Patent Application
20250129706
The present technology pertains to a system for extracting materials from a well to long-term trend analysis for detecting long-term trends in downhole equipment.
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 material within the well, or downhole environment, to displace to the surface.
An operation of an ESP begins with the insertion of the equipment into the downhole environment and continues until the equipment is extracted. An operation can end for various reasons, but typically because there is a failure that prevents continued operation within the downhole environment. Many different operations may occur within the lifecycle of the well, and small subtle changes across the different operations are difficult to detect with conventional techniques because of different configurations, environmental variations such as temperature, seasons, and so forth. There is no possible human operated system that is capable of monitoring trends in the data, which may consist of millions of data points. Trying to converge all datapoints in conventional tools such as spreadsheets would be impractical because of the various complexities, such as equipment may have different reporting data rates, the denoising the data to address anomalies due to scale would be impossible, and so forth.
The disclosed technology addresses the foregoing by normalizing data across operations, combining the normalized data from across all operations of a facility, and analyzing the normalized data with a machine learning model to identify trends that are invisible to the human eye and conventional data analysis techniques. In some aspects, the disclosed technology may provide real-time information pertaining to an operation to enable identification of issues and address equipment operation in real-time. The disclosed technology promotes longer operation periods and can reduce equipment maintenance by assisting decision making.
Various aspects include a system including one or more processors and at least one computer-readable storage medium storing instructions which, when executed by the one or more processors, cause the one or more processors to: receive first measurement data associated with or from equipment submersed into a downhole environment during a portion of a current operation for extracting materials from the downhole environment at a pumping system for a well; normalize the first measurement data; remove portions of normalized data of the pumping system for a well that are associated with scale; combine the normalized data with previous data into complete well data, wherein the previous data is associated with previous operations of the downhole environment; provide the complete well data into a first machine learning model for identifying trends associated with the complete well data; receive labels associated with operation of a first equipment from the first machine learning model; and display information pertaining to trends and events associated with an entirety of the current 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 a 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.
In the case illustrated in
In this case, the flow rate can vary based on the lifecycle of the equipment. For example, the average flow rate of the fourth operation 208 is lower than the average flow rate of the first operation 202. For example, the ESP may experience deterioration that reduces efficiency, and the operator of the pumping system for a well controls the equipment to prolong the life of the fourth operation 208. In some cases, the complete breakdown of equipment can incur more cost rather than controlling the equipment to operate slower and increase the length of the operation. Although the operations are illustrated as ending at the same time, the time axis is normalized based on a beginning time and an ending time. For example, the second operation 204 may be twice as long as the first operation 202 based on the experience gleaned during the first operation 202.
Within the first operation, samples from various equipment are illustrated in
In some aspects, the unit data 220 may include 6 discrete samples representing the time duration between times t0 and t1. That is, the unit data may have a sample rate greater or less than measurement data, and each data item above (e.g., A1 to A4, B1 to B5, and C1 to C8) are interpolated into unit data 220. Detection of trends, identification of events that cause anomalies, and training of machine learning models can be simplified based on normalization in time. In some cases, normalization across units may also occur, such as normalizing parameters dependent on power consumption, with respect to another measurement. For example, the third measurement data 214 (e.g., rotation speed) may be converted to rotation speed based on a current in the first measurement data 210 (e.g., amperage).
The real-time measurement data 340 can include various information relevant to the operations of the pumping system for a well. For example, the pumping system for a well may measure a current of a motor that is driving the ESP and the rotation speed of the ESP. In other aspects, the pumping system for a well can measure other information that affects the extraction of the material. For example, the pumping system for a well 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 ML inference engine 314 may include a plurality of ML models that are configured for various tasks of the pumping system for a well. Non-limiting examples of ML models of the ML inference engine 314 include a missing data model 315, an offline data model 316, and an operation trend model 317. In some aspects, the missing data model 315 is configured to infer measurements when a sensor goes offline. For example, a sensor may become unavailable within a high-pressure environment and may become temporarily available. The ML inference engine 314 is configured to infer missing data from unavailable sensors using the missing data model 315. The offline data model 316 is configured to infer operations of a sensor that is not at all available during the operation. For example, some sensors can be recording data but are unable to provide that data during the operation and the offline data model 316 is configured to infer the measurements based on previous training using recorded data.
In some aspects, the operation trend model 317 is configured to analyze trends across all operations associated with a pumping system for a well and identify and predict long-term changes. For example, the operation trend model 317 may predict that the temperature may rise 0.1 degrees over the next hour, which can be used to assist in controlling the motor of the ESP. The operation trend model 317 is trained based on a combination of pre-existing data associated with other pumping systems for a well that are similar or different. In some cases, the operation trend model 317 is trained based on normalizing all data from previous operations based on time and other units to identify patterns with respect to a number of variations that a person cannot identify (e.g., location, surface materials, temperature).
In some aspects, the operation trend model 317 is configured to detect a change in a measurement parameter based on a specific ratio threshold. In some cases, the change can be an increase, a decrease, or a change in variance (e.g., a deviation in measurement). For example, a measurement may be stable and have low deviation and an increased deviation range can indicate a change in the downhole environment. In one example, the ML model can be configured to build a dynamic sliding window that identifies a trend over a long period of time. For example, as shown in
The runtime monitor 310 may be configured to filter short term events associated with the complex data based real-time measurement data 340 and construct complete well operation data. In one illustrative example, the runtime monitor 310 filters events such as scale and then combines the current operation data with past well operation data. In one case, all operation data that is available, including any different configuration information, can be provided by the operation trend model 317 to identify a long-term trend. The operation trend model 317 operates over a longer period of time and may execute with a larger volume of data with less frequency than the missing data model 315 and the offline data model 316.
In some aspects, the operation trend model 317 is trained based on identifying thresholds around certain behaviors in multiple dimensions. For example, the training may involve horizontally slicing the data based on triggers to corral events for training the ML to detect or predict events. For example, a horizontal trigger may be simple data patterns such as +/−2 standard deviations (20). In another example, a vertical trigger may be an autoregressive model that builds of a model associated a standard operation and generates prediction bands or ranges within that model. In this case, the model may be configured to identify normal operation, and deviation from the model corresponds to an anomaly within the system. Using various models to train the operation trend model 317 enable the identification of anomalies that may be undetectable to a human operator.
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 other dimensions and stores other pertinent information such as temperature, location, pressures, flow rate, etc. Equipment in a downhole environment is not synchronized in both time and in reporting intervals. For example, some equipment reports data in ten minute intervals, some equipment reports data every second, and other equipment reports data between seconds and minutes. Normalizing in time allows a comparison of events within the same downhole environment or across different environments.
Normalizing the data in other dimensions, such as power, allow the identification of relationships between events and enables comparisons to other downhole environments either in the same or different geographical region. In some cases, equipment from downhole environment to downhole environment can vary, and normalizing the data is based on parameters across different downhole environments. For example, two adjacent wells may use different pumps that have different operating frequencies (e.g., in revolutions per minute (RPM)) and power consumption. Normalizing data across different downhole environments allows comparisons to other operating environments and a machine learning model can be configured to learn trends, events, and other factors that relate to improving the lifecycle of the well based on the long-term trends. By analyzing multiple runs of different downhole environments and different geographical regions, the ML model can identify implicit relationships that cannot be identified by humans because an ML model can have a number of hyperparameters from several thousand to hundreds of billions. Normalizing the data on time and magnitude improves the ML model's ability to identify relationships.
In some cases, the runtime monitor 310 also includes multi-variate models that find the relationships within a single downhole environment. In this case, a model and parameters associated with that model can be extracted to identify relationships without normalization. The runtime monitor 310 can preprocess the data prior to training. The actual training process would include normalizing each dataset associated with various downhole environments to provide a quality comparison to identify trends over long terms based on various events within the downhole environment and external to the downhole environment.
The client application 320 is configured to display the performance information based on short-term trends and long-term trends 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.
For example, the user interface 400 includes a graph 410 displaying current and previous flow rate over time. In this case, the time is not normalized to illustrate flow rate of a current operation 412 as compared to a first operation 414 and a second operation 416. The graph 410 can also include a marker 418 that may identify additional information from the measurement data. In some cases, the marker 418 can be annotated based on the ML inference engine 314 to identify an event and/or may be manually annotated to indicate information relevant to the state of the pumping system for a well and equipment.
The graph 420 displays a flow rate differential with respect to an input value. For example, a user may select the differential to be based on the mean of previous flow rates modified by a floor function to denoise start and end of the operation. In this case, the current 422 corresponds to the graph 410. In some aspects, the operator is able to control the application to select a portion of data for training based on issues. As noted above, the operation trend model 317 may use sliding windows to identify long-term trends that can manifest based on minor adjustments. The sliding window 424 in the graph 420 detects a correction action as compared to earlier changes in the flow rate. In some cases, the operation trend model 317 may detect the termination of the trend using the sliding window 424 and the operator may select a data portion and label that data portion for training. For example, a user input control 426 is provided to allow the operator to select a corresponding label. In some aspects, the ML models are configured to identify inflection points within the data set, and identifying trends and other information around the inflection points. In multivariate datasets, identification of an inflection point is more complicated, and the goal is to use various detection mechanisms such as the sliding window 424 to identify deviations from standard behavior as a precipitating information for the events.
The graph 430 displays a current (or power) that is modified by a floor function to denoise start and end data. In this case, the current operation 432 is following a trend similar to a second operation 434 that appears to abruptly end as compared to a third operation 436. Additional context can also be provided such as a production started in an adjacent well or a heatwave caused a variable speed drive to overheat in the second operation 434. External events may provide new information regarding how external events affect operations within the downhole environment. In other cases, the differences in trends between one well and another could be explained by different motors or deviations in equipment (e.g., motors) or other configuration.
The graph 440 displays the revolutions per minute (RPM) of a motor with respect to time. In this example, the current operation 442 is pushing more speed as compared to a first operation 444. The motor may have been replaced after the first operation 444 and was driven at a higher RPM in a third operation 446. In some aspects, the operation trend model 317 may be able to provide output regarding desired operating range based on the collective data of all the operations (or complete well data). For example, the operation trend model 317 may generate a recommendation range 448 for driving the motor.
In some aspects, the method 500 may be executed by a computing system that is operably coupled to the pumping system for a well. For example, an operator of the pumping system for a well may execute a client application (e.g., the client application 320) on a portable tablet device that is wirelessly connected to a centralized service. In this case, the portable tablet device may not receive information directly from the various equipment and sensors of the pumping system for a well. The client application may be executing on a computing system and configured to receive measurements of the equipment and may perform aspects of the method 500. For example, the computing system may store and execute the ML model locally. ML models can be quite large based on the number of trained parameters and the precision of the parameters (e.g., bit sizes, such as a 32-bit floating point or an 8-bit floating point).
At block 505, the computing system may receive first measurement data associated with or from equipment submersed into a downhole environment during a portion of a current operation for extracting materials from the downhole environment at a pumping system for a well.
At block 510, the computing system may normalize the first measurement data based on a correlation associated with the equipment submersed into the downhole environment. For example, the first measurement data may be normalized to have the same timestamp and represent an identical period of time. Non-limiting techniques to normalize the data include linear interpolation, non-linear interpolation, a machine learning interpolation, and so forth. The normalization may also be associated with data and various equipment installed at different well sites (e.g., downhole environments). For example, different motors and other equipment may have different measurement ranges, along with different tolerances, based on their function. In this case, the measurements associated with the equipment cannot be directly compared because of a different magnitude associated with the measurements. The computing system can be configured to correlate different measurements based on relationships in units and other principles and normalize the magnitude of the measurements for analysis.
At block 515, the computing system may remove portions of normalized data of the pumping system for a well that are associated with anomalies of the dataset. For example, hard deposits may occur at random intervals and may provide noise for the machine learning model, which complicates the learning process during training.
At block 520, the computing system may combine the normalized data with previous data into complete well data. The previous data is associated with previous operations of the downhole environment. For example, the previous operations include all previous operations that are available. By combining the normalized data, trends across multiple operations may be detected and corrected.
At block 525, the computing system may provide the complete well data into a first machine learning model for identifying trends associated with the complete well data. In some aspects, the first machine learning model is configured to detect the alarm condition using a sliding window over a period of time using the complete well data. The sliding window can be within a single operation and may also extend across operations. To extend across operations, the sliding window may also use a supplemental function such as a floor function to remove portions of operation data to allow comparisons across operations. For example, flow rate will always be sharply increasing or decreasing during the start and end of an operation, and the floor function may allow direct comparisons of operation data outside of the edges of operations.
At block 530, the computing system may receive labels associated with operation of a first equipment from the first machine learning model. The labels are associated with the portion of the current operation. The labels comprise information indicating changes from the previous operations. For example, the labels may identify a change based on an environmental variable that causes a particular equipment within the downhole environment to operate within a different tolerance range.
At block 535, the computing system may display information pertaining to trends and events associated with an entirety of the current operation. In some cases, the information may induce an operator of the well facility to provide input into a device to control the equipment and avert the trend. In other cases, the computing system may include control system functions and control one or more equipment. For example, power to the motor may be reduced automatically.
As an example of block 535, the computing system may identify an alarm condition associated with a trend regarding operation of the equipment based on a current trend experienced within the downhole environment. The computing system may identify a corrective action to take based on the alarm condition to resolve the trend and then autonomously control the equipment to resolve the trend.
In some cases, the computing system can be used by the operator to label data and provide the labeled data to a data repository. For example, the operator may use the computing system to select a portion of data corresponding to the trend (e.g., identify some aspect of the data) and then provide the portion of data to a supervision system for input into a training dataset.
The computing system can also be configured to use a second machine learning model to detect short-term trends. The second machine learning model is configured to evaluate data over a shorter interval than the first machine learning model and is trained to identify patterns differently from the first machine learning model.
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 calibrating measured data, comprising: receiving first measurement data associated with or from equipment submersed into a downhole environment during a portion of a current operation for extracting materials from the downhole environment at a pumping system for a well; normalizing the first measurement data based on corresponding measurement data associated with other downhole environments; removing portions of normalized data of the pumping system for a well that are associated with scale; combining the normalized data with previous data into complete well data, wherein the previous data is associated with previous operations of the downhole environment; providing the complete well data into a first machine learning model for identifying trends associated with the complete well data; receiving labels associated with operation of a first equipment from the first machine learning model, wherein the labels are associated with the portion of the current operation; and displaying information pertaining to trends and events associated with an entirety of the current operation.
Aspect 2. The method of Aspect 1, further comprising: identifying an alarm condition associated with a trend regarding operation of the equipment based on a current trend experienced within the downhole environment.
Aspect 3. The method of any of Aspects 1 to 2, further comprising: in response to detecting termination of the trend, selecting a portion of data corresponding to the trend and providing the portion of data to a supervision system for input into a training dataset.
Aspect 4. The method of any of Aspects 1 to 3, wherein an operator of the well facility provides input into a device to control the equipment and avert the trend.
Aspect 5. The method of any of Aspects 1 to 4, further comprising: identifying a corrective action to take based on the alarm condition to resolve the trend; and controlling the first equipment based on the corrective action.
Aspect 6. The method of any of Aspects 1 to 5, wherein further comprising: detecting a second alarm condition issue based on the first measurement data using a second machine learning model, wherein the second machine learning model is configured to evaluate data over a shorter interval than the first machine learning model.
Aspect 7. The method of any of Aspects 1 to 6, wherein the first machine learning model is configured to detect the alarm condition using a sliding window over a period of time using the complete well data.
Aspect 8. The method of any of Aspects 1 to 7, wherein the labels comprise information indicating changes from the previous operations.
Aspect 9. A system for detecting long-term trends across operations at a pumping system for a well 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 associated with or from equipment submersed into a downhole environment during a portion of a current operation for extracting materials from the downhole environment at a pumping system for a well; normalizing the first measurement data based on corresponding measurement data associated with other downhole environments; remove portions of normalized data of the pumping system for a well that are associated with scale; combine the normalized data with previous data into complete well data, wherein the previous data is associated with previous operations of the downhole environment; provide the complete well data into a first machine learning model for identifying trends associated with the complete well data; receive labels associated with operation of a first equipment from the first machine learning model, wherein the labels are associated with the portion of the current operation; and display information pertaining to trends and events associated with an entirety of the current operation.
Aspect 10. The system of Aspect 9, wherein the processor is configured to execute the instructions and cause the processor to: identify an alarm condition associated with a trend regarding operation of the equipment based on a current trend experienced within the downhole environment.
Aspect 11. The system of any of Aspects 9 to 10, wherein the processor is configured to execute the instructions and cause the processor to: in response to detecting termination of the trend, select a portion of data corresponding to the trend and providing the portion of data to a supervision system for input into a training dataset.
Aspect 12. The system of any of Aspects 9 to 11, wherein an operator of the well facility provides input into a device to control the equipment and avert the trend.
Aspect 13. The system of any of Aspects 9 to 12, wherein the processor is configured to execute the instructions and cause the processor to: identify a corrective action to take based on the alarm condition to resolve the trend; and control the first equipment based on the corrective action.
Aspect 14. The system of any of Aspects 9 to 13, wherein the processor is configured to execute the instructions and cause the processor to: detect a second alarm condition issue based on the first measurement data using a second machine learning model, wherein the second machine learning model is configured to evaluate data over a shorter interval than the first machine learning model.
Aspect 15. The system of any of Aspects 9 to 14, wherein the first machine learning model is configured to detect the alarm condition using a sliding window over a period of time using the complete well data.
Aspect 16. The system of any of Aspects 9 to 15, wherein the labels comprise information indicating changes from the previous operations.
Aspect 17. A computer readable medium comprising instructions using a computer system. The computer includes a memory (e.g., implemented in circuitry) and a processor (or multiple processors) coupled to the memory. The processor (or processors) is configured to execute the computer readable medium and cause the processor to: receive first measurement data associated with or from equipment submersed into a downhole environment during a portion of a current operation for extracting materials from the downhole environment at a pumping system for a well; normalizing the first measurement data based on corresponding measurement data associated with other downhole environments; remove portions of normalized data of the pumping system for a well that are associated with scale; combine the normalized data with previous data into complete well data, wherein the previous data is associated with previous operations of the downhole environment; provide the complete well data into a first machine learning model for identifying trends associated with the complete well data; receive labels associated with operation of a first equipment from the first machine learning model, wherein the labels are associated with the portion of the current operation; and display information pertaining to trends and events associated with an entirety of the current operation.
Aspect 18. The computer readable medium of Aspect 17, wherein the processor is configured to execute the computer readable medium and cause the processor to: identify an alarm condition associated with a trend regarding operation of the equipment based on a current trend experienced within the downhole environment.
Aspect 19. The computer readable medium of any of Aspects 17 to 18, wherein the processor is configured to execute the computer readable medium and cause the processor to: in response to detecting termination of the trend, select a portion of data corresponding to the trend and providing the portion of data to a supervision system for input into a training dataset.
Aspect 20. The computer readable medium of any of Aspects 17 to 19, wherein an operator of the well facility provides input into a device to control the equipment and avert the trend.
Aspect 21. The computer readable medium of any of Aspects 17 to 20, wherein the processor is configured to execute the computer readable medium and cause the processor to: identify a corrective action to take based on the alarm condition to resolve the trend; and control the first equipment based on the corrective action.
Aspect 22. The computer readable medium of any of Aspects 17 to 21, wherein the processor is configured to execute the computer readable medium and cause the processor to: detect a second alarm condition issue based on the first measurement data using a second machine learning model, wherein the second machine learning model is configured to evaluate data over a shorter interval than the first machine learning model.
Aspect 23. The computer readable medium of any of Aspects 17 to 22, wherein the first machine learning model is configured to detect the alarm condition using a sliding window over a period of time using the complete well data.
Aspect 24. An apparatus for calibrating reliability records comprising one or more means for performing operations according to any of Aspects 1 to 8.
