Geothermal field management using fiber optics

Abstract

Methods and systems for managing enhanced geothermal system fields are provided. The fields may be managed by monitoring the fields using both point and distributed sensors. Measurements from the sensors may be used to model the fields, and potential scenarios for the fields. The measurements may also be used to revise the models for the fields, such as corrections of incorrect assumptions regarding the fields. The models may also be used to proactively alert and / or take automated actions to address occurrence of undesired events in the fields.

Description

GEOTHERMAL FIELD MANAGEMENT USING FIBER OPTICSCROSS REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority to and the benefit of United States NonProvisional Patent Application No. 18 / 792,175, filed August 1, 2024, entitled “GEOTHERMAL FIELD MANAGEMENT USING FIBER OPTICS”, the disclosure of which is incorporated herein by reference in its entirety.BACKGROUND

[0002] Geological formations may host a range of resources. For example, geological formations may include trapped liquids and / or gasses that may include hydrocarbons of various types. These hydrocarbons may be used for a variety of purposes.

[0003] In additional to physical resources such as gasses or fluids, the geological formations may also include geothermal reservoirs. A geothermal reservoir may be part of a geological formation which may be heated due to various geological processes. The heat from a geothermal reservoir may be used for a variety of purposes including, for example, geothermal heating and energy production.SUMMARY

[0004] A summary of certain embodiments disclosed herein is set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of these certain embodiments and that these aspects are not intended to limit the scope of this disclosure. Indeed, this disclosure may encompass a variety of aspects that may not be set forth below.

[0005] In an embodiment, a system is provided. The system may include a hardware sensing system for obtaining measurements of an enhanced geothermal system (EGS) field; and a hardware modeling system that hosts: a reservoir model for the EGS field; a heat exchange model for the EGS field; a fluid dynamics model for the EGS field; a real-time data collection repository for storing the measurements; and a workflow orchestrator adapted to: use the reservoir model, the heat exchange model, the fluid dynamics model, and the real-time data collection repository to obtain predicted behavior for the EGS field.

[0006] The reservoir model may be a three dimensional model or a four dimensional model.

[0007] The heat exchange model may be adapted to provide heat transfer between inject fluid into the EGS field, reservoir fluid of the EGS field, a formation in which the EGS field is positioned, and the Earth’s core.

[0008] The heat exchange model may be further adapted to take into account behavior of critical and supercritical states of fluids of the EGS field, and conducive fracture networks.

[0009] The fluid dynamics model may include a porous media fluid motion model that uses a simplified streamline approach.

[0010] The measurements may include surface pressures for fluids that traverse the EGS field; surface fluid flow rates for the fluids that traverse the EGS field; compositions of the fluids; and surface temperatures of the fluids. The surface measurements may be taken with point sensors positioned on the surface.

[0011] The measurements may also include distributed temperature sensing measurements of the EGS field; and distributed acoustic sensing measurements of the EGS field. The distributed measurements may be taken with downhole distributed measurement components such as fiber optic cables.

[0012] The distributed temperature sensing measurements or the distributed acoustic sensing measurements may be taken using fiber optic cables positioned with the EGS field.

[0013] The fiber optic cables may be positioned in injection and production wells of the EGS field.

[0014] The hardware modeling system may also host: a real-time interpretation model to provide users with distributed flow rates and distributed temperatures along wells of the EGS field.

[0015] The hardware modeling system may also host a data analytic model adapted to: process real-time data for the EGS field from the real-time data collection repository to obtain processed data; and generate, based on the processed data, real-time adjustments to at least one of the reservoir model, the heat exchange model, and the fluid dynamics model.

[0016] The hardware modeling system may also host a reservoir repository that may include flow patterns for the EGS field. The flow patterns may be obtained with chemical or radioactive tracers.

[0017] The workflow orchestrator may be further adapted to provide user access to the predicted behavior for the EGS field to cooperatively develop processes to be performed with respect to the EGS field.

[0018] The EGS field may include injection wells to inject cool fluid, and production wells to produce heated fluid.

[0019] In an embodiment, a method for managing an EGS field is provided. The method may include obtaining measurements of the EGS field; generating, using the measurements, a reservoir model for the EGS field, a heat exchange model for the EGS field, and a fluid dynamics model for the EGS field, predicted behavior of the EGS field; and providing user access to the predicted behavior for the EGS field to cooperatively develop processes to be performed with respect to the EGS field.

[0020] The measurements may include surface pressures for fluids that traverse the EGS field; surface fluid flow rates for the fluids that traverse the EGS field; compositions of the fluids; and surface temperatures of the fluids.

[0021] The measurements may include distributed temperature sensing measurements of the EGS field; and distributed acoustic sensing measurements of the EGS field.

[0022] The distributed temperature sensing measurements or the distributed acoustic sensing measurements may be taken using fiber optic cables positioned with the EGS field.

[0023] The fiber optic cables may be positioned in injection wells of the EGS field.

[0024] The method may also include initiating performance of the processes to update operation of the EGS field.

[0025] Various refinements of the features noted above may be undertaken in relation to various aspects of the present disclosure. Further features may also be incorporated in these various aspects as well. These refinements and additional features may exist individually or in any combination. For instance, various features discussed below in relation to one or more of the illustrated embodiments may be incorporated into any of the above-described aspects of the present disclosure alone or in any combination. The brief summary presented above is intended only to familiarize the reader with certain aspects and contexts of embodiments of the present disclosure without limitation to the claimed subject matter.BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Embodiments disclosed herein are illustrated by way of example and not limitation in the figures of the accompanying drawings in which references indicate similar elements.

[0027] FIGs. 1A-1B show diagrams illustrating a system in accordance with an embodiment.

[0028] FIG. 2 shows a data flow diagram illustrating the operation of a system in accordance with an embodiment.

[0029] FIG. 3 shows a flow diagram illustrating methods in accordance with an embodiment.

[0030] FIG. 4 shows a block diagram illustrating a data processing system in accordance with an embodiment.DETAILED DESCRIPTION

[0031] Various embodiments will be described with reference to details discussed below, and the accompanying drawings will illustrate the various embodiments. The following description and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of various embodiments. However, in certain instances, well-known or conventional details are not described in order to provide a concise discussion of embodiments disclosed herein.

[0032] Reference in the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in conjunction with the embodiment can be included in at least one embodiment. The appearances of the phrases “in one embodiment” and “an embodiment” in various places in the specification do not necessarily all refer to the same embodiment.

[0033] Exploitation of geothermal resources enables electricity and other valuable products to be generated in an economical and environmentally friendly manner. To generate electricity using geothermal resources, a top side power facility may be deployed and connected to an enhanced geothermal system (EGS) field which may include various wells for extracting thermal energy.

[0034] Turning to FIG. 1 A, a diagram of a top side power facility deployed to geological formation 100 in accordance with an embodiment is shown. The top side power facility may generate electricity by extracting heat from geological formation 100.

[0035] For example, geological formation 100 may include any number of layers (e.g., sedimentary layers, hot rock layers, etc.). Ongoing geological processes near and / or in geological formation 100 may generate geothermal reservoir 104. Geothermal reservoir 104 may be a portion of geological formation 100 that tends to remain at elevated temperature due to the ongoing geological processes.

[0036] To use heat to generate electricity, top side power facility 102 may include various components such as, for example, heat exchangers, generators, and cooling systems. The top side power facility may include additional, different, or / or fewer components without departing from embodiments disclosed herein.

[0037] The heat exchangers may use heat extracted from geothermal reservoir 104 to warm a fluid used to drive the generators. The cooling systems may cool the warm fluid to establish a fluid loop (e.g., the cooled fluid may be returned to the heat exchangers to be heated again).

[0038] To obtain the heat used by the heat exchangers to warm the fluid used to drive the generators, a second fluid may be injected into geothermal reservoir 104 via any number of injection wells (e.g., 110), and a second fluid (now heated) may be extracted from geothermal reservoir 104 via any number of production wells (e.g., 112). The second fluid may be warmed by the geothermal reservoir after being injected and prior to being extracted.

[0039] Thus, top side power facility 102 and associated wells may extract and use thermal energy from geothermal reservoir 104 to generate electricity and / or be used for other purposes.

[0040] However, Enhanced Geothermal Systems (EGS) fields present unique challenges and opportunities. EGS field development and planning (FDP) is more expensive than theconventional geothermal reservoirs due to high depth, long horizontal laterals, and hydraulic fracturing operations. The cost of making a mistake is much higher than field development in oil and gas reservoirs due to the lesser energy potential of the geothermal resources (e.g., hot water) when compared to the energy potential of hydrocarbons.

[0041] EGS (Enhanced Geothermal Systems) field development includes multiple hydraulic fractures to render hot water production economically viable. To make the produced hot water usable, the hot water may need to attain prescribed temperatures. To attain the prescribed temperatures, the EGS field may need to be maintained in an equilibrium temperature state, cool water breakthroughs may need to be prevented, and induced seismicity and reservoir cooling and fluid loss may need to be managed.

[0042] In general, embodiments disclosed herein relate to systems and methods for managing EGS fields. To manage the EGS fields, operation of the EGS fields may be (i) subjected to near-real-time monitoring using both point and distributed sensors, and (ii) modeled using the measured data (e.g., and / or other information such as proposed scenarios, discussed below).

[0043] To monitor the EGS fields, any number of point sensors (e.g., 120, 122, 124, illustrated with triangles with black infill) may be positioned with the EGS field. Each of the point sensors may be capable of taking measurements with respect to specific points. For example, point sensors may be positioned with injection wells (e.g., 110) and production wells (e.g., 112) near the surface, and / or downhole. The point sensors may be positioned individually and / or in groups. The point sensors may monitor, for example, flow rates of fluids, pressures of the fluids, temperatures of the fluids and / or other components of the EGS fields, and / or other quantities that may be used to guide operation of the EGS field.

[0044] Similarly, any number of distributed sensors (e.g., 130, illustrated with dashed line) may be positioned with the EGS field. The distributed sensors may include fiber optic cables usable to perform distributed acoustic measurements (DAS), distribute temperaturemeasurements (DTS), distributed pressure measurements (DPS), and / or other types of distributed measurements. The distributed sensors may be permanently or temporarily secured in the wells. For example, the distributed sensors may be permanently placed in injection wells (e g., 110), and may be temporarily placed in production wells (e g., 112) due to limits of the distributed sensors to be exposed to certain temperatures (e.g., may degrade / fail if overheated).

[0045] Measurements from the point and / or distributed sensors may be used to drive modeling processes for the EGS field. For example, during formation fracturing in development of the EGS field to establish fractures 140 that allow for fluid flow between injection wells and production wells, DAS data may be used to infer slurry rates at each cluster, while DTS data may be used to estimate local formation temperatures.

[0046] In another example, the measurements from the distributed sensors may provide for monitoring of injection rates and local temperatures during injection. These measurements may be used to adjust injection rates to make borehole temperatures sensitive to local formation temperatures. Current flow rates may be used to enhance formation local temperature estimation.

[0047] This example data, along with surface data from point sensors regarding pressure and temperature, may serve as inputs to a reservoir model used to model the EGS field.

[0048] The obtained data may be used to model the EGS field. To model the EGS field, a digital reservoir model and automated data interpreter may be used. The automated integration of near-real-time point, and distributed measurements may enable (i) scenarios to be simulated, (ii) modeling errors to be identified and / or corrected, and / or for other operations to manage an EGS field to be performed.

[0049] For example, to manage the field, scenarios may be defined. These scenarios may include, for example, changes to the wells, changes to processes used to produce warmed fluids from the EGS field, etc. To evaluate the impact of these scenarios, the digital reservoirmodel may be used. The digital reservoir model may ingest a scenario and predict the operation of the EGS field should the scenario occur. The predicted operation of the EGS may then be used to (i) identify whether the predicted operation is desirable, and (ii) if desirable, identify actions to be performed based on the scenario (e.g., a field development or operation plan). Refer to FIG. 2 for additional details regarding the digital reservoir model.

[0050] Once the field development plan is obtained, the EGS field may be developed / operated based on the field development / operation plan.

[0051] To provide for analysis of acquired data and modeling of EGS fields, a modeling system may be utilized. Refer to FIG. IB for additional details regarding modeling systems.

[0052] While illustrated in FIG. 1A with a simple EGS field, it will be appreciated that the methods and systems disclosed herein may be applicable to a wide variety of EGS fields that may include any number of injection and production wells.

[0053] Turning to FIG. IB, a block diagram of a modeling system in accordance with an embodiment is shown. The modeling system may be used to analyze measured data, model operation of EGS fields, and / or perform other actions. To provide the above noted functionality, the modeling system of FIG. IB may include user systems 150, model hosting system 156, and communication system 160. Each of these components is discussed below.

[0054] User systems 150 may include any number of user systems (e.g., 152-154) usable by users (e.g., persons / programs) to interact with model hosting system 156. For example, user systems 150 may enable users to (i) obtain measurements of and / or predictions of operation of an EGS field from model hosting system 156, (ii) define and initiate evaluation of scenarios by model hosting system 156, and / or perform other tasks to develop and / or operate EGS fields.

[0055] Model hosting system 156 may host the digital reservoir model usable to modelEGS fields. To facilitate such modeling, model hosting system 156 may communicate withand / or follow instructions issued by user systems 150. Refer to FIG. 2 for additional details regarding the digital reservoir model.

[0056] When providing their functionality, any of user systems 150 and model hosting system 156 may perform all, or a portion, of the actions and methods illustrated in FIGs. 2-3.

[0057] Any of (and / or components thereof) user systems 150 and model hosting system 156 may be implemented using a computing device (also referred to as a data processing system) such as a host or a server, a personal computer (e.g., desktops, laptops, and tablets), a “thin” client, a personal digital assistant (PDA), a Web enabled appliance, a mobile phone (e.g., Smartphone), an embedded system, local controllers, an edge node, and / or any other type of data processing device or system. For additional details regarding computing devices, refer to FIG. 4.

[0058] Any of the components illustrated in FIG. IB may be operably connected to each other (and / or components not illustrated) with communication system 160. In an embodiment, communication system 160 includes one or more networks that facilitate communication between any number of components. The networks may include wired networks and / or wireless networks (e.g., and / or the Internet). The networks may operate in accordance with any number and type of communication protocols (e.g., such as the internet protocol).

[0059] While illustrated in FIG. IB as including a limited number of specific components, a system in accordance with an embodiment may include fewer, additional, and / or different components than those illustrated therein. Additionally, the functionality of any of the components shown in FIG. IB may be integrated into a single device and / or may be divided up over any number of devices.

[0060] Turning to FIG. 2, a diagram illustrating a digital reservoir model in accordance with an embodiment is shown. The digital reservoir model may incorporate measurementsfrom the point / distributed sensors of the system described with respect to FIG. 1 A to provide update to date modeling of an EGS field.

[0061] The digital reservoir model may be orchestrated in accordance with an analytical model (e.g., mathematical model). Flows of data from and operation of components of the model may be based on the analytical model.

[0062] Generally, the digital reservoir model may predict temperature distributions across reservoirs of EGS fields, aid in visualization and decision-making, and facilitate comparisons between local measurements at injection wells and surface measurements at production wells. The model also includes hydrodynamical equations describing fluid transfer in porous media. It may also account for changes in water salinity and composition, which in turn affect the evolution of porosity and permeability.

[0063] The measurements from an EGS field, along with complementary information such as wellbore schematics, may be stored in one or more databases (e.g., repositories). The data may undergo analytics and visualization processes, facilitating corrections to the analytical model. Accumulated historical data along with machine learning may be used to identify trends and anomalies. Consequently, proactive reservoir management may be performed which may aid in decision-making with respect to the EGS fields.

[0064] To provide the above noted functionality, the digital reservoir model may include heat exchange model 200, reservoir model 202, streamlines model 204, EGS field model 206, results / output data 208, comparison data 210, data analytics 212, other data 214, and output 216. Each of these components is discussed below.

[0065] Heat exchange model 200 may include a computational or analytical model of heat transfer in the EGS field. Heat exchange model 200 may consider thermal properties (e.g., heat capacity) of the geological formation in which the wells of the EGS field are drilled, potential fluid injection and / or extraction rates, and / or other factors that may impact temperatures of fluids flowing through the EGS field and the geothermal reservoir.

[0066] Reservoir model 202 may model fluid flow through the geothermal reservoir from which thermal energy is extracted by the EGS field. Reservoir model 202 may be a three dimensional or a four-dimensional (e.g., 3 space and 1 time dimension) model (e.g., may be a finite difference or finite integration technique simulator). Reservoir model 202 may take into account, for example, depths of different portions of the geothermal reservoir, stresses present, pore volume, minerology, types of permeability that are present (e.g., which may depend on the pressure, viscosity, minerology, temperature, etc.), and / or other factors impacting fluid flow and / or other aspects of the geothermal reservoir. To expedite simulation, reservoir model 202 may utilize streamlines for fluids predicted by streamlines model 204.

[0067] Streamlines model 204 may, as noted above, be used to predict streamlines for fluid flows in the EGS field. For example, rather than using a cell-based grid simulation technique such as is commonly used in finite different methods, streamlines model 204 may predict instantaneous flow fields (e.g., steady state flow) due to flows into / out of the injection / production wells. For example, phase saturations and components may be transported along a flow-based grid defined by streamlines (or streamtubes) rather than moved from cell-to-cell. By using streamlines model 204, the computational complexity of reservoir model 202 may be reduced and thereby allow for the model of the EGS field to be updated in near-real-time (e.g., by jumping to the steady state flow solution without needing to simulate transitory action).

[0068] Heat exchange model 200, reservoir model 202, and / or streamlines model 204 may serve as input to EGS field model 206. Generally, EGS field model 206 may model, as noted above, temperatures in the EGS field. By modeling the temperatures in the EGS field, the suitability of different changes to the EGS field and / or operation of the EGS field may be evaluated.

[0069] Generally, EGS field model 206 may operate as the orchestrator invoking and using other model components (e.g., 202-204) to accomplish a desired goal (e.g., simulation of an EGS field). EGS field model 206 may output results / output data 208.

[0070] Output results / output data 208 may include any amount and type of information regarding predicted operation of an EGS field. For example, output results / output data 208 may include information regarding the temperatures of flow of fluids in an EGS field, temperatures of portions of thermal reservoirs of the EGS field, temperatures of flows of fluids into / output of wells of the EGS field, etc.

[0071] The aforementioned information may be compared to measured data (e.g., comparison data 210) from the EGS field obtained with point / distributed sensors. The data may be output as output 216.

[0072] Output 216 may include, for example, plots, tables, etc. of the predicted / measured data regarding operation of an EGS field. Users may view and use the data in analysis of the EGS field, such as selection in how to modify and / or update operation of the EGS field.

[0073] When presented to the user, output 216 may be displayed in graphical user interfaces that enables the users to quickly filter through and select display of desired portions of the data. Accordingly, users may be better able to more easily parse through and act on the data.

[0074] Data analytics 212 may be performed to analyze the predicted / measured data, along with other data 214. Other data 214 may include any other available data for analysis with the simulated / predicted data. For example, other data 214 may include tracers or other types of data.

[0075] During data analytics 212, the data may be compared / analyzed to identify differences between the predicted and simulated data and identify potential causes for the differences. For example, any of models 200-206 may make certain assumptions regardingthe EGS field, operation of the field, the thermal reservoir, etc. These assumptions may be incorrect and may be identified via data analytics 212.

[0076] The incorrect assumptions may be used to identify various model corrections to apply to any of models 200-206. These model corrections may include any number and type of corrections. The corrections may be, for example, based on templates associated with different types of differences in the simulated / measured data. The templates may be populated based on the specifics of the data. Once corrected, new results / output data 208 may be obtained and again analyzed via data analytics 212 until a condition is met (e.g., differences within prescribed limits, certain numbers of loops completed, boundaries on values of the models that would otherwise be corrected are reached, etc.). In this manner, errors in the models may be automatically (e.g., or semi -automated, a user may be in the loop to approved model corrections before being used) eliminated over time.

[0077] In addition, for analyzing for disagreement, the simulated / measured data may be automatically analyzed during data analytics 212 for other types of insights. For example, the data may be analyzed to identify occurrences of desired / undesired conditions impacts EGS fields. When such occurrences are identified, semi -automated and / or fully automated responses may be undertaken. For example, like the incorrect assumptions, template responses to occurrence may be populated. The populated action templates may then either be automatically performed or submitted to a person / other system for review / approval / other action.

[0078] To utilize the digital reservoir model (e.g., to investigate feasibility of potential changes to an EGS field), as discussed above, users may, for example, define scenarios for evaluation. These scenarios may include, for example, changes to numbers of wells in an EGS field, changes in pump rates of fluids through the wells in the EGS fields, and / or other changes to the EGS field and / or operation thereof. Such changes may impact the operation ofany of the models 200-206. Consequently, the resulting results / output data 208 may reflect the operation of the EGS field after some numbers of future actions are performed.

[0079] Any of the aforementioned processes may utilize, as noted above, machine learning and / or predictive analysis. Consequently, trends and anomalies may be proactively identified and brought to the attention of users for response and / or automated responses may be performed (e.g., may automatically change operation of a well of an EGS field).

[0080] Any of the models may be implemented using digital processors (e.g., central processors, processor cores, etc.) that execute corresponding instructions (e.g., computer code / software). Execution of the instructions may cause the digital processors to initiate performance of processes performed by the models. Any portions of the processes may be performed by digital processors and / or other devices. For example, executing the instructions may cause the digital processors to perform actions that directly contribute to performance of the processes, and / or indirectly contribute to performance of the processes by causing (e.g., initiating) other hardware components to perform actions that directly contribute to the performance of the processes.

[0081] Any of the models may be implemented using by special purpose hardware components such as digital signal processors, application specific integrated circuits, programmable gate arrays, graphics processing units, data processing units, and / or other types of hardware components. These special purpose hardware components may include circuitry and / or semiconductor devices adapted to perform the processes. For example, any of the special purpose hardware components may be implemented using complementary metal-oxide semiconductor-based devices (e g., computer chips).

[0082] Any of the data structures (e.g., 200, 202, 204, 206, 208, 210, 214, 216) may be implemented using any type and number of data structures. Additionally, while described as including particular information, it will be appreciated that any of the data structures may include additional, less, and / or different information from that described above. Theinformational content of any of the data structures may be divided across any number of data structures, may be integrated with other types of information, and / or may be stored in any location.

[0083] Turning to FIG. 3, a flow diagram illustrating a method for managing an EGS field in accordance with an embodiment is shown. The method may be performed, for example, by any of the components of the system shown in FIGs. 1A-1B. In the diagram discussed below and shown in FIG. 3, any of the operations may be repeated, performed in different orders, and / or performed in parallel with or in a partially overlapping in time manner with other operations.

[0084] At operation 300, measurements of an EGS field are obtained. The measurements may be obtained by reading them from storage, receiving them from another device, by taking them using point / distributed sensors, and / or via other methods.

[0085] The measurements may include distributed temperature sensing measurements of the EGS field, distributed acoustic sensing measurements of the EGS field, surface pressures for fluids that traverse the EGS field, surface fluid flow rates for the fluids that traverse the EGS field, compositions of the fluids, and surface temperatures of the fluids.

[0086] The distributed temperature sensing measurements and / or the distributed acoustic sensing measurements may be taken using fiber optic cables positioned with the EGS field. The fiber optic cables may be positioned in injection wells of the EGS field.

[0087] At operation 302, predicted behavior of the EGS field is generated using the measurements, a reservoir model for the EGS field, a heat exchange model for the EGS field, and a fluid dynamics model for the EGS field. The predicted behavior may be generated by using the measurements as input for the models. The models may generate predictions based on the measurements and / or other information (e.g., a scenario defined by a user).

[0088] The reservoir model is a three-dimensional model or a four-dimensional model. For example, the reservoir model may simulate three spatial dimensions or three spatial dimensions and one time dimension.

[0089] The heat exchange model may be adapted to provide (e.g., to predict) heat transfer between inject fluid into the EGS field, reservoir fluid (e.g., fluid flow through it, may include inject fluid) of the EGS field, a formation in which the EGS field is positioned, and the Earth’s core. The measurements may be used to provide, for example, information regarding existing conditions and the heat exchange model may predict the likely thermal exchanges with the fluid and / or thermal reservoir so that changes in fluid temperature and temperature of the thermal reservoir may be identified.

[0090] The heat exchange model may also take into account the behavior of critical and / or supercritical states of fluids. Likewise, the heat exchange model may take into account conducive fracture networks present in the EGS field.

[0091] The fluid dynamic model may include a porous media fluid motion model that uses a simplified streamline approach. In other words, a streamline model may be used to identify streamlines for fluid flows, and the fluid dynamics model (e.g., a reservoir model such as 202) may use the streamlines identified by that model in the predictions generated by the fluid dynamic model. For example, the model may use a commercial model such as a reservoir finite difference solver.

[0092] At operation 304, user access to the predicted behavior for the EGS field is provided to cooperatively develop processes to be performed with respect to the EGS field. User access may be provided, for example, by generating and / or displaying various graphical user interfaces. The graphical user interfaces may include plots or other representations of simulate data, measured data (may correspond to the simulated data), suggested actions to be taken (e.g., model corrections, changes to EGS field / operation thereof, etc.), and / or other information.

[0093] For example, to suggest actions to be taken, a real-time interpretation model may be used. The real-time interpretation model may provide users with distributed flow rates and distributed temperatures along wells of the EGS field. The real-time interpretation model may include templates and / or other objects for generating graphical user interfaces.

[0094] To enable real-time interpretation, a data analytic model may be used. The data analytic model may be used to process real-time data for the EGS field from the real-time data collection repository to obtain processed data; and generate, based on the processed data, realtime adjustments to at least one of the reservoir models, the heat exchange model, and the fluid dynamics model.

[0095] To facilitate storage of data, a reservoir repository may be used. The reservoir repository may include flow patterns for the EGS field. The flow patterns may be obtained with chemical or radioactive tracers.

[0096] At operation 306, performance of the processes is initiated to update operation of the EGS field. The performance may be initiated by, for example, sending instructions to various systems in the EGS field. The systems may include computers coupled to various well controls. The instructions may cause the operation of the wells of the EGS field to be operated differently.

[0097] The method may end following operation 306.

[0098] By doing so, embodiments disclosed herein may improve the likelihood of successfully exploiting thermal reservoirs. The likelihood of success may be improved by utilizing near-real-time monitoring and modeling. The approach may provide a view of the EGS field that is more likely to accurately reflect the conditions present in the EGS field.

[0099] Any of the components illustrated in FIGs. 1-2 may be implemented with one or more computing devices. Turning to FIG. 4, a block diagram illustrating an example of a data processing system (e.g., a computing device) in accordance with an embodiment is shown. For example, system 400 may represent any of the data processing systems described aboveperforming any of the processes or methods described above. System 400 can include many different components. These components can be implemented as integrated circuits (ICs), portions thereof, discrete electronic devices, or other modules adapted to a circuit board such as a motherboard or add-in card of the computer system, or as components otherwise incorporated within a chassis of the computer system. Note also that system 400 is intended to show a high-level view of many components of the computer system. However, it is to be understood that additional components may be present in certain implementations and furthermore, different arrangement of the components shown may occur in other implementations. System 400 may represent a desktop, a laptop, a tablet, a server, a mobile phone, a media player, a personal digital assistant (PDA), a personal communicator, a gaming device, a network router or hub, a wireless access point (AP) or repeater, a set-top box, or a combination thereof. Further, while only a single machine or system is illustrated, the term “machine” or “system” shall also be taken to include any collection of machines or systems that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0100] In an embodiment, system 400 includes processor 401, memory 403, and devices 405-407 via a bus or an interconnect 410. Processor 401 may represent a single processor or multiple processors with a single processor core or multiple processor cores included therein. Processor 401 may represent one or more general-purpose processors such as a microprocessor, a central processing unit (CPU), or the like. More particularly, processor 401 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or processor implementing other instruction sets, or processors implementing a combination of instruction sets. Processor 401 may also be one or more special -purpose processors such as an application specific integrated circuit (ASIC), a cellular or baseband processor, a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, agraphics processor, a network processor, a communications processor, a cryptographic processor, a co-processor, an embedded processor, or any other type of logic capable of processing instructions.

[0101] Processor 401, which may be a low power multi-core processor socket such as an ultra-low voltage processor, may act as a main processing unit and central hub for communication with the various components of the system. Such a processor can be implemented as a system on chip (SoC). Processor 401 is configured to execute instructions for performing the operations discussed herein. System 400 may further include a graphics interface that communicates with optional graphics subsystem 404, which may include a display controller, a graphics processor, and / or a display device.

[0102] Processor 401 may communicate with memory 403, which in an embodiment can be implemented via multiple memory devices to provide for a given amount of system memory. Memory 403 may include one or more volatile storage (or memory) devices such as random-access memory (RAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), static RAM (SRAM), or other types of storage devices. Memory 403 may store information including sequences of instructions that are executed by processor 401 , or any other device. For example, executable code and / or data of a variety of operating systems, device drivers, firmware (e g., input output basic system or BIOS), and / or applications can be loaded in memory 403 and executed by processor 401. An operating system can be any kind of operating system, such as, for example, Windows® operating system from Microsoft®, Mac OS® / iOS® from Apple, Android® from Google®, Linux®, Unix®, or other real-time or embedded operating systems such as VxWorks.

[0103] System 400 may further include IO devices such as devices (e.g., 405, 406, 407, 408) including network interface device(s) 405, optional input device(s) 406, and other optional IO device(s) 407. Network interface device(s) 405 may include a wireless transceiver and / or a network interface card (NIC). The wireless transceiver may be a WiFi transceiver, aninfrared transceiver, a Bluetooth transceiver, a WiMax transceiver, a wireless cellular telephony transceiver, a satellite transceiver (e.g., a global positioning system (GPS) transceiver), or other radio frequency (RF) transceivers, or a combination thereof. The NIC may be an Ethernet card.

[0104] Input device(s) 406 may include a mouse, a touch pad, a touch sensitive screen (which may be integrated with a display device of optional graphics subsystem 404), a pointer device such as a stylus, and / or a keyboard (e.g., physical keyboard or a virtual keyboard displayed as part of a touch sensitive screen). For example, input device(s) 406 may include a touch screen controller coupled to a touch screen. The touch screen and touch screen controller can, for example, detect contact and movement or break thereof using any of a plurality of touch sensitivity technologies, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with the touch screen.

[0105] IO devices 407 may include an audio device. An audio device may include a speaker and / or a microphone to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording, and / or telephony functions. Other IO devices 407 may further include universal serial bus (USB) port(s), parallel port(s), serial port(s), a printer, a network interface, a bus bridge (e.g., a PCI-PCI bridge), sensor(s) (e g., a motion sensor such as an accelerometer, gyroscope, a magnetometer, a light sensor, compass, a proximity sensor, etc.), or a combination thereof. IO device(s) 407 may further include an imaging processing subsystem (e.g., a camera), which may include an optical sensor, such as a charged coupled device (CCD) or a complementary metal -oxide semiconductor (CMOS) optical sensor, utilized to facilitate camera functions, such as recording photographs and video clips. Certain sensors may be coupled to interconnect 410 via a sensor hub (not shown), while other devices such as a keyboard or thermal sensor may be controlled by an embedded controller (not shown), dependent upon the specific configuration or design of system 400.

[0106] To provide for persistent storage of information such as data, applications, one or more operating systems and so forth, a mass storage (not shown) may also couple to processor 401. In an embodiment, to enable a thinner and lighter system design as well as to improve system responsiveness, this mass storage may be implemented via a solid-state device (SSD). In an embodiment, the mass storage may primarily be implemented using a hard disk drive (HDD) with a smaller amount of SSD storage to act as an SSD cache to enable non-volatile storage of context state and other such information during power down events so that a fast power up can occur on re-initiation of system activities. Also, a flash device may be coupled to processor 401, e.g., via a serial peripheral interface (SPI). This flash device may provide for non-volatile storage of system software, including basic input / output software (BIOS) as well as other firmware of the system.

[0107] Storage device 408 may include computer-readable storage medium 409 (also known as a machine-readable storage medium or a computer-readable medium) on which is stored one or more sets of instructions or software (e.g., processing module, unit, and / or processing module / unit / logic 428) embodying any one or more of the methodologies or functions described herein. Processing module / unit / logic 428 may represent any of the components described above. Processing module / unit / logic 428 may also reside, completely or at least partially, within memory 403 and / or within processor 401 during execution thereof by system 400, memory 403 and processor 401 also constituting machine-accessible storage media. Processing module / unit / logic 428 may further be transmitted or received over a network via network interface device(s) 405.

[0108] Computer-readable storage medium 409 may also be used to store some software functionalities described above persistently. While computer-readable storage medium 409 is shown in an embodiment to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) that store the one ormore sets of instructions. The terms “computer-readable storage medium” shall also be taken to include any medium that is capable of storing or encoding a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of embodiments disclosed herein. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, or any other non-transitory machine-readable medium.

[0109] Processing module / unit / logic 428, components and other features described herein can be implemented as discrete hardware components or integrated in the functionality of hardware components such as ASICS, FPGAs, DSPs or similar devices. In addition, processing module / unit / logic 428 can be implemented as firmware or functional circuitry within hardware devices. Further, processing module / unit / logic 428 can be implemented in any combination hardware devices and software components.

[0110] Note that while system 400 is illustrated with various components of a data processing system, it is not intended to represent any particular architecture or manner of interconnecting the components; as such details are not germane to embodiments disclosed herein. It will also be appreciated that network computers, handheld computers, mobile phones, servers, and / or other data processing systems which have fewer components or perhaps more components may also be used with embodiments disclosed herein.

[0111] Some portions of the preceding detailed descriptions have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the ways used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities.

[0112] It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as those set forth in the claims below, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system’s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

[0113] Embodiments disclosed herein also relate to an apparatus for performing the operations herein. Such a computer program is stored in a non-transitory computer readable medium. A non-transitory machine-readable medium includes any mechanism for storing information in a form readable by a machine (e.g., a computer). For example, a machine- readable (e.g., computer-readable) medium includes a machine (e.g., a computer) readable storage medium (e.g., read only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media, flash memory devices).

[0114] The processes or methods depicted in the preceding figures may be performed by processing logic that comprises hardware (e.g. circuitry, dedicated logic, etc ), software (e.g., embodied on a non-transitory computer readable medium), or a combination of both. Although the processes or methods are described above in terms of some sequential operations, it should be appreciated that some of the operations described may be performed in a different order. Moreover, some operations may be performed in parallel rather than sequentially.

[0115] Embodiments disclosed herein are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of embodiments disclosed herein.

[0116] In the foregoing specification, embodiments have been described with reference to specific exemplary embodiments thereof. It will be evident that various modifications may be made thereto without departing from the broader spirit and scope of the embodiments disclosed herein as set forth in the following claims. The specification and drawings are, accordingly, to be regarded in an illustrative sense rather than a restrictive sense.

Claims

CLAIMSWhat is claimed is:

1. A system, comprising: a hardware sensing system for obtaining measurements of an enhanced geothermal system (EGS) field; and a hardware modeling system that hosts: a reservoir model for the EGS field; a heat exchange model for the EGS field; a fluid dynamics model for the EGS field; a real-time data collection repository for storing the measurements; and a workflow orchestrator adapted to: use the reservoir model, the heat exchange model, the fluid dynamics model, and the real-time data collection repository to obtain predicted behavior for the EGS field.

2. The system of claim 1, wherein the reservoir model is a three dimensional model or a four dimensional model.

3. The system of claim 1, wherein the heat exchange model is adapted to provide heat transfer between inject fluid into the EGS field, reservoir fluid of the EGS field, a formation in which the EGS field is positioned, and the Earth’s core.

4. The system of claim 3, wherein the heat exchange model is further adapted to take into account behavior of critical and supercritical states of fluids of the EGS field, and conducive fracture networks.

5. The system of claim 1, wherein the fluid dynamics model comprises a porous media fluid motion model that uses a simplified streamline approach.

6. The system of claim 1, wherein the measurements comprise: surface pressures for fluids that traverse the EGS field; surface fluid flow rates for the fluids that traverse the EGS field; compositions of the fluids; and surface temperatures of the fluids.

7. The system of claim 1, wherein the measurements comprise: distributed temperature sensing measurements of the EGS field; and distributed acoustic sensing measurements of the EGS field.

8. The system of claim 7, wherein the distributed temperature sensing measurements or the distributed acoustic sensing measurements are taken using fiber optic cables positioned with the EGS field.

9. The system of claim 8, wherein the fiber optic cables are positioned in injection wells of the EGS field.

10. The system of claim 1, wherein the hardware modeling system further hosts: a real-time interpretation model to provide users with distributed flow rates and distributed temperatures along wells of the EGS field.

11. The system of claim 1, wherein the hardware modeling system further hosts: a data analytic model adapted to: process real-time data for the EGS field from the real-time data collection repository to obtain processed data; and generate, based on the processed data, real-time adjustments to at least one of the reservoir models, the heat exchange model, and the fluid dynamics model.

12. The system of claim 1, wherein the hardware modeling system further hosts: a reservoir repository comprising: flow patterns for the EGS field, the flow patterns being obtained with chemical or radioactive tracers.

13. The system of claim 1, wherein the workflow orchestrator is further adapted to: provide user access to the predicted behavior for the EGS field to cooperatively develop processes to be performed with respect to the EGS field.

14. The system of claim 1, wherein the EGS field comprises injection wells to inject cool fluid, and production wells to produce heated fluid.

15. A method for managing an enhanced geothermal system (EGS) field, the method comprising: obtaining measurements of the EGS field; generating, using the measurements, a reservoir model for the EGS field, a heat exchange model for the EGS field, and a fluid dynamics model for the EGS field, predicted behavior of the EGS field; and providing user access to the predicted behavior for the EGS field to cooperatively develop processes to be performed with respect to the EGS field.

16. The method of claim 15, wherein the measurements comprise: surface pressures for fluids that traverse the EGS field; surface fluid flow rates for the fluids that traverse the EGS field; compositions of the fluids; and surface temperatures of the fluids.

17. The method of claim 15, wherein the measurements comprise: distributed temperature sensing measurements of the EGS field; and distributed acoustic sensing measurements of the EGS field.

18. The method of claim 17, wherein the distributed temperature sensing measurements or the distributed acoustic sensing measurements are taken using fiber optic cables positioned with the EGS field.

19. The method of claim 18, wherein the fiber optic cables are positioned in injection wells of the EGS field.

20. The method of claim 15, further comprising: initiating performance of the processes to update operation of the EGS field.

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