Method and device for optimizing fracturing parameters of new well in tight sandstone gas reservoir and electronic equipment

Abstract

The embodiment of the application provides a method and device for optimizing new well fracturing parameters of a tight sand gas reservoir and electronic equipment, comprising: determining a first data set according to obtained gas field single well geological data and yield data, and determining main control parameters affecting single well EUR from geological parameters, fracturing parameters and position parameters by using the first data set; determining a second data set according to the main control parameters, and the second data set is used for establishing a regression model of the main control parameters and the single well EUR; dividing an optimization range and a grid size of the fracturing parameters to be optimized, and iteratively optimizing the fracturing parameters to obtain optimal fracturing parameters of the regression model. Since the parameters for establishing the single well EUR regression model are the main parameters affecting the single well EUR, the accuracy of the regression model is improved. Therefore, when subsequent optimization objects are the fracturing parameters, other parameter information of the fracturing parameters is used as model coefficients of the regression model, so that the optimization effect of the fracturing parameters is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil exploration, in particular to a method and device for optimizing fracturing parameters of a new well in a tight sandstone gas reservoir and an electronic device. BACKGROUND

[0002] By the end of 2008, the cumulative proven reserves of tight sandstone gas accounted for 63.6% of the entire proven reserves of natural gas in China, and the production of tight sandstone gas accounted for 42.1% of the total natural gas production. Tight sandstone gas has become the main field of natural gas exploration and development in China. At present, this type of gas reservoir is mainly developed by horizontal wells to achieve increased production. The main problems are poor physical properties and poor connectivity of tight reservoirs, which result in rapid decline of horizontal well productivity and low ultimate recovery.

[0003] It has been proved by domestic and foreign development practices that fracturing horizontal wells can greatly improve the reservoir connectivity and increase the single well productivity. Therefore, the optimization of fracturing parameters of new wells in tight sandstone gas reservoirs is particularly important. The traditional optimization methods for fracturing parameters of new wells in gas reservoirs mainly include analytical methods, reservoir numerical simulation methods and physical methods. The traditional methods model according to the artificial fracture and the percolation mechanism of gas, and mainly optimize the fracture parameters such as fracture half-length and conductivity.

[0004] However, the fracturing parameter optimization models based on the analytical method, the reservoir numerical simulation method and the physical method have limited range of parameters, and it is difficult to describe the high-dimensional and nonlinear relationship between the geological parameters, the fracturing process parameters and the fracturing effect. In addition, the traditional methods introduce many assumptions and simplifications by characterizing the artificial fracture and the percolation mechanism of gas reservoirs, which results in poor optimization effect of fracturing parameters. SUMMARY

[0005] Therefore, the present application provides a method and device for optimizing fracturing parameters of a new well in a tight sandstone gas reservoir and an electronic device, so as to solve the problem of poor optimization effect of the fracturing parameter optimization method in the prior art.

[0006] In a first aspect, the embodiments of the present application provide a method for optimizing fracturing parameters of a new well in a tight sandstone gas reservoir, comprising:

[0007] determining a first data set according to the obtained geological data of a single well in a gas field and production data, wherein the geological data comprises geological parameters, fracturing parameters and position parameters, and the production data comprises single well ultimate recoverable reserves data EUR;

[0008] determining a main control parameter affecting the single well EUR from the geological parameters, the fracturing parameters and the position parameters by using the first data set;

[0009] determining a second data set according to the main control parameter, wherein the second data set is used to establish a regression model of the main control parameter and the single well EUR;

[0010] dividing an optimization range and a grid size of the fracturing parameter to be optimized, and iteratively optimizing the fracturing parameter to obtain an optimal fracturing parameter of the regression model.

[0011] In a possible implementation, the determining of the master parameter affecting the EUR from the geological parameter, the fracturing parameter and the location parameter by using the first data set comprises:

[0012] applying the first data set to an XGBoost algorithm to calculate the influence degree of the geological parameter, the fracturing parameter and the location parameter on the single-well EUR;

[0013] determining the master parameter affecting the single-well EUR according to a preset influence degree ratio.

[0014] In a possible implementation, the determining of the second data set according to the master parameter comprises: removing the parameter information except the master parameter from the first data set, and retaining the master parameter affecting the single-well EUR as the second data set.

[0015] In a possible implementation, the regression model of the master parameter and the single-well EUR is established by using the second data set, comprising:

[0016] dividing the second data set into a training set, a validation set and a test set according to a preset ratio;

[0017] performing maximum-minimum normalization processing on the training set data;

[0018] performing maximum-minimum normalization processing on the validation set data and the test set data by using the maximum value and the minimum value of the training set data;

[0019] applying the normalized validation set data and the test set data to an XGBoost algorithm to establish the regression model of the master parameter and the single-well EUR.

[0020] In a possible implementation, the fracturing parameter is iteratively optimized to obtain the optimal fracturing parameter of the regression model, comprising:

[0021] applying a Bayesian optimization algorithm to start iteration with the maximum value of a black-box function as an optimization target;

[0022] setting a maximum iteration number, and calculating the optimal fracturing parameter combination after the iteration ends according to the maximum iteration number.

[0023] In a possible implementation, the optimal fracturing parameter combination is calculated after the iteration ends according to the maximum iteration number, comprising:

[0024] Select a random point, calculate the single well EUR value through the black box function;

[0025] Then an alternative function of the black box function is constructed based on the Gaussian process method, and a high point of the alternative function is found;

[0026] Finally, the high point of the alternative function is added to the data set, and the alternative function is constructed again to find the high point;

[0027] After the maximum number of iterations, the highest point of the final alternative function is output as the optimal fracturing parameter combination.

[0028] In a possible implementation, the geological parameters include water saturation, porosity, brittleness index, and average fracture pressure; the fracturing parameters include fracturing construction discharge, average section length, section number, horizontal section length, proppant amount per section, minimum pump pressure, maximum pump pressure, nitrogen injection amount per section, average construction pressure, and azimuth angle; the position parameters include the longitude and latitude of the single well bottom and the average depth TVD of the development horizon; and the production data includes the monthly gas production curve and the decline rate of the well, which are used to obtain the single well EUR.

[0029] In a second aspect, the embodiments of the present application provide a device for optimizing fracturing parameters of a new well in a tight sandstone gas reservoir, comprising:

[0030] A parameter determination module is configured to determine a first data set according to obtained geological data and production data of a single well in a gas field, wherein the geological data includes geological parameters, fracturing parameters, and position parameters, and the production data includes single well EUR data.

[0031] A main control parameter determination module is configured to determine main control parameters affecting the single well EUR from the geological parameters, fracturing parameters, and position parameters by using the first data set.

[0032] A model establishment module is configured to determine a second data set according to the main control parameters, wherein the second data set is used to establish a regression model of the main control parameters and the single well EUR.

[0033] A fracturing parameter output module is configured to divide an optimization range and a grid size of fracturing parameters to be optimized, and iteratively optimize the fracturing parameters to obtain optimal fracturing parameters of the regression model.

[0034] In a third aspect, the embodiments of the present application provide an electronic device, comprising:

[0035] A processor;

[0036] A memory;

[0037] and a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions, which, when executed by the processor, cause the electronic device to perform the method in any possible implementation of the first aspect.

[0038] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, including a stored program, wherein the program, when executed, controls a device where the computer-readable storage medium is located to perform the method in any possible implementation of the first aspect.

[0039] In the embodiment of the present application, the main control parameters affecting the single-well EUR are determined from the obtained gas field single-well geological data and production data, and then the regression model of the single-well EUR is established by using the main control parameters. Since the parameters for establishing the regression model of the single-well EUR are the main parameters affecting the single-well EUR, the accuracy of the regression model is improved. Therefore, when the subsequent optimization object is the fracturing parameter, the other parameter information except the fracturing parameter is used as the model coefficient of the regression model, so that the optimization effect of the fracturing parameter is improved. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0041] Figure 1 A flowchart of a tight sandstone gas reservoir new well fracturing parameter optimization method provided by an embodiment of the present application is shown in the figure.

[0042] Figure 2 A main control parameter correlation sorting diagram provided by an embodiment of the present application is shown in the figure.

[0043] Figure 3 A Bayesian optimization algorithm diagram provided by an embodiment of the present application is shown in the figure.

[0044] Figure 4 A final optimization result diagram of the fracturing parameter provided by an embodiment of the present application is shown in the figure.

[0045] Figure 5 A schematic diagram of a tight sandstone gas reservoir new well fracturing parameter optimization device provided by an embodiment of the present application is shown in the figure.

[0046] Figure 6 A schematic diagram of an electronic device provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0047] For better understanding of the technical solutions of the present application, the embodiments of the present application are described in detail below with reference to the drawings.

[0048] It should be clear that the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0049] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0050] It should be understood that the term "and / or" used herein is only to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are a "or" relationship.

[0051] Referring to Figure 1 A flowchart of a new well fracturing parameter optimization method for a tight sand gas reservoir provided by the embodiments of the present application is shown in FIG. 1. Figure 1 The new well fracturing parameter optimization method for a tight sand gas reservoir in the embodiments of the present application includes:

[0052] S101, determining a first data set according to the obtained gas field single well geological data and production data.

[0053] In this embodiment, the obtained data includes two kinds of geological data and production data. Further, the geological data includes geological parameters, fracturing parameters and position parameters; the production data includes single well ultimate recoverable reserves data EUR.

[0054] Specifically, the geological parameters in this embodiment include water saturation, porosity, brittleness index, and average fracture pressure. The fracturing parameters include fracturing construction displacement, average section length, section number, horizontal section length, support agent amount per section, minimum pump pressure, maximum pump pressure, nitrogen injection amount per section, average construction pressure, and azimuth angle. The position parameters include the single well bottom longitude, latitude and average depth of the development horizon TVD; the production data includes the monthly gas production curve and decline rate of the well, and the EUR of the single well evaluation calculated therefrom.

[0055] S102, determining the main control parameters affecting the single well EUR from the geological parameters, fracturing parameters and position parameters by using the first data set.

[0056] The XGBoost algorithm is applied in combination with the first data set to calculate the influence degree of the geological parameters, the fracturing parameters and the location parameters on the single-well EUR, and the top 50% variables with the influence degree are selected as the EUR master control parameters. See Figure 2 Among the finally determined master control parameters, the geological master control parameters include water saturation, porosity, brittleness index and average fracture pressure; and the fracturing master control parameters include fracturing construction discharge, segment length, segment number, horizontal segment length and support agent amount per segment.

[0057] In this embodiment, the XGBoost algorithm is a mature and efficient gradient boosting decision tree algorithm, which is improved on the basis of the original GBDT, so that the model effect is greatly improved. As a forward addition model, its core is to use the integrated idea-Boosting idea to integrate multiple weak learners into a strong learner through a certain method. That is, multiple trees are used for joint decision, and the result of each tree is the difference between the target value and the prediction result of all previous trees, and all the results are added up to obtain the final result, so as to improve the effect of the whole model.

[0058] S103, determining a second data set according to the master control parameters, the second data set being used to establish a regression model of the master control parameters and the single-well EUR.

[0059] For the second data set, only the EUR master control parameters are retained to form a new data set, and the new data set is randomly divided into a training set, a validation set and a test set according to a ratio of 6:2:2. First, the maximum and minimum normalization processing is performed on the training set data, and then the maximum and minimum values of the training set data are used to perform the same processing on the validation set and test set data.

[0060] Since the EUR master control parameters are determined to be the parameter data in the geological parameters and the fracturing parameters in S102, the XGBoost algorithm is applied to establish a regression model of the geological parameters, the fracturing parameters and the single-well EUR.

[0061] S104, dividing an optimization range and a grid size of the fracturing parameters to be optimized, and iteratively optimizing the fracturing parameters to obtain optimal fracturing parameters of the regression model.

[0062] In this embodiment, the range and the grid size of the fracturing parameters to be optimized are set, such as the minimum value of the horizontal segment length is 1000, the maximum value is 3500, the grid size is 100, and the integer is taken. The minimum value of the segment number is 20, the maximum value is 60, the grid size is 5, and the integer is taken. The minimum value of the average injection rate is 6, the maximum value is 12, and the grid size is 1.

[0063] The Bayesian optimization algorithm is applied, with the maximum value of the black box function (XGBoost production prediction model) as the optimization goal, and iteration is started to calculate the optimal fracturing parameter combination. The number of iterations is set to 40. The calculation process is as follows Figure 3 As shown in the figure, a random point is first selected and its function value (single-well EUR value) is calculated using a black-box function. A surrogate function is then constructed using the Gaussian process method, and the surrogate function's peak is found. Finally, the surrogate function's peak is added to the dataset, and the surrogate function is constructed again to find the peak. After 40 iterations, the fracturing parameter combination corresponding to the final surrogate function's highest point is output.

[0064] like Figure 4 As shown in the figure, it is a comparison between the optimized fracturing parameters of some old wells in the gas field and the actual fracturing parameters of the operators. The number of optimized fracturing stages is around 50, which far exceeds the original design. The technical iteration also proves that the optimized fracturing parameters are in line with the current status of the close-cut fracturing process. Figure 4 As shown in (b), the optimized single-well EUR is 8% higher than the actual single-well EUR, which proves that the fracturing parameters optimized by this method can provide theoretical help for gas field production increase and have good field application prospects.

[0065] Based on the same XGBoost model and data, a grid search method was applied to optimize fracturing parameters, with the results shown in Table 1. For example, when searching for 4,000 parameter variations, the grid search method required 4,000 searches, while the Bayesian optimization-based parameter optimization method only required 40 calculations, achieving a 94% match with the grid search method. This result demonstrates the superiority of this method in both optimization accuracy and efficiency.

[0066] Table 1 Comparison of Bayesian optimization and grid search results

[0067]

[0068] Corresponding to the method for optimizing fracturing parameters for a new well in a tight sandstone gas reservoir provided in the above embodiment, the present application also provides an embodiment of a device for optimizing fracturing parameters for a new well in a tight sandstone gas reservoir.

[0069] See also Figure 5 The device 20 for optimizing fracturing parameters for a new well in a tight sandstone gas reservoir in the embodiment of the present application includes:

[0070] Parameter determination module 201, configured to determine a first data set based on acquired geological data and production data of a single well in a gas field, wherein the geological data includes geological parameters, fracturing parameters, and location parameters, and the production data includes ultimate recoverable reserves data EUR of the single well;

[0071] The master parameter determination module 202 is configured to determine a master parameter affecting the single-well EUR from the geological parameter, the fracturing parameter and the location parameter by using the first data set.

[0072] The model establishment module 203 is configured to determine a second data set according to the master parameter, and the second data set is used to establish a regression model of the master parameter and the single-well EUR.

[0073] The fracturing parameter output module 204 is configured to divide an optimization range and a grid size of the fracturing parameter to be optimized, and iteratively optimize the fracturing parameter to obtain an optimal fracturing parameter of the regression model.

[0074] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0075] Corresponding to the above-mentioned embodiments, the embodiments of the present application also provide an electronic device.

[0076] Referring to Figure 6 , a structural schematic diagram of an electronic device provided by the embodiments of the present application is shown. As Figure 6 shown, the electronic device 300 can include a processor 301, a memory 302 and a communication unit 303. These components communicate through one or more buses, and those skilled in the art can understand that the electronic device structure shown in the figure does not constitute a limitation on the embodiments of the present application, it can be a bus structure, or a star structure, and can include more or fewer components than shown in the figure, or combine some components, or different component arrangements.

[0077] The communication unit 303 is configured to establish a communication channel, so that the electronic device can communicate with other devices.

[0078] The processor 301 is the control center of the electronic device, which connects all parts of the electronic device through various interfaces and lines, and processes data by running or executing software programs and / or modules stored in the memory 302 and calling data stored in the memory, to perform various functions of the electronic device and / or process data. The processor can be composed of integrated circuits (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs connected together. For example, the processor 301 can only include a central processing unit (CPU). In the embodiments of the present application, the CPU can be a single operation core, or can include multiple operation cores.

[0079] The memory 302 is used to store the execution instructions of the processor 301, and the memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0080] When the execution instructions in the memory 302 are executed by the processor 301, the electronic device 300 is enabled to perform part or all of the steps in the above method embodiments.

[0081] Corresponding to the above embodiments, the embodiments of the present application also provide a computer readable storage medium, wherein the computer readable storage medium can store a program, wherein the program can control the device where the computer readable storage medium is located to perform part or all of the steps in the above method embodiments when the program is running. In specific implementation, the computer readable storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM) and the like.

[0082] Corresponding to the above embodiments, the embodiments of the present application also provide a computer program product, which contains executable instructions, when the executable instructions are executed on a computer, the computer executes part or all of the steps in the above method embodiments.

[0083] In the embodiments of the present application, "at least one" means one or more, and "multiple" means two or more. The "and / or" describes the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B, which can represent the cases of A alone, A and B together, and B alone. Wherein A and B can be singular or plural. The character " / " generally represents that the associated objects before and after are in an "or" relationship. "At least one of the following" and the like expressions mean any combination of these items, including any combination of single or multiple items. For example, at least one of a, b and c can represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b and c can be single or multiple.

[0084] Those skilled in the art can understand that each unit and algorithm step described in the embodiments disclosed herein can be implemented by electronic hardware, computer software or a combination of the two. Whether the functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0085] In several embodiments provided in the present application, any function, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0086] The above is only a specific implementation of the present application. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. The protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for optimizing fracturing parameters for a new well in a tight sandstone gas reservoir, characterized in that: include: Determine a first data set based on the acquired geological data and production data of a single well in the gas field, wherein the geological data includes geological parameters, fracturing parameters, and location parameters, and the production data includes ultimate recoverable reserves data EUR of the single well; Determining, using the first data set from the geological parameters, the fracturing parameters, and the location parameters, a main control parameter that affects the final recoverable reserves data EUR of the single well, including: applying an XGBoost algorithm to the first data set to calculate the degree of influence of the geological parameters, the fracturing parameters, and the location parameters on the final recoverable reserves data EUR of the single well; and determining the main control parameter that affects the final recoverable reserves data EUR of the single well according to a preset influence degree ratio; Determining a second data set based on the master control parameters includes: removing parameter information other than the master control parameters from the first data set, retaining the master control parameters that affect the final recoverable reserves data EUR of the single well as the second data set; using the second data set to establish a regression model between the master control parameters and the final recoverable reserves data EUR of the single well, including: dividing the second data set into a training set, a validation set, and a test set according to a preset ratio; performing maximum and minimum normalization processing on the training set data; performing maximum and minimum normalization processing on the validation set data and the test set data using the maximum and minimum values ​​of the training set data; applying the XGBoost algorithm to the normalized validation set data and the test set data to establish a regression model between the master control parameters and the final recoverable reserves data EUR of the single well; The optimization range and grid size of the fracturing parameters to be optimized are divided, and the fracturing parameters are iteratively optimized to obtain the optimal fracturing parameters of the regression model, including: applying a Bayesian optimization algorithm, starting iteration with the maximum value of a black box function as the optimization target; setting a maximum number of iterations, and calculating the optimal fracturing parameter combination after the maximum number of iterations is completed.

2. The method for optimizing fracturing parameters for a new well in a tight sandstone gas reservoir according to claim 1, wherein: After the maximum number of iterations, the optimal fracturing parameter combination is calculated, including: Select random points and calculate the final recoverable reserves data EUR value of a single well through the black box function; Then, a surrogate function of the black box function is constructed based on the Gaussian process method, and the high point of the surrogate function is found; Finally, add the high point of the surrogate function to the data set and construct the surrogate function again to find the high point; After the maximum number of iterations, the fracturing parameter combination corresponding to the highest point of the final substitution function is output as the optimal fracturing parameter combination.

3. The method for optimizing fracturing parameters for a new well in a tight sandstone gas reservoir according to any one of claims 1 to 2, characterized in that: The geological parameters include water saturation, porosity, brittleness index, and average fracture pressure; the fracturing parameters include fracturing operation displacement, average section length, number of sections, horizontal section length, proppant dosage per section, minimum pump-off pressure, maximum pump-off pressure, nitrogen volume pumped into each section, average operation pressure, and azimuth; the location parameters include the bottom hole longitude and latitude of a single well and the average depth TVD of the development layer; the production data include the monthly gas production curve and decline rate of the well, and the monthly gas production curve and decline rate are used to obtain the ultimate recoverable reserves data EUR of a single well.

4. A device for optimizing fracturing parameters for new wells in tight sandstone gas reservoirs, characterized in that: include: a parameter determination module, configured to determine a first data set based on the acquired geological data and production data of a single well in the gas field, wherein the geological data includes geological parameters, fracturing parameters, and location parameters, and the production data includes the final recoverable reserves data EUR of the single well; a main control parameter determination module, configured to determine, using the first data set, the main control parameters that affect the final recoverable reserves data EUR of the single well from the geological parameters, fracturing parameters, and location parameters, including: applying an XGBoost algorithm to the first data set to calculate the degree of influence of the geological parameters, fracturing parameters, and location parameters on the final recoverable reserves data EUR of the single well; and determining the main control parameters that affect the final recoverable reserves data EUR of the single well according to a preset influence degree ratio; A model building module is used to determine a second data set based on the master control parameters, including: removing parameter information other than the master control parameters from the first data set, retaining the master control parameters that affect the final recoverable reserves data EUR of the single well as the second data set; the second data set is used to establish a regression model between the master control parameters and the final recoverable reserves data EUR of the single well, including: dividing the second data set into a training set, a validation set, and a test set according to a preset ratio; performing maximum and minimum normalization processing on the training set data; performing maximum and minimum normalization processing on the validation set data and the test set data using the maximum and minimum values ​​of the training set data; applying the XGBoost algorithm to the normalized validation set data and the test set data to establish a regression model between the master control parameters and the final recoverable reserves data EUR of the single well; The fracturing parameter output module is used to divide the optimization range and grid size of the fracturing parameters to be optimized, and iteratively optimize the fracturing parameters to obtain the optimal fracturing parameters of the regression model, including: applying a Bayesian optimization algorithm, starting iteration with the maximum value of the black box function as the optimization target; setting a maximum number of iterations, and calculating the optimal fracturing parameter combination after the maximum number of iterations is completed.

5. An electronic device, characterized in that: include: processor; Memory; and a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions, which, when executed by the processor, enable the electronic device to perform the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 3.

Images