Oil and gas well water content prediction method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses an oil and gas well water content prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring historical data during oil-gas well production; the historical data comprises wellhead temperature, wellhead back pressure and liquid phase moisture content; determining a corresponding relation among the wellhead temperature, the wellhead back pressure and the liquid phase water content, and establishing a water content prediction model according to the corresponding relation; and the real-time wellhead temperature and wellhead return pressure of the oil and gas well to be measured are obtained, and the water content of the oil and gas well to be measured is predicted according to the water content prediction model. By adopting the technical scheme of the embodiment of the invention, the water content can be predicted in real time by only depending on the wellhead temperature and the wellhead back pressure without a water content meter; the water content meter is only used as a model correction device, does not need to be installed for a long time and can be recycled among multiple wells, the single well application cost is greatly reduced, the oil and gas field test technology level is comprehensively improved, and oil and gas well fine management and digital transformation are effectively assisted.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of oil and gas metering, and in particular to a method, device, electronic device and storage medium for predicting water content in oil and gas wells. Background Art

[0002] Water content test data is the basis for understanding reservoir conditions, formulating production plans and fine-grained management of oil and gas wells. At the same time, as one of the important data for digital construction of oil fields, test data is the core data of digital twins of oil and gas wells and an important basis for production regulation. Its quality and abundance directly determine the accuracy, effectiveness and stability of oil well big data modeling.

[0003] At present, manual sampling and water content testing in the laboratory are still the main method, which has poor accuracy, low efficiency and high cost, seriously restricting the digital transformation and intelligent development of oil fields. As an instrument installed at the wellhead to collect the water content of oil wells in real time, the water content analyzer is favored by oil fields. However, the water content analyzer itself is relatively expensive, and the large-scale promotion and application of old wells still faces great cost pressure. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, electronic device and storage medium for predicting the water content of oil and gas wells, so as to realize real-time prediction of the water content based on the temperature and back pressure of the wellhead, reduce the use of water meters, and reduce the application cost of a single well.

[0005] In a first aspect, an embodiment of the present invention provides a method for predicting water content in an oil and gas well, comprising:

[0006] Acquire historical data of oil and gas well production; the historical data includes wellhead temperature, wellhead back pressure and liquid phase water content;

[0007] Determine the corresponding relationship between the wellhead temperature, the wellhead back pressure and the liquid phase water content, and establish a water content prediction model based on the corresponding relationship;

[0008] The real-time wellhead temperature and wellhead back pressure of the oil and gas well to be measured are obtained, and the water cut of the oil and gas well to be measured is predicted according to the water cut prediction model.

[0009] In a second aspect, an embodiment of the present invention further provides a device for predicting water content in oil and gas wells, comprising:

[0010] The data acquisition module is specifically used to acquire historical data of oil and gas well production; the historical data includes wellhead temperature, wellhead back pressure and liquid phase water content;

[0011] The model building module is specifically used to determine the corresponding relationship between the wellhead temperature, the wellhead back pressure and the liquid phase water content, and to establish a water content prediction model based on the corresponding relationship;

[0012] The water content prediction module is specifically used to obtain the real-time wellhead temperature and wellhead back pressure of the oil and gas well to be measured, and to predict the water content of the oil and gas well to be measured based on the water content prediction model.

[0013] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0014] one or more processors;

[0015] A storage device for storing one or more programs;

[0016] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for predicting water content in oil and gas wells described in any embodiment of the present invention.

[0017] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for predicting water content in oil and gas wells as described in any embodiment of the present invention.

[0018] The embodiment of the present invention provides a method, device, electronic device and storage medium for predicting the water content of oil and gas wells, by obtaining historical data of oil and gas well production; the historical data includes wellhead temperature, wellhead back pressure and liquid phase water content; determining the correspondence between wellhead temperature, wellhead back pressure and liquid phase water content, and establishing a water content prediction model based on the correspondence; obtaining the real-time wellhead temperature and wellhead back pressure of the oil and gas wells to be measured, and predicting the water content of the oil and gas wells to be measured based on the water content prediction model. By adopting the technical solution of the embodiment of the present invention, the water content meter can be separated, and the real-time prediction of the water content can be performed only by relying on the wellhead temperature and wellhead back pressure; the water content meter is only used as a model correction device, and there is no need to install the water content meter for a long time. It can be circulated among multiple wells, greatly reducing the application cost of a single well, comprehensively improving the level of oil and gas field testing technology, and effectively assisting the refined management and digital transformation of oil and gas wells. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts. In the drawings:

[0020] Figure 1 It is a flow chart of a method for predicting water content in oil and gas wells provided in an embodiment of the present invention;

[0021] Figure 2It is a flow chart of another method for predicting water content of oil and gas wells provided in an embodiment of the present invention;

[0022] Figure 3 It is a structural schematic diagram of a device for predicting water content in oil and gas wells provided in an embodiment of the present invention;

[0023] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0025] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0026] Among them, the acquisition, storage, use and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations.

[0027] Figure 1 This is a flow chart of a method for predicting the water content of oil and gas wells provided in an embodiment of the present invention. This embodiment is applicable to the case where the water content is predicted when the water content of oil and gas wells is tested. The method of this embodiment can be executed by a device for predicting the water content of oil and gas wells, and the device can be implemented in hardware and / or software. The device can be configured in a server for predicting the water content of oil and gas wells. The method specifically includes the following steps:

[0028] S110. Obtain historical data of oil and gas well production.

[0029] Among them, multiphase metering and testing of oil and gas well output fluids is an indispensable daily work in oil fields and the basis for reservoir evaluation, dynamic analysis, and production optimization. China alone has more than 800,000 oil, gas and water wells, more than 20,000 metering rooms and testing stations, and 200,000 oil and gas metering and testing practitioners. Efficient and accurate online metering and testing of multiphase flow is a major proposition for lean management, digital transformation, and quality and efficiency improvement in oil fields.

[0030] At present, manual sampling and water content testing in the laboratory are still the main method, which has poor accuracy, low efficiency and high cost, seriously restricting the digital transformation and intelligent development of oil fields. As an instrument installed at the wellhead to collect the water content of oil wells in real time, the water content meter is favored by oil fields. However, the price of the water content meter itself is relatively high, and the large-scale promotion and application of old wells still faces great cost pressure.

[0031] The present invention proposes a method for predicting the water cut of oil and gas wells. The method is based on neural network modeling and can be separated from the water content meter. The real-time prediction of the water cut is performed only by the wellhead temperature and the wellhead back pressure. The water cut is only used as a model correction device and can be circulated among multiple wells, greatly reducing the application cost of a single well.

[0032] Before modeling, the historical data of oil and gas wells need to be collected and managed. The historical data includes but is not limited to wellhead temperature, wellhead back pressure and liquid phase water content. In addition, the electrical parameters and dynamometer diagram data of the oil and gas wells during production can also be obtained to improve the accuracy of modeling.

[0033] As an optional but non-limiting implementation, the acquisition of historical data during oil and gas well production includes but is not limited to steps A1-A2:

[0034] Step A1: Acquire the first historical data of the oil and gas well during production.

[0035] Step A2: preprocessing the first historical data to obtain historical data for establishing a moisture content prediction model; wherein the preprocessing includes eliminating abnormal data, filling in missing data, and standardizing data format.

[0036] Among them, the first historical data may refer to the wellhead temperature and wellhead back pressure directly obtained by using a temperature and pressure gauge, and the liquid phase water content directly obtained by using a water content meter. Due to the measurement errors of the pressure regulator and the water content meter, the first historical data obtained has errors. The first historical data is preprocessed to eliminate abnormal data. For example, a reasonable numerical range is set for the wellhead temperature, wellhead back pressure and liquid phase water content, and the first historical data that exceeds the numerical range is smoothed or assigned to eliminate abnormal data. Filling missing data may refer to increasing or reducing the data frequency of the first historical data according to model requirements; increasing the data frequency requires interpolation of the data. For example, the water content of sampling and testing is often performed once a day. The amount of data does not meet the modeling requirements and interpolation is required. The daily water content curve can be smoothed to complete the interpolation; reducing the data frequency is generally for temperature and pressure data or electrical parameter data. Frequency reduction can be achieved by averaging multiple data. Standardized data formats include but are not limited to unified data time intervals, units, and the number of valid data bits. After eliminating abnormal data, filling in missing data and standardizing the data format, the first historical data is used to obtain historical data for establishing a moisture content prediction model.

[0037] S120, determining the corresponding relationship among the wellhead temperature, the wellhead back pressure and the liquid phase water content, and establishing a water content prediction model according to the corresponding relationship.

[0038] Among them, by determining the corresponding relationship between wellhead temperature, wellhead back pressure and liquid phase water content, the wellhead temperature and wellhead back pressure are used as model input data, and the liquid phase water content is used as model output data, so as to establish a water content prediction model.

[0039] As an optional but non-limiting implementation, the determination of the correspondence between the wellhead temperature, the wellhead back pressure and the liquid phase water content, and the establishment of a water content prediction model based on the correspondence, includes but is not limited to steps B1-B2:

[0040] Step B1: Divide the historical data into a training set and a validation set for model training.

[0041] Step B2: Determine the corresponding relationship among the wellhead temperature, wellhead back pressure and liquid phase water content in the training set, and establish a water content prediction model based on the corresponding relationship.

[0042] Among them, the historical data is divided into a training set and a validation set. For example, 70% of the data is selected for training the neural network model, and 30% of the data is used to verify the accuracy of the model. In an optional scheme of an embodiment of the present invention, when dividing the historical data, interval sampling is required so that the sampling time span of each part of the data covers the entire life cycle of an industrial process. For example, a single production cycle of a carbon dioxide swallowing well is 4 months, then the data of the 1st and 2nd days can be included in the training set, the data of the 3rd day can be included in the validation set, the data of the 4th and 5th days can be included in the training set, and the data of the 6th day can be included in the validation set, and so on. The historical data obtained in 4 months is divided into a training set and a validation set.

[0043] In an optional scheme for implementing the present invention, a feedforward neural network having neurons and hidden neurons is created and neural network training parameters are set. The feedforward neural network may refer to a feedforward neural network including a first hidden layer of 10 neurons and a second hidden layer of 5 hidden neurons. The neural network training parameters include but are not limited to the number of iterations and the learning rate. The number of iterations is generally 100-300 times, which can be adjusted according to the later model verification effect. Generally, the more iterations, the better the effect, but the time spent and the computing power required for the computer are higher; the learning rate can be set to 0.01.

[0044] After the neural network is created and the training parameters are set, a water cut prediction model is established based on the correspondence between the wellhead temperature, wellhead back pressure and liquid phase water cut. In the embodiment of the present invention, the water cut prediction model is used to predict the water cut of oil and gas wells. The water cut can be predicted by only obtaining the wellhead temperature and wellhead back pressure, which reduces the use of water cut meters and thus reduces the application cost of a single well.

[0045] As an optional but non-limiting implementation, the determination of the correspondence between the wellhead temperature, the wellhead back pressure and the liquid phase water content, and the establishment of a water content prediction model based on the correspondence, also includes but is not limited to steps C1-C4:

[0046] Step C1: input the wellhead temperature and wellhead back pressure in the validation set data into the water cut prediction model to determine the first water cut.

[0047] Step C2: Determine whether a target error between the first moisture content and the second moisture content is less than a preset error threshold; the second moisture content is directly measured using a moisture meter.

[0048] Step C3: If the target error is less than the preset error threshold, the water cut prediction model is used to predict the water cut of the oil and gas well to be measured.

[0049] Step C4: If the target error is not less than the preset error threshold, the number of model training iterations is adjusted to retrain the model.

[0050] The wellhead temperature and wellhead back pressure in the validation set are input into the water cut prediction model, and the water cut prediction model is used to obtain the first water cut; determine whether the target error between the first water cut and the second water cut is less than the preset error threshold; if it is less than the preset error threshold, it indicates that the prediction accuracy of the water cut prediction model is high, and the water cut prediction model can be used to predict the water cut of the oil and gas wells to be measured. If the target error is greater than the preset error threshold, it indicates that the water cut prediction model needs to be adjusted to improve the accuracy of water cut prediction; for example, the model can be retrained by increasing the number of iterations to obtain a water cut prediction model with a target error less than the preset error threshold. The second water cut may refer to the water cut directly measured by the water meter in the historical data, and the second water cut corresponds to the wellhead temperature and wellhead back pressure input in the validation set.

[0051] S130, obtaining the real-time wellhead temperature and wellhead back pressure of the oil and gas well to be measured, and predicting the water cut of the oil and gas well to be measured according to the water cut prediction model.

[0052] After the water cut prediction model is determined, the water cut of the oil and gas well to be measured is predicted by using the water cut prediction model. The water cut of the oil and gas well to be measured is predicted by inputting the acquired real-time wellhead temperature and wellhead back pressure of the oil and gas well to be measured into the water cut prediction model.

[0053] As an optional but non-limiting implementation, the real-time wellhead temperature and wellhead back pressure of the oil and gas well to be measured are obtained, and the water cut of the oil and gas well to be measured is predicted according to the water cut prediction model, including but not limited to steps D1-D2:

[0054] Step D1: Use the temperature and pressure gauge installed at the wellhead to obtain the real-time wellhead temperature and wellhead back pressure of the oil and gas well to be measured.

[0055] Step D2: input the real-time wellhead temperature and wellhead back pressure into the water cut prediction model to determine the water cut of the oil and gas well to be measured.

[0056] Among them, a temperature and pressure gauge installed at the wellhead is used to obtain the real-time wellhead temperature and wellhead back pressure of the oil and gas well to be measured, and the real-time wellhead temperature and wellhead back pressure are input into the water cut prediction model to predict the water cut of the oil and gas well to be measured.

[0057] In the embodiment of the present invention, a correlation model between wellhead temperature, pressure and water content is learned and established, so as to achieve the purpose of predicting water content by only collecting wellhead temperature and wellhead back pressure, reduce the use of water content meters, and reduce the application cost of a single well.

[0058] As an optional but non-limiting implementation, the oil and gas well water content prediction method further includes:

[0059] The water cut prediction model is calibrated using a water cut meter within a preset time interval, and the water cut prediction model after correction is used to predict the water cut of the oil and gas well to be measured.

[0060] Among them, see Figure 2 , the wellhead is connected to a "multi-parameter water content meter" or "water content meter + temperature and pressure gauge", and the installation position is behind the wellhead oil nozzle; the installed water content meter is calibrated by sampling and testing the water content, so that the metering result of the water content meter equipment is accurate. The real-time data of the oil and gas wells to be measured is acquired, and the real-time data includes but is not limited to the wellhead temperature, wellhead back pressure and water content. After the data is collected, it can be temporarily stored in the instrument hard disk space or transmitted to the database; the real-time data is pre-processed to obtain the target real-time data; the target real-time data is compared with the water content obtained by the water content prediction model to determine whether the water content prediction model needs to be corrected. If correction is required, the water content prediction model is corrected by using the water content meter. When the water content prediction model is corrected by using the water content meter, the water content prediction model stops the water content prediction work, and the water content meter is used to predict the water content of the oil and gas wells to be measured; after the correction is completed, the corrected water content prediction model is verified, and the correction is terminated after the verification is passed, and the corrected water content prediction model is used to predict the water content of the oil and gas wells to be measured.

[0061] The embodiment of the present invention provides a method for predicting the water content of oil and gas wells, by obtaining historical data during the production of the oil and gas wells; the historical data includes wellhead temperature, wellhead back pressure and liquid phase water content; determining the correspondence between the wellhead temperature, wellhead back pressure and liquid phase water content, and establishing a water content prediction model based on the correspondence; obtaining the real-time wellhead temperature and wellhead back pressure of the oil and gas wells to be measured, and predicting the water content of the oil and gas wells to be measured based on the water content prediction model. By adopting the technical solution of the embodiment of the present invention, the water content meter can be separated, and the real-time prediction of the water content can be performed only by relying on the wellhead temperature and wellhead back pressure; the water content meter is only used as a model correction device, and there is no need to install the water content meter for a long time. It can be circulated among multiple wells, greatly reducing the application cost of a single well, comprehensively improving the level of oil and gas field testing technology, and effectively assisting the refined management and digital transformation of oil and gas wells.

[0062] Figure 3This is a schematic diagram of the structure of a device for predicting the water content of oil and gas wells provided in an embodiment of the present invention. The technical solution of this embodiment can be applied to the situation of predicting the water content when testing the water content of oil and gas wells. The device can be implemented by software and / or hardware, and is generally integrated on any electronic device with network communication function, including but not limited to: servers, computers, personal digital assistants and other devices. Figure 3 As shown, the oil and gas well water content prediction device provided in this embodiment may include:

[0063] The data acquisition module 310 is specifically used to acquire historical data of oil and gas well production; the historical data includes wellhead temperature, wellhead back pressure and liquid phase water content;

[0064] The model building module 320 is specifically used to determine the corresponding relationship between the wellhead temperature, the wellhead back pressure and the liquid phase water content, and to establish a water content prediction model based on the corresponding relationship;

[0065] The water cut prediction module 330 is specifically used to obtain the real-time wellhead temperature and wellhead back pressure of the oil and gas well to be measured, and to predict the water cut of the oil and gas well to be measured based on the water cut prediction model.

[0066] Based on the above embodiment, optionally, the device further includes:

[0067] A model correction module, specifically used to calibrate the water cut prediction model using a water cut meter within a preset time interval, and use the corrected water cut prediction model to predict the water cut of the oil and gas well to be measured;

[0068] When the water content meter is used to calibrate the water content prediction model, the water content prediction model stops the water content prediction work, and the water content meter is used to predict the water content of the oil and gas well to be measured.

[0069] Based on the above embodiment, optionally, the data acquisition module is specifically used to:

[0070] Get the first historical data of oil and gas well production;

[0071] Preprocessing the first historical data to obtain historical data for establishing a moisture content prediction model;

[0072] The preprocessing includes eliminating abnormal data, filling in missing data and standardizing data format.

[0073] Based on the above embodiment, optionally, the model building module is specifically used to:

[0074] Divide the historical data into training sets and validation sets for model training;

[0075] The corresponding relationship among the wellhead temperature, wellhead back pressure and liquid phase water content in the training set is determined, and a water content prediction model is established based on the corresponding relationship.

[0076] Based on the above embodiment, optionally, the model building module is specifically used to:

[0077] Input the wellhead temperature and wellhead back pressure in the validation set data into the water cut prediction model to determine the first water cut;

[0078] Determine whether a target error between a first moisture content and a second moisture content is less than a preset error threshold; the second moisture content is directly measured by a moisture meter;

[0079] If the target error is less than the preset error threshold, the water cut prediction model is used to predict the water cut of the oil and gas well to be measured;

[0080] If the target error is not less than the preset error threshold, the number of model training iterations is adjusted to retrain the model.

[0081] Based on the above embodiment, optionally, the moisture content prediction module is specifically used to:

[0082] Use the temperature and pressure gauge installed at the wellhead to obtain the real-time wellhead temperature and wellhead back pressure of the oil and gas well to be measured;

[0083] The real-time wellhead temperature and wellhead back pressure are input into the water cut prediction model to determine the water cut of the oil and gas well to be measured.

[0084] The oil and gas well water cut prediction device provided in the embodiment of the present invention can execute the oil and gas well water cut prediction method provided in any of the above-mentioned embodiments of the present invention, and has the corresponding functions and beneficial effects of executing the oil and gas well water cut prediction method. For detailed process, please refer to the relevant operations of the oil and gas well water cut prediction method in the above-mentioned embodiments.

[0085] Figure 4 1 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0086] like Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0087] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0088] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for predicting water content in oil and gas wells.

[0089] In some embodiments, the method for predicting the water content of oil and gas wells may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for predicting the water content of oil and gas wells described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for predicting the water content of oil and gas wells in any other appropriate manner (e.g., by means of firmware).

[0090] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0091] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0092] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0093] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0094] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0095] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0096] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0097] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting water content in oil and gas wells, characterized in that: The method comprises: Acquire historical data of oil and gas well production; the historical data includes wellhead temperature, wellhead back pressure and liquid phase water content; Determine the corresponding relationship between the wellhead temperature, the wellhead back pressure and the liquid phase water content, and establish a water content prediction model based on the corresponding relationship; The real-time wellhead temperature and wellhead back pressure of the oil and gas well to be measured are obtained, and the water cut of the oil and gas well to be measured is predicted according to the water cut prediction model.

2. The method according to claim 1, characterized in that The method for predicting water content in oil and gas wells further comprises: The water content prediction model is calibrated using a water content meter within a preset time interval, and the water content prediction model after correction is used to predict the water content of the oil and gas well to be measured; When the water content meter is used to calibrate the water content prediction model, the water content prediction model stops the water content prediction work, and the water content meter is used to predict the water content of the oil and gas well to be measured.

3. The method according to claim 1, characterized in that The acquisition of historical data of oil and gas well production includes: Get the first historical data of oil and gas well production; Preprocessing the first historical data to obtain historical data for establishing a moisture content prediction model; The preprocessing includes eliminating abnormal data, filling in missing data and standardizing data format.

4. The method according to claim 1, characterized in that: The determining of the correspondence between the wellhead temperature, the wellhead back pressure and the liquid phase water content, and establishing a water content prediction model based on the correspondence, includes: Divide the historical data into training sets and validation sets for model training; The corresponding relationship among the wellhead temperature, wellhead back pressure and liquid phase water content in the training set is determined, and a water content prediction model is established based on the corresponding relationship.

5. The method according to claim 1, characterized in that The determining of the correspondence between the wellhead temperature, the wellhead back pressure and the liquid phase water content, and establishing a water content prediction model based on the correspondence, further includes: Input the wellhead temperature and wellhead back pressure in the validation set data into the water cut prediction model to determine the first water cut; Determine whether a target error between a first moisture content and a second moisture content is less than a preset error threshold; the second moisture content is directly measured by a moisture meter; If the target error is less than the preset error threshold, the water cut prediction model is used to predict the water cut of the oil and gas well to be measured; If the target error is not less than the preset error threshold, the number of model training iterations is adjusted to retrain the model.

6. The method according to claim 1, characterized in that The step of obtaining the real-time wellhead temperature and wellhead back pressure of the oil and gas well to be measured, and predicting the water content of the oil and gas well to be measured based on the water content prediction model, includes: Use the temperature and pressure gauge installed at the wellhead to obtain the real-time wellhead temperature and wellhead back pressure of the oil and gas well to be measured; The real-time wellhead temperature and wellhead back pressure are input into the water cut prediction model to determine the water cut of the oil and gas well to be measured.

7. A device for predicting water content in oil and gas wells, characterized in that: The device comprises: The data acquisition module is specifically used to acquire historical data of oil and gas well production; the historical data includes wellhead temperature, wellhead back pressure and liquid phase water content; The model building module is specifically used to determine the corresponding relationship between the wellhead temperature, the wellhead back pressure and the liquid phase water content, and to establish a water content prediction model based on the corresponding relationship; The water content prediction module is specifically used to obtain the real-time wellhead temperature and wellhead back pressure of the oil and gas well to be measured, and to predict the water content of the oil and gas well to be measured based on the water content prediction model.

8. The device according to claim 7, characterized in that The device also includes: A model correction module, specifically used to calibrate the water cut prediction model using a water cut meter within a preset time interval, and use the corrected water cut prediction model to predict the water cut of the oil and gas well to be measured; When the water content meter is used to calibrate the water content prediction model, the water content prediction model stops the water content prediction work, and the water content meter is used to predict the water content of the oil and gas well to be measured.

9. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method for predicting water content in oil and gas wells as described in any one of claims 1-6.

10. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to execute the method for predicting water content in oil and gas wells as described in any one of claims 1 to 6 when executed by a computer processor.

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