Method, device and equipment for determining fracturing parameters

Abstract

The application discloses a method, device and equipment for determining fracturing parameters, wherein the method comprises the following steps: acquiring historical geologic physical property data, historical engineering data and historical production data of a historical reservoir; constructing a fracture propagation model, and determining fracture morphology data and geologic change information of the historical reservoir based on the fracture propagation model, the historical geologic physical property data and the historical engineering data; taking the historical geologic physical property data, the historical engineering data, the historical production data, the geologic change information and the fracture morphology data of the historical reservoir as training data, and training an oil and gas productivity calculation model; constructing a multi-objective optimization model based on the oil and gas productivity calculation model, a preset cost calculation model and target geologic physical property parameters of a target reservoir, and performing optimization and solving on the multi-objective optimization model to obtain fracturing construction parameters of the target reservoir. Through the above method, more accurate and reliable fracturing construction parameters can be obtained, and fine design of fracturing completion parameters is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas exploration and development, and particularly relates to a method, device and equipment for determining fracturing parameters. BACKGROUND

[0002] Hydraulic fracturing completion is a key technical means for unconventional oil and gas development, and fine design of fracturing completion parameters is an important factor affecting the single-well production of oil and gas wells. Therefore, it is of great significance to realize fine design of fracturing completion parameters for efficient development of oil and gas.

[0003] At present, the design of fracturing completion parameters for oilfield sites is mostly based on field experience, that is, personnel design hydraulic fracturing completion parameters based on expert experience and the construction conditions of oilfield sites, and then construction is carried out based on the designed hydraulic fracturing completion parameters. The design of the above-mentioned hydraulic fracturing parameters is completely realized by relying on the experience of personnel, and the precision, accuracy and reliability of the hydraulic fracturing completion parameters obtained by design are low.

[0004] At present, there is no effective solution to the problem of low precision, accuracy and reliability of the design of hydraulic fracturing completion parameters. SUMMARY

[0005] The purpose of the embodiments of the present specification is to provide a method, device and equipment for determining fracturing parameters to solve the problem of low precision, accuracy and reliability of the design of hydraulic fracturing completion parameters.

[0006] To solve the above technical problem, the first aspect of the present specification provides a method for determining fracturing parameters, comprising:

[0007] obtaining historical geologic physical property data, historical engineering data and historical production data of a historical reservoir;

[0008] constructing a fracture propagation model, and determining fracture morphology data and geologic change information of the historical reservoir based on the fracture propagation model, the historical geologic physical property data and the historical engineering data, the fracture propagation model being used for simulating the morphology of fracture propagation under different engineering parameters;

[0009] using the historical geologic physical property data, the historical engineering data, the historical production data, the geologic change information and the fracture morphology data of the historical reservoir as training data, training to obtain an oil and gas productivity calculation model with geologic physical property parameters and engineering data as input and production data as output;

[0010] constructing a multi-objective optimization model based on the oil and gas productivity calculation model, a preset cost calculation model and target geologic physical property parameters of a target reservoir, and optimizing and solving the multi-objective optimization model to obtain fracturing construction parameters of the target reservoir.

[0011] In some embodiments of the present specification, a fracture propagation model is constructed, comprising:

[0012] obtaining physical property parameters corresponding to historical geologic physical property data and engineering parameters corresponding to historical engineering data;

[0013] determining a first correlation relationship between the fracture morphology parameters and the geologic physical property parameters and the engineering parameters;

[0014] determining a second correlation relationship between the geologic modification parameters and the geologic physical property parameters and the engineering parameters;

[0015] based on the first correlation relationship and the second correlation relationship, the fracture propagation model is constructed.

[0016] In some embodiments of the present specification, based on the fracture propagation model, the historical geologic physical property data and the historical engineering data, the fracture morphology data and the geologic change information of the historical reservoir are determined, comprising:

[0017] based on the first correlation relationship, the historical geologic physical property data and the historical engineering data, the fracture morphology data of the historical reservoir is calculated;

[0018] based on the second correlation relationship, the historical geologic physical property data and the historical engineering data, the geologic modification data of the historical reservoir is calculated;

[0019] based on the geologic modification data and the historical geologic physical property data, the geologic change information is determined.

[0020] In some embodiments of the present specification, the geologic change information includes permeability change information, porosity change information and oil saturation change information.

[0021] In some embodiments of the present specification, the historical geologic physical property data of the historical reservoir, the historical engineering data, the historical production data, the geologic change information and the fracture morphology data are used as training data, and an oil and gas productivity calculation model with geologic physical property parameters and engineering data as input and production data as output is trained, comprising:

[0022] the historical geologic physical property data of the historical reservoir, the historical engineering data, the historical production data, the geologic change information and the fracture morphology data are input into a pre-established artificial intelligence model;

[0023] The artificial intelligence model is used to learn the relationship between the historical geologic property data, the historical engineering data, the geologic change information and the fracture morphology data, and the relationship between the historical geologic property data, the historical engineering data, the geologic change information, the fracture morphology data and the historical production data, and to iteratively update the parameters of the artificial intelligence model to obtain the oil and gas productivity calculation model.

[0024] In some embodiments of the present specification, the oil and gas productivity calculation model comprises a fracturing evolution sub-model and an oil and gas productivity calculation sub-model.

[0025] The fracturing evolution sub-model is used to process the input geologic property data and engineering data, and to output target fracture morphology data and target geologic change information.

[0026] The oil and gas productivity calculation sub-model is used to process the input geologic property data, engineering data, and target fracture morphology data and target geologic change information output by the fracturing evolution sub-model, and to output a productivity calculation result.

[0027] In some embodiments of the present specification, a multi-objective optimization model is constructed based on the oil and gas productivity calculation model, a preset cost calculation model and target geologic property parameters of a target reservoir, and the multi-objective optimization model is optimized and solved to obtain fracturing construction parameters of the target reservoir, comprising:

[0028] A multi-objective optimization model is constructed based on the oil and gas productivity calculation model and the preset cost calculation model, with the cost minimization and the oil and gas production maximization as the target, and the target geologic property parameters of the target reservoir as the constraint condition.

[0029] The initial engineering data of the target reservoir is determined.

[0030] The initial engineering data and the target geologic property parameters are input into the oil and gas productivity calculation model and the preset cost calculation model to output a productivity calculation result and a cost calculation result.

[0031] The initial engineering data is continuously updated and optimized with the cost minimization and the oil and gas production maximization as the target, until a preset optimization condition is reached, and the target engineering data is obtained as the fracturing construction parameters of the target reservoir.

[0032] The second aspect of the present specification provides a fracturing parameter determination device, comprising:

[0033] A data acquisition module is configured to acquire historical geologic property data, historical engineering data and historical production data of a historical reservoir.

[0034] The data determining module is configured to construct a fracture propagation model, and determine fracture morphology data and geological change information of the historical reservoir based on the fracture propagation model, the historical geologic property data and the historical engineering data, the fracture propagation model being configured to simulate the morphology of fracture propagation under different engineering parameters.

[0035] The model training module is configured to train an oil and gas productivity calculation model with the historical geologic property data, the historical engineering data, the historical production data, the geological change information and the fracture morphology data of the historical reservoir as training data, the oil and gas productivity calculation model taking the geologic property parameters and the engineering data as input and taking the production data as output.

[0036] The parameter determining module is configured to construct a multi-objective optimization model based on the oil and gas productivity calculation model, a preset cost calculation model and target geologic property parameters of a target reservoir, and to obtain the fracturing construction parameters of the target reservoir by optimizing and solving the multi-objective optimization model.

[0037] The third aspect of the present specification provides an electronic device, comprising a memory and a processor, the processor and the memory are connected in communication with each other, the memory stores computer instructions, and the processor implements the steps of the method of the first aspect by executing the computer instructions.

[0038] The fourth aspect of the present specification provides a computer readable storage medium, which stores computer program instructions, and the computer program instructions implement the steps of the method of the first aspect when executed.

[0039] The fracturing parameter determination method, apparatus, and equipment provided in the embodiments of this specification acquire historical geological property data, historical engineering data, and historical production data of historical reservoirs; construct a fracture propagation model; and, based on the fracture propagation model, historical geological property data, and historical engineering data, determine the fracture morphology data and geological change information of historical reservoirs. The fracture propagation model is used to simulate the fracture propagation morphology under different engineering parameters. Using historical geological property data, historical engineering data, historical production data, geological change information, and fracture morphology data of historical reservoirs as training data, an oil and gas production capacity calculation model is trained, with geological property parameters and engineering data as input and production data as output. Based on the oil and gas production capacity calculation model, a preset cost calculation model, and the target geological property parameters of the target reservoir, a multi-objective optimization model is constructed. The multi-objective optimization model is optimized and solved to obtain the fracturing construction parameters of the target reservoir. Through the above method, by analyzing the geological change information before and after fracturing, and by combining the characteristics of data such as fracture morphology data and geological change information when training the oil and gas production capacity calculation model, multiple aspects can be considered to calculate oil and gas production capacity more accurately and reliably, thereby improving the prediction accuracy of the oil and gas production capacity calculation model. Furthermore, a multi-objective optimization model constructed based on the oil and gas production capacity calculation model, the preset cost calculation model, and the target geological property parameters can be optimized to obtain more accurate and reliable fracturing construction parameters, thereby achieving fine design of fracturing completion parameters. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0041] Figure 1 The diagram shown is a schematic representation of a method for determining fracturing parameters provided in an embodiment of this specification.

[0042] Figure 2 The diagram shown is a schematic representation of a method for determining crack morphology data and geological change information provided in an embodiment of this specification.

[0043] Figure 3 The diagram shown is a schematic of a machine learning-based fine design method for fracturing completion parameters provided in an embodiment of this specification.

[0044] Figure 4 The diagram shown is a schematic of a device for determining fracturing parameters provided in an embodiment of this specification;

[0045] Figure 5Fig. 1 shows a schematic diagram of an electronic device according to an embodiment of the present specification. DETAILED DESCRIPTION

[0046] In order to enable persons skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, 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 persons skilled in the art without creative labor should fall within the scope of protection of the present application.

[0047] As described above, the fracturing completion parameter design of the oilfield site is mostly based on the site experience, and the precision, accuracy and reliability of the hydraulic fracturing completion parameter design are low. In order to solve the above problems, the present specification provides a fracturing parameter determination method, device and equipment, which obtains historical geologic physical data, historical engineering data and historical production data of a historical reservoir; constructs a fracture propagation model, and determines fracture morphology data and geologic change information of the historical reservoir based on the fracture propagation model, the historical geologic physical data and the historical engineering data, the fracture propagation model being used to simulate the morphology of fracture propagation under different engineering parameters; takes the historical geologic physical data, the historical engineering data, the historical production data, the geologic change information and the fracture morphology data of the historical reservoir as training data, and trains to obtain an oil and gas productivity calculation model taking geologic physical parameters and engineering data as input and production data as output; constructs a multi-objective optimization model based on the oil and gas productivity calculation model, a preset cost calculation model and target geologic physical parameters of a target reservoir, optimizes and solves the multi-objective optimization model, and obtains fracturing construction parameters of the target reservoir.

[0048] In the embodiments of the present specification, by analyzing the geologic change information before and after fracturing, and combining the features of the fracture morphology data and the geologic change information and other data when training the oil and gas productivity calculation model, the oil and gas productivity can be calculated more accurately and reliably from multiple aspects, and the prediction accuracy of the oil and gas productivity calculation model is improved. Further, the multi-objective optimization model constructed based on the oil and gas productivity calculation model, the preset cost calculation model and the target geologic physical parameters can be optimized and solved to obtain more accurate and reliable fracturing construction parameters, and the fine design of the fracturing completion parameter is realized.

[0049] The execution subject of each step of the method provided in the embodiments of the present application can be an electronic device, which refers to an electronic device with data calculation, processing and storage capabilities. The electronic device can be a terminal such as a personal computer (PC), a tablet computer, a smart phone, a wearable device, a smart robot, etc., or a server. The server can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0050] The method for determining fracturing parameters provided in the embodiments of the present application will be first introduced below in combination with the accompanying drawings.

[0051] Figure 1 An example of the method for determining fracturing parameters provided in the embodiments of the present application is shown. Although the method operations or device structures are provided in the embodiments or the drawings as described below, more or some of the operations or module units can be included in the method or device based on convention or without creative labor. The execution order of the steps or the module structure of the device is not limited to the execution order or module structure shown in the embodiments or the drawings in the absence of necessary causal relationship in logic. When the method or module structure is applied in actual devices, servers or terminal products, the method or module structure can be sequentially executed or executed in parallel (for example, in a parallel processor or a multi-thread processing environment, even including a distributed processing, server cluster implementation environment) as shown in the embodiments or the drawings. For example, the method or module structure can be executed in parallel in the form of a plurality of threads in a multi-thread processing environment. Figure 1 As shown, the method can include:

[0052] S101: Obtain historical geologic property data, historical engineering data and historical production data of a historical reservoir.

[0053] It can be understood that the historical reservoir can be a reservoir in which an existing production well is located, the historical geologic property data can be data corresponding to geologic property parameters of the reservoir in which the existing production well is located, the historical engineering data can be data corresponding to engineering parameters of the existing production well during fracturing construction, wherein the engineering parameters can include parameters related to fracturing completion, and the historical production data can be data generated during oil and gas production of the existing production well. The geologic property parameters can include but are not limited to reservoir depth, permeability, porosity, in-situ stress size, direction, etc., the engineering parameters can include but are not limited to fracturing segment number, segment spacing, perforation number, perforation parameter, sand ratio, etc., and the production data can include oil and gas production data of the oil and gas well, etc.

[0054] In some embodiments of the present disclosure, the historical production data can include daily oil and gas production data of the existing production well since the production date and cumulative production data of the oil and gas well, which can include 30-day, 180-day, 1-year, 2-year, 3-year, etc. cumulative production data.

[0055] S102: Construct a fracture propagation model, and determine the fracture morphology data and the geological change information of the historical reservoir based on the fracture propagation model, the historical geologic property data and the historical engineering data, the fracture propagation model being used to simulate the morphology of fracture propagation under different engineering parameters.

[0056] It can be understood that the fracture propagation model can simulate the fracturing process based on the geologic property parameters and the engineering parameters, and the morphology data of the fracture formed by fracturing can be calculated based on the simulation results, and the geological change information of the reservoir after fracturing can also be calculated based on the simulation results. The fracture morphology data can be used to represent the data related to the morphology of the fracture, which can include but is not limited to the fracture length, the fracture width, the fracture height, the fracture complexity, the fracture tortuosity and the effective stimulation volume of the fracture, etc. The geological change information can be used to represent the change of the geologic property parameters of the reservoir after fracturing. Of course, some of the geologic property parameters will not change with the generation of the fracture in the fracturing process, so the geological change information can include the data change information corresponding to the geologic property parameters affected by the fracturing, such as the data change information corresponding to the permeability, the porosity, the oil saturation and other parameters.

[0057] In some embodiments of the present disclosure, the geological change information can include the permeability change information, the porosity change information and the oil saturation change information.

[0058] In some embodiments of the present disclosure, the fracture propagation model can include a plurality of mathematical models, which can include mathematical calculation models related to the fracture morphology parameters such as the fracture length, the fracture width, the fracture height, the fracture complexity, the fracture tortuosity and the effective stimulation volume of the fracture, and can also include mathematical calculation models related to the geological change information such as the permeability, the porosity and the oil saturation. Further, the historical engineering data and the historical geologic property data can be substituted into the constructed mathematical models to calculate the fracture morphology data and the geological change information of the historical reservoir in step S102.

[0059] Reference Figure 2 As shown in some embodiments of the present disclosure, in step S102, when constructing the fracture propagation model, the following steps can be included:

[0060] S201: Obtain the geologic property parameters corresponding to the historical geologic property data and the engineering parameters corresponding to the historical engineering data.

[0061] S202: determining a first correlation relationship between the fracture morphology parameter and the geologic property parameter and the engineering parameter.

[0062] S203: determining a second correlation relationship between the geologic transformation parameter and the geologic property parameter and the engineering parameter.

[0063] S204: constructing the fracture propagation model based on the first correlation relationship and the second correlation relationship.

[0064] With continued reference to Figure 2 As shown in some embodiments of the present specification, in the step S102 of determining the fracture morphology data and the geologic change information of the historical reservoir based on the fracture propagation model, the historical geologic property data and the historical engineering data, the step can include:

[0065] S205: calculating the fracture morphology data of the historical reservoir based on the first correlation relationship, the historical geologic property data and the historical engineering data.

[0066] S206: calculating the geologic transformation data of the historical reservoir based on the second correlation relationship, the historical geologic property data and the historical engineering data.

[0067] S207: determining the geologic change information based on the geologic transformation data and the historical geologic property data.

[0068] It can be understood that the fracture propagation model constructed in the present embodiment can include a plurality of mathematical models for calculating the fracture morphology parameter and the geologic change information. By analyzing whether there is a correlation relationship between each parameter in the geologic property parameter and the engineering parameter and the fracture morphology parameter and the geologic change parameter, at least one parameter affecting the fracture morphology parameter and the geologic change is determined, and a corresponding mathematical model is constructed based on the determined parameter, and then the fracture propagation model is obtained. The fracture propagation model can represent the physical mechanism of fracture propagation. Further, after the fracture propagation model is constructed, the historical geologic property data and the historical engineering data can be substituted into the above fracture propagation model to calculate the fracture morphology data and the geologic transformation data, and the geologic change information can be determined based on the geologic transformation data and the geologic property parameter of the reservoir before fracturing. The geologic change information can include the location of the changed geologic property parameter and the specific change value. Through the above method, the calculation and processing of the historical engineering data and the historical geologic property data can realize the extraction of the features related to the geologic change and the fracture morphology, and then the extracted features can be used as part of the training data in the subsequent model training, which can fully learn the relationship between the historical production data and the features, and provide a data basis for the prediction accuracy of the model.

[0069] In some embodiments of the present disclosure, the geological property data of different positions of the historical reservoir can be different, and a three-dimensional geological model of the historical reservoir can be constructed, which can include values of various geological property parameters of different positions to better show the geological property data at different positions. For example, the three-dimensional model of the historical reservoir can be divided into a plurality of grid cells, each of which corresponds to a plurality of geological property data. The geological property data of the plurality of grid cells can be characterized in the form of a matrix, and each geological property parameter can correspond to a matrix. Each element in the matrix can represent the geological property data corresponding to a certain geological property parameter of a grid cell.

[0070] Further, after the three-dimensional geological model is constructed, the calculated fracture morphology data and the geological modification data can be fused with the three-dimensional geological model, thereby reflecting the change information of the three-dimensional geological space of the historical reservoir after fracturing. For example, based on the fracture propagation model, the change rate of the geological property parameter at the fracture after fracturing can be calculated, such as the increase ratio, the decrease ratio, etc. Then, based on the matrix corresponding to each property parameter in the three-dimensional geological model and the change rate of the property parameter, the geological modification data after fracturing can be calculated, and the difference between the geological modification data after fracturing and the matrix of the corresponding geological property parameter in the original three-dimensional geological model can be calculated to obtain the geological change information of the corresponding geological property parameter. By combining the geological change information of all geological property parameters, the geological change information of the historical reservoir can be obtained.

[0071] In some embodiments of the present disclosure, the fracture propagation model can also be an artificial intelligence model, such as a logistic regression model, a support vector machine model, a multi-layer perception model, a recurrent neural network model, a long short-term memory model, and other deep learning models. A pre-trained model for calculating fracture morphology parameters and geological change information can be obtained by training a large amount of historical data, and then the historical engineering data and the historical geological property data can be input into the pre-trained model to obtain the fracture morphology data and the geological change information of the historical reservoir in step S102.

[0072] S103: Taking the historical geological property data of the historical reservoir, the historical engineering data, the historical production data, the geological change information, and the fracture morphology data as training data, an oil and gas productivity calculation model is trained, which takes geological property parameters and engineering data as input and production data as output.

[0073] It can be understood that the oil and gas production capacity calculation model can be based on the input geological property parameters and engineering data to predict the production data including the oil and gas well production. In the training process, the artificial intelligence model can perform iterative optimization of the model parameters through the historical geological property data, the historical engineering data, and the relationship between the historical geological property data, the historical engineering data, and the corresponding geological change information, the fracture morphology data, and the historical production data, until the preset optimization condition (such as a preset prediction accuracy, a preset iteration number, etc.) is met, to obtain the oil and gas production capacity calculation model. Compared with the oil and gas production capacity calculation using a theoretical mathematical model, the oil and gas production capacity calculation model trained using the above training data can extract multi-level data features of the training data, fully learn the correlation between the training data, and improve the accuracy and practicality of the oil and gas production capacity calculation.

[0074] In some embodiments of the present specification, the historical geological property data can include at least one set of geological property data of a historical reservoir, the historical engineering data can include at least one set of engineering data corresponding to the at least one set of geological property data, and then each set of geological property data and engineering data can have corresponding fracture morphology data, geological change information, and production data. The geological property data, the engineering data, the fracture morphology data, the geological change information, and the production data having mutual corresponding relationship can be taken as one set of training data, each set of training data can participate in iterative training of the model parameters once, and multiple sets of training data can be used to realize iterative optimization of the artificial intelligence model until the preset optimization condition is met, to obtain the oil and gas production capacity calculation model.

[0075] In some embodiments of the present specification, the pre-constructed artificial intelligence model can be a logistic regression model, a support vector machine model, a multi-layer perception model, a recurrent neural network model, a long short-term memory model, or other deep learning models. The specific selection can be based on the data features of the training data and the application requirements of the model, and the present specification does not limit this.

[0076] In some embodiments of the present disclosure, the historical geologic property data of the historical reservoir, the historical engineering data, the historical production data, the geologic change information, and the fracture morphology data are used as training data to train an oil and gas productivity calculation model taking geologic property parameters and engineering data as input and taking production data as output, which can include: inputting the historical geologic property data of the historical reservoir, the historical engineering data, the historical production data, the geologic change information, and the fracture morphology data into a pre-established artificial intelligence model; learning the relationship between the historical geologic property data, the historical engineering data, and the geologic change information and the fracture morphology data, and the relationship between the historical geologic property data, the historical engineering data, the geologic change information, the fracture morphology data, and the historical production data by using the artificial intelligence model, and iteratively updating the parameters of the artificial intelligence model to obtain the oil and gas productivity calculation model.

[0077] It can be understood that the pre-established artificial intelligence model can include two sub-models: a first sub-model and a second sub-model, and the two sub-models can adopt the same type of artificial intelligence model, or can select different types of artificial intelligence models based on the data characteristics of the corresponding training data, and the present disclosure does not limit this. In the training process, the first sub-model is used to learn the relationship between the historical geologic property data, the historical engineering data, and the geologic change information and the fracture morphology data to obtain a fracture evolution sub-model for predicting the fracture morphology after fracturing and the geologic change information, and the second sub-model is used to learn the relationship between the historical geologic property data, the historical engineering data, the geologic change information, the fracture morphology data, and the historical production data to obtain an oil and gas productivity calculation sub-model for oil and gas productivity prediction.

[0078] In some embodiments of the present disclosure, the oil and gas productivity calculation model can include a fracture evolution sub-model and an oil and gas productivity calculation sub-model; the fracture evolution sub-model can be used to process the input geologic property data and engineering data to output target fracture morphology data and target geologic change information; and the oil and gas productivity calculation sub-model can be used to process the input geologic property data, engineering data, and target fracture morphology data and target geologic change information output by the fracture evolution sub-model to output a productivity calculation result.

[0079] Specifically, in actual application, the geologic property parameters and engineering parameter data can be input into the fracture evolution sub-model in the oil and gas productivity calculation model to output predicted target fracture morphology data and target geologic change information, and then the geologic property parameters and engineering parameter data and the predicted target fracture morphology data and target geologic change information can be input into the oil and gas productivity calculation sub-model in the oil and gas productivity calculation model to obtain a final oil and gas production prediction result.

[0080] In some embodiments of the present specification, when training the oil and gas production capacity calculation model, the fracture evolution sub-model and the oil and gas production capacity calculation sub-model can be first trained using part of the training data, then another part of the training data is input into the fracture evolution sub-model, the prediction data of the sub-model is output, and the prediction data is input into the oil and gas production capacity calculation sub-model together with the data related to the geophysical properties and engineering in the training data, the prediction data of the oil and gas production capacity calculation sub-model is output, then based on the difference between the prediction data of the oil and gas production capacity calculation sub-model and the production data as the expected output in the training data, the parameters of the two sub-models are optimized and updated, and the optimized and updated fracture evolution sub-model and oil and gas production capacity calculation sub-model are used as the final oil and gas production capacity calculation model.

[0081] In some embodiments of the present specification, if the crack propagation model in the foregoing step S102 is an artificial intelligence model trained, the crack propagation model can be used as the fracture evolution sub-model in the oil and gas production capacity calculation model.

[0082] S104: constructing a multi-objective optimization model based on the oil and gas production capacity calculation model, a preset cost calculation model, and target geophysical property parameters of a target reservoir, and optimizing and solving the multi-objective optimization model to obtain the fracturing construction parameters of the target reservoir.

[0083] It can be understood that the preset cost calculation model can include, but is not limited to, calculation models of material consumption cost, equipment operation cost, and labor cost, and a cost calculation model for calculating the fracturing construction cost can be constructed based on the above costs.

[0084] In some embodiments of the present specification, constructing a multi-objective optimization model based on the oil and gas production capacity calculation model, a preset cost calculation model, and target geophysical property parameters of a target reservoir, and optimizing and solving the multi-objective optimization model to obtain the fracturing construction parameters of the target reservoir can include: constructing a multi-objective optimization model with the minimum cost and the maximum oil and gas production as the target and the target geophysical property parameters of the target reservoir as the constraint condition based on the oil and gas production capacity calculation model and the preset cost calculation model; determining the initial engineering data of the target reservoir; inputting the initial engineering data and the target geophysical property parameters into the oil and gas production capacity calculation model and the preset cost calculation model to output the production capacity calculation result and the cost calculation result; continuously updating and optimizing the initial engineering data with the minimum cost and the maximum oil and gas production as the target until the preset optimization condition is reached, and obtaining the target engineering data as the fracturing construction parameters of the target reservoir.

[0085] It can be understood that, when performing multi-objective optimization solving, the initial engineering parameters or the optimized engineering parameters can be input into the oil and gas production capacity calculation model and the cost calculation model, and the production capacity calculation result and the cost calculation result are output respectively, and then based on the obtained production capacity calculation result and cost calculation result, the initial engineering parameters or the optimized engineering parameters are iteratively optimized with the minimum cost and the maximum oil and gas production as the target, until the global optimization or the preset iteration number is met, the iterative optimization is ended, and the engineering data input into the oil and gas production capacity calculation model and the cost calculation model for the last time is the target engineering data, which can be used as the designed fracturing construction parameter to perform fracturing construction on the target reservoir by using the fracturing construction parameter. In the embodiments of the present specification, the trained oil and gas production capacity calculation model is used to calculate the oil and gas production, and compared with the calculation of the traditional data model, the accuracy and reliability of the oil and gas production prediction are higher.

[0086] In some embodiments of the present specification, when the multi-objective optimization model performs optimization solving, an optimization algorithm can be used for implementation, and the optimization algorithm includes but is not limited to a particle swarm optimization algorithm, a genetic algorithm, a co-evolution algorithm, etc.

[0087] The embodiments of the present specification also provide a fine design method of fracturing and completion parameters based on machine learning, which predicts the production capacity of a new well through geological and engineering data, thereby optimizing the fracturing and completion parameters and providing a reference for the design of fracturing and completion parameters of an oil and gas well. Moreover, when predicting the production capacity of the new well, the spatio-temporal information field evolution characteristics are fused, which can include the crack morphology data and the geological change information in the foregoing. Figure 3 As shown in the figure, the method can include:

[0088] S1: Obtain historical data. The historical data can include reservoir geologic property parameters, engineering parameter data, and production data of an oil and gas well.

[0089] S2: Establish a numerical simulation library of unconventional crack propagation based on the crack propagation principle.

[0090] Specifically, a fracturing crack propagation numerical model is constructed based on the reservoir geologic property parameters and the engineering parameter data in the historical data, and the crack morphology parameters under the condition of multiple hydraulic fracturing parameters of the existing production well are simulated. For example, based on a three-dimensional geological model, a numerical simulation library of unconventional crack propagation is established, the input geologic property parameters can include reservoir depth, permeability, porosity, in-situ stress size, direction, etc.; the engineering parameter data can include data corresponding to parameters such as fracturing stage number, inter-stage distance, fracturing segment number, segment distance, pump pressure, sand ratio, etc.; the output crack morphology parameters can include but are not limited to crack length, width, height, complexity, tortuosity, effective reconstruction volume, etc.; and the output geological change information can include change data of the geologic property parameters affected by fracturing.

[0091] The following section uses geological change information, including permeability change information, porosity change information, and oil saturation change information, as examples to introduce the specific calculation process of geological change information.

[0092] In 3D geological modeling, geological parameters such as permeability are typically represented by a 3D array (or matrix). Taking permeability as an example, each element corresponds to the permeability value at a specific location in the geological model. Assuming a 3D geological model of size n×m×p, permeability can be represented by a 3D array K, where each element k... ijl This represents the permeability value at location (i, j, l).

[0093] Hydraulic fracturing can significantly affect parameters such as permeability, porosity, and oil saturation in geological models. Assume a three-dimensional geological model with initial parameters of permeability (Kee) and oil saturation (S). o Suppose that fracturing affects certain mesh elements at location (i, j, l), and the set of these elements is denoted as F.

[0094] Assuming the permeability increases by ΔK after fracturing, the updated permeability K′ can be expressed as:

[0095]

[0096] Here, ΔK can represent a scaling factor, for example, ΔK = 0.5 means that the penetration rate increases by 50%.

[0097] Assuming the porosity increases by Δφ after fracturing, the updated porosity φ′ can be expressed as:

[0098] φ′(i,j,l)=φ(i,j,l)×(1+Δφ) Formula (2)

[0099] Δφ can be used to measure a scaling factor; for example, Δφ = 0.1 indicates a 10% increase in porosity.

[0100] Assuming the oil saturation decreases by ΔS after fracturing e The updated oil saturation S o ′ can be represented as:

[0101] S′ o (i,j,l)=S o (i,j,l)×(1-ΔS o ) Formula (3)

[0102] Where, ΔS e It can represent a scaling factor, such as ΔS e =0.1 indicates a 10% reduction in oil saturation.

[0103] For the three-dimensional geological data after fracturing reconstruction, the location information data of all grid cells affected by fracturing, the updated matrix can be included as new features in the model. Assuming that there is a target variable y, the original feature matrix is X e , and the updated feature matrix is X updated .

[0104] The original geological feature matrix can be represented as:

[0105] X origin = [K(i,j,l), φ(i,j,l), S o (i,j,l)...] Equation (4)

[0106] The updated permeability, porosity and oil saturation matrix is added to the original feature matrix as new features, and the difference of three-dimensional geological space change information after fracturing reconstruction (i.e. the geological change information in the foregoing) is:

[0107]

[0108] The difference matrix X change is the quantitative matrix value of the change of the three-dimensional information field obtained by unconventional fracture reconstruction, which can reflect the specific influence of hydraulic fracturing on the parameters of the geological model. By comparing the parameter changes before and after fracturing, the influence of fracturing on the geological model can be quantified. It can be fused with other features as input features for artificial intelligence model training. In this way, the artificial intelligence model can not only use the original geological features, but also use the change information after fracturing reconstruction, thereby improving the prediction accuracy of the model. Of course, the permeability, porosity and oil saturation described above are only one example of this specification, and in other embodiments, the permeability, porosity and oil saturation can also be represented in other forms, or can also include changes in more or fewer geological parameters than the above example.

[0109] S3: Fusion agent model to establish oil and gas production capacity calculation model.

[0110] Specifically, according to the geological data of the new production well (i.e. the target geological property parameters of the target reservoir), the quantitative value of the change of the three-dimensional information field can be predicted through the agent model, and the deep learning model is coupled to establish an intelligent production prediction model. According to the input parameters, define the training set and test set, of which 80% of the data can be used as the training set and 20% of the data can be used as the test set. According to the training set data, the deep learning model is trained to obtain the trained oil and gas production capacity calculation model. Further, in subsequent multi-objective optimization, according to the reservoir geological parameters of the new well, the trained oil and gas production capacity calculation model is used to calculate the production capacity data under different completion parameters.

[0111] S4: obtaining the optimal fracturing and completion parameter combination through the optimization algorithm.

[0112] Specifically, a multi-objective optimization mathematical model for fracturing parameters is established with the highest economic net present value and the maximum production as the target, and the optimal solution of the mathematical model is found through an artificial intelligence algorithm, so as to obtain the best fracturing parameter combination, and whether it conforms to the engineering practice is verified, and finally the fine design of the fracturing and completion parameters is realized.

[0113] The method for fine design of fracturing and completion parameters based on machine learning provided by the embodiments of the present specification can simulate the fracturing fracture expansion based on reservoir geological parameters and engineering parameters, and predict the oil and gas production by establishing an agent model through a simulation database. Based on the reservoir geological parameters of a new well and the predicted cumulative production, the fracturing and completion parameters are optimized to maximize the productivity and economic benefits, so as to greatly improve the production of oil and gas wells.

[0114] Based on the method for determining the fracturing parameters described above, one or more embodiments of the present specification also provide a device for determining the fracturing parameters. The device can include a device (including a distributed system), software (application), module, plug-in, server, client, etc. using the method described in the embodiments of the present specification, and a device combined with necessary implementation hardware. Based on the same innovative concept, the device in one or more embodiments provided by the embodiments of the present specification is as described in the following embodiments. Since the implementation scheme of the device for solving the problem is similar to the method, the implementation of the specific device of the embodiments of the present specification can refer to the implementation of the foregoing method, and the repeated parts will not be described herein. The term "unit" or "module" used below can be a combination of software and / or hardware that can implement a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware or a combination of software and hardware is also possible and is conceived. Figure 4 A schematic diagram of a device for determining fracturing parameters provided by an embodiment of the present application is shown. As shown in the figure, Figure 4 The device for determining fracturing parameters 400 can include:

[0115] The data acquisition module 401 is configured to acquire historical geologic property data, historical engineering data and historical production data of a historical reservoir.

[0116] The data determination module 402 is configured to construct a fracture expansion model, and determine fracture morphology data and geologic change information of the historical reservoir based on the fracture expansion model, the historical geologic property data and the historical engineering data, the fracture expansion model being used to simulate the morphology of fracture expansion under different engineering parameters.

[0117] The model training module 403 is configured to take the historical geologic property data of the historical reservoir, the historical engineering data, the historical production data, the geologic change information, and the fracture morphology data as training data, and train an oil and gas productivity calculation model taking the geologic property parameters and the engineering data as input and taking the production data as output.

[0118] The parameter determination module 404 is configured to construct a multi-objective optimization model based on the oil and gas productivity calculation model, a preset cost calculation model, and target geologic property parameters of a target reservoir, and perform optimization solving on the multi-objective optimization model to obtain the fracturing construction parameters of the target reservoir.

[0119] In some embodiments of the present specification, the data determination module 402, when constructing the fracture propagation model, is specifically configured to: obtain geologic property parameters corresponding to the historical geologic property data and engineering parameters corresponding to the historical engineering data; determine a first correlation relationship between the fracture morphology parameters and the geologic property parameters and the engineering parameters; determine a second correlation relationship between the geologic reconstruction parameters and the geologic property parameters and the engineering parameters; and construct the fracture propagation model based on the first correlation relationship and the second correlation relationship.

[0120] In some embodiments of the present specification, the data determination module 402, when determining the fracture morphology data and the geologic change information of the historical reservoir based on the fracture propagation model, the historical geologic property data, and the historical engineering data, is specifically configured to: calculate the fracture morphology data of the historical reservoir based on the first correlation relationship, the historical geologic property data, and the historical engineering data; calculate the geologic reconstruction data of the historical reservoir based on the second correlation relationship, the historical geologic property data, and the historical engineering data; and determine the geologic change information based on the geologic reconstruction data and the historical geologic property data.

[0121] In some embodiments of the present specification, the geologic change information can include permeability change information, porosity change information, and oil saturation change information.

[0122] In some embodiments of the present specification, the model training module 403 is specifically configured to: input the historical geologic property data of the historical reservoir, the historical engineering data, the historical production data, the geologic change information, and the fracture morphology data into a pre-established artificial intelligence model; learn the relationships between the historical geologic property data, the historical engineering data, and the geologic change information and the fracture morphology data, and the relationships between the historical geologic property data, the historical engineering data, the geologic change information, the fracture morphology data, and the historical production data by using the artificial intelligence model; iteratively update parameters of the artificial intelligence model to obtain the oil and gas productivity calculation model.

[0123] In some embodiments of this specification, the oil and gas production capacity calculation model may include a fracturing evolution sub-model and an oil and gas production capacity calculation sub-model; the fracturing evolution sub-model may be used to process the input geological property data and engineering data, and output target fracture morphology data and target geological change information; the oil and gas production capacity calculation sub-model may be used to process the input geological property data, engineering data, and the target fracture morphology data and target geological change information output by the fracturing evolution sub-model, and output production capacity calculation results.

[0124] In some embodiments of this specification, the parameter determination module 404 can be specifically used to: construct a multi-objective optimization model based on an oil and gas production capacity calculation model and a preset cost calculation model, with the objectives of minimizing cost and maximizing oil and gas production, and with the target geological properties of the target reservoir as constraints; determine the initial engineering data of the target reservoir; input the initial engineering data and the target geological properties into the oil and gas production capacity calculation model and the preset cost calculation model, and output the production capacity calculation results and cost calculation results; continuously update and optimize the initial engineering data with the objectives of minimizing cost and maximizing oil and gas production, until the preset optimization conditions are met, and obtain the target engineering data as the fracturing construction parameters of the target reservoir.

[0125] The descriptions and functions of the above modules can be understood by referring to the section on methods for determining fracturing parameters, and will not be repeated here.

[0126] This application also provides an electronic device, such as... Figure 5 As shown, the electronic device may include a processor 501 and a memory 502, wherein the processor 501 and the memory 502 may be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0127] Processor 501 can be a central processing unit (CPU). Processor 501 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0128] The memory 502, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the fracturing parameter determination method in this embodiment of the invention (e.g., Figure 4 The processor 501 comprises a data acquisition module 401, a data determination module 402, a model training module 403, and a parameter determination module 404. The processor 501 executes various functional applications and data processing by running non-transient software programs, instructions, and modules stored in the memory 502, thereby implementing the fracturing parameter determination method in the above method embodiments.

[0129] Memory 502 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor 501, etc. Furthermore, memory 502 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 502 may optionally include memory remotely located relative to processor 501, and these remote memories may be connected to processor 501 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0130] The one or more modules are stored in the memory 502, and when executed by the processor 501, they perform the following method for determining fracturing parameters:

[0131] Historical geological property data, historical engineering data, and historical production data of historical reservoirs are acquired. A fracture propagation model is constructed, and based on the fracture propagation model, the historical geological property data, and the historical engineering data, the fracture morphology data and geological change information of the historical reservoirs are determined. The fracture propagation model is used to simulate the morphology of fracture propagation under different engineering parameters. Using the historical geological property data, the historical engineering data, the historical production data, the geological change information, and the fracture morphology data of the historical reservoirs as training data, an oil and gas production capacity calculation model is trained, with geological property parameters and engineering data as input and production data as output. Based on the oil and gas production capacity calculation model, a preset cost calculation model, and the target geological property parameters of the target reservoir, a multi-objective optimization model is constructed. The multi-objective optimization model is optimized and solved to obtain the fracturing construction parameters of the target reservoir.

[0132] The specific details of the aforementioned electronic device can be understood by referring to the relevant descriptions and effects in the above method embodiments, and will not be repeated here.

[0133] The specification also provides a computer storage medium storing computer program instructions, which, when executed, implement the steps of the method for determining fracturing parameters.

[0134] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program. The program can be stored in a computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiment methods can be included. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc. The storage medium can also include a combination of the above-mentioned types of memories.

[0135] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0136] The system, device, module or unit described in the above embodiments can be specifically implemented by a computer chip or entity, or by a product with certain functions.

[0137] For the convenience of description, the above device is described as various units respectively described in functions. Of course, the functions of each unit can be implemented in the same or more software and / or hardware in the implementation of the present application.

[0138] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software and the necessary general hardware platform. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a ROM / RAM, a magnetic disc, an optical disc, etc., including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the method of some parts of the embodiments of the present application.

[0139] The application is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well- known computing systems, environments, and / or configurations that can be suitable for use with the application include personal computers, server computers, handheld or laptop devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

[0140] The application can be described in the general context of computer- executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like, that perform particular tasks or implement particular abstract data types. The application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in local and remote computer storage media including memory storage devices.

[0141] While this application has been depicted, described, and is to be understood in connection with specific example embodiments, it will be appreciated that modifications can be made by those skilled in the art without departing from the spirit and scope of the application. It is therefore intended that the application not be limited to the exact example embodiments described above, but should be construed to cover all suitable structures that are pointed out in the appended claims and their equivalents.

Claims

1. A method of determining fracturing parameters, characterized in that, The method comprises the following steps: obtaining historical geologic property data, historical engineering data and historical production data of a historical reservoir; constructing a fracture propagation model, and determining fracture morphology data and geologic change information of the historical reservoir based on the fracture propagation model, the historical geologic property data and the historical engineering data, wherein the fracture propagation model is used to simulate the morphology of fracture propagation under different engineering parameters; training an oil and gas productivity calculation model with geologic property parameters and engineering data as inputs and production data as outputs, by taking the historical geologic property data, the historical engineering data, the historical production data, the geologic change information and the fracture morphology data of the historical reservoir as training data; constructing a multi-objective optimization model based on the oil and gas productivity calculation model, a preset cost calculation model and target geologic property parameters of a target reservoir, and obtaining the fracturing construction parameters of the target reservoir by optimizing and solving the multi-objective optimization model; constructing a fracture propagation model, comprising: obtaining geologic property parameters corresponding to the historical geologic property data and engineering parameters corresponding to the historical engineering data; determining a first correlation between fracture morphology parameters and geologic property parameters and engineering parameters; determining a second correlation between geologic reconstruction parameters and geologic property parameters and engineering parameters; constructing the fracture propagation model based on the first correlation and the second correlation; the oil and gas productivity calculation model comprises a fracturing evolution sub-model and an oil and gas productivity calculation sub-model; the fracturing evolution sub-model is used to process the input geologic property data and engineering data, and output target fracture morphology data and target geologic change information; the oil and gas productivity calculation sub-model is used to process the input geologic property data, engineering data and target fracture morphology data and target geologic change information output by the fracturing evolution sub-model, and output a productivity calculation result.

2. The method of determining fracturing parameters according to claim 1, characterized in that, determining the fracture morphology data and the geologic change information of the historical reservoir based on the fracture propagation model, the historical geologic property data and the historical engineering data, comprises: calculating the fracture morphology data of the historical reservoir based on the first correlation, the historical geologic property data and the historical engineering data; calculating the geologic reconstruction data of the historical reservoir based on the second correlation, the historical geologic property data and the historical engineering data; determining the geologic change information based on the geologic reconstruction data and the historical geologic property data.

3. The method of determining fracturing parameters of claim 2, wherein, The geologic change information comprises permeability change information, porosity change information and oil saturation change information.

4. The method of determining fracturing parameters of claim 1, wherein, training an oil and gas productivity calculation model with geologic property parameters and engineering data as inputs and production data as outputs, by taking the historical geologic property data, the historical engineering data, the historical production data, the geologic change information and the fracture morphology data of the historical reservoir as training data, comprises: inputting the historical geologic property data, the historical engineering data, the historical production data, the geologic change information and the fracture morphology data of the historical reservoir into a pre-established artificial intelligence model; The artificial intelligence model is used to learn the relationship between the historical geologic property data, the historical engineering data, the geologic change information and the fracture morphology data, and the relationship between the historical geologic property data, the historical engineering data, the geologic change information, the fracture morphology data and the historical production data, and parameters of the artificial intelligence model are iteratively updated to obtain the oil and gas productivity calculation model.

5. The method of determining fracturing parameters of claim 1, wherein, Based on the oil and gas productivity calculation model, a preset cost calculation model and target geologic property parameters of a target reservoir, a multi-objective optimization model is constructed, and the multi-objective optimization model is solved to obtain the fracturing construction parameters of the target reservoir, including: Based on the oil and gas productivity calculation model and the preset cost calculation model, a multi-objective optimization model is constructed, with the minimum cost and the maximum oil and gas production as the target and the target geologic property parameters of the target reservoir as the constraint condition; The initial engineering data of the target reservoir is determined; The initial engineering data and the target geologic property parameters are input into the oil and gas productivity calculation model and the preset cost calculation model, and the productivity calculation result and the cost calculation result are output; The initial engineering data is continuously updated and optimized with the minimum cost and the maximum oil and gas production as the target until the preset optimization condition is reached, and the target engineering data is obtained as the fracturing construction parameters of the target reservoir.

6. An apparatus for determining fracturing parameters, the apparatus comprising: Comprising: A data acquisition module is configured to acquire historical geologic property data, historical engineering data and historical production data of a historical reservoir; A data determination module is configured to construct a fracture propagation model, and determine fracture morphology data and geologic change information of the historical reservoir based on the fracture propagation model, the historical geologic property data and the historical engineering data, wherein the fracture propagation model is used to simulate the morphology of fracture propagation under different engineering parameters; A model training module is configured to use the historical geologic property data, the historical engineering data, the historical production data, the geologic change information and the fracture morphology data of the historical reservoir as training data to train an oil and gas productivity calculation model with geologic property parameters and engineering data as input and production data as output; A parameter determination module is configured to construct a multi-objective optimization model based on the oil and gas productivity calculation model, a preset cost calculation model and target geologic property parameters of a target reservoir, and solve the multi-objective optimization model to obtain fracturing construction parameters of the target reservoir; When constructing the fracture propagation model, the data determination module is specifically configured to: Acquire geologic property parameters corresponding to the historical geologic property data and engineering parameters corresponding to the historical engineering data; Determine a first correlation relationship between fracture morphology parameters and geologic property parameters and engineering parameters; Determine a second correlation relationship between geologic modification parameters and geologic property parameters and engineering parameters; Based on the first correlation relationship and the second correlation relationship, the fracture propagation model is constructed; The oil and gas productivity calculation model comprises a fracturing evolution sub-model and an oil and gas productivity calculation sub-model; The fracturing evolution sub-model is used to process input geologic property data and engineering data, and output target fracture morphology data and target geologic change information; The oil and gas production capacity calculation sub-model is used for processing input geological physical property data, engineering data, and target fracture shape data and target geological change information output by the fracturing evolution sub-model, and outputting a production capacity calculation result.

7. An electronic device, comprising: The method comprises the following steps: The memory and the processor are in communication connection with each other, the memory stores computer instructions, and the processor implements the steps of the method according to any one of claims 1 to 5 by executing the computer instructions.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer program instructions, and the computer program instructions are executed by the processor to implement the steps of the method according to any one of claims 1 to 5.

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