A method for matching shale gas liquid accumulation intermittent production wells

Abstract

The present invention belongs to the field of shale gas well production technology, and in particular relates to a method for matching shale gas intermittent production wells with liquid accumulation. The method first obtains multidimensional production data of a target shale gas well in a current preset time period, as well as historical production data of matching shale gas wells. A time sliding window is then constructed, and several sets of window data are generated from the historical production data of the matching shale gas wells according to the constructed time sliding window. An intermittent production well matching model is then constructed, and the obtained multidimensional production data of the target shale gas well in the current preset time period and the several sets of window data are input into the intermittent production well matching model. The model then outputs information on matching shale gas wells that meet intermittent production requirements with the target shale gas well. The present invention can address the low efficiency of existing methods for matching intermittent production wells in shale gas wells due to liquid accumulation.

Description

Technical Field

[0001] The present invention belongs to the technical field of shale gas well production, and in particular relates to a method for matching shale gas liquid accumulation intermittent production wells. Background Art

[0002] During shale gas extraction, liquid accumulation inside gas wells is one of the important factors affecting production efficiency. Specifically, liquid accumulation can lead to a decline in the production capacity of gas wells and may even cause production stagnation. To meet this challenge, an intermittent production strategy is usually adopted, that is, periodically shutting down gas wells, allowing the accumulated liquid to drain naturally, and then reopening the gas wells to resume production.

[0003] Currently, intermittent production is widely used in the shale gas extraction industry to address issues caused by fluid accumulation. This operation involves periodically shutting down gas wells for a period of time, allowing the accumulated fluid to drain naturally or through auxiliary means, thereby mitigating the impact of fluid accumulation on production efficiency. However, the timing of this intermittent production process relies heavily on operator judgment or simple rules, such as fixed shut-down intervals.

[0004] Therefore, existing batch production methods have some problems or defects, including:

[0005] 1. The switching time is not determined accurately enough. The reason is that due to the lack of scientific decision-making support, the current intermittent production strategy often cannot accurately grasp the optimal time for gas wells to enter intermittent production. This may lead to over-closure or under-closure, thus affecting the overall production efficiency.

[0006] 2. Insufficient use of historical data. The reason is that the existing methods do not effectively utilize the production experience and data from other gas wells under similar conditions, which limits the possibility of further optimizing production strategies.

[0007] Therefore, to address the problems existing in the existing technology, a new shale gas well liquid loading intermittent production matching method is needed. Summary of the Invention

[0008] The technical problem solved by the present invention is to provide a method for matching shale gas intermittent production wells with liquid accumulation, so as to solve the problem of low efficiency of the intermittent production well matching method for shale gas wells due to liquid accumulation in the prior art.

[0009] The basic solution provided by the present invention is a method for matching shale gas liquid accumulation intermittent production wells, comprising:

[0010] S1: Obtain multi-dimensional production data of a target shale gas well in a current preset time period, and obtain historical production data of shale gas wells that match the target shale gas well;

[0011] S2: Construct a time sliding window, and generate several sets of window data based on the historical production data of the matching shale gas wells according to the constructed time sliding window;

[0012] S3: Construct an intermittent production well matching model, input the multi-dimensional production data of the target shale gas well in the current preset time period and several groups of window data into the intermittent production well matching model, and output the matching shale gas well information that meets the intermittent production requirements of the target shale gas well.

[0013] Furthermore, in S1, the multi-dimensional production data of the target shale gas well in the current preset time period includes tubing inner diameter data, casing pressure data, oil pressure data, gas production data, water production data, and production pressure difference data.

[0014] Furthermore, the S2 includes:

[0015] S2-1: Construct a time sliding window based on the preset time period of the target shale gas well in S1;

[0016] S2-2: Use a time sliding window to sequentially slide and collect data along the timeline of the historical production data of the matching shale gas wells to obtain several groups of window data. The expression is:

[0017] X=[x1,…,x i ]

[0018] Where i is the number of windows;

[0019] S2-3: Preprocess the acquired window data to obtain data for each window, including tubing inner diameter data, casing pressure data, oil pressure data, gas production data, water production data, and production pressure difference data. The expression is:

[0020] x i =[d i ,c i ,o i ,g i ,w i ,p i ]

[0021] Among them, d i represents the inner diameter data of the oil pipe in the i-th window, c i Indicates the casing pressure data in the i-th window, o i represents the oil pressure data in the i-th window, g i represents the gas production data in the i-th window, w i represents the water production data in the i-th window, p i Represents the production pressure difference data in the i-th window.

[0022] Furthermore, the S3 includes:

[0023] S3-1: Constructing an intermittent production well matching model based on the VAE model;

[0024] S3-2: Divide several groups of window data into training sets and validation sets, input the training sets into the intermittent production well matching model for training, verify the trained intermittent production well matching model with the validation set, and retain the model parameter settings that have passed the validation;

[0025] S3-3: Input several groups of window data into the intermittent production well matching model, and output the two-dimensional feature standard data of each group of window data after dimensionality reduction. The expression is:

[0026] w i =[μ i ,σ i ]

[0027] S3-4: Input the multi-dimensional production data of the target shale gas well in the current preset time period into the intermittent production well matching model, and output the two-dimensional feature matching data [μ, σ] after dimensionality reduction;

[0028] S3-5: Calculate the absolute distance between the two-dimensional feature matching data and the two-dimensional feature standard data. The expression is:

[0029] e i =|μ-μ i |+|σ-σ i |

[0030] Output the well number information of the matching shale gas well with the smallest absolute value distance and the corresponding time sliding window date information.

[0031] Furthermore, the S3 further includes:

[0032] 3-6: Based on the well number information of the matching shale gas well with the smallest absolute value distance obtained in S3-5 and the corresponding time sliding window date information, obtain the intermittent time information and intermittent operation information of the shale gas well after the end of the corresponding time sliding window date information;

[0033] S3-7: Generate the intermittent time and intermittent operation of the target shale gas well according to the acquired intermittent time information and intermittent operation information of the shale gas well.

[0034] A system for matching shale gas liquid accumulation intermittent production wells, applied to the above-mentioned method for matching shale gas liquid accumulation intermittent production wells, comprises:

[0035] A data acquisition module is used to obtain multi-dimensional production data of the target shale gas well in the current preset time period, and to obtain historical production data of shale gas wells that match the target shale gas well;

[0036] The data preprocessing module is used to divide the historical production data of the matching shale gas wells into several groups of window data by constructing a time sliding window;

[0037] The intermittent production matching module is used to construct an intermittent production well matching model, input the multi-dimensional production data of the target shale gas well in the current preset time period and several groups of window data into the intermittent production well matching model, and output the matching shale gas well information that meets the intermittent production requirements of the target shale gas well.

[0038] An electronic device includes a processor and a memory, wherein the memory stores programs or instructions, and the processor executes the above-mentioned method for matching shale gas liquid accumulation intermittent production wells by calling the programs or instructions stored in the memory.

[0039] A computer-readable storage medium stores a program or instruction, wherein the program or instruction enables a computer to execute the method for matching shale gas liquid accumulation intermittent production wells as described above.

[0040] The principles and advantages of the present invention are as follows: In this application, in view of the problem in the prior art that shale gas wells rely heavily on manual judgment and have poor optimization of production strategies when executing intermittent production, the overall solution of this application is to combine the advantages of existing big data models, match the operating data of the target shale gas well with the historical operating data of other similar shale gas wells, and obtain a scientific, effective, and maximally beneficial intermittent production method for production strategies. In the entire technical solution, the innovation of this application lies in the type of target shale gas well data acquisition and the matching method of production data. In the existing big data model field, network models such as neural networks and deep learning are usually used as the basis. After the historical operating data is input into the model for training, the real-time data of the target shale gas well is input into the model. The method of predicting or generating intermittent production of shale gas wells has the disadvantage that a large amount of sample data is required for training such models. The sample data collected in this application is the operating data since the establishment of the shale gas well. If such a large amount of data is used to train the model, the required computing power is very large. In this regard, in the solution of this application, the acquired historical operating data is divided by a time sliding window. For example, according to a time sliding window of every 7 days, the historical operating data is divided into several groups of window data, and then processed by the intermittent production well matching model. Therefore, the data processed by the intermittent production well matching model each time is the historical operating data of every 7 days, rather than the large amount of historical operating data of the shale gas well since the start of operation, which solves the problem of high computing power required for model processing in the prior art.

[0041] On this basis, this application does not adopt the method of directly outputting intermittent production strategies using existing big data models. Instead, it performs absolute value calculations on the reduced-dimensional data of several groups of window data output by the intermittent production well matching model, and on the reduced-dimensional data of the real-time operation data of the target shale gas well, thereby obtaining the comparison results with the real-time operation data of the target shale gas well in each window, and taking the shale gas well information and intermittent production strategy corresponding to the result with the smallest absolute value as the reference object of the intermittent production strategy of the target shale gas well. In this way, the optimal intermittent production strategy for the target shale gas well is obtained, which effectively solves the problem of liquid accumulation and greatly improves the production efficiency of the target shale gas well. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flowchart of an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of the processing logic of an embodiment of the present invention;

[0044] Figure 3 Schematic diagram of VAE model training in an embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of the output results of the intermittent production well matching model in an embodiment of the present invention;

[0046] Figure 5 Schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following is further described in detail through specific implementation methods:

[0048] The symbols in the drawings of the specification include: electronic device 400 , processor 401 , memory 402 , input device 403 , and output device 404 .

[0049] Liquid accumulation in shale gas wells is caused by the fact that in the late stage of gas well production, due to the decrease in formation pressure and gas well production capacity, the gas cylinder temperature gradient increases, and due to the temperature drop, some components of natural gas condense in the wellbore to form condensate. When the gas well production is not enough to bring out part of the condensate, the condensate falls back to the bottom of the well, resulting in liquid accumulation in the wellbore. The generation of liquid accumulation will aggravate the decline in the production capacity of the gas well. For this, intermittent production is required to drain the liquid. In this regard, the technical solution of this application is basically as shown in the attached embodiment. Figure 1 and Figure 2 A method for matching shale gas liquid accumulation intermittent production wells is shown, comprising:

[0050] S1: Obtain multi-dimensional production data of a target shale gas well in a current preset time period, and obtain historical production data of shale gas wells that match the target shale gas well;

[0051] In this embodiment, the multi-dimensional production data of the target shale gas well obtained include tubing inner diameter data, casing pressure data, oil pressure data, gas production data, water production data and production pressure difference data, and the preset time period is 7 days, that is, the tubing inner diameter data, casing pressure data, oil pressure data, gas production data, water production data and production pressure difference data of the target shale gas well in the 7 days before the current time are obtained. The type of multi-dimensional production data selected in this application is selected according to different shale gas well types, so that the shale gas well can ensure the highest adaptability during the data matching and type matching process.

[0052] At the same time, in step S1 of the present application, the multi-dimensional production data of the target shale gas well obtained is continuously updated as the target shale gas well operates, that is, the multi-dimensional production data obtained is the real-time production data of the current target shale gas well within a preset time period. For example, taking the preset time period as 7 days as an example, the multi-dimensional production data obtained uses 7 days as a time window, and continuously fills the latest production data of the target shale gas well into the time window, and production data older than 7 days is eliminated.

[0053] S2: Construct a time sliding window, and generate several sets of window data based on the historical production data of the matching shale gas wells according to the constructed time sliding window; wherein, S2 includes:

[0054] S2-1: Construct a time sliding window based on the preset time period of the target shale gas well in S1;

[0055] S2-2: Use a time sliding window to sequentially slide and collect data along the timeline of the historical production data of the matching shale gas wells to obtain several groups of window data. The expression is:

[0056] X=[x1,…,x i ]

[0057] Where i is the number of windows;

[0058] S2-3: Preprocess the acquired window data to obtain data for each window, including tubing inner diameter data, casing pressure data, oil pressure data, gas production data, water production data, and production pressure difference data. The expression is:

[0059] x i =[d i ,c i ,o i ,g i ,w i ,p i ]

[0060] Among them, d i represents the inner diameter data of the oil pipe in the i-th window, c iIndicates the casing pressure data in the i-th window, o i represents the oil pressure data in the i-th window, g i represents the gas production data in the i-th window, w i represents the water production data in the i-th window, p i Represents the production pressure difference data in the i-th window.

[0061] In this embodiment, the historical operation data of the shale gas well obtained for intermittent production matching with the target shale gas well includes all historical operation data since the matching shale gas well started to operate. At the same time, the number of matching shale gas wells is more than one. Therefore, the amount of historical operation data is very large. In order to better process and calculate the data, this application constructs a time sliding window. The size of the time sliding window is the same as the preset time period in S1, for example, 7 days. The historical operation data is then divided into several window data through the time sliding window. Each window data is then preprocessed so that the data type and data dimension in each window data include the multi-dimensional production data of the target shale gas well obtained, specifically including tubing inner diameter data, casing pressure data, oil pressure data, gas production data, water production data and production pressure difference data. For cases of missing data, data errors, etc., data cleaning, data supplementation and other preprocessing methods are used to improve the data to ensure that each window data contains the data type required to match the target shale gas well.

[0062] S3: Construct an intermittent production well matching model, input the multi-dimensional production data of the target shale gas well in the current preset time period and several sets of window data into the intermittent production well matching model, and output matching shale gas well information that meets the intermittent production requirements of the target shale gas well; S3 includes:

[0063] S3-1: Constructing an intermittent production well matching model based on the VAE model;

[0064] S3-2: Divide several groups of window data into training sets and validation sets, input the training sets into the intermittent production well matching model for training, verify the trained intermittent production well matching model with the validation set, and retain the model parameter settings that have passed the validation;

[0065] S3-3: Input several groups of window data into the intermittent production well matching model, and output the two-dimensional feature standard data of each group of window data after dimensionality reduction. The expression is:

[0066] w i =[μ i ,σ i ]

[0067] S3-4: Input the multi-dimensional production data of the target shale gas well in the current preset time period into the intermittent production well matching model, and output the two-dimensional feature matching data [μ, σ] after dimensionality reduction;

[0068] S3-5: Calculate the absolute distance between the two-dimensional feature matching data and the two-dimensional feature standard data. The expression is:

[0069] e i =|μ-μ i |+|σ-σ i |

[0070] Output the well number information of the matching shale gas well with the smallest absolute value distance and the corresponding time sliding window date information;

[0071] 3-6: Based on the well number information of the matching shale gas well with the smallest absolute value distance obtained in S3-5 and the corresponding time sliding window date information, obtain the intermittent time information and intermittent operation information of the shale gas well after the end of the corresponding time sliding window date information;

[0072] S3-7: Generate the intermittent time and intermittent operation of the target shale gas well according to the acquired intermittent time information and intermittent operation information of the shale gas well.

[0073] In this embodiment, the intermittent production well matching model constructed is based on the VAE model. First, the model is trained using several window data, such as Figure 3 As shown, the tubing inner diameter data, casing pressure data, oil pressure data, gas production data, water production data and production pressure difference data of continuous time periods in the window data are input into the VAE model. The VAE model outputs data of the same type. When the difference between the output value of the same type of data and the input value of the same type of data is within the preset threshold, it means that the training effect of the VAE model meets the requirements, and the parameter settings of the model are retained as the processing result of the multi-dimensional operation data of the target shale gas well.

[0074] In the intermittent production well matching model, the VAE model is used to reduce the dimensionality of the input window data and multi-dimensional feature data. For example, each window data of the input matching shale gas well includes six-dimensional data: tubing inner diameter data, casing pressure data, oil pressure data, gas production data, water production data, and production pressure difference data. After dimensionality reduction by the VAE model, the output is two-dimensional feature data [μ i ,σ i ]; Each window data of the input target shale gas well includes six-dimensional data: tubing inner diameter data, casing pressure data, oil pressure data, gas production data, water production data, and production pressure difference data. After dimensionality reduction through the VAE model, the output is two-dimensional feature data [μ, σ];

[0075] Then calculate the 2D feature data [μ,σ] and each 2D feature data [μ i ,σ i ], and the subsequent intermittent production strategy of the matching shale gas well corresponding to the window data with the smallest absolute value distance in the time period corresponding to the window data is taken as the target shale gas well intermittent production strategy.

[0076] To more clearly describe the present application scheme, take Well A and Well B as an example, where Well A is the target shale gas well and Well B is the matching shale gas well. If the operation time of Well A is greater than the preset time period, for example, the operation time of Well A is greater than 7 days, then the multi-dimensional production data of Well A within 7 days before the current time is obtained;

[0077] Assume that well B has been producing for 10 days since its inception. To this end, we need to find a window of data in well B (with a window length of 7 days) that is closest to the production data of well A in the past 7 days.

[0078] Multidimensional production data includes six dimensions: tubing inner diameter data, casing pressure data, oil pressure data, gas production data, water production data, and production pressure difference data. For ease of demonstration, taking gas production and water production as an example, there are a total of 10 sets of gas production data and water production data in Well B, as shown in Table 1 below:

[0079] Table 1 Production status of well B

[0080]

[0081] Dividing the historical operating data in Table 1 above by a time sliding window of length 7 will generate four sets of exposure data, as shown in Table 2 below:

[0082] Table 2 Sliding window data of well B

[0083]

[0084] The production situation of Well A in the past 7 days is shown in Table 3 below:

[0085] Table 3 Production status of well A in the past 7 days

[0086] date 2024.6.8 6.9 6.10 6.11 6.12 6.13 6.14 Gas production 1700 4000 3800 5000 5500 3100 4700 Water production 1 4 1 6 7 3 1

[0087] The above data are input into the intermittent production well matching model, and the VAE model is used to reduce the dimension of the above window data features and the real-time production data of well A, as shown in the following example: Figure 4 As shown, the two-dimensional feature data shown in Table 4 below is obtained:

[0088] Table 4 μ and σ values ​​for all sliding windows and well A

[0089]

[0090]

[0091] Then, the sum of the absolute values ​​of μ and σ for all sliding windows and μ and σ for well A is calculated. In the final calculation result, the absolute value between sliding window 2 and well A is the smallest, which is 0.1. This means that sliding window 2 is closest to the production situation of well A in the past 7 days. The start date of sliding window 2 is January 12, 2024. This means that the production situation of well B from January 12 to January 18, 2024 is closest to that of well A. The intermittent production strategy after January 18 is used as the intermittent production strategy of well A.

[0092] In this implementation, for the intermittent production strategy time node after the matching window data, by setting every 7 days as a time node, if there is intermittent production within these 7 days, it will be used as the intermittent production strategy for the target shale gas well. If not, the target shale gas well will produce normally within 7 days, and the intermittent production well matching operation will be performed again after the end of 7 days.

[0093] Therefore, in the technical solution of this application, compared with the existing technology, a data-driven approach is adopted, combined with the advantages of existing big data models, by collecting and analyzing historical production data of gas wells, to provide scientific decision-making support for intermittent production, effectively avoiding reliance on manual experience or simple rules, and improving the accuracy and reliability of intermittent production strategies.

[0094] In another embodiment of the present embodiment, a system for matching shale gas liquid accumulation intermittent production wells is further included, which is applied to the above-mentioned method for matching shale gas liquid accumulation intermittent production wells, comprising:

[0095] A data acquisition module is used to obtain multi-dimensional production data of the target shale gas well in the current preset time period, and to obtain historical production data of shale gas wells that match the target shale gas well;

[0096] The data preprocessing module is used to divide the historical production data of the matching shale gas wells into several groups of window data by constructing a time sliding window;

[0097] The intermittent production matching module is used to construct an intermittent production well matching model, input the multi-dimensional production data of the target shale gas well in the current preset time period and several groups of window data into the intermittent production well matching model, and output the matching shale gas well information that meets the intermittent production requirements of the target shale gas well.

[0098] Also included is an electronic device such as Figure 5 As shown, the electronic device 400 includes one or more processors 401 and a memory 402 .

[0099] The processor 401 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 400 to perform desired functions.

[0100] Memory 402 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and processor 401 may execute the program instructions to implement the method for matching shale gas intermittent production wells with liquid accumulation described above in any embodiment of the present invention and / or other desired functions. Various contents such as initial external parameters and thresholds may also be stored in the computer-readable storage medium.

[0101] In one example, electronic device 400 may further include an input device 403 and an output device 404, which are interconnected via a bus system and / or other connection mechanisms (not shown). Input device 403 may include, for example, a keyboard, a mouse, etc. Output device 404 may output various information to the outside, including warning information, braking force, etc. Output device 404 may include, for example, a display, a speaker, a printer, a communication network, and remote output devices connected thereto.

[0102] Of course, to simplify, Figure 5 Only some of the components related to the present invention in the electronic device 400 are shown, and components such as buses, input / output interfaces, etc. are omitted. In addition, the electronic device 400 may further include any other appropriate components according to specific application scenarios.

[0103] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of a method for matching shale gas liquid accumulation intermittent production wells provided in any embodiment of the present invention.

[0104] The computer program product may be written in any combination of one or more programming languages ​​to implement the operations of embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0105] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enables the processor to execute the steps of a method for matching shale gas liquid accumulation intermittent production wells provided in any embodiment of the present invention.

[0106] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable 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 thereof.

[0107] The above are only embodiments of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme are not described in detail here. Ordinary technicians in the field are aware of all common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the field can improve and implement this scheme in combination with their own abilities under the inspiration given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for matching shale gas liquid accumulation intermittent production wells, characterized by: include: S1: Obtain multi-dimensional production data of a target shale gas well in a current preset time period, and obtain historical production data of shale gas wells that match the target shale gas well; S2: Construct a time sliding window, and generate several sets of window data based on the historical production data of the matching shale gas wells according to the constructed time sliding window; S3: Construct an intermittent production well matching model, input the multi-dimensional production data of the target shale gas well in the current preset time period and several groups of window data into the intermittent production well matching model, and output the matching shale gas well information that meets the intermittent production requirements of the target shale gas well; The S3 includes: S3-1: Constructing an intermittent production well matching model based on the VAE model; S3-2: Divide several groups of window data into training sets and validation sets, input the training sets into the intermittent production well matching model for training, verify the trained intermittent production well matching model with the validation set, and retain the model parameter settings that have passed the validation; S3-3: Input several groups of window data into the intermittent production well matching model, and output the two-dimensional feature standard data of each group of window data after dimensionality reduction. The expression is: S3-4: Input the multi-dimensional production data of the target shale gas well in the current preset time period into the intermittent production well matching model, and output the two-dimensional feature matching data after dimensionality reduction. ; S3-5: Calculate the absolute distance between the two-dimensional feature matching data and the two-dimensional feature standard data. The expression is: Output the well number information of the matching shale gas well with the smallest absolute value distance and the corresponding time sliding window date information; Said S3 further comprises: S3-6: Based on the well number information of the matching shale gas well with the smallest absolute value distance obtained in S3-5 and the corresponding time sliding window date information, obtain the intermittent time information and intermittent operation information of the shale gas well after the end of the corresponding time sliding window date information; S3-7: Generate the intermittent time and intermittent operation of the target shale gas well according to the acquired intermittent time information and intermittent operation information of the shale gas well.

2. The method for matching shale gas liquid accumulation intermittent production wells according to claim 1, characterized in that: In S1, the multi-dimensional production data of the target shale gas well in the current preset time period includes tubing inner diameter data, casing pressure data, oil pressure data, gas production data, water production data, and production pressure difference data.

3. The method for matching shale gas liquid accumulation intermittent production wells according to claim 2, characterized in that: The S2 includes: S2-1: Construct a time sliding window based on the preset time period of the target shale gas well in S1; S2-2: Use a time sliding window to sequentially slide and collect data along the timeline of the historical production data of the matching shale gas wells to obtain several groups of window data. The expression is: in, is the number of windows; S2-3: Preprocess the acquired window data to obtain data for each window, including tubing inner diameter data, casing pressure data, oil pressure data, gas production data, water production data, and production pressure difference data. The expression is: in, represents the inner diameter data of the oil pipe in the i-th window, represents the casing pressure data in the i-th window, represents the oil pressure data in the i-th window, represents the gas production data in the i-th window, represents the water production data in the i-th window, Represents the production pressure difference data in the i-th window.

4. A system for matching shale gas liquid accumulation intermittent production wells, applied to the method for matching shale gas liquid accumulation intermittent production wells according to any one of claims 1 to 3, characterized in that: include: A data acquisition module is used to obtain multi-dimensional production data of the target shale gas well in the current preset time period, and to obtain historical production data of shale gas wells that match the target shale gas well; The data preprocessing module is used to divide the historical production data of the matching shale gas wells into several groups of window data by constructing a time sliding window; The intermittent production matching module is used to construct an intermittent production well matching model, input the multi-dimensional production data of the target shale gas well in the current preset time period and several groups of window data into the intermittent production well matching model, and output the matching shale gas well information that meets the intermittent production requirements of the target shale gas well.

5. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores programs or instructions, and the processor executes a method for matching shale gas liquid accumulation intermittent production wells as described in any one of claims 1 to 3 by calling the programs or instructions stored in the memory.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or instruction, and the program or instruction enables a computer to execute a method for matching shale gas liquid accumulation intermittent production wells as described in any one of claims 1 to 3 above.

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