Systems and Methods for Productivity Analysis of Oil and Gas Production Systems

The present application claims priority to U.S. Provisional Patent Application No. 63/661,112 filed on Jun. 18, 2024, which is incorporated by reference in its entirety herein.

Aspects of the presently disclosed technology relate generally to analysis of natural resource production and more specifically to productivity analysis of oil and gas production systems.

Oil and gas production systems use key performance indicators, such as, for example, a productivity index (PI), to assess productivity and monitor changes over time of the production systems. Due to the large number of oil and gas production systems, large datasets are created from data received from a variety of data sources, such as, for example, databases and sensors. With such large amounts of data, ascertaining meaningful analytics for performance of the systems is challenging, especially when one or more sensors are not available to provide accurate data. It is with these observations in mind, among others, that various aspects of the present disclosure were conceived and developed.

Implementations described and claimed herein address the foregoing problems by providing systems and methods for determining key performance indicators of oil and gas production systems when one or more sensors are not available to provide accurate data. The implementations described and claimed herein allow for generating estimated data using a machine learning model to allow for real time determination of key performance indicators of oil and gas production systems.

In some implementations, a system for analyzing natural resource production comprising: a processing system in communication with a computing device, one or more sensors, and one or more databases over a network, the computing device having one or more input systems and one or more output systems, the processing system configured to receive input data from the one or more sensors and the one or more databases, a sensor data generation system having a machine learning model, the sensor data generation system configured to generate estimated bottomhole pressure data for the input data using the machine learning model, the machine learning model built from historical data, and a productivity data generation system configured to generate productivity index data for one or more natural resource production systems using the estimated bottomhole pressure data.

In some implementations, a method for analyzing natural resource production comprising: receiving input data from one or more sensors and one or more databases, generating estimated bottomhole pressure data based on the input data using a machine learning model, the machine learning model built using historical data, generating productivity index data for one or more natural resource production systems using the estimated bottomhole pressure data, and generating output data based on the productivity index data.

In some implementations, a method can comprise: receiving historical data associated with one or more natural resource production systems, generating a training data set based on the historical data, training a machine learning model using the training data set, and validating the machine learning model by comparing estimated bottomhole pressure generated using the machine learning model with sensor data including measured bottom hole pressure

Other implementations are also described and recited herein. Further, while multiple implementations are disclosed, still other implementations of the presently disclosed technology will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative implementations of the presently disclosed technology. As will be realized, the presently disclosed technology is capable of modifications in various aspects, all without departing from the spirit and scope of the presently disclosed technology. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not limiting.

Aspects of the present disclosure involve systems and methods to process communication data. The systems and methods described herein use a machine learning model to generate accurate estimated sensor data for determining key performance indicators for real time analysis of oil and gas production systems. The machine learning model is trained using historical sensor data relating to determining key performance indicators. This results in a more efficient platform that provides accurate estimated sensor data for production systems in the oil and gas industry. Additional advantages of the presently disclosed technology will become apparent from the detailed description below.

To begin a detailed description of an example systemfor productivity analysis of oil and gas production systems. In an implementation, the production systems are one or more wells used to extract oil or gas. In an implementation, the systemprocesses input data and generates estimated sensor data using a machine learning model for use in generating productivity index data, reference is made to. The systemcan include a processing systemconfigured to receive the input data. The input data is received from at least one of a computing device, one or more sensors, or one or more databases. The systemis configured to receive user inputs via one or more input systems using, for example, the computing deviceto input text, audio, and/or interact with an interactive user interface displayed on one or more output systems of, for example, the computing device. The processing system, the computing device, the one or more sensors, and the one or more databasesare configured to interact with one another via a network(s). As illustrated in greater detail below, any and/or all of the processing system, the computing device, and the one or more databasesmay, in some instances, be special-purpose computing devices configured to perform specific functions.

The processing systemincludes one or more computing devices (e.g., servers, routers, user interface devices, internet telephony computing device, and the like) that store and/or retrieve data in the one or more databases, generate user interfaces, execute a sensor data generation system, a productivity data generation system, an output data generation system, etc. by processing instructions. The processing systemmay include a communication interface(s)that is able to communicate with the one or more input systems and one or more output systems via the network(s). For instance, the communication interface(s)may be a network interface configured to support communication between the processing systemand the network(s). The one or more input systems and one or more output systems may be part of the computing deviceor separate from the computing device. The processing systemcan be configured to train and maintain a machine learning modelto execute the techniques, as discussed in greater detail below. The processing systemcan be configured to monitor and store (e.g., with appropriate permissions) sensor data for further analysis and/or training of the machine learning model. In an implementation, the processing systemis configured to transmit the communication to another computing device or database, such as the one or more databases. In an implementation, the processing systemis associated with an organization or entity.

In an implementation, the computing deviceincludes one or more input systems and one or more output systems. For instance, the operator is able to input user data to the processing systemvia one or more interactive user interfaces using the computing device. The computing devicecan be a smartphone, a tablet, a desktop computer, a laptop computer, or other personal computing device that may be used by an individual (e.g., the operator) to receive notification(s) and enter data. In some instances, the computing devicemay be used to display plots, analytical information, notifications and/or other alerts using graphical user interfaces.

In an implementation, the processing systemincludes instructions that direct and/or cause the sensor data generation systemto execute processing techniques on the input data to generate input data subsets that are input into the machine learning model. In an implementation, the input data includes at least one of well head pressure, well head temperature, gas lift rate, watercut, gas-liquid ratio, or liquid rate. In an implementation, at least a portion of the input data is obtained by one or more sensorsdisposed in a well or at a surface, well tests, well logs, or reservoir tests. For instance, liquid rate data is obtained from well tests, reservoir pressure is obtained from formation pressure tests, and permeability is obtained from well logs. In an implementation, the well tests, the well logs, and the reservoir tests are received from the one or more databases.

In an implementation, the machine learning modelis trained to generate estimated sensor data based on the input data. In an implementation, the machine learning modelutilizes a random decision forest machine learning algorithm. In an implementation, the estimated sensor data is bottomhole pressure when a permanent downhole pressure gauge is not available or is otherwise not functioning. The machine learning modelmay be built from historical data that has been previously collected and stored, for example, at the one or more databases. In this implementation, the machine learning modelleverages the historical data to generate the estimated sensor data when the permanent downhole pressure gauge is not able to provide accurate bottomhole pressure. For instance, the training set can include historical data that includes at least one of a bottomhole pressure, well head pressure, well head temperature, gas lift rate, watercut, gas-liquid ration, or liquid rate. In an implementation, the machine learning modelallows the sensor data generation systemto generate estimated sensor data based on the input data and the historical data. The historical data can be received from the one or more databases. Accordingly, the machine learning modelallows the sensor data generation systemto generate estimated sensor data of bottomhole pressure data in real-time to allow for analysis of an oil or gas production system to assist in optimization decisions, such as, for example, restimulations, recompletions, and/or redrills using a large volume of data involving a large number of production systems, despite the presence of missing and/or imbalanced sensor data relating to bottomhole pressure.

In an implementation, the processing systemincludes instructions that direct and/or cause the productivity data generation systemto generate productivity data using sensor data, the estimated sensor data, and reservoir model data. In an implementation, the estimated sensor data is a bottomhole pressure, the sensor data includes a measured flow rate, and the reservoir model data includes a reservoir pressure determined using a historically matched numerical model. In an implementation the sensor data is received from the one or more sensors, and the reservoir model data is received from the one or more databases. In an implementation, the productivity data includes a productivity index value calculated according to equation.

In an implementation to allow for multi-system productivity analysis, the productivity data generation systemnormalizes the productivity index value by a completed permeability-length product according to equation 2:

In an implementation, the processing systemincludes instructions that direct and/or cause the output data generation systemto perform one or more of the functions described herein. For example, the output data generation systemis configured to generate a notification regarding the productivity index data. For instance, the notification is audio, visual, and/or textual notification. In an implementation, the notification indicates a plot of productivity index data for a plurality of production systems. In an implementation, the notification may be sent upon request and/or periodically to the computing device, such as, for example, a report in an e-mail. For instance, the notification may be sent, hourly, daily, weekly, monthly, etc. In another implementation, the notification indicates that one or more production systems require action. In an implementation, the notification is presented via one or more interactive user interfaces generated by the output data generation systemand transmitted, via the communication interface(s), to the computing devicefor display by the output system of the computing device.

The processing systemmay have instructions that direct and/or cause the processing systemto receive input data via the communication interface(s), process the input data, generate estimated sensor data, using the machine learning model, generate productivity index data using the estimated sensor data, generate output data, and transmit the output data to the computing device.

In another implementation, the processing systemmay have instructions that direct and/or cause the processing systemto receive historical data via the one or more databases, process the historical data, generate a training data set, train the machine learning modelusing the training data set, validate the machine learning model, and implement the machine learning modelinto production once validated.

The network(s)can be any combination of one or more of a cellular network such as a 3rd Generation Partnership Project (3GPP) network, a third generation (3G) network, a fourth generation (4G) network, a fifth generation (5G) network, a Long-Term Evolution (LTE), an LTE Advanced Network, a Global System for Mobile Communications (GSM) network, a Universal Mobile Telecommunications System (UMTS) network, and the like. Moreover, the network(s)can include any type of network, such as the Internet, an intranet, a Virtual Private Network (VPN), a Voice over Internet Protocol (VOIP) network, a wireless network (e.g., Bluetooth), a cellular network, a satellite network, combinations thereof, etc. The network(s)can include communications network components such as, but not limited to gateways routers, servers, and registrars, which enable communication across the network(s). In one implementation, the communications network components include multiple ingress/egress routers, which may have one or more ports, in communication with the network(s).

Turning to, a systemto process communication data can include one or more computing devicesfor performing the techniques discussed herein. In one implementation, the one or more computing devicesinclude the computing deviceand/or one or more servers of the processing systemto generate and execute the sensor data generation system, the productivity data generation system, output data generation system, etc. as a software application and/or a module or algorithmic component of software.

In some instances, the computing devicecan include a computer, a personal computer, a desktop computer, a laptop computer, a terminal, a workstation, a server device, a cellular or mobile phone, a mobile device, a smart mobile device a tablet, a wearable device (e.g., a smart watch, smart glasses, a smart epidermal device, etc.) a multimedia console, a television, an Internet-of-Things (IoT) device, a smart home device, a medical device, a virtual reality (VR) or augmented reality (AR) device, a vehicle (e.g., a smart bicycle, an automobile computer, etc.), and/or the like. The computing devicemay be integrated with, form a part of, or otherwise be associated with the systems-. It will be appreciated that specific implementations of these devices may be of differing possible specific computing architectures not all of which are specifically discussed herein but will be understood by those of ordinary skill in the art.

The computing devicemay be a computing system capable of executing a computer program product to execute a computer process. Data and program files may be input to the computing device, which reads the files and executes the programs therein. Some of the elements of the computing deviceinclude one or more processors, one or more memory devices, and/or one or more ports, such as input/output (IO) port(s)and communication port(s). Additionally, other elements that will be recognized by those skilled in the art may be included in the computing devicebut are not explicitly depicted inor discussed further herein. Various elements of the computing devicemay communicate with one another by way of the communication port(s)and/or one or more communication buses, point-to-point communication paths, or other communication means.

The processormay include, for example, a central processing unit (CPU), a microprocessor, a microcontroller, a digital signal processor (DSP), and/or one or more internal levels of cache. There may be one or more processors, such that the processorcomprises a single central-processing unit, or a plurality of processing units capable of executing instructions and performing operations in parallel with each other, commonly referred to as a parallel processing environment.

The computing devicemay be a conventional computer, a distributed computer, or any other type of computer, such as one or more external computers made available via a cloud computing architecture. The presently described technology is optionally implemented in software stored on the data storage device(s) such as the memory device(s), and/or communicated via one or more of the I/O port(s)and the communication port(s), thereby transforming the computing deviceinto a special purpose machine for implementing the operations described herein. Moreover, the computing device, as implemented in the systems-, receives various types of input data (e.g., the input data) and transforms the input data through various stages of the data flow into new types of data files (e.g., estimated sensor data and productivity index data). Moreover, these new data files are transformed further into output data and sent to the computing deviceto provide information regarding the productivity index data, which enables the computing deviceto do something it could not do before—generating estimated bottomhole pressure data using a machine learning model trained using historical data for use in determining productivity index data, thereby leveraging sensor data, estimated data, and reservoir pressure model data.

Additionally, the systems and operations disclosed herein represent an improvement to the technical field of machine learning processing. For instance, the processing systemcan generate productivity index data with vast amounts of data having missing and/or imbalanced data without human intervention. Moreover, data can be leveraged from different data sources with varying levels of abstraction to provide a highly efficient and effective productivity analysis of a number or oil and gas production systems. These techniques are rooted in technology and could not have existed prior to the advent of machine learning analytics.

The one or more memory device(s)may include any non-volatile data storage device capable of storing data generated or employed within the computing device, such as computer executable instructions for performing a computer process, which may include instructions of both application programs and an operating system (OS) that manages the various components of the computing device. The memory device(s)may include, without limitation, magnetic disk drives, optical disk drives, solid state drives (SSDs), flash drives, and the like. The memory device(s)may include removable data storage media, non-removable data storage media, and/or external storage devices made available via a wired or wireless network architecture with such computer program products, including one or more database management products, web server products, application server products, and/or other additional software components. Examples of removable data storage media include Compact Disc Read-Only Memory (CD-ROM), Digital Versatile Disc Read-Only Memory (DVD-ROM), magneto-optical disks, flash drives, and the like. Examples of non-removable data storage media include internal magnetic hard disks, SSDs, and the like. The one or more memory device(s)may include volatile memory (e.g., dynamic random access memory (DRAM), static random access memory (SRAM), etc.) and/or non-volatile memory (e.g., read-only memory (ROM), flash memory, etc.).

Computer program products containing mechanisms to effectuate the systems and methods in accordance with the presently described technology may reside in the memory device(s)which may be referred to as machine-readable media. It will be appreciated that machine-readable media may include any tangible non-transitory medium that is capable of storing or encoding instructions to perform any one or more of the operations of the present disclosure for execution by a machine or that is capable of storing or encoding data structures and/or modules utilized by or associated with such instructions. Machine-readable media may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more executable instructions or data structures.

In some implementations, the computing deviceincludes one or more ports, such as the I/O port(s)and the communication port(s), for communicating with other computing, network, or vehicle computing devices. It will be appreciated that the I/O portand the communication portmay be combined or separate and that more or fewer ports may be included in the computing device.

The I/O portmay be connected to an I/O device, or other device, by which information is input to or output from the computing device. Such I/O devices may include, without limitation, one or more input devices, output devices, and/or environment transducer devices.

In one implementation, the input devices convert a human-generated signal, such as, human voice, physical movement, physical touch or pressure, and/or the like, into electrical signals as input data into the computing devicevia the I/O port. Similarly, the output devices may convert electrical signals received from the computing devicevia the I/O portinto signals that may be sensed as output by a human, such as sound, light, and/or touch. The input device may be an alphanumeric input device, including alphanumeric and other keys for communicating information and/or command selections to the processorvia the I/O port. The input device may be another type of user input device including, but not limited to direction and selection control devices, such as a mouse, a trackball, cursor direction keys, a joystick, and/or a wheel; one or more sensors, such as a camera, a microphone, a positional sensor, an orientation sensor, an inertial sensor, and/or an accelerometer; and/or a touch-sensitive display screen (“touchscreen”). The output devices may include, without limitation, a display, a touchscreen, a speaker, a tactile and/or haptic output device, and/or the like. In some implementations, the input device and the output device may be the same device, for example, in the case of a touchscreen.

The environment transducer devices convert one form of energy or signal into another for input into or output from the computing devicevia the I/O port. For example, an electrical signal generated within the computing devicemay be converted to another type of signal, and/or vice-versa. In one implementation, the environment transducer devices sense characteristics or aspects of an environment local to or remote from the computing device, such as, light, sound, temperature, pressure, magnetic field, electric field, chemical properties, physical movement, orientation, acceleration, gravity, and/or the like.

In one implementation, the communication portis connected to the network(s)so the computing devicecan receive network data useful in executing the methods and systems set out herein as well as transmitting information and network configuration changes determined thereby. Stated differently, the communication portconnects the computing deviceto one or more communication interface devices configured to transmit and/or receive information between the computing deviceand other devices by way of one or more wired or wireless communication networks or connections. Examples of such networks or connections include, without limitation, Universal Serial Bus (USB), Ethernet, Wi-Fi, Bluetooth®, Near Field Communication (NFC), and so on. One or more such communication interface devices may be utilized via the communication portto communicate with one or more other machines, either directly over a point-to-point communication path, over a wide area network (WAN) (e.g., the Internet), over a local area network (LAN), over a cellular network (e.g., third generation (3G), fourth generation (4G), Long-Term Evolution (LTE), fifth generation (5G), etc.) or over another communication means. Further, the communication portmay communicate with an antenna or other link for electromagnetic signal transmission and/or reception.

In an example, the processing system, the sensor data generation system, the productivity data generation system, the output data generation system, etc., and/or other software, modules, services, and operations discussed herein may be embodied by instructions stored on the memory device(s)and executed by the processor.

The system set forth inis but one possible example of a computing deviceor computer system that may be configured in accordance with aspects of the present disclosure. It will be appreciated that other non-transitory tangible computer-readable storage media storing computer-executable instructions for implementing the presently disclosed technology on a computing system may be utilized. In the present disclosure, the methods disclosed may be implemented as sets of instructions or software readable by the computing device.

depicts an example methodfor analyzing key performance indicators of natural resource production systems, which can be performed by any of the systems-discussed herein. The methodcan, in some instances, occur in real time.

At operation, the methodcan receive input data via the communication interface(s)from the one or more sensorsand/or the one or more databases. In an implementation, the input data includes at least one of well head pressure, well head temperature, gas lift rate, watercut, gas-liquid ration, or liquid rate.

At operation, the methodcan process the input data for input into the machine learning model. In an implementation, the processing includes one or more of cleaning/filtering the input data and generating one or more input data sets for the machine learning model.

At operation, the methodcan generate estimated sensor data based on the input data using the machine learning model.

At operation, the methodcan generate productivity index data using the input data, the estimated sensor data, and reservoir pressure model data.

At operation, the methodcan generate output data using the output data generation system.

At operation, the methodcan transmit the output data to the computing device. In an implementation, the output data can be output via the computing device.

depicts an example methodto train a machine learning model, which can be performed by any of the systems-discussed herein. The methodcan, in some instances, occur in real time. In an implementation, methodis performed periodically to restrict model drift. For instance, the methodcan be performed daily, monthly, yearly, etc.

At operation, the methodcan receive historical data via the communication interface(s)from the one or more databases.

At operation, the methodcan process the historical data. In an implementation, the processing includes one or more of cleaning and/or filtering the historical data, thereby generating processed historical data.

At operation, the methodcan generate a training data set using the processed historical data.

At operation, the methodcan train the machine learning modelusing the processed historical data.

At operation, the methodcan validate the model by comparing the estimated sensor data with measured sensor data. If the comparing is below a threshold, the methodreturns to operationto further train the machine learning model.