted reservoirs. The simulation and modeling feature 210 processes all the data at a process action 214, and the machine learning model(s) 107 provide a model of the reservoir at an output 216. Within the model at the output 216 can be multiple sub-models (structural, fluid flow, stratigraphical, seismic, geomechanical, etc.) The output 216 is used to create a mathematical reservoir model 218 that is integrated with the sub-model outputs and represents how the reservoir will respond to a flow of carbon.

[0057] The reservoir model 218 is used to generate a reservoir characterization 220 that defines certain static parameters of the reservoir (e.g. total capacity of the reservoir). The characterization 220 is combined with the model 218 to perform a feasibility study using a set of base reference data 222. The base reference data 222 is a set of historical data of the reservoir.71 CC S-511079-WO-2 BHI0589PCT

[0058] The feasibility study verifies that the reservoir model 218 accurately models the progression of the reservoir metrics over time by applying the base reference data 222 to the model 218 and comparing the output of the model 218 to the current reservoir characterization 220. When the reservoir model 218 accurately predicts the characteristics of the reservoir model 218, the reservoir model portion 208 passes the model to a CCUS cloud 204.

[0059] The CCUS cloud 204 also receives raw data 202 as a data stream from the connected industrial system (e.g., the sensors monitoring the reservoir). The combined model 218 and raw data 202 are provided for the CCUS cloud 204 to an analysis portion 209. The analysis portion 209 includes a set of flow analysis and segmentation algorithms 230 from the reservoir characterization 220. The flow analysis and segmentation algorithms 230 are provided to an analytics algorithm 232.

[0060] The analytics algorithm 232 uses the model 218 to analyze the current state of the reservoir based on the raw data 202. The analysis includes identifying inventory information 234 (e.g. total quantity of carbon captured and transported vis-a-vis total capacity remaining of the reservoir), as well as any metrics information 236. The metrics information 236 may be directly measured (e.g. flow rate, quality, corrosion, temperature, pressure, etc.) and / or determined based on the inventory information 234 and the raw data 202 (e.g., optimal injection rate, leakage potential, flow assurance, well integrity, reservoir integrity, etc.).

[0061] The results of the analytics algorithm 232, the inventory information 234 and the measured and derived metrics 236 are provided to a real time updates feature 238 and the real-time updates feature 238 collates and formats the information into a set of updated information. The set of updated information is provided to a recommendations feature 240.

[0062] The recommendations feature 240 generates a set of recommended actions or alterations to the storage operations based on the set of updated information. In some examples, the set of updated information is determined using a set of predefined rules establishing responses. By way of example, a predefined rule may take the form of “when a reservoir capacity will be met or exceeded within X days at a current flow rate, pause flow and notify user”. In other examples, the recommended actions or alterations may be determined using an optimization problem according to conventional optimization algorithms. In yet further examples, the optimization problem may utilize weights for one or more parameter s and metrics, with the specific weights being tuned using the machine learning algorithm(s) 107 (illustrated in FIG. 1).71 CC S-511079-WO-2 BHI0589PCT

[0063] The recommendations 240 are provided to the CCUS cloud 204 and to a results feature 242. The results feature 242 tracks recommendations provided from the recommendations feature 240 in each iteration of the workflow 200, and correlates results with previous recommendations. The correlated results and recommendations are provided back to the analytics algorithm 232 as a feedback loop, allowing for the analytics algorithm to adapt and learn over time. When the analytics algorithm 232 is developed in whole or in part using the machine learning system(s) 107, the results are added to a training data set allowing for the machine learning algorithm(s) 107 to provide greater accuracy.

[0064] In addition, the results from the results feature 242 are provided back to the real time updates feature 238. This helps in automatically redefining the frequency of measurements and the scale of outputs generated in real-time.

[0065] The CCUS cloud 204 provides an output to a user interface 250 which provides a display to a user 226. The output includes the recommendations from the recommendations feature 240. In some examples, the output can further include displaying all or portions of the reservoir model 218. The user 226 is, in some examples, able to request specific information (e.g. metrics 236, inventory information 234) on demand using the user interface 250. By being presented with the displayed data, and being able to request specific data, the user 226 is able to monitor a reservoir capacity in real time.

[0066] In some examples, when a recommendation can be implemented automatically through the CCUS cloud 204, the recommendations feature 240 causes the CCUS cloud 204 to implement one or more modifications to the operations of the industrial system (e.g. a reservoir site 201). By way of example, these modifications can include adjusting a flow rate, optimizing operating parameters to prevent downtime, recommending additional tests to prevent leakage, etc.

[0067] When the implementation is time sensitive, the CCUS 204 immediately implements the recommendation, and the user is provided an opportunity to confirm that the reservoir site 201 should continue with the modified operation through the user interface 250. When the implementation is not time sensitive, the CCUS 204 can cause the user interface 204 to alert the user 226 and allow the user 226 to decide if the recommendation should be implemented before automatically adjusting the operations at the reservoir stie 201.

[0068] In yet further implementations, the recommendations and results, as well as the determined inventory information and metrics provided in a given iteration of the workflow 200 may be passed back to the reservoir modeling portion 208 through the CCUS 204 as a feedback. This information is then used to update the training set of the machine71 CC S-511079-WO-2 BHI0589PCTlearning model(s) 107 (illustrated in FIG. 1), and the accuracy of the reservoir model 218 is improved.

[0069] In some implementations, such as when the workflow 200 is initially brought online, this feedback may happen every iteration of the workflow 200. In other examples, such as when the workflow 200 has been operating for a period of time and has provided a significant amount of refining to the reservoir model 218, the feedback may be reserved and only provided periodically (e.g., weekly, monthly, or any similar period), thereby saving on computational resources when the reservoir model 218 is substantially accurate.

[0070] With continued reference to the systems 100, 101 of FIG. 1, and the workflow 200 of FIG. 2, FIG. 3 illustrates a method 300 for providing accurate real-time analysis of a carbon storage reservoir and providing recommendations regarding the operations of the carbon storage reservoir.

[0071] Initially, available data sources (input data sources 212, real-time data 202) and historical data from the available data sources are identified in an identify data sources and historical data step 310. The data sources and the historical data are then used to generate the reservoir model 218 using the reservoir modeling portion 208 of the workflow 200 in a generate reservoir model step 320.

[0072] After the reservoir model 218 has been generated, the CCUS cloud 204 provides a stream of raw data 202 from the reservoir 201 to an analysis portion 209 of the workflow 200 in a receive and analyze reservoir data step 330.

[0073] The analyzed reservoir data is used to generate one or more recommendations regarding storage operations at the reservoir site in a generate recommendations step 340. Once recommendations have been generated they can be accessed by a user who can choose to act, or not act on them.

[0074] In some implementations, optional steps, indicated by dashed lines, may be implemented. In one example, the optional steps include a automatic implementation of the recommendation at an implement recommendation step 350. In other examples, the optional steps include displaying an alert to the user through a user interface at a display alert step 360, followed by implementation of the recommendation in response to the user approving the alert through the user interface in an implement approved recommendation step 370, or the user can act otherwise 380.

[0075] While described throughout within the context of a carbon capture and storage reservoir, it is appreciated that the systems, workflows, and methods described herein may be71 CC S-511079-WO-2 BHI0589PCTapplied to one or more similarly structured industrial systems and are not limited to carbon capture storage reservoirs.

[0076] In the descriptions of the flowcharts herein, the operations may be performed in a different order than the order shown, or the operations may be performed in different orders or at different times. Certain operations may also be left out of the flowcharts, one or more operations may be repeated, or other operations may be added to the flowcharts.

[0077] In the descriptions of the flowcharts herein, the operations may be performed in a different order than the order shown, or the operations may be performed in different orders or at different times. Certain operations may also be left out of the flowcharts, one or more operations may be repeated, or other operations may be added to the flowcharts.

[0078] Set forth below are some embodiments of the foregoing disclosure:

[0079] Embodiment 1. A method characterized by training an artificial intelligence / machine learning (AI / ML) algorithm to generate a reservoir model representative of a storage reservoir and generating the reservoir model, a computing device containing the trained reservoir model receives a set of real-time reservoir data generated at the storage reservoir, the set of real-time reservoir data being representative of measured reservoir operations, and analyzes the real-time reservoir data using the trained reservoir model. The computing device generates at least one recommended alteration to reservoir operations based on an output of the reservoir model.

[0080] Embodiment 2. A method as in any previous embodiment, wherein the storage reservoir is a carbon capture storage reservoir.

[0081] Embodiment 3. A method as in any previous embodiment, wherein generating at least one recommended alteration to reservoir operations further comprises providing the recommended alteration to a reservoir site and causing at least one operation of the reservoir to be altered according to the recommended alteration without human intervention.

[0082] Embodiment 4. A method as in any previous embodiment, wherein generating at least one recommended alteration to reservoir operations further comprises displaying the recommended alteration to a user and implementing the recommended alteration responsive to the user approving the recommended alteration.

[0083] Embodiment 5. A method as in any previous embodiment, wherein generating the reservoir model comprises simulating operation of the reservoir based on a set of available input data sources and a set of contextual data.

[0084] Embodiment 6. A method as in any previous embodiment, wherein the set of contextual data includes seismo-stratigraphic information, petrophysical datasets, geological71 CC S-511079-WO-2 BHI0589PCTand basin modeling inputs, rock mechanics, geomechanical data, and well and production related information in case of depleted reservoirs.

[0085] Embodiment 7. A method as in any previous embodiment wherein the analysis comprises a flow analysis and a segmentation analysis using set of real-time reservoir data.

[0086] Embodiment 8. A method as in any previous embodiment, wherein the reservoir model is a mathematical model representing how the reservoir will respond to a flow of carbon.

[0087] Embodiment 9. A method as in any previous embodiment, wherein training the artificial intelligence / machine learning (AI / ML) algorithm to generate the reservoir model representative of the storage reservoir and generating the reservoir model further comprises verifying an accuracy of the generated reservoir model by performing a feasibility study of the generated reservoir model.

[0088] Embodiment 10. A method as in any previous embodiment, wherein the feasibility study comprise applying a set of historic real-time data to the generated reservoir model and determining a magnitude of deviation between an output of the reservoir model and subsequent historic real-time data.

[0089] Embodiment 11. A method as in any previous embodiment, wherein the generated reservoir model is accepted responsive to the deviation being below a predefined magnitude.

[0090] Embodiment 12. A system including analysis equipment comprising a processor and a memory, wherein the memory comprises instructions stored thereon that, when executed by the processor, cause the processor to perform operations including: training an artificial intelligence / machine learning (AI / ML) algorithm to generate a reservoir model representative of a storage reservoir and generating the reservoir model, receiving, at a computing device containing the trained reservoir model, a set of real-time reservoir data generated at the storage reservoir, the set of real-time reservoir data being representative of measured reservoir operations, and analyzing the real-time reservoir data using the trained reservoir model, and generating at least one recommended alteration to reservoir operations based on an output of the reservoir model.

[0091] Embodiment 13. A system according to any previous embodiment, wherein the storage reservoir is a carbon capture storage reservoir.

[0092] Embodiment 14. A system according at any previous embodiment, wherein generating at least one recommended alteration to reservoir operations further comprises71 CC S-511079-WO-2 BHI0589PCTproviding the recommended alteration to a reservoir site and causing at least one operation of the reservoir to be altered according to the recommended alteration without human intervention.

[0093] Embodiment 15. A system according to any previous embodiment, wherein generating at least one recommended alteration to reservoir operations further comprises displaying the recommended alteration to a user and implementing the recommended alteration responsive to the user approving the recommended alteration.

[0094] Embodiment 16. A system according to any previous embodiment, wherein generating the reservoir model comprises simulating operation of the reservoir based on a set of available input data sources and a set of contextual data, and wherein the set of contextual data includes seismo-stratigraphic information, petrophysical datasets, geological and basin modeling inputs, rock mechanics, geomechanical data, and well and production related information in case of depleted reservoirs.

[0095] Embodiment 17. A system according to any previous embodiment, wherein the analysis comprises a flow analysis and a segmentation analysis using set of real-time reservoir data.

[0096] Embodiment 18. A system according to any previous embodiment, wherein the reservoir model is a mathematical model representing how the reservoir will respond to a flow of carbon.

[0097] Embodiment 19. A system according to any previous embodiment, wherein training the artificial intelligence / machine learning (AI / ML) algorithm to generate the reservoir model representative of the storage reservoir and generating the reservoir model further comprises verifying an accuracy of the generated reservoir model by performing a feasibility study of the generated reservoir model.

[0098] Embodiment 20. A system according to any previous embodiment, wherein the feasibility study comprises applying a set of historic real-time data to the generated reservoir model and determining a magnitude of deviation between an output of the reservoir model and subsequent historic real-time data, and the generated reservoir model is accepted responsive to the deviation being below a predefined magnitude.

[0099] While the embodiments are presented as an ordered list, it is appreciated that each particular embodiment may be combined with any, all, or none, of the other embodiments, and the embodiments are not limited by the order presented.

[0100] The use of the terms “a” and “an” and “the” and similar referents in the context of describing the invention (especially in the context of the following claims) are to71 CC S-511079-WO-2 BHI0589PCTbe construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. Further, it should be noted that the terms “first,” “second,” and the like herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The terms “about”, “substantially” and “generally” are intended to include the degree of error associated with measurement of the particular quantity based upon the equipment available at the time of filing the application. For example, “about” and / or “substantially” and / or “generally” can include a range of ± 8% of a given value.

[0101] The teachings of the present disclosure may be used in a variety of well operations. These operations may involve using one or more treatment agents to treat a formation, the fluids resident in a formation, a borehole, and / or equipment in the borehole, such as production tubing. The treatment agents may be in the form of liquids, gases, solids, semi-solids, and mixtures thereof. Illustrative treatment agents include, but are not limited to, fracturing fluids, acids, steam, water, brine, anti-corrosion agents, cement, permeability modifiers, drilling muds, emulsifiers, demulsifiers, tracers, flow improvers etc. Illustrative well operations include, but are not limited to, hydraulic fracturing, stimulation, tracer injection, cleaning, acidizing, steam injection, water flooding, cementing, etc.

[0102] While the invention has been described with reference to an exemplary embodiment or embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out this invention, but that the invention will include all embodiments falling within the scope of the claims. Also, in the drawings and the description, there have been disclosed exemplary embodiments of the invention and, although specific terms may have been employed, they are unless otherwise stated used in a generic and descriptive sense only and not for purposes of limitation, the scope of the invention therefore not being so limited.

Claims

71 CC S-511079-WO-2 BHI0589PCTCLAIMSWhat is claimed is:

1. A computer-implemented method (300) characterized by:training an artificial intelligence / machine learning (AI / ML) algorithm to generate a reservoir model (108) representative of a storage reservoir and generating the reservoir model (108);receiving, at a computing device (105) containing the trained reservoir model (108), a set of real-time reservoir data (157) generated at the storage reservoir (108), the set of realtime reservoir data (157) being representative of measured reservoir operations, and analyzing the real-time reservoir data (157) using the trained reservoir model (108);generating at least one recommended alteration to reservoir operations based on an output (216) of the reservoir model (108), and providing the recommended alteration to a reservoir site (201) and causing at least one operation of the reservoir to be altered according to the recommended alteration without human intervention, by immediately implementing the recommendation; andproviding the user an opportunity to confirm that the reservoir site (201) should continue with the modified operation through the user interface (250).

2. The computer-implemented method of claim 1, wherein the storage reservoir is a carbon capture storage reservoir.

3. The computer-implemented method of claim 1, wherein generating at least one recommended alteration to reservoir operations further comprises displaying the recommended alteration to a user (226) and implementing the recommended alteration responsive to the user (226) approving the recommended alteration.

4. The computer-implemented method of claim 1, wherein generating the reservoir model (108) comprises simulating operation of the reservoir based on a set of available input data sources and a set of contextual data.

5. The computer-implemented method of claim 4, wherein the set of contextual data includes sei smo- stratigraphic information, petrophysical datasets, geological and basin modeling inputs, rock mechanics, geomechanical data, and well and production related information in case of depleted reservoirs.

6. The computer-implemented method of claim 1, wherein the analysis comprises a flow analysis and a segmentation analysis using set of real-time reservoir data (157).

7. The computer-implemented method of claim 1, wherein the reservoir model (108) is a mathematical model (218) representing how the reservoir will respond to a flow of carbon.71 CC S-511079-WO-2 BHI0589PCT8. The computer-implemented method of claim 1, wherein training the artificial intelligence / machine learning (AI / ML) algorithm to generate the reservoir model (218) representative of the storage reservoir and generating the reservoir model (218) further comprises verifying an accuracy of the generated reservoir model by performing a feasibility study of the generated reservoir model (218).

9. The computer-implemented method of claim 8, wherein the feasibility study comprise applying a set of historic real-time data (157) to the generated reservoir model (218) and determining a magnitude of deviation between an output (216) of the reservoir model and subsequent historic real-time data (157).

10. The computer-implemented method of claim 9, wherein the generated reservoir model (108) is accepted responsive to the deviation being below a predefined magnitude.

11. A system (100) characterized by:analysis equipment comprising a processor and a memory, wherein the memory comprises instructions stored thereon that, when executed by the processor, cause the processor to perform operations characterized by:training an artificial intelligence / machine learning (AI / ML) algorithm to generate a reservoir model representative of a storage reservoir and generating the reservoir model; receiving, at a computing device containing the trained reservoir model, a set of realtime reservoir data generated at the storage reservoir, the set of real-time reservoir data being representative of measured reservoir operations, and analyzing the real-time reservoir data using the trained reservoir model;generating at least one recommended alteration to reservoir operations based on an output (216) of the reservoir model;providing the recommended alteration to a reservoir site and causing at least one operation of the reservoir to be altered according to the recommended alteration without human intervention, by immediately implementing the recommendation; andproviding the user (226) an opportunity to confirm that the reservoir site should continue with the modified operation through the user interface.

12. The system (100) of claim 11, wherein the storage reservoir is a carbon capture storage reservoir.

13. The system (100) of claim 11, wherein generating at least one recommended alteration to reservoir operations further comprises displaying the recommended alteration to71 CC S-511079-WO-2 BHI0589PCTa user (226) and implementing the recommended alteration responsive to the user (226) approving the recommended alteration.

14. The system (100) of claim 11, wherein generating the reservoir model (108) comprises simulating operation of the reservoir based on a set of available input data sources and a set of contextual data, and wherein the set of contextual data includes seismo-stratigraphic information, petrophysical datasets, geological and basin modeling inputs, rock mechanics, geomechanical data, and well and production related information in case of depleted reservoirs.

15. The system (100) of claim 11, wherein the analysis comprises a flow analysis and a segmentation analysis using set of real-time reservoir data (157).16 The system (100) of claim 11, wherein the reservoir model is a mathematical model representing how the reservoir will respond to a flow of carbon.

17. The system (100) of claim 11, wherein training the artificial intelligence / machine learning (AI / ML) algorithm to generate the reservoir model representative of the storage reservoir and generating the reservoir model further comprises verifying an accuracy of the generated reservoir model by performing a feasibility study of the generated reservoir model (108).

18. The system (100) of claim 17, wherein the feasibility study comprise applying a set of historic real-time data (157) to the generated reservoir model (108) and determining a magnitude of deviation between an output (216) of the reservoir model (108) and subsequent historic real-time data (157), and the generated reservoir model (108) is accepted responsive to the deviation being below a predefined magnitude.

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