Gas well classification method, device, equipment and medium

Abstract

The embodiment of the invention discloses a gas well classification method, device and equipment and a medium. The method comprises the steps of obtaining yield characteristic data of oil and gas fields in different development stages, and clustering the yield characteristic data to obtain oil and gas field classification; for the oil and gas fields of the same type, classifying the gas wells in the oil and gas fields according to the unit yield data of the continuous unit time of the gas wells in the oil and gas fields and the accumulated yield data of the preset time period; and for the gas wells of the same type, the gas wells are classified based on a preset recovery ratio threshold value, and classification results of the high-quality wells and the low-yield wells are obtained. According to the technical scheme, fine classification of the gas wells can be achieved, and the accuracy of gas well classification is improved.

Description

Technical Field

[0001] The present application relates to the field of oil and gas field development, and in particular to a gas well classification method, device, equipment and medium. Background Art

[0002] In recent years, my country's rapid economic development has led to an increasing demand for energy. The supply of conventional oil and natural gas resources can no longer meet this demand, resulting in a growing dependence on foreign oil and gas. Energy is becoming the primary factor constraining economic development. Low-permeability gas reservoirs have played an increasingly important role in the long-term development of natural gas and are of great strategic significance to my country's natural gas energy. However, in the actual development of natural gas, gas well reservoirs exhibit strong heterogeneity, production characteristics vary widely, and production varies significantly between wells. Therefore, it is necessary to conduct gas well classification research to better guide the rational production of gas wells. Scientific and effective gas well classification helps clarify the production status of gas wells and understand their production characteristics, thereby formulating targeted and refined individual well management strategies.

[0003] Current gas well classification research primarily considers three aspects: static reservoir parameters, dynamic production parameters, and a comprehensive classification of static and dynamic parameters. Static reservoir parameter classification primarily relies on formation thickness and permeability, namely the formation coefficient (Kh value), as well as various parameters from well logging interpretation. Dynamic production parameter classification primarily involves parameters such as wellhead casing pressure, wellhead gas production, open-flow rate (ORF), and gas production per unit pressure drop. With the continuous advancement of reservoir stimulation technologies such as acid fracturing, gas well productivity has significantly increased. Therefore, static reservoir parameters do not fully represent the actual production capacity of a gas well. Dynamic production parameter classification, such as ORF, is based solely on short-term pre-production gas testing results, is not stable, and does not fully represent the actual capacity of the gas well. Current approaches to comprehensive classification of both static and dynamic gas well parameters often produce inconsistent results, leading to inaccurate classification of some wells. Summary of the Invention

[0004] The present application provides a gas well classification method, apparatus, equipment and medium to efficiently and accurately classify oil and gas fields and gas wells.

[0005] According to one aspect of the present application, a gas well classification method is provided, the method comprising:

[0006] Obtain production characteristic data of oil and gas fields at different development stages, and cluster the production characteristic data to obtain oil and gas field classification;

[0007] For oil and gas fields of the same type, the gas wells in the oil and gas fields are classified according to the continuous unit production data of the gas wells in the oil and gas fields per unit time and the cumulative production data of the preset time period;

[0008] For gas wells of the same type, the gas wells are classified based on the preset recovery rate threshold to obtain the classification results of high-quality wells and low-yield wells.

[0009] According to another aspect of the present application, a gas well classification device is provided, comprising:

[0010] The oil and gas field classification module is used to obtain the production characteristic data of oil and gas fields at different development stages and cluster the production characteristic data to obtain the oil and gas field classification;

[0011] A gas well classification module is used to classify the gas wells in the oil and gas fields of the same type according to the continuous unit production data of the gas wells in the oil and gas fields per unit time and the cumulative production data of the preset time period;

[0012] The fine classification module is used to classify gas wells of the same type based on preset recovery rate thresholds to obtain classification results of high-quality wells and low-yield wells.

[0013] According to another aspect of the present application, a gas well classification device is provided, the device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute the gas well classification method of any embodiment of the present application.

[0017] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the gas well classification method of any embodiment of the present application when executed.

[0018] The technical solution of the embodiment of the present application obtains production characteristic data of oil and gas fields at different development stages and clusters the production characteristic data to obtain oil and gas field classifications. For oil and gas fields of the same type, the gas wells in the oil and gas field are classified based on the continuous unit production data of the gas wells in the oil and gas field per unit time and the cumulative production data of the preset time period. For gas wells of the same type, the gas wells are classified based on preset recovery rate thresholds to obtain classification results of high-quality wells and low-yield wells. The technical solution of the embodiment of the present application can achieve refined classification of gas wells and improve the accuracy of gas well classification.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1 This is a flow chart of a gas well classification method provided according to Example 1 of the present application;

[0022] Figure 2 This is a flow chart of a gas well classification method provided according to Example 2 of the present application;

[0023] Figure 3 This is a schematic structural diagram of a gas well classification device provided according to the third embodiment of the present application;

[0024] Figure 4 It is a structural schematic diagram of a gas well classification device for implementing a gas well classification method provided in Example 4 of the present application. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0026] It should be noted that the terms "first", "second", "third", "fourth", "actual", "preset", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Example 1

[0028] Figure 1 This is a flowchart of a gas well classification method provided in Example 1 of the present application. This embodiment of the present application is applicable to interpolating missing seismic data. The method can be performed by a gas well classification device, which can be implemented in hardware and / or software and can be configured in a gas well classification device.

[0029] like Figure 1 As shown, the method includes:

[0030] S110 , obtaining production characteristic data of oil and gas fields at different development stages, and clustering the production characteristic data to obtain oil and gas field classifications.

[0031] An oil or gas field is a fully developed oil or gas field. The development stage encompasses the entire development and production phase of the oil or gas field, including, for example, the peak production period, the stable production period, and the declining production period. The number of oil or gas fields can be a preset number, which can be determined based on actual conditions and is not limited here. The production characteristic data can be the total oil and gas production of the oil or gas field at each production time in each production stage.

[0032] For example, the production characteristic data of multiple oil and gas fields at different development stages can be clustered, and oil and gas fields with similar production characteristic data can be classified into one category. Oil and gas fields belonging to the same classification cluster belong to the same oil and gas field, and oil and gas fields can be divided into multiple categories.

[0033] In the embodiments of the present application, before clustering the yield characteristic data, the data can be cleaned and preprocessed to remove impurities. The yield characteristic data is selected to discriminate between different oil and gas fields, at different production stages, and over time. The yield characteristic data is standardized and normalized to ensure that constant characteristic data is within the same magnitude range. Clustering is then performed on the processed yield characteristic data to improve clustering effectiveness.

[0034] S120 . For oil and gas fields of the same type, classify the gas wells in the oil and gas fields according to the continuous unit production data per unit time and the cumulative production data of the gas wells in a preset time period.

[0035] For example, after classifying multiple types of oil and gas fields, for gas wells in the same type of oil and gas fields, the unit production data of the gas wells per unit time and the cumulative production data of the preset time period are determined. The unit time can be determined according to actual conditions. For example, it can be set to months, in which case the unit production data is monthly production data; it can be set to days, in which case the unit production data is daily production data; the preset time period can be determined according to actual conditions. For example, it can be set to a quarter, i.e., three months, in which case the cumulative production data is the cumulative production data of three months. If the gas well is an oil-producing well, the unit production is the unit oil production, and the cumulative production data is the cumulative oil production. If the gas well is a gas-producing well, the unit production is the unit gas production, and the cumulative production data is the cumulative gas production. The unit production data can also be the unit water production, and the cumulative production data can also be the cumulative water production.

[0036] Exemplarily, the gas wells in the oil and gas field are classified according to the unit production data and cumulative production data of the gas wells in the oil and gas field of the same type, so that the gas wells in the same oil and gas field are divided into at least two types.

[0037] S130 , for gas wells of the same type, classify the gas wells based on preset recovery rate thresholds to obtain classification results of high-quality wells and low-yield wells.

[0038] The preset recovery rate threshold can be determined based on actual conditions, for example, a demarcation value between high-quality and low-yield gas wells. For example, for gas wells of the same type, the preset recovery rate threshold can be used to classify them into high-quality and low-yield wells, thereby achieving refined classification of gas wells and ensuring that gas wells in the same category have similar characteristics.

[0039] In the embodiment of the present application, for gas wells of the same type, the gas wells are classified based on preset recovery rate thresholds to obtain classification results of high-quality wells and low-yield wells, including:

[0040] For gas wells of the same type, determining the actual recovery rate of the gas wells;

[0041] The gas wells whose actual recovery rate is higher than or equal to the preset recovery rate threshold are regarded as high-quality wells, and the gas wells whose actual recovery rate is lower than the preset recovery rate threshold are regarded as low-yield wells.

[0042] For example, for gas wells of the same type, the actual recovery rate of the gas wells can be statistically determined, and the actual recovery rate of each gas well can be compared with a preset recovery rate threshold. If the actual recovery rate of the gas well is higher than or equal to the preset recovery rate threshold, the gas well is regarded as a high-quality well. If the actual recovery rate of the gas well is lower than the preset recovery rate threshold, the gas well is regarded as a low-yield well. In this way, the gas wells can be more directly and finely classified in terms of recovery rate, which facilitates more accurate determination of the characteristics and differences between different types of gas wells.

[0043] In the embodiment of the present application, after the gas wells are classified based on the preset recovery rate thresholds and the classification results of high-quality wells and low-yield wells are obtained, the method further includes:

[0044] Determine the geological conditions, production parameters and reservoir characteristics of high-quality wells and low-yield wells respectively;

[0045] The development method of the gas well to be developed is determined based on the predicted type of the gas well to be developed, as well as the geological conditions, production parameters and reservoir characteristics of the high-quality well and the low-yield well.

[0046] For example, for high-quality wells and low-yield wells, the common geological conditions, production parameters, and reservoir characteristics of high-quality wells are determined, and the common geological conditions, production parameters, and reservoir characteristics of low-yield wells are determined, and the differences between the characteristics of high-quality wells and low-yield wells are compared. For undeveloped gas wells, the predicted type of the undeveloped gas well is determined according to the scheme of the above embodiment based on the production characteristic data, unit production data, cumulative production data, and actual recovery rate of the undeveloped gas well in the production development stage. Or, for undeveloped undeveloped gas wells, the predicted type of the undeveloped gas well is predicted based on data such as geographical location, geological conditions, and reservoir characteristics. Based on the predicted type of the undeveloped gas well, as well as the geological conditions, production parameters, and reservoir characteristics of the high-quality wells and low-yield wells, the development method of the undeveloped gas well is determined. For example, if the undeveloped gas well is a high-quality well, the undeveloped gas well is developed according to the production parameters of the high-quality well to maximize the development production. If the gas well to be developed is a low-yield well, it should be developed according to the production parameters of the low-yield well so that the development method matches the gas well type and avoids waste of resources caused by mismatch between input and output.

[0047] The technical solution of the embodiment of the present application obtains production characteristic data of oil and gas fields at different development stages and clusters the production characteristic data to obtain oil and gas field classifications. For oil and gas fields of the same type, the gas wells in the oil and gas field are classified based on the continuous unit production data of the gas wells in the oil and gas field per unit time and the cumulative production data of the preset time period. For gas wells of the same type, the gas wells are classified based on preset recovery rate thresholds to obtain classification results of high-quality wells and low-yield wells. The technical solution of the embodiment of the present application can achieve refined classification of gas wells and improve the accuracy of gas well classification.

[0048] Example 2

[0049] Figure 2 This is a flow chart of a gas well classification method provided in Example 2 of this application. This embodiment of the application is optimized based on the above embodiment. For solutions not fully described in this embodiment of the application, please refer to the above embodiment. Figure 2 As shown, the method of the embodiment of the present application specifically includes the following steps:

[0050] S210: Obtain production characteristic data of oil and gas fields at different development stages, and cluster the production characteristic data to obtain oil and gas field classifications.

[0051] In the embodiment of the present application, the production characteristic data of the oil and gas fields at different development stages are obtained, and the production characteristic data are clustered to obtain the oil and gas field classification, including:

[0052] Obtaining the total production of a preset number of oil and gas fields at each production time in different development stages; wherein the development stage includes at least one of a peak production period, a stable production period, and a declining production period;

[0053] The total production corresponding to each production time in different development stages is clustered based on the K-means clustering algorithm to obtain the oil and gas field classification.

[0054] The preset number can be determined based on actual conditions, for example, based on the actual number of developed oil and gas fields. The total production of the preset number of oil and gas fields at different production times during different development stages can be obtained. The different development stages can include at least one of a peak production period, a stable production period, and a declining production period.

[0055] The K-means clustering algorithm is an iterative cluster analysis algorithm. It involves pre-dividing the data into K groups, randomly selecting K objects as initial cluster centers, and then calculating the distance between each object and each seed cluster center, assigning each object to the cluster center closest to it. The cluster centers and the objects assigned to them represent a cluster. In this embodiment of the present application, the K-means clustering algorithm can be used to cluster the total production corresponding to each production time in different development stages to obtain a classification of oil and gas fields.

[0056] S220. For each gas well in the same type of oil and gas field, calculate a first Pearson correlation coefficient between the gas well and other gas wells based on the unit production data, and calculate a second Pearson correlation coefficient between the gas well and other gas wells based on the cumulative production data.

[0057] For example, unit production data is continuous data, which refers to the production data within a continuous unit of time. Cumulative production data is discrete data, which refers to the cumulative production data within a preset time period. Continuous data and discrete data can be calculated separately.

[0058] Specifically, for each gas well in the same type of oil and gas field, each gas well is traversed, and the Pearson correlation coefficient between the gas well and other gas wells is determined during the traversal process. The first Pearson correlation coefficient between the gas well and other gas wells is calculated based on the unit production data, and the second Pearson correlation coefficient between the gas well and other gas wells is calculated based on the cumulative production data.

[0059] Specifically, the Pearson correlation coefficient is a statistical method used to measure the degree of correlation between two variables. The magnitude of the Pearson correlation coefficient indicates the degree of correlation between the two variables. The larger the absolute value of the correlation coefficient, the stronger the correlation. Therefore, it can be used as an indicator of the degree of correlation between gas fields. It is defined as the quotient of the covariance between the two variables and the product of the standard deviations of the two variables. The formula is as follows:

[0060]

[0061] X, Y are two sets of variables. In the embodiment of the present application, if R X,Y The first Pearson correlation coefficient is calculated, then X, Y represent the unit production data of the traversed gas well and the unit production data of other gas wells respectively. If R X,Y The second Pearson correlation coefficient is calculated, then X, Y represent the cumulative production data of the traversed gas well and the cumulative production data of other gas wells, cov(X,Y) represents the covariance between variables X, Y, σ X represents the standard deviation of X, σ Y represents the standard deviation of Y. E represents the mathematical expectation.

[0062] S230: Fusing the first Pearson correlation coefficient and the second Pearson correlation coefficient to obtain a correlation coefficient distance vector between the gas well and other gas wells.

[0063] For example, the first Pearson correlation coefficient and the second Pearson correlation coefficient are correlation coefficients between two gas wells calculated based on unit production data and cumulative production data, respectively, and both represent the correlation coefficients between the two gas wells. Therefore, the first Pearson correlation coefficient and the second Pearson correlation coefficient can be fused to obtain the correlation coefficient distance vector between the two gas wells.

[0064] In the embodiment of the present application, the first Pearson correlation coefficient and the second Pearson correlation coefficient are fused to obtain the correlation coefficient distance vector between the gas well and other gas wells, including:

[0065] determining a weight of the first Pearson correlation coefficient and a weight of the second Pearson correlation coefficient;

[0066] The first Pearson correlation coefficient and the second Pearson correlation coefficient are weightedly summed according to the weight to obtain a correlation coefficient distance vector between the gas well and other gas wells.

[0067] For example, the weight of the first Pearson correlation coefficient R1 can be determined to be w1, and the weight of the second Pearson correlation coefficient R2 can be determined to be w2. The first and second Pearson correlation coefficients are then weighted and fused to obtain the fused correlation coefficient between a pair of gas wells: R = R1*w1+R2*w2. The fused correlation coefficients for a gas well and all other gas wells can be combined to form a correlation coefficient distance vector between the gas well and the other gas wells. w1 can be 80%, and w2 can be 20%.

[0068] S240: Classify the gas wells in the oil and gas field according to the correlation coefficient distance vector.

[0069] For example, the gas wells in the oil and gas field may be classified according to the correlation coefficient distance vector that identifies the correlation between the gas wells, so that correlation and similarity exist between gas wells of the same category.

[0070] In an embodiment of the present application, classifying the gas wells in the oil and gas field according to the correlation coefficient distance vector includes:

[0071] For each gas well, the correlation coefficient distance vectors between the gas well and other gas wells are combined to obtain a correlation coefficient distance matrix;

[0072] Hierarchical clustering is performed on the correlation coefficient distance matrix to obtain a classification of the gas wells in the oil and gas field.

[0073] For example, during the traversal process, each time a gas well is traversed, a correlation coefficient distance vector is obtained between that gas well and other gas wells. For multiple gas wells in the same oil and gas field, multiple correlation coefficient distance vectors can be obtained. Multiple correlation coefficient distance vectors can be combined to obtain a correlation coefficient distance matrix for multiple gas wells in the same oil and gas field. Hierarchical clustering is performed on the correlation coefficient distance matrix to classify the gas wells in the oil and gas field. Because the correlation coefficient distance matrix contains the correlation between two gas wells, the similarity between gas wells can be analyzed based on the correlation coefficient distance matrix, and samples with high similarity can be clustered together to form a single class, thereby classifying the gas wells.

[0074] S250: For gas wells of the same type, classify the gas wells based on preset recovery rate thresholds to obtain classification results of high-quality wells and low-yield wells.

[0075] An embodiment of the present application provides a gas well classification method, which calculates a first Pearson correlation coefficient between the gas well and other gas wells based on the unit production data for each gas well in the same type of oil and gas field, and calculates a second Pearson correlation coefficient between the gas well and other gas wells based on the cumulative production data; the first Pearson correlation coefficient and the second Pearson correlation coefficient are fused to obtain a correlation coefficient distance vector between the gas well and other gas wells; and the gas wells in the oil and gas field are classified according to the correlation coefficient distance vector. By using the Pearson correlation coefficient matrix to calculate the correlation between gas wells, the similarity between gas wells can be accurately determined, thereby improving the accuracy of classification. By classifying gas wells, the characteristics of gas wells in each category can be better understood, thereby formulating effective development strategies for gas wells in a targeted manner and improving the efficiency of gas field development.

[0076] Example 3

[0077] Figure 3 This is a schematic diagram of the structure of a gas well classification device provided in Example 3 of this application. The device can execute the gas well classification method provided in any embodiment of this application and has the corresponding functional modules and beneficial effects of the execution method. Figure 3 As shown, the device includes:

[0078] The oil and gas field classification module 310 is used to obtain the production characteristic data of the oil and gas fields at different development stages and cluster the production characteristic data to obtain the oil and gas field classification;

[0079] The gas well classification module 320 is used to classify the gas wells in the oil and gas fields of the same type according to the continuous unit production data of the gas wells in the oil and gas fields per unit time and the cumulative production data of the gas wells in the preset time period;

[0080] The fine classification module 330 is used to classify gas wells of the same type based on preset recovery rate thresholds to obtain classification results of high-quality wells and low-yield wells.

[0081] In the embodiment of the present application, the oil and gas field classification module 310 is specifically configured to include:

[0082] Obtaining the total production of a preset number of oil and gas fields at each production time in different development stages; wherein the development stage includes at least one of a peak production period, a stable production period, and a declining production period;

[0083] The total production corresponding to each production time in different development stages is clustered based on the K-means clustering algorithm to obtain the oil and gas field classification.

[0084] In the embodiment of the present application, the gas well classification module 320 is specifically used to:

[0085] For each gas well in the same type of oil and gas field, a first Pearson correlation coefficient between the gas well and other gas wells is calculated based on the unit production data, and a second Pearson correlation coefficient between the gas well and other gas wells is calculated based on the cumulative production data;

[0086] The first Pearson correlation coefficient and the second Pearson correlation coefficient are combined to obtain a correlation coefficient distance vector between the gas well and other gas wells;

[0087] The gas wells in the oil and gas field are classified according to the correlation coefficient distance vector.

[0088] In the embodiment of the present application, the gas well classification module 320 is specifically used to:

[0089] For each gas well, the correlation coefficient distance vectors between the gas well and other gas wells are combined to obtain a correlation coefficient distance matrix;

[0090] Hierarchical clustering is performed on the correlation coefficient distance matrix to obtain a classification of the gas wells in the oil and gas field.

[0091] In the embodiment of the present application, the gas well classification module 320 is specifically used to:

[0092] determining a weight of the first Pearson correlation coefficient and a weight of the second Pearson correlation coefficient;

[0093] The first Pearson correlation coefficient and the second Pearson correlation coefficient are weightedly summed according to the weight to obtain a correlation coefficient distance vector between the gas well and other gas wells.

[0094] In the embodiment of the present application, the fine classification module 330 is specifically configured to:

[0095] For gas wells of the same type, determining the actual recovery rate of the gas wells;

[0096] The gas wells whose actual recovery rate is higher than or equal to the preset recovery rate threshold are regarded as high-quality wells, and the gas wells whose actual recovery rate is lower than the preset recovery rate threshold are regarded as low-yield wells.

[0097] In an embodiment of the present application, the device further includes:

[0098] Parameter determination module, used to determine the geological conditions, production parameters and reservoir characteristics of high-quality wells and low-yield wells respectively;

[0099] The development mode determination module is used to determine the development mode of the gas well to be developed based on the predicted type of the gas well to be developed, as well as the geological conditions, production parameters and reservoir characteristics of the high-quality well and the low-yield well.

[0100] A gas well classification device provided in an embodiment of the present application can execute a gas well classification method provided in any embodiment of the present application, and has functional modules and beneficial effects corresponding to the execution method.

[0101] Example 4

[0102] Figure 4 A schematic diagram of a gas well classification apparatus 10 that can be used to implement an embodiment of the present application is shown. The gas well classification apparatus 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 gas well classification apparatus can also represent various forms of mobile devices, such as personal digital assistants, 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 application described and / or claimed herein.

[0103] like Figure 4As shown, the gas well classification device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from the storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the gas well classification device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0104] Multiple components in the gas well classification device 10 are connected to an 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 magnetic 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 gas well classification device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0105] The processor 11 can be any general-purpose and / or specialized processing component 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 specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the gas well classification method.

[0106] In some embodiments, the gas well classification method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the gas well classification device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the gas well classification method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the gas well classification method in any other suitable manner (e.g., via firmware).

[0107] Various embodiments of the systems and techniques described herein can be implemented in digital gas well classification circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising 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.

[0108] Computer programs for implementing the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable gas well classification device, such that when executed by the processor, the computer programs implement the functions / operations specified in the flowcharts and / or block diagrams. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0109] In the context of the present application, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use with or in conjunction with an instruction execution system, device, or apparatus. A computer-readable storage medium may include, but is not limited to, a gas well classification, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media 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.

[0110] To provide for user interaction, the systems and techniques described herein can be implemented on a gas well classification 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 pointing device (e.g., a mouse or trackball) through which the user can provide input to the gas well classification device. Other types of devices can also be used to provide for user interaction; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0111] The systems and techniques described herein can be implemented in a computing system that includes back-end 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 front-end components (e.g., a user computer with a graphical user interface or 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 back-end components, middleware components, or front-end components. The components of the system can 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.

[0112] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0113] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired information of the technical solution of this application can be achieved. This document is not limited here.

[0114] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A gas well classification method, characterized in that: The method comprises: Obtain production characteristic data of oil and gas fields at different development stages, and cluster the production characteristic data to obtain oil and gas field classification; For oil and gas fields of the same type, the gas wells in the oil and gas fields are classified according to the continuous unit production data of the gas wells in the oil and gas fields per unit time and the cumulative production data of the preset time period; For gas wells of the same type, the gas wells are classified based on the preset recovery rate threshold to obtain the classification results of high-quality wells and low-yield wells.

2. The method according to claim 1, characterized in that Obtain production characteristic data of oil and gas fields at different development stages and cluster the production characteristic data to obtain oil and gas field classification, including: Obtaining the total production of a preset number of oil and gas fields at each production time in different development stages; wherein the development stage includes at least one of a peak production period, a stable production period, and a declining production period; The total production corresponding to each production time in different development stages is clustered based on the K-means clustering algorithm to obtain the oil and gas field classification.

3. The method according to claim 1, characterized in that Classifying the gas wells in the oil and gas field according to the continuous unit production data per unit time and the cumulative production data of the gas wells in the preset time period, including: For each gas well in the same type of oil and gas field, a first Pearson correlation coefficient between the gas well and other gas wells is calculated based on the unit production data, and a second Pearson correlation coefficient between the gas well and other gas wells is calculated based on the cumulative production data; The first Pearson correlation coefficient and the second Pearson correlation coefficient are combined to obtain a correlation coefficient distance vector between the gas well and other gas wells; The gas wells in the oil and gas field are classified according to the correlation coefficient distance vector.

4. The method according to claim 3, characterized in that Classifying the gas wells in the oil and gas field according to the correlation coefficient distance vector includes: For each gas well, the correlation coefficient distance vectors between the gas well and other gas wells are combined to obtain a correlation coefficient distance matrix; Hierarchical clustering is performed on the correlation coefficient distance matrix to obtain a classification of the gas wells in the oil and gas field.

5. The method according to claim 3, characterized in that The first Pearson correlation coefficient and the second Pearson correlation coefficient are combined to obtain a correlation coefficient distance vector between the gas well and other gas wells, including: determining a weight of the first Pearson correlation coefficient and a weight of the second Pearson correlation coefficient; The first Pearson correlation coefficient and the second Pearson correlation coefficient are weightedly summed according to the weight to obtain a correlation coefficient distance vector between the gas well and other gas wells.

6. The method according to claim 1, characterized in that For gas wells of the same type, the gas wells are classified based on the preset recovery rate thresholds to obtain the classification results of high-quality wells and low-yield wells, including: For gas wells of the same type, determining the actual recovery rate of the gas wells; The gas wells whose actual recovery rate is higher than or equal to the preset recovery rate threshold are regarded as high-quality wells, and the gas wells whose actual recovery rate is lower than the preset recovery rate threshold are regarded as low-yield wells.

7. The method according to claim 1, characterized in that After classifying the gas wells based on the preset recovery rate thresholds to obtain classification results of high-quality wells and low-yield wells, the method further includes: Determine the geological conditions, production parameters and reservoir characteristics of high-quality wells and low-yield wells respectively; The development method of the gas well to be developed is determined based on the predicted type of the gas well to be developed, as well as the geological conditions, production parameters and reservoir characteristics of the high-quality well and the low-yield well.

8. A gas well classification device, characterized in that: The device comprises: The oil and gas field classification module is used to obtain the production characteristic data of oil and gas fields at different development stages and cluster the production characteristic data to obtain the oil and gas field classification; A gas well classification module is used to classify the gas wells in the oil and gas fields of the same type according to the continuous unit production data of the gas wells in the oil and gas fields per unit time and the cumulative production data of the preset time period; The fine classification module is used to classify gas wells of the same type based on preset recovery rate thresholds to obtain classification results of high-quality wells and low-yield wells.

9. A gas well classification device, characterized in that: The device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the gas well classification method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the gas well classification method according to any one of claims 1 to 7 when executed.

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