Patent Application
20250207486
The present disclosure relates to methods and systems for developing unconventional hydrocarbon resources.
Although unconventional shale reservoirs can have significant potential for oil and gas production, their development requires long horizontal wells (e.g., 3,000 to 10,000 feet) with multiple hydraulic fracture stages for commercial production. Example unconventional reservoir deposits can include coal-bed methane, tight sandstone reservoirs, chalks, and/or self-sourced oil and gas in shale accumulations. Stimulation in the form of hydraulic fracturing can enhance the flow of oil and gas from a well by creating fractures in the rock formation surrounding the well, and thus improve the performance of the well.
The present disclosure involves methods and systems for developing unconventional hydrocarbon resources. One example computer-implemented method includes generating a synthetic database for unconventional hydrocarbon resources. A machine learning model that models unconventional reservoirs is generated based on the synthetic database. Hydraulic fracturing stimulation performance of an unconventional reservoir is determined using the machine learning model. The hydraulic fracturing stimulation performance is provided for unconventional hydrocarbon resource development of the unconventional reservoir.
The previously described implementation is implementable using a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer system including a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method or the instructions stored on the non-transitory, computer-readable medium. These and other embodiments may each optionally include one or more of the following features.
In some implementations, the synthetic database includes simulated data generated from a physics model-based reservoir simulation tool.
In some implementations, providing the hydraulic fracturing stimulation performance for the unconventional hydrocarbon resource development of the unconventional reservoir includes increasing, based on the hydraulic fracturing stimulation performance, a flow rate of produced hydrocarbon fluid from the unconventional reservoir.
In some implementations, before generating the synthetic database for unconventional hydrocarbon resources, determining multiple reservoir characteristics that affect the hydraulic fracturing stimulation performance, and generating the synthetic database for unconventional hydrocarbon resources includes generating, based on the multiple reservoir characteristics, the synthetic database for unconventional hydrocarbon resources.
In some implementations, the multiple reservoir characteristics includes at least one of formation permeability, in-situ stress distribution, reservoir fluid viscosity, skin factor, reservoir pressure, or reservoir depth.
In some implementations, before generating the synthetic database for unconventional hydrocarbon resources, determining multiple stimulation parameters that affect the hydraulic fracturing stimulation performance, and generating the synthetic database for unconventional hydrocarbon resources includes generating, based on the multiple stimulation parameters, the synthetic database for unconventional hydrocarbon resources.
In some implementations, the multiple stimulation parameters include at least one of a type of fractures, a size of fractures, a type of fluid for hydraulic fracturing, a type of proppant for hydraulic fracturing, or an injection pressure for hydraulic fracturing.
In some implementations, generating the synthetic database includes generating, using a physics-based fracture propagation model, the synthetic database.
In some implementations, generating the synthetic database includes generating, using a physics-based reservoir production model, the synthetic database.
In some implementations, the synthetic database includes fracture geometry and corresponding reservoir production data.
In some implementations, the hydraulic fracturing stimulation performance of the unconventional reservoir includes at least one of a fracture half length, a fracture height, a size of stimulated reservoir volume (SRV), or a permeability of the SRV.
While generally described as computer-implemented software embodied on tangible media that processes and transforms the respective data, some or all of the aspects may be computer-implemented methods or further included in respective systems or other devices for performing this described functionality. The details of these and other aspects and implementations of the present disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the disclosure will be apparent from the description and drawings, and from the claims.
Like reference numbers and designations in the various drawings indicate like elements.
This disclosure describes systems and methods for using a combination of physics-based models and data-driven/machine learning (ML) models in the hydrocarbon resource development of unconventional reservoirs. Although this disclosure generally discusses shale reservoirs, the systems and methods can be applied to other types of unconventional reservoirs.
The assessment methodology and production practices for unconventional reservoirs can vary from those used for conventional resources, for instance, due to the unique characteristics of unconventional reservoirs. As an example, shales generally have extremely low permeability (e.g., in the nanodarcy range) due to their very fine grain size. Additionally, shales can form excellent seals for oil and gas fields. These characteristics not only make shales different than conventional reservoirs, but also hydrocarbon production more difficult. For example, in unconventional reservoirs, offset well performance can exhibit a repeatable statistical distribution of estimated ultimate recoveries (EURs) and may not be a reliable predictor of undeveloped location performance. As another example, continuous unconventional hydrocarbon systems can be regional in extent, can require extensive stimulation to produce at economical rates, can have limited in-situ water, and may not exhibit an obvious seal or trap. Additionally, free hydrocarbons (non-absorbed) in unconventional reservoirs are not held in place by hydrodynamics.
As a further example, low permeability (e.g., less than 0.1 millidarcy) shale bulk permeabilities can be less than 0.001 millidarcy. Measurement of shale permeability can be challenging, and the permeability measurement results may not be sufficiently accurate. Gas flow through the shale matrix can be extremely limited and insufficient for commercial production. Shale porosities can also be relatively low, for example, from less than 5% to 12%.
For at least these reasons, physics-based models (which are used in existing systems) may not be sufficient for evaluating or modeling the stimulation performance of unconventional shale reservoirs. To overcome this deficiency, this disclosure describes using data-driven methods to analyze the significance and impact of key components/parameters on diagnostic, predictive, and prescriptive analytics regarding the performance of stimulation jobs in unconventional shale wells. This information can be used to improve (e.g., optimize) the production performance of an unconventional reservoir.
To evaluate stimulation performance of an unconventional shale reservoir, a computer system can use a machine learning-based method that uses a synthetically generated universal database, which encompasses a set of reservoir, operational and geomechanical parameters associated with unconventional shale reservoirs. A machine learning model can be built for a case of interest to predict the performance of stimulation jobs performed on the unconventional shale reservoir. The output parameters of the ML model can include fracture half length, fracture height, and size and permeability of Stimulated Reservoir Volume (SRV). The input parameters of the ML model can include permeability, Young's modulus, Poisson's ratio, stress, and injection pressures and rates.
The disclosed systems and methods provide many advantages over existing systems. As an example, the disclosed systems and methods provide a universal machine learning-based solution that can be tailored to use for a well in any reservoir. This tailored approach is not available in existing systems. As another example, the disclosed systems and methods combine physics-based models with machine learning-based models to predict the stimulation performance of an unconventional reservoir, which is not achievable in existing systems. By predicting the stimulation performance of an unconventional reservoir, the disclosed systems and methods can improve (e.g., optimize) the production performance of wells in the unconventional reservoir.
The following is a discussion of reservoir, completion, and/or stimulation parameters and their importance for modeling the stimulation performance of an unconventional shale reservoir.
In some examples, reservoir/rock parameters that affect hydraulic fracturing of an unconventional wellbore can include formation permeability, in-situ stress distribution, reservoir fluid viscosity, skin factor, reservoir pressure, reservoir depth, and/or condition of the wellbore. For example, the skin factor can indicate whether the reservoir is already stimulated or, in some cases, damaged. As another example, to increase well productivity, formation permeability can be increased by hydraulic fracturing of the formation. In some cases, formation permeability can be very low, and initial acidizing pressure can exceed the pressure required to lift the overburden layer. In unconventional reservoirs, pore interconnections can be small and reduced in aperture by the liquid wetting phase, and consequently, fluid flow can be very low. In the case of oil or gas-condensate reservoirs, low mobility of the viscous liquid phase and multi-phase flow can further reduce fluid flow.
Hydraulic fracturing stimulation can enhance the flow of oil and gas from a well by creating fractures in the surrounding rock formation. The stimulation process aims to increase the formation's permeability, allowing more oil and gas to flow through the well. The performance of the stimulation process depends on many factors, including the type and size of the fractures created, the fluid used, and the pressure applied. In some cases, one of the most important parameters that affect hydraulic fracturing stimulation performance is the type of proppant used. Proppants are solid materials, such as sand or ceramic beads, mixed with the fracturing fluid and pumped into the formation to prop open the fractures when the pressure is released. Different types of proppants have different properties and can be used to achieve different results. For example, some proppants are designed to be more resistant to crushing, while others are designed to be more permeable. In some cases, the choice of proppant can be based on the type of formation being stimulated and the desired outcome.
In some implementations, in unpropped wedge scenarios, fine-mesh proppants can produce similar or sometimes better results when compared to 20/40 mesh proppant, because smaller proppant particles have less tendency to bridge and pack off in the fracture. 40/70 mesh proppant can be a proppant type in stimulating the unconventional reservoirs. The properties of an unpropped wedge can be insensitive to the material characteristics of the proppant. Consequently, wells treated with non-API-spec proppants may produce similarly to wells treated with standard proppants. When unpropped wedge mechanism is validated in a particular application, formerly substandard proppant sources can be approved for use, reducing demand for the limited supply of high-quality 20/40 and/or 40/70 mesh sand.
In some examples, the size of the fractures created is a parameter that can affect hydraulic fracturing stimulation performance. In some cases, larger fractures can provide more permeability but may be more difficult to create and may require higher pump pressures. Therefore, when selecting the type of fracturing fluid to use and the pump pressure to apply, it is important to consider the size of the fractures that can be achieved with the selected parameters.
In some examples, the type of fracturing fluid used is also a parameter that can affect stimulation performance. Different fracturing fluids have other properties, such as viscosity, density, and gel strength, and can be used to achieve different results. For instance, some fluids are designed to reduce friction and improve the flow of the proppant, while others are designed to increase viscosity and reduce fluid loss.
In some examples, the pump pressure applied during the stimulation process is a parameter that can affect stimulation performance. Higher pressures can create larger fractures and improve permeability, but they can also cause damage to the formation and reduce production. Therefore, the pressure selected for the formation being stimulated can affect the performance of the stimulation process.
In some implementations, water can be used as a base fluid in unconventional reservoir treatments. Water is economical and can be re-used, especially if chemical quality control standards are broad, as in water fracturing applications (e.g., using non-gelled, non-viscosified water). In some cases, however, unconventional reservoir rock can be chemically unreactive to water as pore throats are too small to accept much fluid, and the majority of flow and leak-off goes to fractures. Additionally, mobile or swelling clay minerals are not a component of fracture-fill material or matrix pore-wall linings. Thus, water becomes an issue when its physical properties, high density, and capillary pressure gradient in small pore networks render it immobile in low-energy systems.
In some implementations, over-pressured or higher-energy reservoirs can have the potential for high sustained post-treatment flow rates. A high and controlled flow rate can facilitate fracture fluid clean-up and the use of breaker-laden high-viscosity crosslinked fluids if warranted. Conversely, under-pressured reservoirs are more prone to clean-up issues, for example, with high gelant loading fracturing fluids, and can respond better to gas assist, foam, slick water (low viscosity), and oil. In cases of ultra-low reservoir pressure, all-gas treatments can be effective, such as the CO2 dry frac process, N2 coiled tubing fracturing treatments, and/or Devonian Shale plays.
In some implementations, measuring completion and/or stimulation effectiveness can be challenging in unconventional reservoirs where horizontal or multiple-stage completions have been applied because of multiple production entry points and/or separate reservoir compartments. Conventional single-layer analysis techniques can be used, but the result can be speculative. Other methods can use three or more production-log surveys to assign interzonal flow rate contributions and then apply analytic or numerical techniques to estimate kh, effective fracture length, and/or drainage area/volume. In some cases, the lack of resolution and confidence with infrequent and small sampling and suboptimal conditions for liquid removal (tubing must be landed above all perforations) can also limit the reliability of measuring completion and/or stimulation effectiveness. Consequently, evaluations of completion and/or stimulation effectiveness can compare the cumulative hydrocarbon production of differently completed wells at fixed periods of elapsed time (e.g., cumulative 90-day output). In this way, the relative effectiveness of different techniques can be assessed.
Evaluations of completion and/or stimulation effectiveness can provide important insights, especially when the sample size of wells is very large, but the evaluation results can be misleading when the sample size is small. In some cases, comparative analysis can provide insight into the relative contributions of the reservoir and fracture flow properties. The development and refinement of technologies such as distributed temperature surveys (DTS) can offer the potential to obtain a large amount of continuous flow rate and bottom-hole pressure data on a layer-by-layer basis to use analytic methods effectively. The obtained flow rate and bottom-hole pressure measurements can be complemented with pre-completion techniques to obtain initial reservoir pressure, which can be input in drawdown analysis.
In some implementations, a physics-based model can represent the governing laws of nature that innately embed the concepts of time, space, causality, and generalizability. These laws of nature can define how physical, chemical, biological, and geological processes evolve. A physics-based ML model can integrate data, partial differential equations (PDEs), and mathematical models to solve data shift problems in machine learning. Physics-based ML models can be trained to solve supervised learning tasks while following the laws of physics described by general nonlinear equations. In some cases, binary classification, multiclass classification, and/or regression can be used in the physics-based ML model, depending on the parameters to be predicted.
In some implementations, a physics-based model can model complex processes and predict future events with sufficient information about the current situation.
In some implementations, machine learning models can be used to make predictions. In some cases, choosing between a physics-based model and an ML model depends on the problem to be solved. For example, an ML-based model can be applied in use cases with many example outcomes. In some cases, given enough example outcomes (i.e., the training data), an ML model can learn an underlying pattern between the information about the system (i.e., the input variables) and the outcome to be predicted (i.e., the output variables).
In some implementations, a physics-based model can be used if a problem can be described mathematically using the physics-based model. In some cases, however, machine learning can still be used to solve problems that can be characterized using physics-based modeling.
In some implementations, a combination of physics with machine learning in a hybrid modeling scheme can be used to solve problems. For the hybrid modeling scheme, machine-learning can be applied to a system even if in principle the system can be described using a physics-based model.
In some implementations, ML models can learn physics governing a physical system. For example, given enough examples of how a physical system behaves, an ML model can learn the behaviors of the physical system and make predictions that follow the physics governing the physical system.
In some implementations, one consideration of implementing an ML-based approach when a physics-based model that describes a system is available is computational cost. In some cases, even though a physics-based model can describe a procedure, solving the physics-based model can be time-consuming. Therefore, a physics-based approach might not be used for real-time predictions based on live data. In this case, an ML-based model could be used instead. In some cases, the computational complexity of an ML model is mainly associated with the training phase of the ML model. Once the ML model has finished training, making predictions on new data can be computationally efficient. Therefore, the hybrid approach of combining machine learning and physics-based modeling can be used to solve problems.
In some implementations, high-resolution datasets and the decrease in sensor, storage, and computing costs can enable ML models to improve predictions and perform more comprehensive field decisions.
In some implementations, reservoir parameters that can be significant for assessing economic viability, development, and well-completion techniques for shale production can include parameters such as total organic carbon (TOC) content, kerogen type, thermal maturity, mineralogy/lithology, brittleness, natural fractures, depositional environment, thickness, porosity, and/or pressure.
In some implementations, cost-effective development of unconventional resources can follow a life-cycle approach, for example, a six-step, phased approach that addresses exploration, appraisal, development, production, rejuvenation, and abandonment phases. In the life-cycle process, the exploitation strategy (e.g., the placement of wells and stimulation locations and aspects associated with the process followed by a rejuvenation phase) can be extrapolated based on the differences between the three groups of unconventional resources. Furthermore, the increased operational efficiency garnered from the life-cycle process can reduce costs, depending on the scale and scope of the related project.
In some implementations, a manufacturing approach to oil and gas exploration, which includes standardization, factory lines, and strategic use of inventory, can be adopted.
In some implementations, the operating practices of three key segments can include conventional players who have significant unconventional investments, large independents that primarily have an unconventional focus, and smaller independents.
In some implementations, production evaluation and reservoir forecasting can lead to multiple simulations to cover various field decisions under uncertainty. Multiple factors, for example, a lack of rock/fluid data, short production history, and/or a limited understanding of the physics governing the flow process, can constrain the capability to analyze multiple scenarios and therefore compromise the reliability of field management decisions. In such situations, a set of analytical models (proxies) can be used to predict flow/production scenarios, monitor, and perform forecasts that can either alleviate the overwhelming computational burden entailed by existing simulation tools or shed light to improve the physical understanding in the absence of these tools.
In some implementations, ML modeling approaches can use information from previously collected data as training data to identify the measured pressure, temperature, or production rate characteristics and predict the future trend. In some cases, physics-based approaches assume that a physical model describing the behavior behind these measurements is available and sufficiently accurate and self-contained to predict future behavior. Hybrid approaches combining previously collected data with material assumptions can be used to connect the previously collected data with quantities that can be predicted and/or controlled in the reservoir.
In some implementations, the characteristics of the reservoir and the design of the stimulation can impact hydraulic fracturing stimulation performance in unconventional shale reservoirs. In some cases, unconventional shales can be characterized by low permeability and porosity, making it challenging to produce hydrocarbons. To overcome this challenge, hydraulic fracturing can increase permeability and stimulate production. Therefore, the reservoir's characteristics can impact the performance of the hydraulic fracturing stimulation. Additionally, unconventional shales can be naturally fractured and have complex geology, making it challenging to predict the fracture network that will form. Therefore, reservoir characteristics, for example, rock properties, stress field, natural fracture network, and/or fluid production potential, can impact the design of hydraulic fracturing stimulation in unconventional shale reservoirs.
In some implementations, the stimulation design can also be important for improving the performance of hydraulic fracturing. The stimulation design can consider the reservoir characteristics and the goals of the stimulation. The stimulation design can include selecting the fracturing fluid and proppant, determining the injection rate and pressure, and designing the fracture geometry. The stimulation design can also include reservoir management strategies, such as well spacing and completion design, which can affect the performance of the stimulation. Therefore, the performance of hydraulic fracturing in unconventional shales can be highly dependent on the characteristics of the reservoir and the design of the stimulation. Consequently, an understanding of the reservoir and an effective stimulation design can be important for improving the performance of the stimulation.
At 102, a computer system evaluates and selects parameters that affect or influence hydraulic fracturing stimulation performance. In some implementations, the parameters can include the aforementioned reservoir, completion, and/or stimulation parameters from relevant existing reservoirs (e.g., reservoirs that have similar characteristics to the target reservoir, such as type of reservoir).
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In some implementations, time and spatial dependency can be major factors in resolving uncertainties associated with oil and gas occurrence and performance. Therefore, hybrid approaches considering both ML modeling and physics modeling that considers fundamental analytic relations of time and space can be applied. Additionally, the multiple time and space scales involved in the oil field can require methods for how to validate and/or interpret data, as well as communicate results for decision making in a timely manner.
Multiple realizations using parameters in Table 1 are generated. These realizations correspond to different hydraulic fracturing configurations. The resulting data from these configurations can be used to train a machine learning model to predict gas production as a function of time, lateral length, fracture half length, number of stages, permeability, porosity, and net pay.
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At 110, the computer system applies predictive analytics to the case of interest. In some cases, predictive analytics can provide predictions and/or estimations on the output of the ML model using the data relationships between the input and the output of the ML model established in the universal database. In some cases, the computer system can apply the ML model to the case of interest to predict the outcome of the case of interest.
In some implementations, the parameters in Table 1 can be expanded to include other parameters (e.g., Young's modulus, Poisson ratio, fracture height, fracture width, and stimulated reservoir volume) to predict gas production or other parameters relevant to oil and gas production.
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At 802, a computer system generates a synthetic database for unconventional hydrocarbon resources.
At 804, the computer system generates, based on the synthetic database, a machine learning model that models unconventional reservoirs.
At 806, the computer system determines, using the machine learning model, hydraulic fracturing stimulation performance of an unconventional reservoir.
At 808, the computer system provides the hydraulic fracturing stimulation performance for unconventional hydrocarbon resource development of the unconventional reservoir.
The illustrated computer 902 is intended to encompass any computing device such as a server, a desktop computer, an embedded computer, a laptop/notebook computer, a wireless data port, a smart phone, a personal data assistant (PDA), a tablet computing device, or one or more processors within these devices, including physical instances, virtual instances, or both. The computer 902 can include input devices such as keypads, keyboards, and touch screens that can accept user information. Also, the computer 902 can include output devices that can convey information associated with the operation of the computer 902. The information can include digital data, visual data, audio information, or a combination of information. The information can be presented in a graphical user interface (UI) (or GUI). In some implementations, the inputs and outputs include display ports (such as DVI-I+2× display ports), USB 3.0, GbE ports, isolated DI/O, SATA-III (6.0 Gb/s) ports, mPCIe slots, a combination of these, or other ports. In instances of an edge gateway, the computer 902 can include a Smart Embedded Management Agent (SEMA), such as a built-in ADLINK SEMA 2.2, and a video sync technology, such as Quick Sync Video technology supported by ADLINK MSDK+. In some examples, the computer 902 can include the MXE-5400 Series processor-based fanless embedded computer by ADLINK, though the computer 902 can take other forms or include other components.
The computer 902 can serve in a role as a client, a network component, a server, a database, a persistency, or components of a computer system for performing the subject matter described in the present disclosure. The illustrated computer 902 is communicably coupled with a network 930. In some implementations, one or more components of the computer 902 can be configured to operate within different environments, including cloud-computing-based environments, local environments, global environments, and combinations of environments.
At a high level, the computer 902 is an electronic computing device operable to receive, transmit, process, store, and manage data and information associated with the described subject matter. According to some implementations, the computer 902 can also include, or be communicably coupled with, an application server, an email server, a web server, a caching server, a streaming data server, or a combination of servers.
The computer 902 can receive requests over network 930 from a client application (for example, executing on another computer 902). The computer 902 can respond to the received requests by processing the received requests using software applications. Requests can also be sent to the computer 902 from internal users (for example, from a command console), external (or third) parties, automated applications, entities, individuals, systems, and computers.
Each of the components of the computer 902 can communicate using a system bus 903. In some implementations, any or all of the components of the computer 902, including hardware or software components, can interface with each other or the interface 904 (or a combination of both), over the system bus. Interfaces can use an application programming interface (API) 912, a service layer 913, or a combination of the API 912 and service layer 913. The API 912 can include specifications for routines, data structures, and object classes. The API 912 can be either computer-language independent or dependent. The API 912 can refer to a complete interface, a single function, or a set of APIs 912.
The service layer 913 can provide software services to the computer 902 and other components (whether illustrated or not) that are communicably coupled to the computer 902. The functionality of the computer 902 can be accessible for all service consumers using this service layer 913. Software services, such as those provided by the service layer 913, can provide reusable, defined functionalities through a defined interface. For example, the interface can be software written in JAVA, C++, or a language providing data in extensible markup language (XML) format. While illustrated as an integrated component of the computer 902, in alternative implementations, the API 912 or the service layer 913 can be stand-alone components in relation to other components of the computer 902 and other components communicably coupled to the computer 902. Moreover, any or all parts of the API 912 or the service layer 913 can be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of the present disclosure.
The computer 902 can include an interface 904. Although illustrated as a single interface 904 in
The computer 902 includes a processor 905. Although illustrated as a single processor 905 in
The computer 902 can also include a database 906 that can hold data for the computer 902 and other components connected to the network 930 (whether illustrated or not). For example, database 906 can be an in-memory, conventional, or a database storing data consistent with the present disclosure. In some implementations, the database 906 can be a combination of two or more different database types (for example, hybrid in-memory and conventional databases) according to particular needs, desires, or particular implementations of the computer 902 and the described functionality. Although illustrated as a single database 906 in
The computer 902 also includes a memory 907 that can hold data for the computer 902 or a combination of components connected to the network 930 (whether illustrated or not). Memory 907 can store any data consistent with the present disclosure. In some implementations, memory 907 can be a combination of two or more different types of memory (for example, a combination of semiconductor and magnetic storage) according to particular needs, desires, or particular implementations of the computer 902 and the described functionality. Although illustrated as a single memory 907 in
An application 908 can be an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer 902 and the described functionality. For example, an application 908 can serve as one or more components, modules, or applications 908. Multiple applications 908 can be implemented on the computer 902. Each application 908 can be internal or external to the computer 902.
The computer 902 can also include a power supply 914. The power supply 914 can include a rechargeable or non-rechargeable battery that can be configured to be either user- or non-user-replaceable. In some implementations, the power supply 914 can include power-conversion and management circuits, including recharging, standby, and power management functionalities. In some implementations, the power-supply 914 can include a power plug to allow the computer 902 to be plugged into a wall socket or a power source to, for example, power the computer 902 or recharge a rechargeable battery.
There can be any number of computers 902 associated with, or external to, a computer system including computer 902, with each computer 902 communicating over network 930. Further, the terms “client”, “user”, and other appropriate terminology can be used interchangeably without departing from the scope of the present disclosure. Moreover, the present disclosure contemplates that many users can use one computer 902 and one user can use multiple computers 902.
Examples of field operations 1010 include forming/drilling a wellbore, hydraulic fracturing, producing through the wellbore, injecting fluids (such as water) through the wellbore, to name a few. In some implementations, methods of the present disclosure can trigger or control the field operations 1010. For example, the methods of the present disclosure can generate data from hardware/software including sensors and physical data gathering equipment (e.g., seismic sensors, well logging tools, flow meters, and temperature and pressure sensors). The methods of the present disclosure can include transmitting the data from the hardware/software to the field operations 1010 and responsively triggering the field operations 1010 including, for example, generating plans and signals that provide feedback to and control physical components of the field operations 1010. Alternatively or in addition, the field operations 1010 can trigger the methods of the present disclosure. For example, implementing physical components (including, for example, hardware, such as sensors) deployed in the field operations 1010 can generate plans and signals that can be provided as input or feedback (or both) to the methods of the present disclosure.
Examples of computational operations 1012 include one or more computer systems 1020 that include one or more processors and computer-readable media (e.g., non-transitory computer-readable media) operatively coupled to the one or more processors to execute computer operations to perform the methods of the present disclosure. The computational operations 1012 can be implemented using one or more databases 1018, which store data received from the field operations 1010 and/or generated internally within the computational operations 1012 (e.g., by implementing the methods of the present disclosure) or both. For example, the one or more computer systems 1020 process inputs from the field operations 1010 to assess conditions in the physical world, the outputs of which are stored in the databases 1018. For example, seismic sensors of the field operations 1010 can be used to perform a seismic survey to map subterranean features, such as facies and faults. In performing a seismic survey, seismic sources (e.g., seismic vibrators or explosions) generate seismic waves that propagate in the earth and seismic receivers (e.g., geophones) measure reflections generated as the seismic waves interact with boundaries between layers of a subsurface formation. The source and received signals are provided to the computational operations 1012 where they are stored in the databases 1018 and analyzed by the one or more computer systems 1020.
In some implementations, one or more outputs 1022 generated by the one or more computer systems 1020 can be provided as feedback/input to the field operations 1010 (either as direct input or stored in the databases 1018). The field operations 1010 can use the feedback/input to control physical components used to perform the field operations 1010 in the real world.
For example, the computational operations 1012 can process the seismic data to generate three-dimensional (3D) maps of the subsurface formation. The computational operations 1012 can use these 3D maps to provide plans for locating and drilling exploratory wells. In some operations, the exploratory wells are drilled using logging-while-drilling (LWD) techniques which incorporate logging tools into the drill string. LWD techniques can enable the computational operations 1012 to process new information about the formation and control the drilling to adjust to the observed conditions in real-time.
The one or more computer systems 1020 can update the 3D maps of the subsurface formation as information from one exploration well is received and the computational operations 1012 can adjust the location of the next exploration well based on the updated 3D maps. Similarly, the data received from production operations can be used by the computational operations 1012 to control components of the production operations. For example, production well and pipeline data can be analyzed to predict slugging in pipelines leading to a refinery and the computational operations 1012 can control machine operated valves upstream of the refinery to reduce the likelihood of plant disruptions that run the risk of taking the plant offline.
In some implementations of the computational operations 1012, customized user interfaces can present intermediate or final results of the above-described processes to a user. Information can be presented in one or more textual, tabular, or graphical formats, such as through a dashboard. The information can be presented at one or more on-site locations (such as at an oil well or other facility), on the Internet (such as on a webpage), on a mobile application (or app), or at a central processing facility.
The presented information can include feedback, such as changes in parameters or processing inputs, that the user can select to improve a production environment, such as in the exploration, production, and/or testing of petrochemical processes or facilities. For example, the feedback can include parameters that, when selected by the user, can cause a change to, or an improvement in, drilling parameters (including drill bit speed and direction) or overall production of a gas or oil well. The feedback, when implemented by the user, can improve the speed and accuracy of calculations, streamline processes, improve models, and solve problems related to efficiency, performance, safety, reliability, costs, downtime, and the need for human interaction.
In some implementations, the feedback can be implemented in real-time, such as to provide an immediate or near-immediate change in operations or in a model. The term real-time (or similar terms as understood by one of ordinary skill in the art) means that an action and a response are temporally proximate such that an individual perceives the action and the response occurring substantially simultaneously. For example, the time difference for a response to display (or for an initiation of a display) of data following the individual's action to access the data can be less than 1 millisecond (ms), less than 1 second(s), or less than 5 s. While the requested data need not be displayed (or initiated for display) instantaneously, it is displayed (or initiated for display) without any intentional delay, taking into account processing limitations of a described computing system and time required to, for example, gather, accurately measure, analyze, process, store, or transmit the data.
Events can include readings or measurements captured by downhole equipment such as sensors, pumps, bottom hole assemblies, or other equipment. The readings or measurements can be analyzed at the surface, such as by using applications that can include modeling applications and machine learning. The analysis can be used to generate changes to settings of downhole equipment, such as drilling equipment. In some implementations, values of parameters or other variables that are determined can be used automatically (such as through using rules) to implement changes in oil or gas well exploration, production/drilling, or testing. For example, outputs of the present disclosure can be used as inputs to other equipment and/or systems at a facility. This can be especially useful for systems or various pieces of equipment that are located several meters or several miles apart, or are located in different countries or other jurisdictions.
Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly embodied computer software or firmware; in computer hardware, including the structures disclosed in this specification and their structural equivalents; or in combinations of one or more of them. Software implementations of the described subject matter can be implemented as one or more computer programs. Each computer program can include one or more modules of computer program instructions encoded on a tangible, non-transitory, computer-readable computer-storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively, or additionally, the program instructions can be encoded in/on an artificially generated propagated signal. For example, the signal can be a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to a suitable receiver apparatus for execution by a data processing apparatus. The computer-storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of computer-storage mediums.
The terms “data processing apparatus”, “computer”, and “electronic computer device” (or equivalent as understood by one of ordinary skill in the art) refer to data processing hardware. For example, a data processing apparatus can encompass all kinds of apparatuses, devices, and machines for processing data, including by way of example, a programmable processor, a computer, or multiple processors or computers. The apparatus can also include special purpose logic circuitry including, for example, a central processing unit (CPU), a field programmable gate array (FPGA), or an application specific integrated circuit (ASIC). In some implementations, the data processing apparatus or special purpose logic circuitry (or a combination of the data processing apparatus and special purpose logic circuitry) can be hardware- or software-based (or a combination of both hardware- and software-based). The apparatus can optionally include code that creates an execution environment for computer programs, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of execution environments. The present disclosure contemplates the use of data processing apparatuses with or without conventional operating systems, for example, Linux, Unix, Windows, Mac OS, Android, or iOS.
A computer program, which can also be referred to or described as a program, software, a software application, a module, a software module, a script, or code can be written in any form of programming language. Programming languages can include, for example, compiled languages, interpreted languages, declarative languages, or procedural languages. Programs can be deployed in any form, including as stand-alone programs, modules, components, subroutines, or units for use in a computing environment. A computer program can, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data, for example, one or more scripts stored in a markup language document; in a single file dedicated to the program in question; or in multiple coordinated files storing one or more modules, sub programs, or portions of code. A computer program can be deployed for execution on one computer or on multiple computers that are located, for example, at one site or distributed across multiple sites that are interconnected by a communication network. While portions of the programs illustrated in the various figures may be shown as individual modules that implement the various features and functionality through various objects, methods, or processes; the programs can instead include a number of sub-modules, third-party services, components, and libraries. Conversely, the features and functionality of various components can be combined into single components as appropriate. Thresholds used to make computational determinations can be statically, dynamically, or both statically and dynamically determined.
The methods, processes, or logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The methods, processes, or logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, for example, a CPU, an FPGA, or an ASIC.
Computers suitable for the execution of a computer program can be based on one or more of general and special purpose microprocessors and other kinds of CPUs. The elements of a computer are a CPU for performing or executing instructions and one or more memory devices for storing instructions and data. Generally, a CPU can receive instructions and data from (and write data to) a memory. A computer can also include, or be operatively coupled to, one or more mass storage devices for storing data. In some implementations, a computer can receive data from, and transfer data to, the mass storage devices including, for example, magnetic, magneto optical disks, or optical disks. Moreover, a computer can be embedded in another device, for example, a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive.
Computer readable media (transitory or non-transitory, as appropriate) suitable for storing computer program instructions and data can include all forms of permanent/non-permanent and volatile/non-volatile memory, media, and memory devices. Computer readable media can include, for example, semiconductor memory devices such as random access memory (RAM), read only memory (ROM), phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices. Computer readable media can also include, for example, magnetic devices such as tape, cartridges, cassettes, and internal/removable disks. Computer readable media can also include magneto optical disks, optical memory devices, and technologies including, for example, digital video disc (DVD), CD ROM, DVD+/−R, DVD-RAM, DVD-ROM, HD-DVD, and BLURAY. The memory can store various objects or data, including caches, classes, frameworks, applications, modules, backup data, jobs, web pages, web page templates, data structures, database tables, repositories, and dynamic information. Types of objects and data stored in memory can include parameters, variables, algorithms, instructions, rules, constraints, and references. Additionally, the memory can include logs, policies, security or access data, and reporting files. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
Implementations of the subject matter described in the present disclosure can be implemented on a computer having a display device for providing interaction with a user, including displaying information to (and receiving input from) the user. Types of display devices can include, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), a light-emitting diode (LED), or a plasma monitor. Display devices can include a keyboard and pointing devices including, for example, a mouse, a trackball, or a trackpad. User input can also be provided to the computer through the use of a touchscreen, such as a tablet computer surface with pressure sensitivity or a multi-touch screen using capacitive or electric sensing. Other kinds of devices can be used to provide for interaction with a user, including to receive user feedback, for example, sensory feedback including visual feedback, auditory feedback, or tactile feedback. Input from the user can be received in the form of acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to, and receiving documents from, a device that is used by the user. For example, the computer can send web pages to a web browser on a user's client device in response to requests received from the web browser.
The term “graphical user interface,” or “GUI,” can be used in the singular or the plural to describe one or more graphical user interfaces and each of the displays of a particular graphical user interface. Therefore, a GUI can represent any graphical user interface, including, but not limited to, a web browser, a touch screen, or a command line interface (CLI) that processes information and efficiently presents the information results to the user. In general, a GUI can include a plurality of user interface (UI) elements, some or all associated with a web browser, such as interactive fields, pull-down lists, and buttons. These and other UI elements can be related to or represent the functions of the web browser. Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back end component, for example, as a data server, or that includes a middleware component, for example, an application server. Moreover, the computing system can include a front-end component, for example, a client computer having one or both of a graphical user interface or a Web browser through which a user can interact with the computer. The components of the system can be interconnected by any form or medium of wireline or wireless digital data communication (or a combination of data communication) in a communication network. Examples of communication networks include a local area network (LAN), a radio access network (RAN), a metropolitan area network (MAN), a wide area network (WAN), Worldwide Interoperability for Microwave Access (WIMAX), a wireless local area network (WLAN) (for example, using 802.11 a/b/g/n or 802.20 or a combination of protocols), all or a portion of the Internet, or any other communication system or systems at one or more locations (or a combination of communication networks). The network can communicate with, for example, Internet Protocol (IP) packets, frame relay frames, asynchronous transfer mode (ATM) cells, voice, video, data, or a combination of communication types between network addresses.
The computing system can include clients and servers. A client and server can generally be remote from each other and can typically interact through a communication network. The relationship of client and server can arise by virtue of computer programs running on the respective computers and having a client-server relationship.
Cluster file systems can be any file system type accessible from multiple servers for read and update. Locking or consistency tracking may not be necessary since the locking of exchange file system can be done at application layer. Furthermore, Unicode data files can be different from non-Unicode data files.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of features that may be specific to particular implementations. Certain features that are described in this specification in the context of separate implementations can also be implemented, in combination, or in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations, separately, or in any suitable sub-combination. Moreover, although previously described features may be described as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can, in some cases, be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
Particular implementations of the subject matter have been described. Other implementations, alterations, and permutations of the described implementations are within the scope of the following claims as will be apparent to those skilled in the art. While operations are depicted in the drawings or claims in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed (some operations may be considered optional), to achieve desirable results. In certain circumstances, multitasking or parallel processing (or a combination of multitasking and parallel processing) may be advantageous and performed as deemed appropriate.
Moreover, the separation or integration of various system modules and components in the previously described implementations should not be understood as requiring such separation or integration in all implementations; and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Accordingly, the previously described example implementations do not define or constrain the present disclosure. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of the present disclosure.
Furthermore, any claimed implementation is considered to be applicable to at least a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer system comprising a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method or the instructions stored on the non-transitory, computer-readable medium.
Embodiment 1: A computer-implemented method comprising generating a synthetic database for unconventional hydrocarbon resources; generating, based on the synthetic database, a machine learning model that models unconventional reservoirs; determining, using the machine learning model, hydraulic fracturing stimulation performance of an unconventional reservoir; and providing the hydraulic fracturing stimulation performance for unconventional hydrocarbon resource development of the unconventional reservoir.
Embodiment 2: The computer-implemented method of embodiment 1, wherein the synthetic database comprises simulated data generated from a physics model-based reservoir simulation tool.
Embodiment 3: The computer-implemented method of embodiment 1 or 2, wherein providing the hydraulic fracturing stimulation performance for the unconventional hydrocarbon resource development of the unconventional reservoir comprises increasing, based on the hydraulic fracturing stimulation performance, a flow rate of produced hydrocarbon fluid from the unconventional reservoir.
Embodiment 4: The computer-implemented method of any one of embodiments 1 to 3, further comprising before generating the synthetic database for unconventional hydrocarbon resources, determining a plurality of reservoir characteristics that affect the hydraulic fracturing stimulation performance, and wherein generating the synthetic database for unconventional hydrocarbon resources comprises generating, based on the plurality of reservoir characteristics, the synthetic database for unconventional hydrocarbon resources.
Embodiment 5: The computer-implemented method of embodiment 4, wherein the plurality of reservoir characteristics comprises at least one of formation permeability, in-situ stress distribution, reservoir fluid viscosity, skin factor, reservoir pressure, or reservoir depth.
Embodiment 6: The computer-implemented method of any one of embodiments 1 to 5, further comprising before generating the synthetic database for unconventional hydrocarbon resources, determining a plurality of stimulation parameters that affect the hydraulic fracturing stimulation performance, and wherein generating the synthetic database for unconventional hydrocarbon resources comprises generating, based on the plurality of stimulation parameters, the synthetic database for unconventional hydrocarbon resources.
Embodiment 7: The computer-implemented method of embodiment 6, wherein the plurality of stimulation parameters comprise at least one of a type of fractures, a size of fractures, a type of fluid for hydraulic fracturing, a type of proppant for hydraulic fracturing, or an injection pressure for hydraulic fracturing.
Embodiment 8: The computer-implemented method of any one of embodiments 1 to 7, wherein generating the synthetic database comprises generating, using a physics-based fracture propagation model, the synthetic database.
Embodiment 9: The computer-implemented method of any one of embodiments 1 to 8, wherein generating the synthetic database comprises generating, using a physics-based reservoir production model, the synthetic database.
Embodiment 10: The computer-implemented method of any one of embodiments 1 to 9, wherein the synthetic database comprises fracture geometry and corresponding reservoir production data.
Embodiment 11: The computer-implemented method of any one of embodiments 1 to 10, wherein the hydraulic fracturing stimulation performance of the unconventional reservoir comprises at least one of a fracture half length, a fracture height, a size of stimulated reservoir volume (SRV), or a permeability of the SRV.
Embodiment 12: A non-transitory computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising: generating a synthetic database for unconventional hydrocarbon resources; generating, based on the synthetic database, a machine learning model that models unconventional reservoirs; determining, using the machine learning model, hydraulic fracturing stimulation performance of an unconventional reservoir; and providing the hydraulic fracturing stimulation performance for unconventional hydrocarbon resource development of the unconventional reservoir.
Embodiment 13: The non-transitory computer-readable medium of embodiment 12, wherein the synthetic database comprises simulated data generated from a physics model-based reservoir simulation tool.
Embodiment 14: The non-transitory computer-readable medium of embodiment 12 or 13, wherein the operations further comprise: before generating the synthetic database for unconventional hydrocarbon resources, determining a plurality of reservoir characteristics that affect the hydraulic fracturing stimulation performance, and wherein generating the synthetic database for unconventional hydrocarbon resources comprises generating, based on the plurality of reservoir characteristics, the synthetic database for unconventional hydrocarbon resources.
Embodiment 15: The non-transitory computer-readable medium of any one of embodiments 12 to 14, wherein the operations further comprise: before generating the synthetic database for unconventional hydrocarbon resources, determining a plurality of stimulation parameters that affect the hydraulic fracturing stimulation performance, and wherein generating the synthetic database for unconventional hydrocarbon resources comprises generating, based on the plurality of stimulation parameters, the synthetic database for unconventional hydrocarbon resources.
Embodiment 16: The non-transitory computer-readable medium of any one of embodiments 12 to 15, wherein the synthetic database comprises fracture geometry and corresponding reservoir production data.
Embodiment 17: A computer-implemented system, comprising one or more computers; and one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising: generating a synthetic database for unconventional hydrocarbon resources; generating, based on the synthetic database, a machine learning model that models unconventional reservoirs; determining, using the machine learning model, hydraulic fracturing stimulation performance of an unconventional reservoir; and providing the hydraulic fracturing stimulation performance for unconventional hydrocarbon resource development of the unconventional reservoir.
Embodiment 18: The computer-implemented system of embodiment 17, wherein the synthetic database comprises simulated data generated from a physics model-based reservoir simulation tool.
Embodiment 19: The computer-implemented system of embodiment 17 or 18, wherein the one or more operations further comprise: before generating the synthetic database for unconventional hydrocarbon resources, determining a plurality of reservoir characteristics that affect the hydraulic fracturing stimulation performance, and wherein generating the synthetic database for unconventional hydrocarbon resources comprises generating, based on the plurality of reservoir characteristics, the synthetic database for unconventional hydrocarbon resources.
Embodiment 20: The computer-implemented system of any one of embodiments 17 to 19, wherein the one or more operations further comprise: before generating the synthetic database for unconventional hydrocarbon resources, determining a plurality of stimulation parameters that affect the hydraulic fracturing stimulation performance, and wherein generating the synthetic database for unconventional hydrocarbon resources comprises generating, based on the plurality of stimulation parameters, the synthetic database for unconventional hydrocarbon resources.
