Systems and methods for developing and utilizing a surrogate model to estimate production flow rates for a well

Abstract

A method including receiving a plurality of constraints related to extraction of hydrocarbons from a subsurface formation, running one or more simulations based on the constraints, determining an interference boundary based on the one or more simulations, wherein the interference boundary includes a distance between fractures where the interactions between the fractures are below a threshold value, organizing results of the one or more simulations into a first dataset associated with distances between the fractures less than the interference boundary and a second dataset associated with distances between the fractures greater than the interference boundary, generating a first predictive model based on the constraints and the first dataset and a second predictive model based on the constraints and the second dataset, generating a surrogate model based on the first predictive model and the second predictive model, and controlling one or more drilling tools based on the surrogate model.

Description

SYSTEMS AND METHODS FOR DEVELOPING AND UTILIZING A SURROGATE MODEL TO ESTIMATE PRODUCTION FLOW RATES FOR A WELLCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority to and the benefit of United States Provisional Patent Application Serial No. 63 / 691,875 filed September 6, 2024, the disclosure of which is incorporated herein by reference in its entirety.BACKGROUND

[0002] The present disclosure generally relates to systems and methods for developing and utilizing a surrogate model to estimate production flow rates for a hydraulically fractured horizontal well.

[0003] This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present disclosure, which are described and / or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it may be understood that these statements are to be read in this light, and not as admissions of prior art.

[0004] Horizontal drilling with multi-stage hydraulic fracturing (MHF) produces oil and gas at economical levels from reservoirs with low permeability. However, developing a reservoir model and running production simulations using the reservoir model to estimate initial production flow rates of horizontal wells employing MHF may be time and resource consuming. Additionally, analytical approaches for estimating initial production flow rates of horizontal wells employing MHF may not take into account the interference effect between transverse fractures of the horizonal wells, thereby over-estimating the initial production flow rate significantly.SUMMARY

[0005] 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 intendedto limit the scope of this disclosure. Indeed, this disclosure may encompass a variety of aspects that may not be set forth below.

[0006] In certain embodiments, a method includes receiving a plurality of constraints related to extraction of hydrocarbons from a subsurface formation, running one or more simulations based on the plurality of constraints, determining an interference boundary based on the one or more simulations, wherein the interference boundary includes a distance between fractures where the interactions between the fractures are below a threshold value, organizing results of the one or more simulations into a first dataset associated with distances between the fractures less than the interference boundary and a second dataset associated with the interference boundary and distances between the fractures greater than the interference boundary, generating a first predictive model based on the plurality of constraints and the first dataset, generating a second predictive model based on the plurality of constraints and the second dataset, generating a surrogate model based on the first predictive model and second predictive model, and controlling one or more drilling tools based on the surrogate model.

[0007] In certain embodiments, a non-transitory computer-readable medium includes computer-executable instructions that, when executed, cause a processing system to perform operations including receive a plurality of constraints related to extraction of hydrocarbons from a subsurface formation, run one or more simulations based on the plurality of constraints, determine an interference boundary based on the one or more simulations, wherein the interference boundary includes a distance between fracture where the interactions between the fractures are below a threshold value, organize results of the one or more simulations into a first dataset associated with distances between the fractures less than the interference boundary and a second dataset associated with the interference boundary and distances between the fractures greater than the interference boundary, generate a first predictive model based on the plurality of constraints and the first dataset, generate a second predictive model based on the plurality of constraints and the second dataset, generate a surrogate model based on the first predictive model and the second predictive model, and control one or more drilling tools based on the surrogate model.

[0008] In certain embodiments, a system includes a storage component including a plurality of constraints related to extraction of hydrocarbons from a subsurfaceformation and a processing system. The processing system is configured to receive the plurality of constraints, run one or more simulations based on the plurality of constraints, determine an interference boundary based on the one or more simulations, wherein the interference boundary includes a distance between fracture where the interactions between the fractures are below a threshold value, organize results of the one or more simulations into a first dataset associated with distances between the fractures less than the interference boundary and a second dataset associated with the interference boundary and distances between the fractures greater than the interference boundary, generate a first predictive model based on the plurality of constraints and the first dataset, generate a second predictive model based on the plurality of constraints and the second dataset, generate a surrogate model based on the first predictive model and the second predictive model, and control one or more drilling tools based on the surrogate model.BRIEF DESCRIPTION OF DRAWINGS

[0009] These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

[0010] FIG. 1 is an example of a system that includes a workspace framework, in accordance with aspects of the present disclosure;

[0011] FIG. 2 is an example of a wellsite, in accordance with aspects of the present disclosure;

[0012] FIG. 3 is an example of an approach for estimating the flow rate of a horizontal well with MHF, in accordance with aspects of the present disclosure;

[0013] FIG. 4 is a flow chart for developing a surrogate model to estimate a production flow rate for a horizontal well with MHF, in accordance with aspects of the present disclosure;

[0014] FIG. 5 is an example model of horizontal well, in accordance with aspects of the present disclosure;

[0015] FIG. 6 is a plot of the production flow rate per fracture based on the distance between fractures for reservoirs with varying permeability, in accordance with aspects of the present disclosure;

[0016] FIG. 7 is a plot of the scaling factor of an individual fracture based on the distance between fractures for reservoirs with varying permeability, in accordance with aspects of the present disclosure;

[0017] FIG. 8 is a series of plots comparing a ratio of production flow rate of the horizontal well to production flow rate of a vertical well determined via a simulated modeling with a ratio determined via a predictive model for a low interference horizontal well, in accordance with aspects of the present disclosure;

[0018] FIG. 9 is a series of plots comparing scaling factor of a simulated model with a scaling factor of a predictive model for a high interference horizontal well, in accordance with aspects of the present disclosure; and

[0019] FIG. 10 is a plot comparing an estimated production flow rate of a simulated model with an estimated production flow rate of a surrogate model, in accordance with aspects of the present disclosure.DETAILED DESCRIPTION

[0020] One or more specific embodiments of the present disclosure will be described below. In an effort to provide a concise description of these embodiments, all features of an actual implementation may not be described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers’ specific goals, such as compliance with system- related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.

[0021] When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. Any examples of operating parameters and / or environmental conditions are not exclusive of other parameters / conditions of the disclosed embodiments.

[0022] Certain embodiments commensurate in scope with the present disclosure are summarized below. These embodiments are not intended to limit the scope of the disclosure, but rather these embodiments are intended only to provide a brief summary of certain disclosed embodiments. Indeed, the present disclosure may encompass a variety of forms that may be similar to or different from the embodiments set forth below.

[0023] The present disclosure relates to developing and utilizing a surrogate model to estimate production flow rates for a hydraulically fractured horizontal well. A processing system may perform steps of a workflow for developing the surrogate model for estimating the hydrocarbon production flow rates for the horizontal well taking an interference effect between fractures into account and control drilling tools based on the results of the surrogate model. The processing system first receives and / or defines a set of constraints related to extraction of hydrocarbons from a subsurface formation. For example, the set of constraints may include reservoir characteristics, geologic characteristics, boundary flow characteristics, and the like. The processing system then runs simulations based on the set of constraints. For example, the processing system may run multiple simulations with varying well characteristics (e.g., fracture characteristics, permeability, minerology, flow conditions, etc.) to estimate the production flow rate of the horizontal well. The processing system may analyze the results of the simulations. For example, the processing system may determine an interference boundary and a scaling factor based on the one or more simulations. The interference boundary is a distance between fractures at which the interactions between the fractures are negligible. The processing system may organize results of the one or more simulations into a high interference dataset associated with distances between the fractures less than the interference boundary and a low interference dataset associatedwith the interference boundary and distances between the fractures greater than the interference boundary. The scaling factor is a ratio of a production flow rate of a vertical well with one fracture to a production flow rate of an individual fracture of the horizontal well, which is indicative of the interference effect.

[0024] Based on the set of constraints and the low interference dataset, the processing system generates a low interference predictive model. The low interference predictive model may return estimated production flow rates for low interference horizontal wells. Based on the set of constraints, the high interference dataset, and the scaling factor, the processing system generates a high interference predictive model. In some embodiments, the high interference predictive model may return scaling factors that characterize the interference effect for high interference horizontal wells.

[0025] The processing system generates a surrogate model based on the high interference predictive model and the low interference predictive model. For example, the processing system may modify the low interference predictive model to take into account the scaling factor of the high interference predictive model. As such, the surrogate model may be used to estimate the production flow rates across high and low interference horizontal wells with similar accuracy to running multiple simulations while being time and resource efficient. Furthermore, the processing system may control one or more drilling tools based on the surrogate model. For example, the processing system may determine, via the surrogate model, where to drill a horizontal well, the size and shape of the horizontal well, a number of fractures to drill, and the like. The processing system may then control the drilling tools according to the results of the surrogate model.

[0026] By way of introduction, FIG. 1 shows an example of a system 100 that includes a workspace framework 110 that may provide for instantiation of, rendering of, and / or interactions with a graphical user interface (GUI) 120. In the example of FIG. 1, the GUI 120 may include graphical controls for computational frameworks (e.g., applications) 121, projects 122, visualization features 123, one or more other features 124, data access 125, and data storage 126.

[0027] In the example of FIG. 1, the workspace framework 110 may be tailored to a particular geologic environment such as an example geologic environment 150. Forexample, the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and that may be intersected by a fault 153. As an example, the geologic environment 150 may be outfitted with a variety of sensors, detectors, actuators, and the like. For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, and the like. Other equipment 156 may be located remote from a wellsite and include sensing, detecting, emitting, or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, and the like. As an example, one or more satellites may be provided for purposes of communications, data acquisition, and the like. For example, FIG. 1 shows a satellite in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or alternatively include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric).

[0028] FIG. 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting). As an example, the equipment 157 and / or 158 may include components and / or systems for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, and the like.

[0029] In the example of FIG. 1, the GUI 120 shows some examples of computational frameworks, including the DRILLPLAN, DRILLOPS, PETREL, TECHLOG, PETROMOD, ECLIPSE, PIPESIM, and INTERSECT frameworks (SLB, Houston, Texas).

[0030] The DRILLPLAN framework provides for digital well construction planning and includes features for automation of repetitive tasks and validation workflows,enabling improved quality drilling programs (e.g., digital drilling plans) to be produced quickly with assured coherency.

[0031] The DRILLOPS framework may execute a digital drilling plan and ensure plan adherence, while delivering goal-based automation. The DRILLOPS framework may generate activity plans automatically and individual operations, whether they are monitored and / or controlled on the rig or in town. Automation may utilize data analysis and learning systems to assist and optimize tasks, such as, for example, setting ROP to drilling a stand. A preset menu of automatable drilling tasks may be rendered, and, using data analysis and models, a plan may be executed in a manner to achieve a specified goal, where, for example, measurements may be utilized for calibration. The DRILLOPS framework provides flexibility to modify and replan activities dynamically, for example, based on a live appraisal of various factors (e.g., equipment, personnel, and supplies). Well construction activities (e.g., tripping, drilling, cementing) may be continually monitored and dynamically updated using feedback from operational activities. The DRILLOPS framework may provide for various levels of automation based on planning and / or re-planning (e.g., via the DRILLPLAN framework), feedback, and the like.

[0032] The PETREL framework may be part of the DELFI environment for utilization in geosciences and geoengineering, for example, to analyze subsurface data from exploration to production of fluid from a reservoir. The DELFI cognitive exploration and production (E&P) environment (SLB, Houston, Texas), referred to herein as the DELFI environment or DELFI framework, is a secure, cognitive, cloudbased collaborative environment that integrates data and workflows with digital technologies, such as artificial intelligence and machine learning.

[0033] The PETREL framework provides components that allow for optimization of various exploration, development and production operations. The PETREL framework includes seismic to simulation software components that may output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) may develop collaborative workflows and integrate operations to streamline processes (e.g., with respect to one or more geologic environments, etc.). Such a framework may be considered anapplication (e.g., executable using one or more devices) and may be considered a data- driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

[0034] The TECHLOG framework may handle and process field and laboratory data for a variety of geologic environments (e.g., deepwater exploration, shale). The TECHLOG framework may structure wellbore data for analyses, planning, and the like.

[0035] The PETROMOD framework provides petroleum systems modeling capabilities that may combine one or more of seismic, well, and geological information to model the evolution of a sedimentary basin. The PETROMOD framework may predict if, and how, a reservoir has been charged with hydrocarbons, including the source and timing of hydrocarbon generation, migration routes, quantities, and hydrocarbon type in the subsurface or at surface conditions.

[0036] The ECLIPSE framework provides a reservoir simulator (e.g., as a computational framework) with numerical solutions for fast and accurate prediction of dynamic behavior for various types of reservoirs and development schemes.

[0037] The INTERSECT framework provides a high-resolution reservoir simulator for simulation of detailed geological features and quantification of uncertainties, for example, by creating accurate production scenarios and, with the integration of precise models of the surface facilities and field operations, the INTERSECT framework may produce reliable results, which may be continuously updated by real-time data exchanges (e.g., from one or more types of data acquisition equipment in the field that may acquire data during one or more types of field operations). The INTERSECT framework may provide completion configurations for complex wells where such configurations may be built in the field, may provide detailed enhanced-oil-recovery (EOR) formulations where such formulations may be implemented in the field, may analyze application of steam injection and other thermal EOR techniques for implementation in the field, advanced production controls in terms of reservoir coupling and flexible field management, and flexibility to script customized solutions for improved modeling and field management control. The INTERSECT framework, as with the other example frameworks, may be utilized as part of the DELFI environment, for example, for rapid simulation of multiple concurrent cases. Forexample, a workflow may utilize one or more of the DELFI environment on demand reservoir simulation features.

[0038] Additional computational frameworks made be provided. For example, an additional framework may provide an advanced bit engine (e.g., as a computational framework) for simulating drilling data associated with drilling a particular formation using a polycrystalline diamond bit. As another example, an extra framework may provide a drilling simulator (e.g., as a computational framework) for simulating a performance of a bit design with respect to a calibrated formation model.

[0039] The aforementioned DELFI environment provides various features for workflows as to subsurface analysis, planning, construction and production, for example, as illustrated in the workspace framework 110. As shown in FIG. 1, outputs from the workspace framework 110 may be utilized for directing or controlling one or more processes in the geologic environment 150 and, feedback 160 may be received via one or more interfaces in one or more forms (e.g., acquired data as to operational conditions, equipment conditions, environment conditions). As an example, a workflow may progress to a geology and geophysics (G&G) service provider, which may generate a well trajectory, which may involve execution of one or more G&G frameworks (e.g., consider the PETREL framework, etc.).

[0040] In the example of FIG. 1, the visualization features 123 may be implemented via the workspace framework 110, for example, to perform tasks associated with one or more of subsurface regions, planning operations, constructing wells and / or surface fluid networks, and producing from a reservoir.

[0041] As an example, a visualization process may implement one or more of various features that may be suitable for one or more web applications. For example, a template may involve use of the JAVASCRIPT object notation format (JSON) and / or one or more other languages / formats. As an example, a framework may include one or more converters. For example, consider a JSON to PYTHON converter and / or a PYTHON to JSON converter. Such an approach may provide for compatibility of devices and / or frameworks with respect to one or more sets of instructions.

[0042] As an example, visualization features may provide for visualization of various earth models, properties, and the like in one or more dimensions. As an example, visualization features may provide for rendering of information in multiple dimensions, which may optionally include multiple resolution rendering. In such an example, information being rendered may be associated with one or more frameworks and / or one or more data stores. As an example, visualization features may include one or more control features for control of equipment, which may include, for example, field equipment that may perform one or more field operations. As an example, a workflow may utilize one or more frameworks to generate information that may be utilized to control one or more types of field equipment (e.g., drilling equipment, wireline equipment, fracturing equipment).

[0043] While several simulators are illustrated in the example of FIG. 1, one or more other simulators may be utilized, additionally or alternatively. For example, consider the VISAGE geomechanics simulator (SLB, Houston Texas) or the PIPESIM network simulator (SLB, Houston Texas).

[0044] As an example, a workflow may utilize one or more types of data for one or more processes (e.g., stratigraphic modeling, basin modeling, completion designs, drilling, production, injection). As an example, one or more tools may provide data that may be used in a workflow or workflows that may implement one or more frameworks (e.g, PETREL, TECHLOG, PETROMOD, ECLIPSE).

[0045] In the example of FIG. 1, drilling may be performed in the geologic environment 150, for example, to access the reservoir 151, which may be accessed from land or offshore. In FIG. 1, the downhole equipment 154 may be, for example, part of a bottom hole assembly (BHA). The BHA may be used to drill a well. The downhole equipment 154 may communicate information to equipment at the surface and may receive instructions and information from the equipment at the surface. During a well construction process, a variety of operations (such as cementing, wireline evaluation, testing) may be conducted. In such embodiments, data collected by tools and sensors and used for reasons such as reservoir characterization may be collected and transmitted.

[0046] A well may include a substantially horizontal portion (e.g., lateral portion) that may intersect with one or more fractures. For example, a well in a shale formation may pass through natural fractures, artificial fractures (e.g., hydraulic fractures), or a combination thereof. Such a well may be constructed using directional drilling techniques as described herein. However, these same techniques may be used in connection with other types of directional wells (such as slant wells, S-shaped wells, deep inclined wells, and others) and are not limited to horizontal wells.

[0047] FIG. 2 shows an example of a wellsite system 200 (e.g., at a wellsite that may be onshore or offshore). As shown, the wellsite system 200 may include a mud tank 201 for holding mud and other material (e.g., where mud may be a drilling fluid), a suction line 203 that serves as an inlet to a mud pump 204 for pumping mud from the mud tank 201 such that mud flows to a vibrating hose 206, a drawworks 207 for winching drill line or drill lines 212, a standpipe 208 that receives mud from the vibrating hose 206, a kelly hose 209 that receives mud from the standpipe 208, a gooseneck or goosenecks 210, a traveling block 211, a crown block 213 for carrying the traveling block 211 via the drill line or drill lines 212, a derrick 214, a kelly 218 or a top drive 240, a kelly drive bushing 219, a rotary table 220, a drill floor 221, a bell nipple 222, one or more blowout preventers (BOPs) 223, a drill string 225, a drill bit 226, a casing head 227 and a flow pipe 228 that carries mud and other material to, for example, the mud tank 201.

[0048] In the example system of FIG. 2, a borehole 232 is formed in subsurface formations 230 by rotary drilling. The wellsite system may additionally and / or alternatively use directional drilling.

[0049] As shown in the example of FIG. 2, the drill string 225 is suspended within the borehole 232 and has a drill string assembly 250 that includes the drill bit 226 at its lower end. As an example, the drill string assembly 250 may be a bottom hole assembly (BHA).

[0050] The wellsite system 200 may provide for operation of the drill string 225 and other operations. As shown, the wellsite system 200 includes the platform and the derrick 214 positioned over the borehole 232. As mentioned, the wellsite system 200may include the rotary table 220 where the drill string 225 may pass through an opening in the rotary table 220.

[0051] As shown in the example of FIG. 2, the wellsite system 200 may include the kelly 218 and associated components or a top drive 240 and associated components. The kelly 218 may be a square or hexagonal metal / alloy bar with a hole drilled therein that serves as a mud flow path. The kelly 218 may be used to transmit rotary motion from the rotary table 220 via the kelly drive bushing 219 to the drill string 225, while allowing the drill string 225 to be lowered or raised during rotation. The kelly 218 may pass through the kelly drive bushing 219, which may be driven by the rotary table 220. As an example, the rotary table 220 may include a master bushing that operatively couples to the kelly drive bushing 219 such that rotation of the rotary table 220 may turn the kelly drive bushing 219 and hence the kelly 218. The kelly drive bushing 219 may include an inside profile matching an outside profile (e.g., square, hexagonal) of the kelly 218; however, with slightly larger dimensions so that the kelly 218 may freely move up and down inside the kelly drive bushing 219.

[0052] The top drive 240 may provide functions performed by a kelly and a rotary table. The top drive 240 may turn the drill string 225. As an example, the top drive 240 may include one or more motors (e.g., electric and / or hydraulic) connected with appropriate gearing to a short section of pipe called a quill, that in turn may be screwed into a saver sub or the drill string 225 itself. The top drive 240 may be suspended from the traveling block 211, so the rotary mechanism is free to travel up and down the derrick 214. As an example, a top drive 240 may allow for drilling to be performed with more joint stands than a kelly / rotary table approach.

[0053] In the example of FIG. 2, the mud tank 201 may hold mud, which may be one or more types of drilling fluids. As an example, a wellbore may be drilled to produce fluid, inject fluid or both (e.g., hydrocarbons, minerals, water).

[0054] In the example of FIG. 2, the drill string 225 (e.g., including one or more downhole tools) may be composed of a series of pipes coupled together to form a long tube with the drill bit 226 at the lower end thereof. As the drill string 225 is advanced into a wellbore for drilling, at some point in time prior to or coincident with drilling, the mud may be pumped by the pump 204 from the mud tank 201 (e.g., or other source)via the lines (e.g., hoses, standpipes) 206, 208 and 209 to a port of the kelly 218 or, for example, to a port of the top drive 240. The mud may then flow via one or more passages in the drill string 225 and out of ports located on the drill bit 226 (see, e.g., a directional arrow). As the mud exits the drill string 225 via ports in the drill bit 226, it may then circulate upwardly through an annular region between an outer surface(s) of the drill string 225 and surrounding wall(s) (e.g., open borehole, casing), as indicated by directional arrows. In such a manner, the mud lubricates the drill bit 226 and carries heat energy (e.g., frictional or other energy) and formation cuttings to the surface where the mud and / or cuttings may be returned to the mud tank 201, for example, for recirculation (e.g., with processing to remove cuttings).

[0055] The mud pumped by the pump 204 into the drill string 225 may, after exiting the drill string 225, form a mudcake that lines the wellbore which, among other functions, may reduce friction between the drill string 225 and surrounding wall(s) (e.g., borehole, casing). A reduction in friction may facilitate advancing or retracting the drill string 225. During a drilling operation, the entire drill string 225 may be pulled from a wellbore and optionally replaced, for example, with a new or sharpened drill bit, a smaller diameter drill string, etc. As mentioned, the act of pulling a drill string out of a hole or replacing it in a hole is referred to as tripping. A trip may be referred to as an upward trip or an outward trip or as a downward trip or an inward trip depending on trip direction.

[0056] As an example, consider a downward trip where upon arrival of the drill bit 226 of the drill string 225 at a bottom of a wellbore, pumping of the mud commences to lubricate the drill bit 226 for purposes of drilling to enlarge the wellbore. As mentioned, the mud may be pumped by the pump 204 into a passage of the drill string 225 and, upon filling of the passage, the mud may be used as a transmission medium to transmit energy, for example, energy that may encode information as in mud-pulse telemetry.

[0057] As an example, mud-pulse telemetry equipment may include a downhole device configured to effect changes in pressure in the mud to create an acoustic wave or waves upon which information may be modulated. In such an example, information from downhole equipment (e.g., one or more modules of the drill string 225) may betransmitted uphole to an uphole device, which may relay such information to other equipment for processing and control.

[0058] As an example, telemetry equipment may operate via transmission of energy via the drill string 225 itself. For example, consider a signal generator that imparts coded energy signals to the drill string 225 and repeaters that may receive such energy and repeat it to further transmit the coded energy signals.

[0059] As an example, the drill string 225 may be fitted with telemetry equipment 252 that includes a rotatable drive shaft, a turbine impeller mechanically coupled to the drive shaft such that the mud may cause the turbine impeller to rotate, a modulator rotor mechanically coupled to the drive shaft such that rotation of the turbine impeller causes said modulator rotor to rotate, a modulator stator mounted adjacent to or proximate to the modulator rotor such that rotation of the modulator rotor relative to the modulator stator creates pressure pulses in the mud, and a controllable brake for selectively braking rotation of the modulator rotor to modulate pressure pulses. In such example, an alternator may be coupled to the aforementioned drive shaft where the alternator includes at least one stator winding electrically coupled to a control circuit to selectively short the at least one stator winding to electromagnetically brake the alternator and thereby selectively brake rotation of the modulator rotor to modulate the pressure pulses in the mud.

[0060] In the example of FIG. 2, a control / data acquisition system 262 may include circuitry to sense pressure pulses generated by telemetry equipment 252 and, for example, communicate sensed pressure pulses or information derived therefrom for process and control. With this in mind, the control / data acquisition system 262 may include a number of components to perform the various operations described herein. For example, the control / data acquisition system 262 may include a communication component, processing units, a memory, a storage, input / output (IO) ports, a display, and the like. The communication component may be a wireless or wired communication component that facilitates communication between the multi-tenant queuing system, the client systems, and any other suitable electronic device.

[0061] Each of the processing units may include multiple processor devices that may be of any type of computer processor or microprocessor capable of executing computer-executable code. Each processing unit may also include multiple processors that may perform the operations described below.

[0062] The memory and the storage may be any suitable article of manufacture that may serve as media to store processor-executable code, data, or the like. These articles of manufacture may represent computer-readable media (i.e., any suitable form of memory or storage) that may store the processor-executable code used by the processing units to perform the presently disclosed techniques. The memory and the storage may represent non-transitory computer-readable media (e.g., any suitable form of memory or storage) that may store the processor-executable code used by the processing units to perform various techniques described herein. It should be noted that non-transitory merely indicates that the media is tangible and not a signal.

[0063] The IO ports may couple to the drilling design system, one or more input devices, one or more displays, or the like to facilitate human or machine interaction with the control / data acquisition system 262. The display may operate to depict visualizations associated with software or executable code being processed by the processing units. In one embodiment, the display may be a touch display capable of receiving inputs from an operator of the control / data acquisition system 262. The display may be any suitable type of display, such as a liquid crystal display (LCD), plasma display, or an organic light emitting diode (OLED) display, for example. Additionally, in one embodiment, the display may be provided in conjunction with a touch- sensitive mechanism (e.g., a touch screen) that may function as part of a control interface for the control / data acquisition system 262.

[0064] Although the components of the control / data acquisition system 262 are described with respect to the control / data acquisition system 262, it should be noted that any other computing or processing device described herein may also include the same or similar components to perform, or facilitate performing, the various operations described herein. Moreover, it should be understood that the components described above are exemplary figures and the control / data acquisition system 262 and other suitable computing systems may include additional or fewer components as detailed above.

[0065] The assembly 250 of the illustrated example includes a logging-whiledrilling (LWD) module 254 (e.g., a LWD tool), a measuring-while-drilling (MWD) module 256 (e.g., a MWD tool), an optional module 258, a rotary steerable system (RSS), an at-bit steerable system (ABSS), and / or a motor 260, and the drill bit 226. Such components or modules may be referred to as tools where a drill string 225 may include a plurality of tools.

[0066] As an example, an RSS may provide for directional drilling with continuous rotation from the surface, for example, without having to utilize a slide mode (e.g., sliding mode using a mud motor). An RSS may be deployed when drilling directional, horizontal, and / or extended-reach wells. As an example, an RSS may provide for applying a relatively consistent side force (e.g., akin to a stabilizer) that rotates with a drill string 225 or otherwise orients a drill bit in a desired direction while continuously rotating at the same number of rotations per minute as the drill string 225.

[0067] As an example, an ABSS may be a type of RSS. As an example, an ABSS may include a steering sleeve assembly. For example, consider a sleeve assembly that may include one or more features of an ABSS such as the NEOSTEER system (SLB, Houston, Texas). As an example, an ABSS may include an actuating system that may controllably exert pressure against a borehole wall. For example, consider a number of integrated pistons that may provide for enhancing curvature leverage within a cutting structure. In such an example, such leveraging may provide for achieving desirable build rates. An ABSS may provide for meeting curvature requirements in a curve section and directional control in a lateral section. As an example, a steering unit may incorporate metal-to-metal hydraulic seals that may help to minimize erosion and enhance hydraulic design capacity for improved performance. As an example, an ABSS may be configured within a motor-assisted BHA to provide suitable RPM levels accompanied by directional control and reliable steerability.

[0068] As an example, directional drilling may involve the use of a mud motor; however, in various scenarios, a mud motor may present some challenges depending on factors such as rate of penetration (ROP) and transferring weight to a drill bit (e.g., weight on bit, WOB) due to friction. A mud motor may be a positive displacement motor (PDM) that operates to drive a drill bit (e.g., during directional drilling). A PDMoperates as drilling fluid is pumped through it where the PDM converts hydraulic power of the drilling fluid into mechanical power to cause the drill bit to rotate.

[0069] As explained, one or more technologies may be utilized for directional drilling. Directional drilling involves drilling into the Earth to form a deviated bore such that the trajectory of the bore is not vertical; rather, the trajectory deviates from vertical along one or more portions of the bore. As an example, consider a target that is located at a lateral distance from a surface location where a rig may be stationed. In such an example, drilling may commence with a vertical portion and then deviate from vertical such that the bore is aimed at the target and, eventually, reaches the target. Directional drilling may be implemented where a target may be inaccessible from a vertical location at the surface of the Earth, where material exists in the Earth that may impede drilling or otherwise be detrimental (e.g., consider a salt dome), where a formation is laterally extensive (e.g., consider a relatively thin yet laterally extensive reservoir), where multiple bores are to be drilled from a single surface bore, where a relief well is desired, and the like.

[0070] In the example of FIG. 2, the LWD module 254 may be housed in a suitable type of drill collar and may contain one or a plurality of selected types of logging tools. It will also be understood that more than one LWD and / or MWD module may be employed. Where the position of a module is mentioned, as an example, it may refer to a module at the position of the LWD module 254, the MWD module 256, etc. An LWD module may include capabilities for measuring, processing, and storing information, as well as for communicating with the surface equipment. In the illustrated example, the LWD module 254 may include a seismic measuring device.

[0071] In the example of FIG. 2, the MWD module 256 may be housed in a suitable type of drill collar and may contain one or more devices for measuring characteristics of the drill string 225 and the drill bit 226. As an example, the MWD module 256 may include equipment for generating electrical power, for example, to power various components of the drill string 225. As an example, the MWD module 256 may include the telemetry equipment 252, for example, where the turbine impeller may generate power by flow of the mud; it being understood that other power and / or battery systems may be employed for purposes of powering various components. As an example, the MWD module 256 may include one or more of the following types of measuringdevices: a weight-on-bit measuring device, a torque measuring device, a vibration measuring device, a shock measuring device, a stick slip measuring device, a direction measuring device, and an inclination measuring device.

[0072] FIG. 2 also shows some examples of types of holes that may be drilled. For example, consider a slant hole 272, an S-shaped hole 274, a deep inclined hole 276, and a horizontal hole 278. As an example, a directional well may include several shapes where each of the shapes may aim to meet particular operational demands. As an example, a drilling process may be performed on the basis of information as and when it is relayed to a drilling engineer. As an example, inclination and / or direction may be modified based on information received during a drilling process. As an example, deviation of a bore may be accomplished in part by use of one or more of an RSS, a downhole motor and / or a turbine. As to a motor, for example, a drill string 225 may include a positive displacement motor (PDM).

[0073] As an example, a system may be a steerable system and include equipment to perform a method such as geosteering. As an example, a steerable system may include a PDM or a turbine on a lower part of a drill string 225 which, just above a drill bit, a bent sub may be mounted. As an example, above a PDM, MWD equipment that provides real time or near real time data of interest (e.g., inclination, direction, pressure, temperature, real weight on the drill bit, torque stress) and / or LWD equipment may be installed. As to the latter, LWD equipment may make it possible to send to the surface various types of data of interest, including for example, geological data (e.g., gamma ray log, resistivity, density and sonic logs).

[0074] The coupling of sensors providing information on the course of a well trajectory, in real time or near real time, with, for example, one or more logs characterizing the formations from a geological viewpoint, may allow for implementing a geosteering method. Such a method may include navigating a subsurface environment, for example, to follow a desired route to reach a desired target or targets.

[0075] As an example, a drill string 225 may include an azimuthal density neutron (ADN) tool for measuring density and porosity; a MWD tool for measuring inclination, azimuth and shocks; a compensated dual resistivity (CDR) tool for measuring resistivity and gamma ray related phenomena; one or more variable gauge stabilizers; one or morebend joints; and a geosteering tool, which may include a motor and optionally equipment for measuring and / or responding to one or more of inclination, resistivity and gamma ray related phenomena.

[0076] As an example, geosteering may include intentional directional control of a wellbore based on results of downhole geological logging measurements in a manner that aims to keep a directional wellbore within a desired region or zone (e.g., a pay zone). As an example, geosteering may include directing a wellbore to keep the wellbore in a particular section of a reservoir, for example, to minimize gas and / or water breakthrough and, for example, to maximize economic production from a well that includes the wellbore.

[0077] Referring again to FIG. 2, the wellsite system 200 may include one or more sensors 264 that are operatively coupled to the control / data acquisition system 262. As an example, a sensor or sensors may be at surface locations. As an example, a sensor or sensors may be at downhole locations. As an example, a sensor or sensors may be at one or more remote locations that are not within a distance of the order of about one hundred meters from the wellsite system 200. As an example, a sensor or sensor may be at an offset wellsite where the wellsite system 200 and the offset wellsite are in a common field (e.g., oil and / or gas field). As another example, one or more of the sensors 264 may be provided for tracking pipe, tracking movement of at least a portion of a drill string 225, and the like.

[0078] As an example, the system 200 may include one or more sensors 266 that may sense and / or transmit signals to a fluid conduit such as a drilling fluid conduit (e.g., a drilling mud conduit). For example, in the system 200, the one or more sensors 266 may be operatively coupled to portions of the standpipe 208 through which mud flows. As an example, a downhole tool may generate pulses that may travel through the mud and be sensed by one or more of the one or more sensors 266. In such an example, the downhole tool may include associated circuitry such as, for example, encoding circuitry that may encode signals, for example, to reduce demands as to transmission. As an example, circuitry at the surface may include decoding circuitry to decode encoded information transmitted at least in part via mud-pulse telemetry. As an example, circuitry at the surface may include encoder circuitry and / or decoder circuitry and circuitry downhole may include encoder circuitry and / or decoder circuitry. As anexample, the system 200 may include a transmitter that may generate signals that may be transmitted downhole via mud (e.g., drilling fluid) as a transmission medium.

[0079] Various types of data associated with field operations may be 1-D series data. For example, consider data as to one or more of a drilling system, downhole states, formation attributes, and surface mechanics being measured as single or multi-channel time series data.

[0080] With the foregoing in mind, the control / data acquisition system 262 develops and utilizes a surrogate model to estimate a production flow rate of hydrocarbons from a horizontal well using MHF. For example, FIG. 3 is an example 300 of an approach for estimating the flow rate of hydrocarbons from a horizontal well 302 with MHF. The basic concept of this approach, as illustrated, is that each fracture of the horizontal well 302 may be represented by a vertical well 304 with one hydraulic fracture 306. For example, the production of the horizontal well 302 may be represented by the following equation:QH=Qv ’ f (1)

[0081] In equation (1), QH is the flow rate of the horizontal well 302, Qvis the flow rate of the vertical well 304 with one hydraulic fracture 306, and Nf is the number of fractures of the horizontal well 302. The flow rate of the vertical well 304 can be estimated using a history-matched simulation model, a predictive model, or field data. However, equation (1) is only applicable if the fractures of the horizontal well 302 are a sufficient distance apart such that the interference effect does not apply.

[0082] As such, the surrogate model is developed to account for the interference effect. FIG. 4 is a flow chart of a workflow 400 for developing a surrogate model to estimate a production flow rate for a horizontal well with MHF and using the surrogate model in a drilling operation. The workflow 400 may be implemented via the control / data acquisition system 262 in the present embodiment. However, the workflow 400 may be implemented on any suitable computing system and / or processor. Although the workflow 400 is described in a particular order, it should be understood that the workflow 400 may be implemented in any suitable order.

[0083] At block 402, the control / data acquisition system 262 may define model constraints related to extraction of hydrocarbons from a subsurface formation. For example, the set of constraints may include reservoir characteristics 404, geologic characteristics 406, boundary flow characteristics 408, and the like. The reservoir characteristics 404 may include permeability, thickness, an American Petroleum Institute (API) gravity of the hydrocarbons, and the like. The geologic characteristics 406 may include mineralogy, discretized structural stratification, fracture network presence, and the like of the subsurface formation. The boundary flow characteristics 408 include characteristics of the flow of fluid at the boundary of the reservoir, such as a transient flow condition, a middle time flow condition, a late time flow condition (e.g., a pseudo-steady state flow condition), and the like. In some embodiments, the control / data acquisition system 262 receives the model constraints (e.g., from a user device).

[0084] At block 410, the control / data acquisition system 262 may run one or more simulations based on the constraints. The simulation may be a numerical simulation configured to create hydraulic transverse fractures. In some embodiments, the simulation may use a simple model of a horizontal well to simulate the production of the horizontal well. In some embodiments, the simulation may include any of the frameworks discussed with reference to FIG. 1. As illustrated, the control / data acquisition system 262 runs a series of simulations (e.g., eighty to one hundred simulations; however, it should be appreciated that the control / data acquisition system 262 may run more or fewer simulations). Additionally, the control / data acquisition system 262 may run simulations with varying constraints. For example, the control / data acquisition system 262 may run multiple simulations with varying well characteristics (e.g., fracture characteristics, permeability, minerology, flow conditions, etc.). In some embodiments, the control / data acquisition system 262 may identify combinations of constraints for the simulation that minimize the number of simulations that are run. The results of the simulations (e.g., the simulated model) may be used to characterize the interference effect.

[0085] FIGS. 5-10 are examples of inputs and results of the workflow 300 deriving from the inputs. As instance, FIG. 5 is an example model 500 of horizontal well 502.Each grid 504 represents a fracture along the horizontal well 502. Table 1 includes the basic properties associated with the example model 502.Table 1. Basic properties Associated with Example Model 502

[0086] In one test case, the control / data acquisition system 262 ran the simulation for the example model 500 over 200 times. During these runs, the control / data acquisition system 262 varied the reservoir permeability between 0.1-15 millidarcy (md), the reservoir pressure between 150-350 bar, and the distance between fractures between 20-600 meters.

[0087] Returning to FIG. 4, at block 412, the control / data acquisition system 262 may analyze the results of the simulations and structure the dataset based on the analysis. For example, the control / data acquisition system 262 may identify a scaling factor 414 and an interference boundary 416 based on the simulations. The interference boundary 416 is a distance between fractures at which the interference effect becomes negligible (e.g., where the production rate of an individual fracture is within five percent of the production rate of a vertical well with a hydraulic fracture). The interference effect refers to a change (e.g., decrease) in the production of a second fracture due to interactions between a first fracture and the second fracture. Thus, fractures that are closer together are more likely to interact. Certain conditions of the subsurface formation, such as permeability, may affect the interference boundary 416. Low interference occurs at distances that are at or higher than interference boundary 416. Generally, the production flow rate of a fracture with low interference only slightly deviates from a linear relationship (e.g., deviates less than 10%, 5%, 1%, or any other suitable percentage from linear) for specific geological conditions. High interferenceoccurs at distances lower than the interference boundary 416. The production flow rate of a fracture with high interference may deviate substantially from a linear relationship, as described further below.

[0088] The control / data acquisition system 262 may identify the interference boundary based on the results of the simulation and split the dataset into a high interference dataset and a low interference dataset. For example, FIG. 6 is a plot 600 of the production flow rate per fracture based on the distance between fractures for reservoirs with varying permeability of 0.1-15 md. The interference effect, as discussed above, changes the rate of production of each fracture of a well. Therefore, distances between fractures resulting in low interference have negligible effects on the production flow rate and therefore include measurements that are substantially linear. As seen in FIG. 6, the measurements become substantially linear around 600 meters between fractures. As such, the control / data acquisition system 262 may identify 600 meters as the interference boundary 602. As such, the control / data acquisition system 262 may identify the high interference area 604, where the interference effect substantially affects the production flow rate, as including distances between fractures that are less than 600 meters. The control / data acquisition system 262 may identify the low interference area 606, where interference effect has little or negligible effects on the production flow rate, as including distances between fractures at or that are more than 600 meters. The data in the high interference area 604 may be categorized as the high interference dataset, and the data in the low interference area 606 may be categorized as the low interference dataset.

[0089] Returning to FIG. 4, the scaling factor 414 is a ratio of a production flow rate of a vertical well with one fracture (e.g., a vertical well with hydraulic fracturing) to a production flow rate of an individual fracture of the horizontal well, as described in the following equation.

[0090] In equation (2), SF is the scaling factor, Qvis the production flow rate of the vertical well with hydraulic fracturing in meters cubed per day (m3 / day) and QHI is the production flow rate of the individual fracture with high interference in meters cubed per day (m3 / day). The production flow rate of the individual fracture may be obtainedby dividing the total production flow rate of the horizontal well with MHF by the numbers of fractures in a well. The scaling factor 414 is indicative of the interference effect. As such, as the distance between fractures approaches the interference boundary, the scaling factor 414 should approach one.

[0091] For high interference horizontal wells, the scaling factor may be dependent on the reservoir permeability. For example, FIG. 7 is a plot 700 of the scaling factor of an individual fracture based on the distance between fractures for reservoirs with varying permeability of 0.1-15 md. As shown, reservoirs with higher permeability have greater scaling factors compared to reservoirs with lower permeability when the distance between fractures are lower. This is due, at least in part, to fractures in reservoirs with higher permeability being more likely to interact, thereby reducing the production flow rate of each fracture. As such, a scaling factor for a fracture in a higher permeability reservoir is likely to be higher than a scaling factor for a fracture in a lower permeability reservoir. However, as illustrated, the difference in scaling factor due to reservoir permeability decreases as the distance between fractures increases.

[0092] Returning to FIG. 4 with the foregoing in mind, at block 418, the control / data acquisition system 262 may generate and run a predictive model based on the plurality of constraints. In some embodiments, the control / data acquisition system 262 may develop a high interference predictive model for the high interference dataset and a low interference predictive model for the low interference dataset. The predictive models may be a regression model 420 (e.g., multivariate regression model, a machine learning based regression model) a classification algorithm 422, an unsupervised learning type clustering algorithm 424, and the like. The control / data acquisition system 262 may determine a type of predictive model to utilize based on the constraints to be globalized via the surrogate model (e.g., the constraints that may be inputs to the surrogate model). In some embodiments, the control / data acquisition system 262 may update, calibrate, and / or reinforce the predictive models based on real-field production data 426.

[0093] For low interference fractures, a production flow rate of the horizontal well increases linearly with an increasing number of fractures in accordance with equation (1). Therefore, the control / data acquisition system 262 may generate a low interference predictive model that describes the linear relationship between the production flow rateof the horizontal well and the number of fractures. For example, the control / data acquisition system 262 may develop the following equation to describe the relationship.7 = (A - 1) ■ (P ■ yi + kh ■ y2+ N ■ y3+ y4) + 1 (3)

[0094] In equation (3), QH is a production flow rate at the specific number of fractures (m3 / day), Qvis a production flow rate of a vertical well with hydraulic fracturing (m3 / day), N is the number of fractures, P is the reservoir pressure (bar), k is the reservoir permeability (md), h is the reservoir thickness (m), and y, , yi, and v are constants.

[0095] As an example, FIG. 8 includes a first plot 800 of a ratio of production flow rate of the horizontal well to production flow rate of a vertical well per number of fractures for a low interference well. The first plot 800 includes the measurements 802 generated by the simulated model with a distance of 600 meters between fractures and lines 804 generated by the predictive model. The first plot 800 may include a first line 806 representing the ratio for a formation with a permeability of 15 md and a second line 808 representing the ratio for a formation with a permeability of less than 0.1 md. The control / data acquisition system 262 may develop an equation to describe the relationship between the ratio and the number of fractures based on the simulated model. In some embodiments, the control / data acquisition system 262 may develop an equation to describe the relationship between the ratio, the number of fractures, and the permeability. For example, based on the first plot 800, the control / data acquisition system 262 developed equation (3) and defined the constants as y / = 0.000036, y2 = 0.00058, y^ = 0.000249, andyv = 1.0034.

[0096] FIG. 8 also includes a second plot 810 comparing the ratio of production flow rate of the horizontal well to production flow rate of a vertical well determined via a simulated model with the ratio determined via a predictive model for the low interference horizontal well. In particular, the second plot 810 includes data points corresponding to the ratio determined via the predictive model by the ratio determined via the simulated model. If the predictive model substantially matches the simulated model, the data points should be substantially linear with a high R2value. The second plot 810 shows that the predictive model (e.g., equation (3)) substantially matches the results of the simulation as the data points have an R2value of 0.9993. Therefore, thepredictive model for low interference horizontal wells developed by the control / data acquisition system 262 may estimate the production flow rate of a horizontal well with similar accuracy to running multiple simulations while being time and resource efficient.

[0097] Returning to block 418 of FIG. 4, the control / data acquisition system 262 may also develop the high interference predictive model for the high interference dataset. For high interference fractures, a production flow rate of the horizontal well is characterized by a logarithmic function. Therefore, the control / data acquisition system 262 may determine an equation describing the interference effect to account for the logarithmic relationship between the production flow rate of the horizontal well and the distance between fractures for the high interference area. Because scaling factor is indicative of the interference effect between a fracture in the horizontal well and the vertical well with hydraulic fracturing and the relationship between the production flow rate of the horizontal well and the distance between fractures is logarithmic, the control / data acquisition system 262 may determine a definition of a natural log of the scaling factor. For example, the control / data acquisition system 262 may develop the following equation to describe the natural log of the scaling factor.3(ln(d))2+ x4ln(d) In (fc) + x5(ln(fc))2+ x6(4)

[0098] In equation (4), SF is the scaling factor, dis a distance between fractures (m), k is the reservoir permeability (md), and xy, X2, X3, X4, xs, and X6 are constants. The constants may be set such that the scaling factor reaches one as the distance approaches the interference boundary.

[0099] As an example, FIG. 9 includes a first plot 900 of a ratio of production flow rate per fracture per distance between fractures for varying reservoir permeabilities for a high interference horizontal well. The first plot 900 includes the measurements 902 generated by the simulated model illustrating the logarithmic relationship between production flow rate and distance between fractures. As illustrated by the first plot 900, the logarithmic relationship is less pronounced for horizontal wells with lower permeability. The control / data acquisition system 262 may develop an equation to describe the relationship between the scaling factor and the distance between fractures based on the results of the simulation. In some embodiments, the control / dataacquisition system 262 may develop an equation to describe the relationship between the scaling factor, the distance between fractures, and the permeability. For example, based on the first plot 900, the control / data acquisition system 262 developed equation (4) and defined the constants as xi = -1.793, X2 = 0.396, X3 = 0.126, X4 = -0.054, xs = - 0.016, and X6 = 6.28. As such, if the distance between fractures is greater than or equal to the interference boundary of 600 meters, the scaling factor equals one.

[0100] FIG. 9 also includes a second plot 904 comparing a scaling factor determined via the simulated model with a scaling factor determined via the predictive model for a high interference horizontal well. In particular, the second plot 904 includes data points corresponding to the scaling factor determined via the predictive model by the scaling factor determined via the simulated model. If the scaling factor of the predictive model substantially matches the scaling factor of the simulated model, the data points should be substantially linear with a high R2value. The second plot 904 shows that the scaling factor of the predictive model (e.g., equation (4)) substantially matches the scaling factor of the simulated model as the data points have an R2value of 0.9917. Therefore, the predictive model for high interference horizontal wells developed by the control / data acquisition system 262 may determine the interference effect with similar accuracy to running multiple simulations while being time and resource efficient.

[0101] Returning to FIG. 4, the control / data acquisition system 262 may determine whether the predictive models have acceptable accuracy in decision block 428. That is, the control / data acquisition system 262 may determine whether the predictive models is within a similarity threshold to the simulated model as done in the second plot 810 of FIG. 8 and the second plot 904 of FIG. 9. If the accuracy of the predictive model is not acceptable (e.g., the results of the predictive model are below the similarity threshold), the control / data acquisition system 262 conducts additional simulation runs at block 410.

[0102] If the accuracy of the predictive model is acceptable (e.g., the results of the predictive model are below the similarity threshold), the control / data acquisition system 262 defines a global surrogate model for the constraint definition at block 430. The surrogate model is a model that can be used to estimate the production flow rate of the horizontal well for both low interference horizontal wells and high interference horizontal wells. For example, the control / data acquisition system 262 may combinethe equation for estimating the production flow rate for a low interference horizontal well (e.g., equation (3)) with the equation for estimating the interference effect (e.g., equation (4)), resulting in the following equation.

[0103] In equation (5), QH is a production flow rate at the specific number of fractures (m3 / day), Qv is a production flow rate of a vertical well with hydraulic fracturing (m3 / day), TV is the number of fractures, P is the reservoir pressure (bar), k is the reservoir permeability (md), h is the reservoir thickness (m), SF is the scaling factor, and yj, y 2, ys, andjv are constants.

[0104] The surrogate model may be used to determine well characteristics that result in the highest production flow rate for the horizontal well. For example, the surrogate model may be run multiple times for varying well characteristics to determine a value of the well characteristic that results in the highest production flow rate. The well characteristics may include fracture characteristics, reservoir characteristics (e.g., permeability, thickness, porosity, etc.), geologic characteristics (e.g., minerology, stratification, etc.), flow conditions, and the like. As an example, equation (5) may be used for varying the number of fractures and the distance between fractures. The surrogate model, once developed, may be used to determine where to drill a horizontal well, the size and shape of the horizontal well, a number of fractures to drill, and the like.

[0105] FIG. 10 is a plot 1000 comparing an estimated production flow rate of the simulated model with an estimated production flow rate of the surrogate model. In particular, the plot 1000 includes data points corresponding to the production flow rate determined via the predictive model by the production flow rate determined via the simulated model. If the production flow rate of the predictive model substantially matches the production flow of the simulated model, the data points should be substantially linear with a high R2value. The plot 1000 shows that the production flow rate of the predictive model (e.g., equation (5)) substantially matches the production flow rate of the simulated model as the data points have an R2value of 0.9995. Therefore, the predictive model for horizontal wells developed by the control / data acquisition system 262 may determine the production flow rate across high and lowinterference areas with similar accuracy to running multiple simulations while being time and resource efficient.

[0106] Returning to FIG. 4, at block 432, the control / data acquisition system 262 controls one or more drilling tools based on the surrogate model. In particular, the control / data acquisition system 262 may control the one or more drilling tools according to the well characteristics the surrogate model determined result in the highest production flow rate. For example, the control / data acquisition system 262 may drill a horizontal well according to a size and shape (e.g., a length of well, depth of well, geometry of the horizontal angle, etc.) determined via the surrogate model. As another example, the control / data acquisition system 262 may hydraulically fracture the horizontal well according to a number of fractures and / or a distance between fractures determined via the surrogate model. As a further example, the control / data acquisition system 262 may control the rate of penetration of the drill bit according to the surrogate model.

[0107] Technical effects of the disclosed embodiments include a system and method for developing and utilizing a surrogate model to estimate production flow rates for a hydraulically fractured horizontal well. A processing system then runs multiple simulations with varying well characteristics for a set of constraints related to the extraction of hydrocarbons from a subsurface formation to estimate the production flow rate of the horizontal well. The processing system analyzes the results of the simulations to determine an interference boundary where an interference effect between fractures is negligible. The processing system splits the results of the simulations into a high interference dataset associated with distances between the fractures less than the interference boundary and a low interference dataset associated with the interference boundary and distances between the fractures greater than the interference boundary. This dataset split allows the processing system to determine the relationship between the varied well characteristics and the production flow rate via analysis of the low interference dataset. Therefore, the processing system generates a low interference predictive model that estimates a production flow rate without taking the interference effect into account based on the analysis. The dataset split also allows the processing system to determine the effect of the interference effect on the estimated productionflow rate. Therefore, the processing system generates a high interference predictive model indicative of the interference effect.

[0108] The processing system generates a surrogate model based on the high interference predictive model and the low interference predictive model. As such, the surrogate model may be used to estimate the production flow rates across high and low interference horizontal wells with similar accuracy to running multiple simulations. Therefore, for future operations, the processing system does not need to run the time and resource intensive simulations, thereby reducing the number of simulations run overall and conserving time and resources. The predictive models, and therefore the surrogate model, may also be updated based on field production data to improve and / or tailor the surrogate model for estimating the production flow rate for the horizontal well. Furthermore, the processing system may control one or more drilling tools based on the surrogate model, thereby ensuring horizontal wells drilled have high production flow rates and reducing the time and resources consumed when drilling a less productive well.

[0109] The subject matter described in detail above may be defined by one or more clauses as set forth below.

[0110] According to a first aspect, a method includes receiving a plurality of constraints related to extraction of hydrocarbons from a subsurface formation, running one or more simulations based on the plurality of constraints, determining an interference boundary based on the one or more simulations, wherein the interference boundary includes a distance between fractures where the production rate of an individual fracture is within five percent of the production rate of a vertical well with a hydraulic fracture, organizing results of the one or more simulations into a first dataset comprising data associated with distances between the fractures less than the interference boundary and a second dataset comprising data associate with the interference boundary and distances between the fractures greater than the interference boundary, generating a first predictive model based on the plurality of constraints and the first dataset, generating a second predictive model based on the plurality of constraints and the second dataset, generating a surrogate model based on the first predictive model and the second predictive model, and controlling one or more drilling tools based on the surrogate model.

[0111] The method of the preceding clause, including determining results of the first predictive model are not within a similarity threshold of the results of the one or more simulations and running one or more additional simulations.

[0112] The method of any preceding clause, wherein the plurality of constraints comprise permeability of reservoir, porosity of a reservoir, fluid American Petroleum Institute (API) gravity, dominant minerology of a formation, discretized structural stratification inputs, fracture network presence in the formation, boundary flow conditions, and the like.

[0113] The method of any preceding clause, including determining a scaling factor based on the one or more simulations, wherein the scaling factor is indicative of an interference effect caused by interactions between fractures and wherein generating the first predictive model is based on the plurality of constraints, the first dataset, and the scaling factor.

[0114] The method of any preceding clause, including selecting a type of predicative model based on the plurality of constraints.

[0115] The method of any preceding clause, wherein the first predictive model is generated via a regression model, a classification model, or a clustering model.

[0116] The method of any preceding clause, wherein the first predictive model is generated via machine learning.

[0117] The method of any preceding clause, wherein controlling the one or more drilling tools based on the surrogate model includes determining a number of fractures to drill in a well via the surrogate model and controlling the one or more drilling tools to drill the number of fractures determined via the surrogate model.

[0118] The method of any preceding clause, including receiving field production data associated with a well and updating the first predictive model based the field production data.

[0119] According to a second aspect, a non-transitory computer-readable medium includes computer-executable instructions that, when executed, cause a processing system to perform operations including receive a plurality of constraints related toextraction of hydrocarbons from a subsurface formation, run one or more simulations based on the plurality of constraints, determine an interference boundary based on the one or more simulations, wherein the interference boundary includes a distance between fracture where the interactions between the fractures are below a threshold value, organize results of the one or more simulations into a first dataset associated with distances between the fractures less than the interference boundary and a second dataset associated with the interference boundary and distances between the fractures greater than the interference boundary, generate a first predictive model based on the plurality of constraints and the first dataset, generate a second predictive model based on the plurality of constraints and the second dataset, generate a surrogate model based on the first predictive model and the second predictive model, and control one or more drilling tools based on the surrogate model.

[0120] The non-transitory computer-readable medium of the preceding clause, wherein the computer-executable instructions that, when executed, cause a processing system to perform operations including determine results of the first predictive model are not within a similarity threshold of the results of the one or more simulations and run one or more additional simulations.

[0121] The non-transitory computer-readable medium of any preceding clause, wherein the computer-executable instructions that, when executed, cause a processing system to perform operations including determine a scaling factor based on the one or more simulations, wherein the scaling factor is indicative of an interference effect caused by interactions between the fractures and wherein generating the first predictive model is based on the plurality of constraints, the first dataset, and the scaling factor.

[0122] The non-transitory computer-readable medium of any preceding clause, wherein the plurality of constraints comprises permeability of reservoir, porosity of a reservoir, fluid American Petroleum Institute (API) gravity, dominant minerology of a formation, discretized structural stratification inputs, fracture network presence in the formation, boundary flow conditions, and the like.

[0123] The non-transitory computer-readable medium of any preceding clause, wherein the first predictive model is generated via machine learning.

[0124] The non-transitory computer-readable medium of any preceding clause, wherein controlling the one or more drilling tools based on the surrogate model includes determine a number of fractures to drill in a well via the surrogate model and control the one or more drilling tools to drill the number of fractures in the well determined via the surrogate model.

[0125] The non-transitory computer-readable medium of any preceding clause, wherein the computer-executable instructions that, when executed, cause a processing system to perform operations including receive field production data associated with a well and update the first predictive model based on the field production data.

[0126] According to a third aspect, a system includes a storage component including a plurality of constraints related to extraction of hydrocarbons from a subsurface formation and a processing system. The processing system is configured to receive the plurality of constraints, run one or more simulations based on the plurality of constraints, determine an interference boundary based on the one or more simulations, wherein the interference boundary includes a distance between fracture where the interactions between the fractures are below a threshold value, organize results of the one or more simulations into a first dataset associated with distances between the fractures less than the interference boundary and a second dataset associated with the interference boundary and distances between the fractures greater than the interference boundary, generate a first predictive model based on the plurality of constraints and the first dataset, generate a second predictive model based on the plurality of constraints and the second dataset, generate a surrogate model based on the first predictive model and the second predictive model, and control one or more drilling tools based on the surrogate model.

[0127] The system of the preceding clause, wherein the processing system is configured to determine results of the first predictive model are not within a similarity threshold of the results of the one or more simulations and run one or more additional simulations.

[0128] The system of any preceding clause, wherein the processing system is configured to determine a scaling factor based on the one or more simulations, wherein the scaling factor is indicative of an interference effect caused by interactions betweenfractures and wherein generating the first predictive model is based on the plurality of constraints, the first dataset, and the scaling factor.

[0129] The system of any preceding clause, wherein the processing system is configured to receive field production data associated with a well and update the first predictive model based on the field production data.

[0130] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods described herein are illustrated and described may be re-arranged, and / or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principals of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosure and various embodiments with various modifications as are suited to the particular use contemplated.

[0131] Finally, the techniques presented and claimed herein are referenced and applied to material objects and concrete examples of a practical nature that demonstrably improve the present technical field and, as such, are not abstract, intangible or purely theoretical. Further, if any claims appended to the end of this specification contain one or more elements designated as “means for [perform]ing [a function] ...” or “step for [perform]ing [a function] ... ”, it is intended that such elements are to be interpreted under 35 U.S.C. 112(f). However, for any claims containing elements designated in any other manner, it is intended that such elements are not to be interpreted under 35 U.S.C. 112(f).

Claims

CLAIMS1. A method, comprising: receiving a plurality of constraints related to extraction of hydrocarbons from a subsurface formation; running one or more simulations based on the plurality of constraints; determining an interference boundary based on the one or more simulations, wherein the interference boundary comprises a distance between fractures where the interactions between the fractures are below a threshold value; organizing results of the one or more simulations into a first dataset associated with distances between the fractures less than the interference boundary and a second dataset associated with the interference boundary and distances between the fractures greater than the interference boundary; generating a first predictive model based on the plurality of constraints and the first dataset; generating a second predictive model based on the plurality of constraints and the second dataset; generating a surrogate model based on the first predictive model and the second predictive model; and controlling one or more drilling tools based on the surrogate model.

2. The method of claim 1, comprising: determining results of the first predictive model are not within a similarity threshold of the results of the one or more simulations; and running one or more additional simulations.

3. The method of claim 1, wherein the plurality of constraints comprise permeability of reservoir, porosity of a reservoir, fluid American Petroleum Institute (API) gravity, dominant minerology of the subsurface formation, discretized structural stratification inputs, fracture network presence in the formation, boundary flow conditions, and the like.

4. The method of claim 1, comprising determining a scaling factor based on the one or more simulations, wherein the scaling factor is indicative of an interferenceeffect caused by interactions between fractures and wherein generating the first predictive model is based on the plurality of constraints, the first dataset, and the scaling factor.

5. The method of claim 1, comprising selecting a type of predicative model based on the plurality of constraints.

6. The method of claim 1, wherein the first predictive model is generated via a regression model, a classification model, or a clustering model.

7. The method of claim 1, wherein the first predictive model is generated via machine learning.

8. The method of claim 1, wherein controlling the one or more drilling tools based on the surrogate model comprises: determining a number of fractures to drill in a well via the surrogate model; and controlling the one or more drilling tools to drill the number of fractures determined via the surrogate model.

9. The method of claim 1, comprising: receiving field production data associated with a well; and updating the first predictive model based the field production data.

10. A non-transitory computer-readable medium comprising computer-executable instructions that, when executed, cause a processing system to perform operations comprising: receive a plurality of constraints related to extraction of hydrocarbons from a subsurface formation; run one or more simulations based on the plurality of constraints; determine an interference boundary based on the one or more simulations, wherein the interference boundary comprises a distance between fracture where the interactions between the fractures are below a threshold value;organize results of the one or more simulations into a first dataset associated with distances between the fractures less than the interference boundary and a second dataset associated with the interference boundary and distances between the fractures greater than the interference boundary; generate a first predictive model based on the plurality of constraints and the first dataset; generate a second predictive model based on the plurality of constraints and the second dataset; generate a surrogate model based on the first predictive model and the second predictive model; and control one or more drilling tools based on the surrogate model.

11. The non-transitory computer-readable medium of claim 10, wherein the computer-executable instructions that, when executed, cause a processing system to perform operations comprising: determine results of the first predictive model are not within a similarity threshold of the results of the one or more simulations; and run one or more additional simulations.

12. The non-transitory computer-readable medium of claim 10, wherein the computer-executable instructions that, when executed, cause a processing system to perform operations comprising determine a scaling factor based on the one or more simulations, wherein the scaling factor is indicative of an interference effect caused by interactions between the fractures and wherein generating the first predictive model is based on the plurality of constraints, the first dataset, and the scaling factor.

13. The non-transitory computer-readable medium of claim 10, wherein the plurality of constraints comprises permeability of reservoir, porosity of a reservoir, fluid American Petroleum Institute (API) gravity, dominant minerology of a formation, discretized structural stratification inputs, fracture network presence in the formation, boundary flow conditions, and the like.

14. The non-transitory computer-readable medium of claim 10, wherein the first predictive model is generated via machine learning.

15. The non-transitory computer-readable medium of claim 10, wherein controlling the one or more drilling tools based on the surrogate model comprises: determine a number of fractures to drill in a well via the surrogate model; and control the one or more drilling tools to drill the number of fractures in the well determined via the surrogate model.

16. The non-transitory computer-readable medium of claim 10, wherein the computer-executable instructions that, when executed, cause a processing system to perform operations comprising: receive field production data associated with a well; and update the first predictive model based on the field production data.

17. A system, comprising: a storage component comprising a plurality of constraints related to extraction of hydrocarbons from a subsurface formation; and a processing system configured to: receive the plurality of constraints; run one or more simulations based on the plurality of constraints; determine an interference boundary based on the one or more simulations, wherein the interference boundary comprises a distance between fracture where the interactions between the fractures are below a threshold value; organize results of the one or more simulations into a first dataset associated with distances between the fractures less than the interference boundary and a second dataset associated with the interference boundary and distances between the fractures greater than the interference boundary; generate a first predictive model based on the plurality of constraints and the first dataset; generate a second predictive model based on the plurality of constraints and the low interference dataset;generate a surrogate model based on the first predictive model and the second predictive model; and control one or more drilling tools based on the surrogate model.

18. The system of claim 17, wherein the processing system is configured to: determine results of the first predictive model are not within a similarity threshold of the results of the one or more simulations; and run one or more additional simulations.

19. The system of claim 17, wherein the processing system is configured to determine a scaling factor based on the one or more simulations, wherein the scaling factor is indicative of an interference effect caused by interactions between fractures and wherein generating the first predictive model is based on the plurality of constraints, the first dataset, and the scaling factor.

20. The system of claim 15, wherein the processing system is configured to: receive field production data associated with a well; and update the first predictive model based on the field production data.

Images