Systems and methods for productivity analysis of oil and gas production systems

Abstract

Implementations claimed and described herein provide systems and methods for analyzing natural resource production. The systems and methods use a machine learning model to generate estimated sensor data associated with input data. The machine learning model is built from historical data.

Description

SYSTEMS AND METHODS FOR PRODUCTIVITY ANALYSIS OF OIL AND GAS PRODUCTION SYSTEMSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority to U.S. Provisional Patent Application No. 63 / 661,112 filed on June 18, 2024, which is incorporated by reference in its entirety herein.FIELD

[0002] 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.BACKGROUND

[0003] 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.SUMMARY

[0004] 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.

[0005] 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.

[0006] 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.

[0007] 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

[0008] 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.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] FIG. 1 illustrates an example communication system.

[0010] FIG. 2 illustrates an example processing system.

[0011] FIG. 3 illustrates an example computing system that may implement various aspects of the communication system.

[0012] FIG. 4 illustrates example operations for analyzing key performance indicators of natural resource production systems.

[0013] FIG. 5 illustrates example operations for training a machine learning model.

[0014] FIG. 6 illustrates results using an example machine learning model.

[0015] FIG. 7 illustrates results using an example machine learning model.DETAILED DESCRIPTION

[0016] 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.

[0017] To begin a detailed description of an example system 100 for 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 system 100 processes input data and generates estimated sensor data using a machine learning model for use in generating productivity index data, reference is made to FIGs. 1-7. The system 100 can include a processing system 102 configured to receive the input data. The input data is received from at least one of a computing device 104, one or more sensors 106, or one or more databases 110. The system 100 is configured to receive user inputs via one or more input systems using, for example, the computing device 104 to input text, audio, and / or interact with an interactive user interface displayed on one or more output systems of, for example, the computing device 104. The processing system 102, the computing device 104, the one or more sensors 106, and the one or more databases 110 are configured to interact with one another via a network(s) 112. As illustrated in greater detail below, any and / or all of the processing system 102, the computing device 104, and the one or moredatabases 110 may, in some instances, be special-purpose computing devices configured to perform specific functions.

[0018] The processing system 102 includes 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 110, generate user interfaces, execute a sensor data generation system 114, a productivity data generation system 116, an output data generation system 122, etc. by processing instructions. The processing system 102 may include a communication interface(s) 118 that is able to communicate with the one or more input systems and one or more output systems via the network(s) 112. For instance, the communication interface(s) 118 may be a network interface configured to support communication between the processing system 102 and the network(s) 112. The one or more input systems and one or more output systems may be part of the computing device 104 or separate from the computing device 104. The processing system 102 can be configured to train and maintain a machine learning model 120 to execute the techniques, as discussed in greater detail below. The processing system 102 can be configured to monitor and store (e.g., with appropriate permissions) sensor data for further analysis and / or training of the machine learning model 120. In an implementation, the processing system 102 is configured to transmit the communication to another computing device or database, such as the one or more databases 110. In an implementation, the processing system 102 is associated with an organization or entity.

[0019] In an implementation, the computing device 104 includes one or more input systems and one or more output systems. For instance, the operator is able to input user data to the processing system 102 via one or more interactive user interfaces using the computing device 104. The computing device 104 can 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 device 104 may be used to display plots, analytical information, notifications and / or other alerts using graphical user interfaces.

[0020] In an implementation, the processing system 102 includes instructions that direct and / or cause the sensor data generation system 114 to execute processing techniques on the input data to generate input data subsets that are input into the machine learning model 120. 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 ofthe input data is obtained by one or more sensors 106 disposed 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 110.

[0021] In an implementation, the machine learning model 120 is trained to generate estimated sensor data based on the input data. In an implementation, the machine learning model 120 utilizes 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 model 120 may be built from historical data that has been previously collected and stored, for example, at the one or more databases 110. In this implementation, the machine learning model 120 leverages 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 model 120 allows the sensor data generation system 114 to 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 110. Accordingly, the machine learning model 120 allows the sensor data generation system 114 to 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.

[0022] In an implementation, the processing system 102 includes instructions that direct and / or cause the productivity data generation system 116 to 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 106, and thereservoir model data is received from the one or more databases 110. In an implementation, the productivity data includes a productivity index value calculated according to equation 1.PI = — - — (Equation 1),Pr-Pwf where:PI = productivity index; = flow rate; pr= reservoir pressure; and pwf = bottomhole pressure

[0023] In an implementation to allow for multi-system productivity analysis, the productivity data generation system 116 normalizes the productivity index value by a completed permeability -length product according to equation 2:(Equation 2), where:PI = productivity index; k = permeability; andL = completed well depth.

[0024] In an implementation, the processing system 102 includes instructions that direct and / or cause the output data generation system 122 to perform one or more of the functions described herein. For example, the output data generation system 122 is 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 104, 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 system 122 and transmitted, via the communication interface(s) 118, to the computing device 104 for display by the output system of the computing device 104.

[0025] The processing system 102 may have instructions that direct and / or cause the processing system 102 to receive input data via the communication interface(s) 118, process the input data, generate estimated sensor data, using the machine learning model 120, generate productivity index data using the estimated sensor data, generate output data, and transmit the output data to the computing device 104.

[0026] In another implementation, the processing system 102 may have instructions that direct and / or cause the processing system 102 to receive historical data via the one or more databases 110, process the historical data, generate a training data set, train the machine learning model 120 using the training data set, validate the machine learning model 120, and implement the machine learning model 120 into production once validated.

[0027] The network(s) 112 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) 112 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) 112 can include communications network components such as, but not limited to gateways routers, servers, and registrars, which enable communication across the network(s) 112. 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) 112.

[0028] Turning to FIG. 3, a system 300 to process communication data can include one or more computing devices 302 for performing the techniques discussed herein. In one implementation, the one or more computing devices 302 include the computing device 104 and / or one or more servers of the processing system 102 to generate and execute the sensor data generation system 114, the productivity data generation system 116, output data generation system 122, etc. as a software application and / or a module or algorithmic component of software.

[0029] In some instances, the computing device 302 can 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 smartwatch, smart glasses, a smart epidermal device, etc.) a multimedia console, a television, an Internet-of-Things (loT) 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 device 302 may be integrated with, form a part of, or otherwise be associated with the systems 100-300. 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.

[0030] The computing device 302 may 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 302, which reads the files and executes the programs therein. Some of the elements of the computing device 302 include one or more processors 304, one or more memory devices 306, and / or one or more ports, such as input / output (IO) port(s) 308 and communication port(s) 310. Additionally, other elements that will be recognized by those skilled in the art may be included in the computing device 302 but are not explicitly depicted in FIG. 3 or discussed further herein. Various elements of the computing device 302 may communicate with one another by way of the communication port(s) 310 and / or one or more communication buses, point-to-point communication paths, or other communication means.

[0031] The processor 304 may 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 304, such that the processor 304 comprises 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.

[0032] The computing device 302 may 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) 306, and / or communicated via one or more of the I / O port(s) 308 and the communication port(s) 310, thereby transforming the computing device 302 in FIG. 3 to a special purpose machine for implementing the operations described herein. Moreover, the computing device 302, as implemented in the systems 100-300, receives various types of input data (e.g., the input data) and transforms the input data throughvarious 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 device 104 to provide information regarding the productivity index data, which enables the computing device 302 to 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 .

[0033] Additionally, the systems and operations disclosed herein represent an improvement to the technical field of machine learning processing. For instance, the processing system 102 can 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.

[0034] The one or more memory device(s) 306 may include any non-volatile data storage device capable of storing data generated or employed within the computing device 302, 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 302. The memory device(s) 306 may include, without limitation, magnetic disk drives, optical disk drives, solid state drives (SSDs), flash drives, and the like. The memory device(s) 306 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) 306 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.).

[0035] 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) 306 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.

[0036] In some implementations, the computing device 302 includes one or more ports, such as the I / O port(s) 308 and the communication port(s) 310, for communicating with other computing, network, or vehicle computing devices. It will be appreciated that the I / O port 308 and the communication port 310 may be combined or separate and that more or fewer ports may be included in the computing device 302.

[0037] The I / O port 308 may be connected to an I / O device, or other device, by which information is input to or output from the computing device 302. Such I / O devices may include, without limitation, one or more input devices, output devices, and / or environment transducer devices.

[0038] 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 device 302 via the I / O port 308. Similarly, the output devices may convert electrical signals received from the computing device 302 via the I / O port 308 into 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 processor 304 via the I / O port 308. 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.

[0039] The environment transducer devices convert one form of energy or signal into another for input into or output from the computing device 302 via the I / O port 308. For example, an electrical signal generated within the computing device 302 may 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 302, such as, light, sound, temperature, pressure, magnetic field, electric field, chemical properties, physical movement, orientation, acceleration, gravity, and / or the like.

[0040] In one implementation, the communication port 310 is connected to the network(s) 112 so the computing device 302 can 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 port 310 connects the computing device 302 to one or more communication interface devices configured to transmit and / or receive information between the computing device 302 and 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 port 310 to 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 port 310 may communicate with an antenna or other link for electromagnetic signal transmission and / or reception.

[0041] In an example, the processing system 102, the sensor data generation system 114, the productivity data generation system 116, the output data generation system 122, etc., and / or other software, modules, services, and operations discussed herein may be embodied by instructions stored on the memory device(s) 306 and executed by the processor 304.

[0042] The system set forth in FIG. 3 is but one possible example of a computing device 302 or 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 acomputing 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 302.

[0043] FIG. 4 depicts an example method 400 for analyzing key performance indicators of natural resource production systems, which can be performed by any of the systems 100-300 discussed herein. The method 400 can, in some instances, occur in real time.

[0044] At operation 402, the method 400 can receive input data via the communication interface(s) 118 from the one or more sensors 106 and / or the one or more databases 110. 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.

[0045] At operation 404, the method 400 can process the input data for input into the machine learning model 120. 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 120.

[0046] At operation 406, the method 400 can generate estimated sensor data based on the input data using the machine learning model 120.

[0047] At operation 408, the method 400 can generate productivity index data using the input data, the estimated sensor data, and reservoir pressure model data.

[0048] At operation 410, the method 400 can generate output data using the output data generation system.

[0049] At operation 412, the method 400 can transmit the output data to the computing device 104. In an implementation, the output data can be output via the computing device 104.

[0050] FIG. 5 depicts an example method 500 to train a machine learning model, which can be performed by any of the systems 100-300 discussed herein. The method 500 can, in some instances, occur in real time. In an implementation, method 500 is performed periodically to restrict model drift. For instance, the method 500 can be performed daily, monthly, yearly, etc.

[0051] At operation 502, the method 400 can receive historical data via the communication interface(s) 118 from the one or more databases 110.

[0052] At operation 504, the method 500 can 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.

[0053] At operation 506, the method 500 can generate a training data set using the processed historical data.

[0054] At operation 508, the method 500 can train the machine learning model 120 using the processed historical data.

[0055] At operation 510, the method 500 can validate the model by comparing the estimated sensor data with measured sensor data. If the comparing is below a threshold, the method 500 returns to operation 508 to further train the machine learning model 120.

[0056] At operation 512, the method 500 can implement the machine learning model 120 into production if the comparing is within the threshold.

[0057] It is to be understood that the specific order or hierarchy of operations in the methods depicted in FIGs. 4 and 5 and throughout this disclosure are instances of example approaches and can be rearranged while remaining within the disclosed subject matter. For instance, any of the operations depicted in FIGs. 4 and 5 may be omitted, repeated, performed in parallel, performed in a different order, and / or combined with any other of the operations depicted in FIGs. 4 and 5 or discussed herein.

[0058] The system and methods described herein facilitate decisions to optimize well performance through restimulations, recompletions, and redrills to maximize the producing potential of wells in more than one field. The generated dataset is also valuable in providing lessons learned from retrospective studies about historical completion and stimulation practices over the history of a field.

[0059] The system and methods described herein facilitate generating productivity index data when bottomhole flowing pressures are not known. In an implementation, well properties can be obtained from logs including using 1’ log data to pull average properties within completed intervals and using KL weighted values to aggregate up to the well level from unique zones. In another implementation, reservoir pressures can be obtained using first exporting model layer pressures within a 1000 ft radius around each well within each perforated layer. This reservoir pressure is first KL-weighted to account for the multiple completed intervals in each well and then corrected at t=0 with real pressures.

[0060] FIGs. 6 and 7 illustrate the accuracy of the bottomhole pressure estimation using the machine learning model 120 in accordance with the system and methods described herein. Duringtesting, the estimated bottomhole pressure determined using the machine learning model 120 was found to have an absolute error of less than 4% compared to the measured bottomhole pressure.

[0061] Furthermore, any term of degree such as, but not limited to, “substantially,” as used in the description and the appended claims, should be understood to include an exact, or a similar, but not exact configuration. Similarly, the terms “about” or “approximately,” as used in the description and the appended claims, should be understood to include the recited values or a value that is three times greater or one third of the recited values. For example, about 3 mm includes all values from 1 mm to 9 mm, and approximately 50 degrees includes all values from 16.6 degrees to 150 degrees.

[0062] Lastly, the terms “or” and “and / or,” as used herein, are to be interpreted as inclusive or meaning any one or any combination. Therefore, “A, B, or C” or “A, B, and / or C” mean any of the following: “A,” “B,” or “C”; “A and B”; “A and C”; “B and C”; “A, B and C.” An exception to this definition will occur only when a combination of elements, functions, steps or acts are in some way inherently mutually exclusive.

[0063] While the present disclosure has been described with reference to various implementations, it will be understood that these implementations are illustrative and that the scope of the present disclosure is not limited to them. Many variations, modifications, additions, and improvements are possible. More generally, implementations in accordance with the present disclosure have been described in the context of particular implementations. Functionality may be separated or combined differently in various implementations of the disclosure or described with different terminology. These and other variations, modifications, additions, and improvements may fall within the scope of the disclosure as defined in the claims that follow.

Claims

CLAIMSWHAT IS CLAIMED IS:

1. 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.

2. The method of claim 1, further comprising: transmitting the output data to a computing device to cause the output data to be presented using one or more output systems of the computing device.

3. The method of claim 2, wherein the output data includes a notification associated with the productivity index data.

4. The method of claim 3, wherein the notification includes a plot of the productivity index data.

5. The method of claim 2, wherein the computing device includes at least one of a smartphone, a tablet, a desktop computer, a laptop computer, or a personal computing device.

6. The method of any one of claims 1-5, wherein the input data includes at least one of well head pressure, well head temperature, gas lift rate, watercut, gas-liquid ration, or liquid rate.

7. The method of any one of claims 1-6, further comprising: normalizing the productivity index data by a completed permeability -length product.

8. The method of any one of claims 1-7, wherein the productivity index data is generated using reservoir model data.

9. The method of claim 8, wherein the reservoir model data includes a reservoir pressure determined using a historically matched numerical model.

10. The method of any one of claims 1-9, further comprising: processing the input data before inputting the input data into the machine learning model.

11. The method of any one of claims 1-10, wherein the machine learning model is validated by comparing estimated bottomhole pressure generated using the machine learning model with sensor data including measured bottom hole pressure.

12. The method of claim 11, wherein the machine learning model is implemented into production based on the validating.

13. The method of any one of claims 1-12, wherein the input data is obtained during at least one well test / 14. A system comprising at least one processor, and a storage medium storing instructions, which when executed by the at least one processor, causes the system to carry out the method of any one of claims 1 to 13.

15. A machine-readable medium carrying machine readable instructions, which when executed by at least one processor of at least one computing device, causes the at least one computing device to carry out the method of any one of claims 1 to 13.

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