Patent Application
20250129713
The present technology pertains to a well system for extracting materials, and more particularly, to a real-time feedback and machine learning system for downhole environments.
A well system comprises a well-drilling system to form the well and a well-pumping system to retrieve materials from the well. A well-drilling system is a setup of equipment and machinery designed to extract natural resources, such as water, oil, or gas, from the ground. The system typically includes a drilling rig, which is used to bore a hole into the earth's crust, and a casing, which is a steel pipe that lines the well and prevents the walls from collapsing. The drilling process begins with the placement of a drill bit at the end of a drill string. The drill bit is then rotated, using a motor or a manual mechanism, to create a hole in the ground. As the hole is drilled, the drill string is gradually lengthened by adding more sections of pipe. The process continues until the desired depth is reached.
Once the drilling is complete, a casing is installed into the well to protect it from collapse and prevent contamination of the extracted resources. The casing is typically cemented into place to seal off any potential pathways for groundwater to enter the well. Once the well is prepared, a well-pumping system is installed to extract the resources from the well. The type of pump used depends on the type of resource being extracted, as well as the depth and diameter of the well. For example, a submersible pump, a sub pump, or a reciprocating pump may be used for an oil well.
In order to describe the manner in which the various advantages and features of the disclosure may be obtained, a more particular description of the principles described herein will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only example embodiments of the disclosure and are not to be considered to limit its scope, the principles herein are described and explained with additional specificity and detail through the use of the drawings in which:
Certain aspects of this disclosure are provided below. Some of these aspects may be applied independently and some of them may be applied in combination as would be apparent to those of skill in the art. In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of aspects of the application. However, it will be apparent that various aspects may be practiced without these specific details. The figures and descriptions are not intended to be restrictive.
The ensuing description provides example aspects only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the example aspects will provide those skilled in the art with an enabling description for implementing an example aspect. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the application as set forth in the appended claims.
The terms “exemplary” and/or “example” are used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” and/or “example” is not necessarily to be construed as preferred or advantageous over other aspects. Likewise, the term “aspects of the disclosure” does not require that all aspects of the disclosure include the discussed feature, advantage or mode of operation.
As previously described, a well system (or a well site) includes a large number of interoperating components, and many of these components experience wear and tear, failure, adverse conditions, and other general issues that may affect operation of the well site. In one illustrative aspect, an electric submersible pump (ESP) system, which is also referred to as an artificial lift pumping system, can be deployed into the downhole environment (e.g., into the well) and experiences high temperature, immense pressure, fluid-borne abrasives, excessive gas, scale, and variable flow rate environments. The electrical submersible pump (ESP) forces materials within the well, or downhole environment, to displace to the surface.
A performance curve of the ESP is generated at a testing facility prior to installation into the downhole environment, and identifies various parameters with respect to a flow rate. An example performance curve is further described below and shown in
The downhole environment is significantly different than a calibration environment and unknown variables can affect performance of the ESP. Measurement data from the sensors of the ESP may be provided to the ground surface equipment to provide feedback regarding performance of the ESP. The ground surface equipment also has information, such as motor amperage, motor voltage, the alternating current frequency, along with telemetry collected from the downhole gauge. In some cases, data may be unavailable because the downhole environment is harsh and can cause communication links to fail for various reasons. In other cases, the sensor is unable to communicate data based on its configuration and data from the sensors are made available once extracted from the downhole environment. In other cases, a sensor for certain aspects of relevant data may not be installed.
The performance curve of the ESP may change during runtime operation due to various intrinsic and extrinsic reasons. For example, material temperature may affect efficiency of the operation due to changes in material viscosity. The ESP may also encounter scale, which is a mineral deposit that affects operation of the well pumping system. The performance curve of the ESP that is generated at a test facility is static and does not change after installation into a downhole environment. Additionally, other physical phenomenon such as viscosity of the fluid, fluid density, presence of multi-phase flow, scale, and/or particles such as sand can affect the performance of the well pumping system.
The disclosed technology addresses the foregoing by using measurement data and machine learning to infer unavailable data and provide real-time performance curves and confidence of the real-time curves. In one aspect, the disclosed systems and techniques improve the extraction of resources from the downhole environment by scaling the flow rate based on real-time information and inferred information that is unavailable. The systems and techniques may display a performance curve and various parameters and ranges that provide visual feedback to the well pumping system operator and enable informed decision making. For example, the well pumping system operator may reduce the flow rate of the extracted material when efficiency decreases due to various issues that the ESP may encounter. Informed decision making also affects the deterioration of components of the ESP during operation.
Additional details and aspects of the present disclosure are described in more detail below with respect to the figures.
In some aspects, a drilling arrangement is shown that exemplifies an LWD configuration in a wellbore drilling scenario 100. The LWD typically incorporates sensors that acquire formation data. The drilling arrangement of
In some aspects, one or more logging tools 126 may be integrated into the bottom-hole assembly 125 near the drill bit 114. As the drill bit 114 extends the wellbore 116 through the subterranean formations 118, logging tools 126 collect measurements relating to various formation properties as well as the orientation of the tool and various other drilling conditions. In some cases, the logging tools interface with various sensors and equipment. The bottom-hole assembly 125 may also include a telemetry sub 128 to transfer measurement data to a surface receiver 132 and to receive commands from the surface. In at least some cases, the telemetry sub 128 communicates with a surface receiver 132 using mud pulse telemetry. In some instances, the telemetry sub 128 does not communicate with the surface, but rather stores logging data for later retrieval at the surface when the logging assembly is recovered.
Each of the logging tools 126 may include one or more tool components spaced apart from each other and communicatively coupled by one or more wires and/or another communication arrangement. The logging tools 126 may also include one or more computing devices communicatively coupled with one or more of the tool components. The one or more computing devices may be configured to control or monitor the performance of the tool, process logging data, and/or carry out one or more aspects of the methods and processes of the present disclosure.
In at least some instances, one or more of the logging tools 126 may communicate with a surface receiver 132 by a wire, such as a wired drill pipe. In other cases, the one or more of the logging tools 126 may communicate with a surface receiver 132 by wireless signal transmission, such as ground penetrating radar. In at least some cases, one or more of the logging tools 126 may receive electrical power from a wire that extends to the surface, including wires extending through a wired drill pipe.
In some aspects, a collar 134 is a frequent component of a drill string 108 and generally resembles a very thick-walled cylindrical pipe, typically with threaded ends and a hollow core for the conveyance of drilling fluid. In some cases, multiple collars 134 may be included in the drill string 108 and are constructed and intended to be heavy to apply weight on the drill bit 114 to assist the drilling process. Because of the thickness of the collar's wall, pocket-type cutouts or other type recesses may be provided into the collar's wall without negatively impacting the integrity (strength, rigidity, and the like) of the collar 134 as a component of the drill string 108.
The tool body 146 may be lowered into the wellbore 116 by wireline conveyance 144. The wireline conveyance 144 may be anchored in the drill rig 142 or by a portable device such as a truck 145. The wireline conveyance 144 may include one or more wires, slicklines, cables, and/or the like, as well as tubular conveyances such as coiled tubing, joint tubing, or other tubulars.
The wireline conveyance 144 provides power and support for the tool, as well as enabling communication between processing systems 148 on the surface. In some examples, the wireline conveyance 144 may include electrical and/or fiber optic cabling for performing any communications. The wireline conveyance 144 is sufficiently strong and flexible to tether the tool body 146 through the wellbore 116, while also permitting communication through the wireline conveyance 144 to one or more of the processing systems 148, which may include local and/or remote processors. In some cases, power may be supplied via the wireline conveyance 144 to meet the power requirements of the tool. For slickline or coiled tubing configurations, power may be supplied downhole with a battery or via a downhole generator.
The head capacity 202, the efficiency 204, and the BHP 206 operate based on a flow rate. Manufacturers generally publish representative polynomial equations for head capacity 202 and BHP 206 with respect to performance on fresh water at a temperature of 60° F.
However, the head capacity 202, the efficiency 204, and the BHP 206 will be different in field environments. For example, the extracted material may be at a lower temperature, and viscosity of the material may be very different, which affects the amount of power consumed by the pump. The efficiency also deviates over time based on additional parameters not considered by the performance curve 200, such as produced solids increasing erosion of the pump stages. The deterioration of the ESP is also not considered which increases BHP required relative to the head produced and further reducing the efficiency of the ESP.
The performance curve 200 includes a recommended operating range 208 that corresponds to an ideal operation for a well pumping system. The recommended operating range 208 represents a region where the pump axial thrust characteristics have minimal negative impact to stage operation and represent the highest efficiency.
In some aspects, the systems and techniques herein dynamically produce a performance curve, or a part of the performance curve based on real-time data. Based on displaying the performance curve, an operator of the well pumping system may make informed decisions about the operation of the well pumping system to efficiently extract materials, improve the length of the run, and improve the lifecycle of equipment within the downhole environment.
The real-time measurement data 340 can include various information relevant to the operations of the well pumping system. For example, the well pumping system may measure a current of a motor that is driving the ESP and the rotation speed of the ESP. In other aspects, the well pumping system can measure other information that affects the extraction of the material. For example, the well pumping system may include various temperature sensors. In one example, a temperature sensor may measure a temperature of the material being extracted, or a temperature sensor may measure a temperature of a surface within the well.
The runtime monitor 310 includes a performance calculator 312 and a machine learning (ML) inference engine 314 that implements various techniques (e.g., a neural network, etc.) to learn complex behavior in a non-linear manner similar to human neurons. The performance calculator is configured to calculate the performance curve using the runtime monitor 310 and other objective information such as statistics, modeling data associated with the various components of the system (e.g., the polynomial curves of an ESP provided by the manufacturer), and inferred information. In some cases, the performance calculator 312 may use data that cannot be directly measured at runtime. In this case, the performance calculator 312 may request the ML inference engine 314 to infer the data based on the real-time measurement data 340. In other cases, the downhole environment may cause a communication malfunction, and a sensor within the downhole environment may be unavailable. In that case, the performance calculator 312 may request the ML inference engine 314 to infer the unavailable data based on the real-time measurement data 340.
In some cases, the ML inference engine 314 can include one or more ML models. Each ML model is configured for a particular task, such as for inferring missing data from a particular sensor. In this case, the sensor is trained based on normalized data associated with the measurement and other variables such as ground surface material, subterranean material, temperature, etc.
The performance calculator 312 is configured to generate a performance curve similar to the performance curve 200 but can be configured to add additional detail and context. Examples of performance curves are shown in
In some cases, the runtime monitor 310 is also configured to store the real-time measurement data 340 and any inferred information in the database 330 for use in subsequent training. In one illustrative example, the database 330 is a time series database that normalizes all data with respect to time and stores other pertinent information such as temperature, location, pressures, flow rate, etc. In some aspects, other aspects of the measurements can be normalized, such as based on power input, power input, or other correlated or semi-correlated measurement.
The client application 320 is configured to display the performance curves generated by the performance calculator 312 in real-time. The client application 320 can be any type of application, such as a web application that renders in a browser, a platform framework that uses a desktop render to render the app or a browser to render, or a hybrid framework (e.g., Xamarin, .Net MAUI, Electron, etc.) that uses a common language runtime and an embedded browser to execute the application. In some cases, the client application can be a server rendered application.
The user interface 400 includes a performance curve 410 identifying the performance of the ESP in the downhole environment based on real-time data. For example, the performance curve 410 includes the head capacity 412, the efficiency 414, and the BHP 416 of current measurement data. The performance curve 410 may also include a cursor 418 that identifies a real-time operation point.
The user interface 400 also includes a control section 420 for controlling the view of the performance curve 410 based on past, present, and future data. For example, the control section 420 includes a previous data control for controlling data changes over a duration, in this case 60 minutes. The performance curve 410 also is configured to display past data based on the control section, such as the past head capacity 422 and the past efficiency 424. The performance curve 410 can also identify other variations, such as the temperature difference, the flow rate difference, etc. In the non-limiting example shown in the user interface 400, the temperature differential, the flow rate differential, and the downhole pressure differential are illustrated.
The control section 420 can also be configured to project performance based on inferred data. In this case, the control section 420 includes a confidence level that controls the statistical calculations and inference levels. The control section 420 also includes a projection control that allows a user to select a variable (e.g., temperature, pressure, etc.) to identify future performance. In this manner, a user can make informed determinations of the performance of the well pumping system.
In this case, the user interface is configured to only display a portion of the performance curves, for example within the recommended operation range (e.g., the recommended operating region 208). The head capacity 452 and the efficiency 454 are displayed with a band illustrating maximum and minimum regions. The maximum and minimum regions are associated with statistical calculations based on measured and inferred data. Further, a confidence in the prediction is illustrated. Based on the confidence and the bands associated with the head capacity 452 and the efficiency 454, an operator of the well pumping system may identify a priority region 456 in which material extraction may be better than a cursor 458 that identifies current performance.
In some aspects, a conventional curve may be generated based on equipment that will be placed into the downhole environment. For example, before beginning the run, which is the time period that the equipment is disposed into the downhole environment and continues until the equipment is extracted from the downhole environment, an initial performance curve is generated. As described above, the performance curve may change based on measurement data during the run. The initial performance curve of the first equipment is based on a standardized performance test of the first equipment using a calibration material.
At block 505, a computing system may receive first measurement data from a first equipment submersed into a downhole environment during a run for extracting material from the downhole environment.
At block 510, a computing system may predict a second measurement from a second equipment submersed into the downhole environment during the run by using a first machine learning model, wherein the performance curve is further based on the second measurement. For example, the first machine learning model is at least partially trained based on measurement data from the downhole environment. In some cases, the first machine learning model comprises a multi-label classifier configured to identify classifications of the downhole environment and predict the second measurement based on training data similar to the downhole environment.
At block 515, a computing system may determine that a third equipment becomes unavailable during the run and, in response to this determination, predict third measurement data associated with the third equipment during a time that the third equipment is unavailable. In some aspects, the prediction of the missing data may be performed by a second machine learning model configured to infer missing data for a specific sensor. For example, the sensor may go offline during various events such as a high-pressure event in the downhole environment. The first machine learning model may also be further trained based on downhole environments having different characteristics, and the first machine learning model can identify corresponding learned features and apply the learning to the prediction.
At block 520, a computing system may determine a performance curve based on the measurement data, the performance curve identifying an extraction rate of the material with respect to efficiency and head capacity. In one aspect, the determination of the performance curve can include predicting a minimum value, a maximum value, and an actual value of the efficiency of the equipment based on a flow rate of material. The minimum value, the maximum value, and the actual value of the efficiency are provided to the operator to enable the operator of the well pumping system to control the equipment for maximum efficiency and prolong the duration of the run, as well as reduce deterioration events.
At block 525, a computing system may provide the performance curve to an operator of the run for real-time feedback associated with performance of equipment in the downhole environment. For example, as shown in
In some cases, the computing system may estimate a recommended flow rate for the material to minimize deterioration of the equipment based on the downhole environment. In this case, the equipment can be preserved in the downhole environment longer, which is more efficient for the extraction of materials.
In some aspects, computing system 600 is a distributed system in which the functions described in this disclosure may be distributed within a datacenter, multiple data centers, a peer network, etc. In some aspects, one or more of the described system components represents many such components each performing some or all of the function for which the component is described. In some aspects, the components may be physical or virtual devices.
Example computing system 600 includes at least one processing unit (CPU or processor) 610 and connection 605 that couples various system components including system memory 615, such as ROM 620 and RAM 625 to processor 610. Computing system 600 may include a cache 612 of high-speed memory connected directly with, in close proximity to, or integrated as part of processor 610.
Processor 610 may include any general purpose processor and a hardware service or software service, such as services 632, 634, and 636 stored in storage device 630, configured to control processor 610 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. Processor 610 may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.
To enable user interaction, computing system 600 includes an input device 645, which may represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech, etc. Computing system 600 may also include output device 635, which may be one or more of a number of output mechanisms. In some instances, multimodal systems may enable a user to provide multiple types of input/output to communicate with computing system 600. Computing system 600 may include communications interface 640, which may generally govern and manage the user input and system output. The communication interface may perform or facilitate receipt and/or transmission wired or wireless communications using wired and/or wireless transceivers, including those making use of an audio jack/plug, a microphone jack/plug, a universal serial bus (USB) port/plug, an Apple® Lightning® port/plug, an Ethernet port/plug, a fiber optic port/plug, a proprietary wired port/plug, a Bluetooth® wireless signal transfer, a BLE wireless signal transfer, an IBEACON® wireless signal transfer, an RFID wireless signal transfer, near-field communications (NFC) wireless signal transfer, dedicated short range communication (DSRC) wireless signal transfer, 802.11 WiFi wireless signal transfer, WLAN signal transfer, Visible Light Communication (VLC), Worldwide Interoperability for Microwave Access (WiMAX), IR communication wireless signal transfer, Public Switched Telephone Network (PSTN) signal transfer, Integrated Services Digital Network (ISDN) signal transfer, 3G/4G/5G/LTE cellular data network wireless signal transfer, ad-hoc network signal transfer, radio wave signal transfer, microwave signal transfer, infrared signal transfer, visible light signal transfer, ultraviolet light signal transfer, wireless signal transfer along the electromagnetic spectrum, or some combination thereof. The communications interface 640 may also include one or more Global Navigation Satellite System (GNSS) receivers or transceivers that are used to determine a location of the computing system 600 based on receipt of one or more signals from one or more satellites associated with one or more GNSS systems. GNSS systems include, but are not limited to, the US-based GPS, the Russia-based Global Navigation Satellite System (GLONASS), the China-based BeiDou Navigation Satellite System (BDS), and the Europe-based Galileo GNSS. There is no restriction on operating on any particular hardware arrangement, and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.
Storage device 630 may be a non-volatile and/or non-transitory and/or computer-readable memory device and may be a hard disk or other types of computer readable media which may store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, a floppy disk, a flexible disk, a hard disk, magnetic tape, a magnetic strip/stripe, any other magnetic storage medium, flash memory, memristor memory, any other solid-state memory, a compact disc read only memory (CD-ROM) optical disc, a rewritable compact disc (CD) optical disc, digital video disk (DVD) optical disc, a blu-ray disc (BDD) optical disc, a holographic optical disk, another optical medium, a secure digital (SD) card, a micro secure digital (microSD) card, a Memory Stick® card, a smartcard chip, a EMV chip, a subscriber identity module (SIM) card, a mini/micro/nano/pico SIM card, another integrated circuit (IC) chip/card, RAM, static RAM (SRAM), dynamic RAM (DRAM), ROM, programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash EPROM (FLASHEPROM), cache memory (L1/L2/L3/L4/L5/L #), resistive random-access memory (RRAM/ReRAM), phase change memory (PCM), spin transfer torque RAM (STT-RAM), another memory chip or cartridge, and/or a combination thereof.
The storage device 630 may include software services, servers, services, etc., that when the code that defines such software is executed by the processor 610, it causes the system to perform a function. In some aspects, a hardware service that performs a particular function may include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as processor 610, connection 605, output device 635, etc., to carry out the function. The term “computer-readable medium” includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other mediums capable of storing, containing, or carrying instruction(s) and/or data. A computer-readable medium may include a non-transitory medium in which data may be stored and that does not include carrier waves and/or transitory electronic signals propagating wirelessly or over wired connections. Examples of a non-transitory medium may include, but are not limited to, a magnetic disk or tape, optical storage media such as CD or DVD, flash memory, memory or memory devices. A computer-readable medium may have stored thereon code and/or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, or the like.
In some cases, the computing device or apparatus may include various components, such as one or more input devices, one or more output devices, one or more processors, one or more microprocessors, one or more microcomputers, one or more cameras, one or more sensors, and/or other component(s) that are configured to carry out the steps of processes described herein. In some examples, the computing device may include a display, one or more network interfaces configured to communicate and/or receive the data, any combination thereof, and/or other component(s). The one or more network interfaces may be configured to communicate and/or receive wired and/or wireless data, including data according to the 3G, 4G, 5G, and/or other cellular standard, data according to the Wi-Fi (802.11x) standards, data according to the Bluetooth™ standard, data according to the IP standard, and/or other types of data.
The components of the computing device may be implemented in circuitry. For example, the components may include and/or may be implemented using electronic circuits or other electronic hardware, which may include one or more programmable electronic circuits (e.g., microprocessors, GPUs, DSPs, CPUs, and/or other suitable electronic circuits), and/or may include and/or be implemented using computer software, firmware, or any combination thereof, to perform the various operations described herein.
In some aspects the computer-readable storage devices, mediums, and memories may include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.
Specific details are provided in the description above to provide a thorough understanding of the aspects and examples provided herein. However, it will be understood by one of ordinary skill in the art that the aspects may be practiced without these specific details. For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software. Additional components may be used other than those shown in the figures and/or described herein. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the aspects in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the aspects.
Individual aspects may be described above as a process or method which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations may be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but may have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination may correspond to a return of the function to the calling function or the main function.
Processes and methods according to the above-described examples may be implemented using computer-executable instructions that are stored or otherwise available from computer-readable media. Such instructions may include, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or a processing device to perform a certain function or group of functions. Portions of computer resources used may be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, source code, etc. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on.
Devices implementing processes and methods according to these disclosures may include hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof, and may take any of a variety of form factors. When implemented in software, firmware, middleware, or microcode, the program code or code segments to perform the necessary tasks (e.g., a computer-program product) may be stored in a computer-readable or machine-readable medium. A processor(s) may perform the necessary tasks. Typical examples of form factors include laptops, smart phones, mobile phones, tablet devices, or other small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. The functionality described herein also may be embodied in peripherals or add-in cards. Such functionality may also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.
The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are example means for providing the functions described in the disclosure.
In the foregoing description, aspects of the application are described with reference to specific aspects thereof, but those skilled in the art will recognize that the application is not limited thereto. Thus, while illustrative aspects of the application have been described in detail herein, it is to be understood that the inventive concepts may be otherwise variously embodied and employed, and that the appended claims are intended to be construed to include such variations, except as limited by the prior art. Various features and aspects of the above-described application may be used individually or jointly. Further, aspects may be utilized in any number of environments and applications beyond those described herein without departing from the broader spirit and scope of the specification. The specification and drawings are, accordingly, to be regarded as illustrative rather than restrictive. For the purposes of illustration, methods were described in a particular order. It should be appreciated that in alternate aspects, the methods may be performed in a different order than that described.
One of ordinary skill will appreciate that the less than (“<”) and greater than (“>”) symbols or terminology used herein may be replaced with less than or equal to (“≤”) and greater than or equal to (“≥”) symbols, respectively, without departing from the scope of this description.
Where components are described as being “configured to” perform certain operations, such configuration may be accomplished, for example, by designing electronic circuits or other hardware to perform the operation, by programming programmable electronic circuits (e.g., microprocessors, or other suitable electronic circuits) to perform the operation, or any combination thereof.
The phrase “coupled to” refers to any component that is physically connected to another component either directly or indirectly, and/or any component that is in communication with another component (e.g., connected to the other component over a wired or wireless connection, and/or other suitable communication interface) either directly or indirectly.
Claim language or other language reciting “at least one of” a set and/or “one or more” of a set indicates that one member of the set or multiple members of the set (in any combination) satisfy the claim. For example, claim language reciting “at least one of A and B” or “at least one of A or B” means A, B, or A and B. In another example, claim language reciting “at least one of A, B, and C” or “at least one of A, B, or C” means A, B, C, or A and B, or A and C, or B and C, or A and B and C. The language “at least one of” a set and/or “one or more” of a set does not limit the set to the items listed in the set. For example, claim language reciting “at least one of A and B” or “at least one of A or B” may mean A, B, or A and B, and may additionally include items not listed in the set of A and B.
The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the aspects disclosed herein may be implemented as electronic hardware, computer software, firmware, or combinations thereof. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The techniques described herein may also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques may be implemented in any of a variety of devices such as general purposes computers, wireless communication device handsets, or integrated circuit devices having multiple uses including application in wireless communication device handsets and other devices. Any features described as modules or components may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques may be realized at least in part by a computer-readable data storage medium comprising program code including instructions that, when executed, performs one or more of the methods described above. The computer-readable data storage medium may form part of a computer program product, which may include packaging materials. The computer-readable medium may comprise memory or data storage media, such as RAM such as synchronous dynamic random access memory (SDRAM), ROM, non-volatile random access memory (NVRAM), EEPROM, flash memory, magnetic or optical data storage media, and the like. The techniques additionally, or alternatively, may be realized at least in part by a computer-readable communication medium that carries or communicates program code in the form of instructions or data structures and that may be accessed, read, and/or executed by a computer, such as propagated signals or waves.
The program code may be executed by a processor, which may include one or more processors, such as one or more DSPs, general purpose microprocessors, an application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Such a processor may be configured to perform any of the techniques described in this disclosure. A general purpose processor may be a microprocessor; but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Accordingly, the term “processor,” as used herein may refer to any of the foregoing structure, any combination of the foregoing structure, or any other structure or apparatus suitable for implementation of the techniques described herein.
Illustrative aspects of the disclosure include:
Aspect 1. A method of providing performance feedback of an extraction facility, comprising: receiving first measurement data from a first equipment submersed into a downhole environment during a run for extracting material from the downhole environment; determining a performance curve based on the first measurement data, the performance curve identifying an extraction rate of the material with respect to efficiency and head capacity; and providing the performance curve to an operator of the run for real-time feedback associated with performance of equipment in the downhole environment.
Aspect 2. The method of Aspect 1, further comprising: predicting a second measurement from a second equipment submersed into the downhole environment during the run by using a first machine learning model, wherein the performance curve is further based on the second measurement.
Aspect 3. The method of any of Aspects 1 to 2, wherein the first machine learning model is at least partially trained based on measurement data from the downhole environment.
Aspect 4. The method of any of Aspects 1 to 3, wherein the first machine learning model is further trained based on downhole environments having different characteristics.
Aspect 5. The method of any of Aspects 1 to 4, wherein the first machine learning model comprises a multi-label classifier configured to identify classifications of the downhole environment and predict the second measurement based on training data similar to the downhole environment.
Aspect 6. The method of any of Aspects 1 to 5, further comprising: determining that a third equipment becomes unavailable during the run; and predicting third measurement data associated with the third equipment during a time that the third equipment is unavailable.
Aspect 7. The method of any of Aspects 1 to 6, wherein calculating the performance curve based on the first measurement data comprises: predicting a minimum value, a maximum value, and an actual value of an efficiency of the equipment based on a flow rate of material, wherein the minimum value, the maximum value, and the actual value of the efficiency are provided to the operator.
Aspect 8. The method of any of Aspects 1 to 7, wherein, before beginning the run, an initial performance curve is generated, and the performance curve changes based on measurement data during the run.
Aspect 9. The method of any of Aspects 1 to 8, wherein the initial performance curve of the first equipment is based on a standardized performance test of the first equipment using a calibration material.
Aspect 10. The method of any of Aspects 1 to 9, further comprising: estimating a recommended flow rate for the material to minimize deterioration of the equipment based on the downhole environment.
Aspect 11. A system for providing performance feedback of an extraction facility includes a storage (implemented in circuitry) configured to store instructions and a processor. The processor configured to execute the instructions and cause the processor to: receive first measurement data from a first equipment submersed into a downhole environment during a run for extracting material from the downhole environment; determine a performance curve based on the first measurement data, the performance curve identifying an extraction rate of the material with respect to efficiency and head capacity; and provide the performance curve to an operator of the run for real-time feedback associated with performance of equipment in the downhole environment.
Aspect 12. The system of Aspect 11, wherein the processor is configured to execute the instructions and cause the processor to: predict a second measurement from a second equipment submersed into the downhole environment during the run by using a first machine learning model, wherein the performance curve is further based on the second measurement.
Aspect 13. The system of any of Aspects 11 to 12, wherein the first machine learning model is at least partially trained based on measurement data from the downhole environment.
Aspect 14. The system of any of Aspects 11 to 13, wherein the first machine learning model is further trained based on downhole environments having different characteristics.
Aspect 15. The system of any of Aspects 11 to 14, wherein the first machine learning model comprises a multi-label classifier configured to identify classifications of the downhole environment and predict the second measurement based on training data similar to the downhole environment.
Aspect 16. The system of any of Aspects 11 to 15, wherein the processor is configured to execute the instructions and cause the processor to: determine that a third equipment becomes unavailable during the run; and predict third measurement data associated with the third equipment during a time that the third equipment is unavailable.
Aspect 17. The system of any of Aspects 11 to 16, wherein the processor is configured to execute the instructions and cause the processor to: predict a minimum value, a maximum value, and an actual value of an efficiency of the equipment based on a flow rate of material, wherein the minimum value, the maximum value, and the actual value of the efficiency are provided to the operator.
Aspect 18. The system of any of Aspects 11 to 17, wherein an initial performance curve is generated, and the performance curve changes based on measurement data during the run.
Aspect 19. The system of any of Aspects 11 to 18, wherein the initial performance curve of the first equipment is based on a standardized performance test of the first equipment using a calibration material.
Aspect 20. The system of any of Aspects 11 to 19, wherein the processor is configured to execute the instructions and cause the processor to: estimate a recommended flow rate for the material to minimize deterioration of the equipment based on the downhole environment.
Aspect 21. A non-transitory computer-readable storage medium comprising instructions stored thereon which, when executed by at least one processor, causes the at least one processor to perform operations according to any of Aspects 1 to 10.
Aspect 22. An apparatus for providing performance feedback of an extraction facility comprising one or more means for performing operations according to any of Aspects 1 to 10.
