Shale gas production capacity prediction method and device

Abstract

The present application discloses a shale gas production capacity prediction method and device, belonging to the technical field of shale gas exploitation. The method includes: determining an initial value range of fracture property parameters and an initial value range of matrix property parameters; establishing a first preset number of initial geological models based on the initial value ranges of the fracture property parameters and the matrix property parameters; respectively inputting the first preset number of initial geological models into a reservoir simulator to obtain predicted production data corresponding to each initial geological model for a first preset time period; determining a plurality of target geological models based on the predicted production data and the actual production data; respectively determining the predicted production data corresponding to each target geological model for a second preset time period; and determining the production capacity statistical data of the target shale gas well. By using the present application, a plurality of target geological models that are more in line with the actual geological conditions can be determined, so as to obtain more accurate predicted production data and production capacity statistical data of the target shale gas well.

Description

Technical Field

[0001] This application relates to the technical field of shale gas exploitation, and particularly relates to a method and device for predicting shale gas production capacity. Background Art

[0002] With the consumption and shortage of world energy, the exploitation of unconventional energy such as shale gas reservoirs has attracted more and more attention, and the prediction and evaluation of the production capacity of shale gas wells have gradually become an important part of the exploitation of shale gas reservoirs.

[0003] The common method for predicting the production capacity of shale gas wells is as follows: First, based on the well logging interpretation data and micro-seismic data corresponding to the shale gas well, the value ranges of the fracture property parameters and matrix property parameters in the geological model are determined. Then, according to the experience of the staff and the actual production data of shale gas, multiple value combinations of each fracture property parameter and matrix property parameter representing the geological conditions near the shale gas well are determined, so as to determine multiple target geological models corresponding to different value combinations. The multiple target geological models are input into the reservoir simulator to obtain the predicted production data corresponding to each target geological model.

[0004] However, in the above method, the values of each fracture property parameter and matrix property parameter are subjectively determined by the staff based on their work experience, which may have relatively large errors, and ultimately lead to inaccurate predicted production data of the shale gas well. Summary of the Invention

[0005] The embodiments of this application provide a method for predicting shale gas production capacity, which can solve the problem that the production data of shale gas wells are not accurately predicted in the prior art.

[0006] In a first aspect, a method for predicting shale gas production capacity is provided. The method includes:

[0007] Based on the well logging interpretation data and micro-seismic data corresponding to the target shale gas well, determine the initial value range of the fracture property parameters and the initial value range of the matrix property parameters, where the fracture property reference includes natural fracture property parameters and artificial fracture property parameters;

[0008] Based on the initial value range of the fracture property parameters and the initial value range of the matrix property parameters, establish the first preset number of initial geological models of the target shale gas well;

[0009] Respectively input the first preset number of initial geological models into the reservoir simulator to obtain the predicted production data of the first preset time period corresponding to each initial geological model;

[0010] Determine a plurality of target geological models based on the predicted production data of the first preset period corresponding to each initial geological model and the actual production data of the first preset period of the target shale gas well;

[0011] Input the plurality of target geological models into the reservoir simulator respectively, and obtain the predicted production data of the second preset period corresponding to each target geological model;

[0012] Determine the productivity statistical data of the target shale gas well based on the predicted production data of the second preset period corresponding to each target geological model.

[0013] In a possible implementation manner, the natural fracture attribute parameters include at least one of the lengths, widths, heights, and flow conductivities of a plurality of natural fractures, the artificial fracture attribute parameters include at least one of the lengths, widths, heights, and flow conductivities of a plurality of artificial fractures, and the matrix attribute parameters include at least one of matrix porosity, matrix water saturation, and matrix permeability.

[0014] In a possible implementation manner, the establishing of the first preset number of initial geological models of the target shale gas well based on the initial value ranges of the fracture attribute parameters and the initial value ranges of the matrix attribute parameters includes:

[0015] Perform multiple random value-taking processes respectively within the initial value ranges of the fracture attribute parameters and the initial value ranges of the matrix attribute parameters to obtain multiple groups of parameter value combinations composed of fracture attribute parameter values and matrix attribute parameter values, wherein the number of times of the random value-taking process and the number of groups of parameter value combinations are both the same as the first preset number;

[0016] Determine the first preset number of initial geological models based on multiple groups of parameter value combinations.

[0017] In a possible implementation manner, the predicted production data of the first preset period includes the predicted daily gas production, predicted daily water production, predicted gas-water ratio, and predicted bottom-hole flowing pressure of the first preset period, and the actual production data of the first preset period includes the actual daily gas production, actual daily water production, actual gas-water ratio, and actual bottom-hole flowing pressure of the first preset period.

[0018] In a possible implementation manner, the determining of the plurality of target geological models based on the predicted production data of the first preset period corresponding to each initial geological model and the actual production data of the first preset period of the target shale gas well includes:

[0019] Calculate the global error value between the predicted production data corresponding to each initial geological model for the first preset period and the actual production data of the target shale gas well for the first preset period;

[0020] Determine multiple target geological models based on the global error value corresponding to each initial geological model and a preset global error threshold.

[0021] In a possible implementation, the first preset period is composed of multiple consecutive unit periods;

[0022] The calculating the global error value between the predicted production data corresponding to each initial geological model for the first preset period and the actual production data of the target shale gas well for the first preset period includes:

[0023] Based on the actual production data of the first preset period, determine multiple target unit periods in the first preset period, and obtain the actual production data of the multiple target unit periods;

[0024] Respectively obtain the predicted production data of the multiple target unit periods from the predicted production data of the first preset period corresponding to each initial geological model;

[0025] For each initial geological model, determine the global error value of each target unit period corresponding to the initial geological model based on the predicted production data of the multiple target unit periods corresponding to the geological model and the actual production data of the multiple target unit periods.

[0026] In a possible implementation, the determining multiple target geological models based on the global error value corresponding to each initial geological model and a preset global error threshold includes:

[0027] Determine multiple first reference geological models as those initial geological models for which the global error value of each corresponding target unit period is less than the preset global error threshold;

[0028] Determine the fracture property parameter values of the fracture property parameters corresponding to each first reference geological model among the multiple first reference geological models and the matrix property parameter values of the matrix property parameters corresponding to each first reference geological model;

[0029] Respectively adjust the initial value range of the fracture property parameters and the initial value range of the matrix property parameters based on the fracture property parameter values and the matrix property parameter values corresponding to each first reference geological model to obtain the reference value range of the fracture property parameters and the reference value range of the matrix property parameters;

[0030] Based on the reference value ranges of the fracture property parameters and the reference value ranges of the matrix property parameters, establish a second preset number of second reference geological models;

[0031] Input the second preset number of second reference geological models into the reservoir simulator to obtain the predicted production data for the first preset time period corresponding to each second reference geological model;

[0032] Based on the predicted production data for the first preset time period corresponding to each second reference geological model and the actual production data for the first preset time period, determine the multiple target geological models.

[0033] In a possible implementation manner, the determining the multiple target geological models based on the predicted production data for the first preset time period corresponding to each second reference geological model and the actual production data for the first preset time period includes:

[0034] Based on the predicted production data for the first preset time period corresponding to each second reference geological model and the actual production data for the first preset time period, determine the global error value for each target unit time period corresponding to each second reference geological model;

[0035] If the global error value for each target unit time period corresponding to each second reference geological model is less than the preset global error threshold, then determine the multiple second reference geological models as the multiple target geological models;

[0036] If there is a global error value greater than or equal to the preset global error threshold among the global error values for each target unit time period corresponding to each second reference geological model, then determine the multiple second reference geological models in which the global error value for each target unit time period in the corresponding multiple target unit time periods is less than the preset global error threshold as multiple first reference geological models, and go to execute determining the fracture property parameter values of the fracture property parameters corresponding to each first reference geological model among the multiple first reference geological models and the matrix property parameter values of the matrix property parameters corresponding to each first reference geological model.

[0037] In a possible implementation manner, the determining the productivity statistical data of the target shale gas well based on the predicted production data for the second preset time period corresponding to each target geological model includes:

[0038] Based on the predicted production data for the second preset time period corresponding to each target geological model, determine the cumulative production data for the second preset time period corresponding to each target geological model;

[0039] Based on the cumulative production data of the second preset period corresponding to each target geological model, determine the shale gas production capacity statistical data of the target shale gas well, where the shale gas production capacity statistical data includes the P10 value, P50 value, and P90 value of multiple cumulative production data.

[0040] In a second aspect, a shale gas production capacity prediction device is provided, and the device includes:

[0041] A parameter determination module, configured to determine the initial value range of fracture property parameters and the initial value range of matrix property parameters based on well logging interpretation data and microseismic data corresponding to a target shale gas well, where the fracture property reference includes natural fracture property parameters and artificial fracture property parameters;

[0042] A modeling module, configured to establish a first preset number of initial geological models of the target shale gas well based on the initial value range of the fracture property parameters and the initial value range of the matrix property parameters;

[0043] A first numerical simulation module, configured to input the first preset number of initial geological models into a reservoir simulator respectively to obtain the predicted production data of the first preset period corresponding to each initial geological model;

[0044] A model determination module, configured to determine multiple target geological models based on the predicted production data of the first preset period corresponding to each initial geological model and the actual production data of the first preset period of the target shale gas well;

[0045] A second numerical simulation module, configured to input the multiple target geological models into the reservoir simulator respectively to obtain the predicted production data of the second preset period corresponding to each target geological model;

[0046] A production capacity statistics module, configured to determine the production capacity statistical data of the target shale gas well based on the predicted production data of the second preset period corresponding to each target geological model.

[0047] In a possible implementation manner, the natural fracture property parameters include at least one of the lengths, widths, heights, and conductivities of multiple natural fractures, the artificial fracture property parameters include at least one of the lengths, widths, heights, and conductivities of multiple artificial fractures, and the matrix property parameters include at least one of matrix porosity, matrix water saturation, and matrix permeability.

[0048] In a possible implementation manner, the modeling module is configured to:

[0049] Perform multiple random value-taking processes within the initial value range of the fracture property parameters and the initial value range of the matrix property parameters respectively to obtain multiple groups of parameter value combinations composed of fracture property parameter values and matrix property parameter values, where the number of times of the random value-taking process and the number of groups of parameter value combinations are both the same as the first preset number;

[0050] Based on multiple groups of parameter value combinations, determine the first preset number of initial geological models.

[0051] In a possible implementation manner, the predicted production data of the first preset period includes the predicted daily gas production, predicted daily water production, predicted water-gas ratio, and predicted bottom-hole flowing pressure of the first preset period, and the actual production data of the first preset period includes the actual daily gas production, actual daily water production, actual water-gas ratio, and actual bottom-hole flowing pressure of the first preset period.

[0052] In a possible implementation manner, the model determination module is used for:

[0053] Based on the predicted production data of the first preset period corresponding to each initial geological model and the actual production data of the first preset period of the target shale gas well, calculate the global error value between the predicted production data and the actual production data corresponding to each initial geological model;

[0054] Based on the global error value corresponding to each initial geological model and the preset global error threshold, determine multiple target geological models.

[0055] In a possible implementation manner, the first preset period is composed of multiple consecutive unit periods;

[0056] The model determination module is used for:

[0057] Based on the actual production data of the first preset period, determine multiple target unit periods in the first preset period, and obtain the actual production data of the multiple target unit periods;

[0058] Respectively obtain the predicted production data of the multiple target unit periods in the predicted production data of the first preset period corresponding to each initial geological model;

[0059] For each initial geological model, based on the predicted production data of the multiple target unit periods corresponding to the geological model and the actual production data of the multiple target unit periods, determine the global error value of each target unit period corresponding to the initial geological model.

[0060] In a possible implementation manner, the model determination module is used for:

[0061] Determine multiple initial geological models in which the global error value for each corresponding target unit time period is less than the preset global error threshold as multiple first reference geological models;

[0062] Determine the fracture property parameter values of the fracture property parameters corresponding to each first reference geological model and the matrix property parameter values of the matrix property parameters corresponding to each first reference geological model among the multiple first reference geological models;

[0063] Based on the fracture property parameter values and matrix property parameter values corresponding to each first reference geological model respectively, adjust the initial value range of the fracture property parameters and the initial value range of the matrix property parameters to obtain the reference value range of the fracture property parameters and the reference value range of the matrix property parameters;

[0064] Based on the reference value range of the fracture property parameters and the reference value range of the matrix property parameters, establish a second preset number of second reference geological models;

[0065] Input the second preset number of second reference geological models into the reservoir simulator to obtain the predicted production data for the first preset time period corresponding to each second reference geological model;

[0066] Based on the predicted production data for the first preset time period corresponding to each second reference geological model and the actual production data for the first preset time period, determine the multiple target geological models.

[0067] In a possible implementation manner, the model determination module is configured to:

[0068] Based on the predicted production data for the first preset time period corresponding to each second reference geological model and the actual production data for the first preset time period, determine the global error value for each target unit time period corresponding to each second reference geological model;

[0069] If the global error value for each target unit time period corresponding to each second reference geological model is less than the preset global error threshold, then determine the multiple second reference geological models as the multiple target geological models;

[0070] If there is a global error value greater than or equal to the preset global error threshold among the global error values of each target unit time period corresponding to each second reference geological model, then determine the second reference geological model in which the global error value of each target unit time period among the corresponding multiple target unit time periods is less than the preset global error threshold as the first reference geological model, and go to execute determining the fracture attribute parameter values of the fracture attributes corresponding to each first reference geological model among the multiple first reference geological models and the matrix attribute parameter values of the matrix attributes corresponding to each first reference geological model.

[0071] In a possible implementation manner, the production capacity statistics module is configured to:

[0072] Determine the cumulative production data of each second preset time period corresponding to each target geological model based on the predicted production data of the second preset time period corresponding to each target geological model;

[0073] Determine the shale gas production capacity statistics data of the target shale gas well based on the cumulative production data of the second preset time period corresponding to each target geological model, where the shale gas production capacity statistics data includes the P10 value, P50 value, and P90 value of multiple cumulative production data.

[0074] In a third aspect, a computer device is provided, where the computer device includes a processor and a memory, and at least one instruction is stored in the memory, and the instruction is loaded and executed by the processor to implement the operations performed by the shale gas production capacity prediction method.

[0075] In a fourth aspect, a computer-readable storage medium is provided, and at least one instruction is stored in the storage medium, and the instruction is loaded and executed by the processor to implement the operations performed by the shale gas production capacity prediction method.

[0076] The beneficial effects brought by the technical solution provided in the embodiment of the present application are as follows: In the solution mentioned in the embodiment of the present application, the initial value ranges of fracture property parameters and matrix property parameters can be determined first based on the well logging interpretation data and microseismic data corresponding to the target shale gas well. Then, based on the determined initial value ranges, the first preset number of initial geological models of the target shale gas well are established. These first preset number of initial geological models are input into the reservoir simulator to obtain the predicted production data for the first preset time period corresponding to each initial geological model. Then, based on the predicted production data for the first preset time period and the actual production data of the shale gas well for the first preset time period, multiple target geological models that are more in line with the actual geological situation are determined. These multiple target geological models are input into the reservoir simulator to obtain the predicted production data for the second preset time period corresponding to each target geological model, thereby determining the production capacity statistical data of the target shale gas well. By using the present application, multiple target geological models that are more in line with the actual geological situation can be determined, so as to obtain more accurate predicted production data and production capacity statistical data of the target shale gas well. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0078] Figure 1 is a flowchart of a shale gas production capacity prediction method provided by an embodiment of the present application;

[0079] Figure 2 is a schematic diagram of the distribution of complex fractures provided by an embodiment of the present application;

[0080] Figure 3 is a schematic diagram of the statistical data of the cumulative gas production provided by an embodiment of the present application;

[0081] Figure 4 is a schematic diagram of the statistical data of the cumulative water production provided by an embodiment of the present application;

[0082] Figure 5 is a schematic diagram of a target unit time period provided by an embodiment of the present application;

[0083] Figure 6 is a flowchart of determining a target geological model provided by an embodiment of the present application;

[0084] Figure 7 is a schematic diagram of the predicted bottom hole flowing pressure and the actual bottom hole flowing pressure for multiple iterations provided by an embodiment of the present application;

[0085] Figure 8 It is a schematic diagram of predicting the water-gas ratio and the actual water-gas ratio through multiple iterations provided by an embodiment of the present application;

[0086] Figure 9 It is a schematic structural diagram of a shale gas production capacity prediction device provided by an embodiment of the present application;

[0087] Figure 10 It is a structural block diagram of a terminal provided by an embodiment of the present application;

[0088] Figure 11 It is a structural block diagram of a server provided by an embodiment of the present application. Detailed implementation manners

[0089] To make the objectives, technical solutions, and advantages of the present application clearer, the embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0090] An embodiment of the present application provides a method for predicting shale gas production capacity, and this method for predicting shale gas production capacity can be implemented by a computer device. The computer device can be a terminal, a server, etc. The terminal can be a desktop computer, a notebook computer, a tablet computer, a mobile phone, etc. The server can be a single server or a server cluster, etc. The computer device can include a processor, a memory, a communication component, etc.

[0091] The processor can be a central processing unit (CPU). The processor can be used to determine the initial value range of fracture property parameters and the initial value range of matrix property parameters, establish an initial geological model, obtain the predicted production data of the first preset period corresponding to each initial geological model through a reservoir simulator, determine multiple target geological models, determine the predicted production data of the second preset period corresponding to each target geological model, determine the production capacity statistical data of the target shale gas well, and so on.

[0092] The memory can be various volatile memories or non-volatile memories, such as a solid state disk (SSD), a dynamic random access memory (DRAM), etc. The memory can be used for data storage. For example, storing the well logging interpretation data and microseismic data corresponding to the target shale gas well, storing the data during the process of establishing the first preset number of initial geological models, storing the predicted production data corresponding to each obtained initial geological model, storing the data during the process of determining multiple target geological models, storing the predicted production data of the second preset period corresponding to each obtained target geological model, storing the determined production capacity statistical data of the target shale gas well, and so on.

[0093] The communication component can be a wired network connector, a wireless fidelity (WiFi) module, a Bluetooth module, a cellular network communication module, etc. The communication component can be used for data transmission with other devices.

[0094] Figure 1 It is a flowchart of a shale gas production capacity prediction method provided by an embodiment of the present application. Refer to Figure 1 , this embodiment includes:

[0095] 101. Based on the logging interpretation data and microseismic data corresponding to the target shale gas well, determine the initial value ranges of the fracture property parameters and the matrix property parameters.

[0096] Among them, the fracture property references include natural fracture property parameters and artificial fracture property parameters.

[0097] In implementation, after the staff drills a well to obtain the target shale gas well, the logging interpretation data corresponding to the target shale gas well can be obtained through logging technology, and then multiple artificial fractures are created by hydraulic fracturing. The artificial fractures generated during the fracturing process can connect the target shale gas wellbore and nearby natural fractures, thereby providing a fast flow channel for the shale gas underground, enabling people to extract the shale gas underground through the target shale gas well. After the hydraulic fracturing is completed, a complex fracture network composed of natural fractures and artificial fractures is formed. At this time, the microseismic data corresponding to the target shale gas well can be obtained. As a reference, Figure 2 shows the distributions of two different complex fracture networks composed of natural fractures and artificial fractures, Figure 2 where the thick straight lines in represent the shale gas wells, multiple artificial fractures are evenly arranged around the shale gas wells, and multiple natural fractures are discretely distributed around the shale gas wells.

[0098] The staff can estimate the values of the fracture property parameters and the matrix property parameters of the complex fractures through the logging interpretation data and microseismic data corresponding to the target shale gas well, and obtain the initial value ranges of the fracture property parameters and the matrix property parameters.

[0099] Optionally, the specific property parameter settings for the fracture property parameters and the matrix property parameters can be: the natural fracture property parameters include at least one of the lengths, widths, heights, and conductivities of multiple natural fractures, the artificial fracture property parameters include at least one of the lengths, widths, heights, and conductivities of multiple artificial fractures, and the matrix property parameters include at least one of the matrix porosity, matrix water saturation, and matrix permeability.

[0100] 102. Based on the initial value ranges of fracture property parameters and the initial value ranges of matrix property parameters, establish the first preset number of initial geological models for the target shale gas well.

[0101] In implementation, after determining the initial value ranges of fracture property parameters and the initial value ranges of matrix property parameters, values can be taken within the initial value ranges of each property parameter, so as to obtain a set of parameter value combinations composed of fracture property parameter values and matrix property parameter values. Then, using the EDFM (Embedded Discrete Fracture Model) technology and according to the determined parameter value combinations, establish an initial geological model containing complex fractures intertwined with artificial fractures and natural fractures. According to the preset first preset number, obtain the first preset number of groups of parameter value combinations, thereby establishing the first preset number of initial geological models.

[0102] Optionally, the process of obtaining parameter value combinations within the initial value ranges of each property parameter to establish the first preset number of initial geological models can be as follows:

[0103] Perform multiple random value-taking processes respectively within the initial value ranges of fracture property parameters and the initial value ranges of matrix property parameters to obtain multiple groups of parameter value combinations composed of fracture property parameter values and matrix property parameter values, where the number of random value-taking processes and the number of groups of parameter value combinations are both the same as the first preset number. Based on the multiple groups of parameter value combinations, determine the first preset number of initial geological models.

[0104] In implementation, a distribution can be assigned to the initial value ranges of fracture property parameters and the initial value ranges of matrix property parameters respectively according to experience. For example, a uniform distribution can be assigned to the initial value range of the length of a natural fracture. When a value is randomly obtained within the initial value range of the length of this natural fracture, the probability of each value within the initial value range being selected is the same. Another example, a Gaussian distribution can be assigned to the conductivity of an artificial fracture. When a value is randomly obtained within the initial value range of the conductivity of this artificial fracture, the probability of a value in the middle part of the initial value range being selected is higher than that of the values at both ends. Optionally, a uniform distribution can be assigned to all the initial value ranges of fracture property parameters and the initial value ranges of matrix property parameters. Of course, it can also be other distributions, and the embodiments of the present application do not limit this.

[0105] After assigning a distribution to each property parameter, the Monte Carlo sampling method can be used to randomly select values within the initial value ranges of each fracture property parameter and each matrix property parameter, obtaining the fracture property parameter values corresponding to each fracture property parameter and the matrix property parameter values corresponding to each matrix property parameter, thereby obtaining a set of parameter value combinations composed of fracture property parameters and matrix property parameters. Based on this parameter value combination, an initial geological model corresponding to this parameter value combination can be established using the EDFM technique.

[0106] Using the above method, perform random value selection a first preset number of times within the initial value ranges of each fracture property parameter and each matrix property parameter, thereby obtaining a first preset number of groups of parameter value combinations and establishing a first preset number of initial geological models.

[0107] Optionally, the setting of the specific value of the first preset number can be any reasonable setting, and the embodiments of the present application do not limit this. One setting method can be to set the first preset number as a multiple of the total number of property parameters of the fracture property parameters and the matrix property parameters. For example, if the total number of all fracture property parameters and matrix property parameters is 10, the first preset number can be set as 5 times the total number of property parameters, that is, the first preset number is 50.

[0108] 103. Input the first preset number of initial geological models into the reservoir simulator respectively to obtain the predicted production data for the first preset period corresponding to each initial geological model.

[0109] In practice, input the obtained first preset number of initial geological models into the reservoir simulator respectively, and perform numerical simulation on each initial geological model, thereby obtaining the predicted production data for the first preset period corresponding to each initial geological model.

[0110] Among them, the first preset period can be the period with actual production data. For example, if the target shale gas well has been developed for two years and the actual production data of the target shale gas well for two years can already be obtained, then the first preset period can be two years after the target shale gas well is exploited.

[0111] Optionally, the predicted production data can include predicted daily gas production, predicted daily water production, predicted gas-water ratio, and predicted bottom-hole flowing pressure.

[0112] The predicted daily gas production in the first preset period is the production of shale gas per day in the first preset period predicted based on the initial geological model. The predicted daily water production in the first preset period is the production of fracturing fluid per day in the first preset period predicted based on the initial geological model. The predicted water-gas ratio in the first preset period is the ratio of the predicted daily water production to the predicted daily gas production per day in the first preset period. The predicted bottom-hole flowing pressure in the first preset period is the pressure at the bottom of the well per day in the first preset period predicted based on the initial geological model.

[0113] 104. Determine multiple target geological models based on the predicted production data of the first preset period corresponding to each initial geological model and the actual production data of the first preset period of the target shale gas well.

[0114] In implementation, obtain the actual production data of the first preset period of the target shale gas well. The actual production data of the first preset period includes the actual daily gas production, actual daily water production, actual water-gas ratio, and actual bottom-hole flowing pressure in the first preset period.

[0115] The actual daily gas production in the first preset period is the production of shale gas per day in the first preset period of the target shale gas well. The actual daily water production in the first preset period is the production of fracturing fluid per day in the first preset period of the target shale gas well. The actual water-gas ratio in the first preset period is the ratio of the daily water production to the daily gas production per day in the first preset period. The actual bottom-hole flowing pressure in the first preset period is the bottom-hole pressure of the shale gas well per day in the first preset period of the target shale gas well.

[0116] Among them, the actual daily gas production and actual daily water production can be directly obtained at the mining site of the target shale gas well, and the actual water-gas ratio can be obtained through ratio calculation. For the actual bottom-hole flowing pressure, the staff can collect the actual wellhead pressure at the mining site of the target shale gas well, and the actual bottom-hole flowing pressure can be calculated based on the actual wellhead pressure. The corresponding calculation formula can be:

[0117]

[0118] Among them, P wf is the bottom-hole flowing pressure, P wh is the wellhead pressure, γ g is the relative density of the gas, H is the vertical depth of the middle of the pay zone, is the gas deviation factor under the conditions of the average pressure and average temperature in the wellbore, is the average temperature in the wellbore, f is the friction resistance coefficient, P sc is the standard atmospheric pressure, q 配产 is the average production in the third preset period after the trial production and before the formal production, L is the measured depth of the middle of the pay zone considering the well deviation factor, and d is the inner diameter of the tubing.

[0119] Based on the predicted production data of the first preset period corresponding to each initial geological model and the actual production data of the first preset period of the target shale gas well, the predicted production data with a relatively small difference from the actual production data can be determined, and multiple target geological models can be determined based on the initial geological models corresponding to these predicted production data with relatively small differences.

[0120] Optionally, the further processing for determining the target geological model can be as follows:

[0121] Based on the predicted production data of the first preset period corresponding to each initial geological model and the actual production data of the first preset period of the target shale gas well, calculate the global error value between the predicted production data and the actual production data corresponding to each initial geological model. Based on the global error value corresponding to each initial geological model and the preset global error threshold, determine multiple target geological models.

[0122] In implementation, based on the predicted production data of the first preset period corresponding to each initial geological model and the actual production data of the first preset period of the target shale gas well, the global error value between the predicted production data and the actual production data for each day in the first preset period can be calculated. Then, according to the preset global error threshold pre-set by the staff, select the initial geological models that meet the requirements, define them as the first reference geological models, and then determine the target geological models based on these first reference geological models. The first reference geological models can be directly determined as the target geological models, or other processing can be performed on the first reference geological models to obtain the target geological models.

[0123] The fracture attribute parameter values and matrix attribute parameter values corresponding to the target geological model are parameter values that are determined to be relatively close to the actual geological situation. Therefore, the predicted production data obtained based on the target geological model will be relatively close to the actual production data. By predicting the future production data of the target shale gas well according to this target geological model, relatively accurate production data can be obtained.

[0124] Optionally, there can be multiple methods for determining the first reference geological model. The following are two of them:

[0125] The first method: The initial geological models in which the global error value between the predicted production data and the actual production data for each day in the first preset period is less than the preset global error threshold can be determined as the first reference geological models.

[0126] The second method: For each initial geological model, the average value of the global error values for each day in the corresponding first preset period can be calculated, and the initial geological models with the average value less than the preset global error threshold can be determined as the first reference geological models.

[0127] The third method: For each initial geological model, select the predicted production data and actual production data for multiple days during the first preset period. Calculate the global error value between the selected predicted production data and actual production data for multiple days corresponding to each initial geological model. Determine the initial geological models for which the global error values for the selected multiple days are all less than the preset global error threshold as the first reference geological models.

[0128] Any of the above methods can be used to determine the first reference geological model, or other reasonable methods can be used for determination. The embodiments of the present application do not limit this.

[0129] 105. Input multiple target geological models into the reservoir simulator respectively to obtain the predicted production data for the second preset period corresponding to each target geological model.

[0130] In implementation, the staff can preset the period of the production data to be predicted in advance, that is, preset the second preset period. Then input these multiple target geological models into the reservoir simulator respectively, so as to obtain the predicted production data for the second preset period corresponding to each target geological model.

[0131] Optionally, the second preset period can be any reasonable value, which can be one month or two months, or it can also be twenty years or thirty years. The embodiments of the present application do not limit this.

[0132] 106. Based on the predicted production data for the second preset period corresponding to each target geological model, determine the production capacity statistical data of the target shale gas well.

[0133] In implementation, according to multiple target geological models, the predicted production data for the second preset period corresponding to each target geological model can be obtained. Then, certain statistical processing can be performed on these predicted production data to obtain the production capacity statistical data of the target shale gas well that is convenient for people to view. The corresponding statistical processing process can be as follows:

[0134] Based on the predicted production data for the second preset period corresponding to each target geological model, determine the cumulative production data for the second preset period corresponding to each target geological model. Based on the cumulative production data for the second preset period corresponding to each target geological model, determine the shale gas production capacity statistical data of the target shale gas well, where the shale gas production capacity statistical data includes the P10 value, P50 value, and P90 value of multiple cumulative production data.

[0135] In implementation, for each target geological model, the daily gas production in the predicted production data for the corresponding second preset period can be summed up to obtain the cumulative data of the daily gas production for the second preset period corresponding to each target geological model. Similarly, for each target geological model, the daily water production in the predicted production data for the corresponding second preset period can be summed up to obtain the cumulative data of the daily water production for the second preset period corresponding to each target geological model. The cumulative data of the daily gas production and the cumulative data of the daily water production for the second preset period corresponding to each target geological model are the cumulative production data for the second preset period corresponding to the target geological model.

[0136] For the production data of daily gas production, each target geological model corresponds to a cumulative gas production for the second preset period. For the production data of daily water production, each target geological model corresponds to a cumulative water production for the second preset period.

[0137] Statistical processing can be performed on the cumulative gas production for the second preset period corresponding to each target geological model to obtain the P10 value, P50 value, and P90 value of the cumulative gas production. The P10 value of the cumulative gas production is the minimum value among the top 10% of the cumulative gas production when the cumulative gas production is arranged from largest to smallest among the cumulative gas productions corresponding to multiple target geological models. Similarly, the P50 value of the cumulative gas production is the minimum value among the top 50% of the cumulative gas production when the cumulative gas production is arranged from largest to smallest among the cumulative gas productions corresponding to multiple target geological models. The P90 value of the cumulative gas production is the minimum value among the top 90% of the cumulative gas production when the cumulative gas production is arranged from largest to smallest among the cumulative gas productions corresponding to multiple target geological models.

[0138] Statistical processing can be performed on the cumulative water production for the second preset period corresponding to each target geological model to obtain the P10 value, P50 value, and P90 value of the cumulative water production. The P10 value of the cumulative water production is the minimum value among the top 10% of the cumulative water production when the cumulative water production is arranged from largest to smallest among the cumulative water productions corresponding to multiple target geological models. Similarly, the P50 value of the cumulative water production is the minimum value among the top 50% of the cumulative water production when the cumulative water production is arranged from largest to smallest among the cumulative water productions corresponding to multiple target geological models. The P90 value of the cumulative water production is the minimum value among the top 90% of the cumulative water production when the cumulative water production is arranged from largest to smallest among the cumulative water productions corresponding to multiple target geological models.

[0139] For example, the shale gas production capacity statistical data of the target shale gas wells shown in Table 1 for 30 years.

[0140] Table 1

[0141]

[0142] For another example, as Figure 3 and Figure 4 shown, it is also possible to count the shale gas production capacity statistics data corresponding to each day in a period of time. Figure 3 shows the change in the cumulative gas production in a period of time. Figure 4 shows the change in the cumulative water production in a period of time.

[0143] Optionally, in step 104, for each initial geological model, it is necessary to calculate the global error value between the predicted production data and the actual production data in its corresponding first preset period. After obtaining the predicted production data and the actual production data in the first preset period, the global error value between the predicted production data and the actual production data for each day in the first preset period can be calculated according to the following formula:

[0144]

[0145] where i is the serial number of each type of predicted production data and actual production data, and E i is the error value between the i-th type of predicted production data and the i-th type of actual production data, P i is the i-th type of predicted production data, and M i is the i-th type of actual production data.

[0146] The error value between each type of predicted production data and the corresponding actual production data for each day in the first preset period corresponding to each initial geological model can be calculated according to formula (2), and then the global error value between the predicted production data and the actual production data for each day in the first preset period corresponding to each initial geological model can be calculated according to the following formula (3):

[0147]

[0148] where G is the global error value between the predicted production data and the actual production data, and D i is the weight value corresponding to the i-th type of predicted production data (which can also be called the weight value corresponding to the i-th type of actual production data).

[0149] Optionally, the staff can preset the weight value corresponding to each type of predicted production data.

[0150] The following takes the predicted production data including the predicted water-gas ratio and the predicted bottom-hole flowing pressure (similarly, the actual production data is the actual water-gas ratio and the actual bottom-hole flowing pressure) as an example for illustration. The staff can preset the weight value corresponding to the predicted water-gas ratio as D 1 , and set the weight value corresponding to the predicted bottom-hole flowing pressure as D 2 .

[0151]

[0152]

[0153] Among them, P 1 is the predicted water-gas ratio, M 1 is the actual water-gas ratio, and E 1 is the error value between the predicted water-gas ratio and the actual water-gas ratio. P 2 is the predicted bottom-hole flowing pressure, M 2 is the actual bottom-hole flowing pressure, and E 2 is the error value between the predicted bottom-hole flowing pressure and the actual bottom-hole flowing pressure.

[0154]

[0155] Among them, G is the global error value between the predicted production data and the actual production data.

[0156] For the three methods of determining the first reference geological model listed in step 104, the following takes the third method as an example for further detailed description:

[0157] Optionally, the first preset time period can be divided into multiple consecutive unit time periods. In the embodiments of the present application, the unit time period is one day. Of course, it can also be other time periods, and the embodiments of the present application do not limit this. For the case where the duration of the first preset time period is relatively long, in order to save processing time, multiple target unit time periods can be selected in the first preset time period, so as to obtain multiple global error values corresponding to each initial geological model. The corresponding processing can be as follows:

[0158] Based on the actual production data of the first preset time period, determine multiple target unit time periods in the first preset time period, and obtain the actual production data of the multiple target unit time periods. Respectively obtain the predicted production data of the multiple target unit time periods in the predicted production data of the first preset time period corresponding to each initial geological model. For each initial geological model, based on the predicted production data of the multiple target unit time periods corresponding to the geological model and the actual production data of the multiple target unit time periods, determine the global error value of each target unit time period corresponding to the initial geological model.

[0159] In implementation, there are various methods for selecting target unit time periods. For example, the preset number of time periods of the target unit time period can be preset, and the preset number of unit time periods are evenly selected in the first preset time period and determined as the target unit time periods. Or, after obtaining the actual production data of the first preset time period, based on the situation of the actual production data, some representative unit time periods can be selected as the target unit time periods. Specifically, the inflection points, maximum values or minimum values, etc. with better quality in the actual production data can be selected as the target unit time periods. For example, as Figure 5 shown Figure 5The target unit time periods in [it] are the relative positions of the determined multiple unit time periods on the actual bottom-hole flowing pressure data. The distribution of these multiple target unit time periods is relatively uniform and they are time periods that can reflect the variation of the actual production data. It can be understood that the unit time periods are determined based on all the actual production data, and here the relevant positions of the unit time periods in the actual water-gas ratio data are not shown anymore. It is the same as the unit time periods in Figure 5 .

[0160] After obtaining the multiple target unit time periods, the global error value of each target unit time period in the multiple target unit time periods corresponding to each initial geological model can be determined. Subsequently, based on the global error value of each target unit time period corresponding to each initial geological model, the target geological model can be determined. The corresponding processing can be as follows:

[0161] Determine multiple initial geological models whose global error values of the corresponding each target unit time period are all less than the preset global error threshold as multiple first reference geological models. Determine the fracture property parameter values of the fracture property parameters corresponding to each first reference geological model among the multiple first reference geological models and the matrix property parameter values of the matrix property parameters corresponding to each first reference geological model. Based on the fracture property parameter values and the matrix property parameter values corresponding to each first reference geological model respectively, adjust the initial value ranges of the fracture property parameters and the initial value ranges of the matrix property parameters to obtain the reference value ranges of the fracture property parameters and the reference value ranges of the matrix property parameters. Based on the reference value ranges of the fracture property parameters and the reference value ranges of the matrix property parameters, establish a second preset number of second reference geological models. Input the second preset number of second reference geological models into the reservoir simulator to obtain the predicted production data of the first preset time period corresponding to each second reference geological model. Based on the predicted production data of the first preset time period corresponding to each second reference geological model and the actual production data of the first preset time period, determine multiple target geological models.

[0162] In implementation, after determining the global error value of each target unit time period corresponding to each initial geological model, the initial geological models whose global error values of the corresponding each target unit time period are all greater than the preset error threshold can be determined as the first reference geological models, thereby obtaining multiple first reference geological models.

[0163] Then, for each type of fracture property parameter, perform the following processing: Determine the maximum value and the minimum value in the fracture property parameter values corresponding to the first reference geological model, adjust the minimum value in the initial value range of this fracture property parameter to the minimum value in the fracture property parameter values corresponding to the first reference geological model, and adjust the maximum value in the initial value range of this fracture property parameter to the maximum value in the fracture property parameter values corresponding to the first reference geological model to obtain the adjusted reference value range of the fracture property parameter.

[0164] Similarly, for each matrix property parameter, the following processing is performed: determine the maximum and minimum values of the matrix property parameter values corresponding to the first reference geological model, adjust the minimum value in the initial value range of the matrix property parameter to the minimum value of the matrix property parameter values corresponding to the first reference geological model, and adjust the maximum value in the initial value range of the matrix property parameter to the maximum value of the matrix property parameter values corresponding to the first reference geological model, so as to obtain the reference value range of the adjusted matrix property parameter.

[0165] Randomly select a second preset number of values from the reference value range of the fracture property parameter and the reference value range of the matrix property parameter respectively to obtain a second preset number of parameter value combinations. According to these second preset number of parameter value combinations respectively, use the EDFM technology to establish a second reference geological model corresponding to each parameter value combination, and obtain a second preset number of second reference geological models.

[0166] Input these second preset number of second reference geological models into the reservoir simulator respectively to obtain the predicted production data of the first preset period corresponding to each second reference geological model. Then, based on the predicted production data of the first preset period corresponding to each second reference geological model and the actual production data of the first preset period of the target shale gas well, determine the predicted production data with a smaller difference from the actual production data, and determine multiple target geological models according to the second reference geological models corresponding to these predicted production data with smaller differences.

[0167] Optionally, as Figure 6 shown, the subsequent processing of determining the target geological model can be:

[0168] Based on the predicted production data of the first preset period corresponding to each second reference geological model and the actual production data of the first preset period, determine the global error value of each target unit period corresponding to each second reference geological model. If the global error value of each target unit period corresponding to each second reference geological model is less than the preset global error threshold, then determine the multiple second reference geological models as multiple target geological models. If there is a global error value greater than or equal to the preset global error threshold among the global error values of each target unit period corresponding to each second reference geological model, then determine the multiple second reference geological models with the global error value of each target unit period less than the preset global error threshold in the corresponding multiple target unit periods as multiple first reference geological models, and go to execute determining the fracture property parameter values of the fracture property parameters corresponding to each first reference geological model among the multiple first reference geological models and the matrix property parameter values of the matrix property parameters corresponding to each first reference geological model.

[0169] In implementation, for the predicted production data of the first preset time period corresponding to each second reference geological model, obtain the predicted production data of multiple target unit time periods corresponding to each second reference geological model, calculate the global error value between the predicted production data of each target unit time period and the corresponding actual production data, and obtain the global error values of multiple target unit time periods corresponding to each second reference geological model.

[0170] Then, compare the global error values of multiple target unit time periods corresponding to each second reference geological model with the preset global error threshold. If the global error values of each target unit time period corresponding to all second reference geological models are less than the preset global error threshold, it indicates that the fracture property parameter values and matrix property parameter values corresponding to these second reference geological models are relatively close to the actual geological conditions. Therefore, the second reference geological model can be directly determined as the target geological model.

[0171] If there are global error values greater than or equal to the preset global error threshold among the global error values of multiple target unit time periods corresponding to multiple second reference geological models, it indicates that there are inaccurate geological models among these second reference geological models. At this time, these second reference geological models can all be determined as the first reference geological models, then determine the fracture property parameter values and matrix property parameter values corresponding to these re-determined multiple first reference geological models, and then determine the maximum and minimum values of the fracture property parameter values corresponding to these multiple first reference geological models, as well as the maximum and minimum values of the matrix property parameter values corresponding to these multiple first reference geological models. Based on the determined maximum and minimum values of the fracture property parameter values and the maximum and minimum values of the matrix property parameter values, adjust the reference value ranges of the fracture property parameters and the reference value ranges of the matrix property parameters to obtain the adjusted reference value ranges of the fracture property parameters and the adjusted reference value ranges of the matrix property parameters.

[0172] Randomly select a second preset number of values from the adjusted reference value ranges of the fracture property parameters and the adjusted reference value ranges of the matrix property parameters to obtain the corresponding second preset number of second reference geological models. Input these second preset number of second reference geological models into the reservoir simulator to obtain the predicted production data for the first preset time period corresponding to these multiple second reference geological models. Based on the predicted production data for the first preset time period corresponding to these multiple second reference geological models and the actual production data for the first preset time period, use the above judgment method to judge again whether the global error value for each target unit time period corresponding to these multiple second reference geological models is less than the preset global error threshold. If so, determine all of these multiple second reference geological models as target geological models. If not, determine these multiple second reference geological models as first reference geological models again, and repeat the above process until the target geological models are determined. Optionally, the number of times of adjusting the fracture property parameters and the matrix property parameters based on the fracture property parameter values and matrix property parameter values corresponding to the first reference geological model can be called the number of iterations. For example, after determining multiple initial geological models with the global error value for each target unit time period less than the preset global error threshold as multiple first reference geological models, adjusting the fracture property parameters and the matrix property parameters according to these multiple first reference geological models can be called the first iteration of the initial geological models. Then, determine the second reference geological models, and then determine multiple first reference geological models from multiple second reference geological models with the global error value for each target unit time period less than the preset global error threshold. Adjusting the fracture property parameters and the matrix property parameters according to the newly determined multiple first reference geological models can be called the second iteration of the initial geological models, and so on. The effect of multiple iterations can be as shown in Figure 7 and Figure 8 shown, Figure 7 and Figure 8 is the comparison between the actual production data and the predicted production data for multiple iterations corresponding to one target unit time period. The diagonal line in the figure is used to represent that the predicted production data is equal to the actual production data. If the points in the figure are closer to the diagonal line, it indicates that the predicted production data is closer to the actual production data and the predicted production data is more accurate. From Figure 7 and Figure 8 in the data of multiple iterations, it can be seen that as the number of iterations increases, the points in the figure are closer to the diagonal line, indicating that the predicted production data is closer to the actual production data and the predicted production data is more accurate.

[0173] Alternatively, other methods can also be used to determine the target geological model. For example, a threshold for the number of times of adjusting the reference value range of fracture attribute parameters and the reference value range of matrix attribute parameters can be set. According to the above method, the reference value range of fracture attribute parameters and the reference value range of matrix attribute parameters are adjusted. When the number of adjustment times reaches the threshold, the second reference geological model determined after the last adjustment can be determined as the target geological model.

[0174] All the above optional technical solutions can be combined arbitrarily to form optional embodiments of the present application, which will not be elaborated one by one here.

[0175] In the embodiments of the present application, the following steps can be taken. First, based on the well logging interpretation data and microseismic data corresponding to the target shale gas well, the initial value ranges of fracture attribute parameters and matrix attribute parameters are determined. Then, based on the determined initial value ranges, the first preset number of initial geological models of the target shale gas well are established. These first preset number of initial geological models are input into the reservoir simulator to obtain the predicted production data for the first preset time period corresponding to each initial geological model. Then, based on the predicted production data for the first preset time period and the actual production data of the shale gas well for the first preset time period, multiple target geological models that are more in line with the actual geological situation are determined. These multiple target geological models are input into the reservoir simulator to obtain the predicted production data for the second preset time period corresponding to each target geological model, so as to determine the production capacity statistical data of the target shale gas well. By using the present application, multiple target geological models that are more in line with the actual geological situation can be determined, so as to obtain more accurate predicted production data and production capacity statistical data of the target shale gas well.

[0176] The embodiments of the present application provide a shale gas production capacity prediction device, which can be the computer device in the above embodiments, such as Figure 9 As shown, the device includes:

[0177] A parameter determination module 910, configured to determine the initial value ranges of fracture attribute parameters and matrix attribute parameters based on the well logging interpretation data and microseismic data corresponding to the target shale gas well, wherein the fracture attribute reference includes natural fracture attribute parameters and artificial fracture attribute parameters;

[0178] A modeling module 920, configured to establish the first preset number of initial geological models of the target shale gas well based on the initial value ranges of the fracture attribute parameters and the matrix attribute parameters;

[0179] A first numerical simulation module 930, configured to input the first preset number of initial geological models into the reservoir simulator respectively to obtain the predicted production data for the first preset time period corresponding to each initial geological model;

[0180] A model determination module 940, configured to determine a plurality of target geological models based on the predicted production data of the first preset period corresponding to each initial geological model and the actual production data of the first preset period of the target shale gas well.

[0181] A second numerical simulation module 950, configured to respectively input the plurality of target geological models into the reservoir simulator to obtain the predicted production data of the second preset period corresponding to each target geological model.

[0182] A production capacity statistics module 960, configured to determine the production capacity statistics data of the target shale gas well based on the predicted production data of the second preset period corresponding to each target geological model.

[0183] In a possible implementation manner, the natural fracture attribute parameters include at least one of the lengths, widths, heights, and flow conductivities of a plurality of natural fractures, the artificial fracture attribute parameters include at least one of the lengths, widths, heights, and flow conductivities of a plurality of artificial fractures, and the matrix attribute parameters include at least one of matrix porosity, matrix water saturation, and matrix permeability.

[0184] In a possible implementation manner, the modeling module 920 is configured to:

[0185] Perform multiple random value-taking processes respectively within the initial value range of the fracture attribute parameters and the initial value range of the matrix attribute parameters to obtain multiple groups of parameter value combinations composed of fracture attribute parameter values and matrix attribute parameter values, where the number of times of the random value-taking process and the number of groups of parameter value combinations are both the same as the first preset number;

[0186] Determine a first preset number of initial geological models based on the multiple groups of parameter value combinations.

[0187] In a possible implementation manner, the predicted production data of the first preset period includes the predicted daily gas production, predicted daily water production, predicted water-gas ratio, and predicted bottom-hole flowing pressure of the first preset period, and the actual production data of the first preset period includes the actual daily gas production, actual daily water production, actual water-gas ratio, and actual bottom-hole flowing pressure of the first preset period.

[0188] In a possible implementation manner, the model determination module 940 is configured to:

[0189] Calculate the global error value between the predicted production data and the actual production data corresponding to each initial geological model based on the predicted production data of the first preset period corresponding to each initial geological model and the actual production data of the first preset period of the target shale gas well.

[0190] Determine a plurality of target geological models based on the global error value corresponding to each initial geological model and a preset global error threshold.

[0191] In a possible implementation, the first preset time period is composed of a plurality of consecutive unit time periods;

[0192] The model determination module 940 is configured to:

[0193] Determine a plurality of target unit time periods in the first preset time period based on the actual production data of the first preset time period, and obtain the actual production data of the plurality of target unit time periods;

[0194] Obtain the predicted production data of the plurality of target unit time periods from the predicted production data of the first preset time period corresponding to each initial geological model respectively;

[0195] For each initial geological model, determine the global error value of each target unit time period corresponding to the initial geological model based on the predicted production data of the plurality of target unit time periods corresponding to the geological model and the actual production data of the plurality of target unit time periods.

[0196] In a possible implementation, the model determination module 940 is configured to:

[0197] Determine a plurality of initial geological models in which the global error value of each corresponding target unit time period is less than the preset global error threshold as a plurality of first reference geological models;

[0198] Determine the fracture attribute parameter value of the fracture attribute parameter corresponding to each first reference geological model among the plurality of first reference geological models and the matrix attribute parameter value of the matrix attribute parameter corresponding to each first reference geological model;

[0199] Adjust the initial value range of the fracture attribute parameter and the initial value range of the matrix attribute parameter respectively based on the fracture attribute parameter value and the matrix attribute parameter value corresponding to each first reference geological model, to obtain a reference value range of the fracture attribute parameter and a reference value range of the matrix attribute parameter;

[0200] Establish a second preset number of second reference geological models based on the reference value range of the fracture attribute parameter and the reference value range of the matrix attribute parameter;

[0201] Input the second preset number of second reference geological models into the reservoir simulator to obtain the predicted production data of the first preset time period corresponding to each second reference geological model;

[0202] Determine the multiple target geological models based on the predicted production data of the first preset period corresponding to each of the second reference geological models and the actual production data of the first preset period.

[0203] In a possible implementation manner, the model determination module 940 is configured to:

[0204] Determine the global error value of each target unit period corresponding to each of the second reference geological models based on the predicted production data of the first preset period corresponding to each of the second reference geological models and the actual production data of the first preset period;

[0205] If the global error value of each target unit period corresponding to each of the second reference geological models is less than the preset global error threshold, determine the multiple second reference geological models as the multiple target geological models;

[0206] If there is a global error value greater than or equal to the preset global error threshold among the global error values of each target unit period corresponding to each of the second reference geological models, determine the second reference geological model in which the global error value of each target unit period in the corresponding multiple target unit periods is less than the preset global error threshold as the first reference geological model, and go to execute the fracture property parameter value of the fracture property parameter corresponding to each first reference geological model and the matrix property parameter value of the matrix property parameter corresponding to each first reference geological model among the multiple first reference geological models.

[0207] In a possible implementation manner, the production capacity statistics module 960 is configured to:

[0208] Determine the cumulative production data of the second preset period corresponding to each of the target geological models based on the predicted production data of the second preset period corresponding to each of the target geological models;

[0209] Determine the shale gas production capacity statistics data of the target shale gas well based on the cumulative production data of the second preset period corresponding to each of the target geological models, where the shale gas production capacity statistics data includes the P10 value, P50 value, and P90 value of multiple cumulative production data.

[0210] It should be noted that: when the shale gas production capacity prediction device provided in the above embodiment performs shale gas production capacity prediction, only the division of the above functional modules is used for illustration. In practical applications, the above functions can be assigned to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the shale gas production capacity prediction device provided in the above embodiment and the embodiment of the shale gas production capacity prediction method belong to the same concept. For the specific implementation process, please refer to the method embodiment, which will not be elaborated here.

[0211] Figure 10 FIG. shows a structural block diagram of a terminal 1000 provided by an exemplary embodiment of the present application. The terminal may be the computer device in the above embodiment. The terminal 1000 may be: a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a notebook computer, or a desktop computer. The terminal 1000 may also be referred to by other names such as user equipment, portable terminal, laptop terminal, desktop terminal, etc.

[0212] Generally, the terminal 1000 includes a processor 1001 and a memory 1002.

[0213] The processor 1001 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 1001 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), PLA (Programmable Logic Array). The processor 1001 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the wake state, also known as the CPU; the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 1001 may be integrated with a GPU (Graphics Processing Unit), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 1001 may also include an AI (Artificial Intelligence) processor, and the AI processor is used to process computational operations related to machine learning.

[0214] The memory 1002 may include one or more computer-readable storage media, which may be non-transitory. The memory 1002 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash storage devices. In some embodiments, the non-transitory computer-readable storage media in the memory 1002 is used to store at least one instruction for being executed by the processor 1001 to implement the shale gas production capacity prediction method provided in the method embodiments of the present application.

[0215] In some embodiments, the terminal 1000 may further optionally include: a peripheral device interface 1003 and at least one peripheral device. The processor 1001, the memory 1002, and the peripheral device interface 1003 may be connected through a bus or signal lines. Each peripheral device may be connected to the peripheral device interface 1003 through a bus, signal lines, or a circuit board. Specifically, the peripheral device includes at least one of a radio frequency circuit 1004, a display screen 1005, a camera 1006, an audio circuit 1007, a positioning component 1008, and a power supply 1009.

[0216] The peripheral device interface 1003 may be used to connect at least one peripheral device related to I / O (input / output) to the processor 1001 and the memory 1002. In some embodiments, the processor 1001, the memory 1002, and the peripheral device interface 1003 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 1001, the memory 1002, and the peripheral device interface 1003 may be implemented on a separate chip or circuit board, and this embodiment does not limit this.

[0217] The radio frequency circuit 1004 is used to receive and transmit RF (radio frequency) signals, also known as electromagnetic signals. The radio frequency circuit 1004 communicates with a communication network and other communication devices through electromagnetic signals. The radio frequency circuit 1004 converts an electrical signal into an electromagnetic signal for transmission, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 1004 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. The radio frequency circuit 1004 may communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to: a metropolitan area network, each generation of mobile communication networks (2G, 3G, 4G, and 5G), a wireless local area network, and / or a WiFi network. In some embodiments, the radio frequency circuit 1004 may further include a circuit related to NFC (near field communication), and this application does not limit this.

[0218] The display screen 1005 is used to display the UI (user interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 1005 is a touch display screen, the display screen 1005 also has the ability to collect touch signals on or above the surface of the display screen 1005. The touch signals can be input to the processor 1001 as control signals for processing. At this time, the display screen 1005 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, there may be one display screen 1005, which is disposed on the front panel of the terminal 1000; in other embodiments, there may be at least two display screens 1005, which are respectively disposed on different surfaces of the terminal 1000 or are in a folding design; in still other embodiments, the display screen 1005 may be a flexible display screen, which is disposed on the curved surface or folding surface of the terminal 1000. Even further, the display screen 1005 can also be set to an irregular non-rectangular shape, that is, a special-shaped screen. The display screen 1005 can be prepared from materials such as LCD (liquid crystal display) and OLED (organic light-emitting diode).

[0219] The camera module 1006 is used to collect images or videos. Optionally, the camera module 1006 includes a front camera and a rear camera. Generally, the front camera is disposed on the front panel of the terminal, and the rear camera is disposed on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth-of-field camera, a wide-angle camera, and a telephoto camera, so as to implement the function of background blurring by fusing the main camera and the depth-of-field camera, the function of panoramic shooting by fusing the main camera and the wide-angle camera, and the VR (virtual reality) shooting function or other fused shooting functions. In some embodiments, the camera module 1006 may further include a flash. The flash can be a single-color-temperature flash or a two-color-temperature flash. The two-color-temperature flash refers to the combination of a warm-light flash and a cold-light flash, which can be used for light compensation under different color temperatures.

[0220] The audio circuit 1007 may include a microphone and a speaker. The microphone is used to collect sound waves of the user and the environment, and convert the sound waves into electrical signals for input to the processor 1001 for processing, or input to the radio frequency circuit 1004 to achieve voice communication. For the purpose of stereo collection or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the terminal 1000. The microphone may also be an array microphone or an omnidirectional collection microphone. The speaker is used to convert the electrical signal from the processor 1001 or the radio frequency circuit 1004 into sound waves. The speaker may be a traditional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for uses such as ranging. In some embodiments, the audio circuit 1007 may further include a headphone jack.

[0221] The positioning component 1008 is used to locate the current geographical location of the terminal 1000 to achieve navigation or LBS (location-based service). The positioning component 1008 may be a positioning component based on the GPS (Global Positioning System) of the United States, the Beidou system of China, the GLONASS system of Russia, or the Galileo system of the European Union.

[0222] The power supply 1009 is used to supply power to each component in the terminal 1000. The power supply 1009 may be alternating current, direct current, a primary battery, or a rechargeable battery. When the power supply 1009 includes a rechargeable battery, the rechargeable battery may support wired charging or wireless charging. The rechargeable battery may also be used to support fast charging technology.

[0223] In some embodiments, the terminal 1000 further includes one or more sensors 1010. The one or more sensors 1010 include but are not limited to: an acceleration sensor 1011, a gyroscope sensor 1012, a pressure sensor 1013, a fingerprint sensor 1014, an optical sensor 1015, and a proximity sensor 1016.

[0224] The acceleration sensor 1011 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established with the terminal 1000. For example, the acceleration sensor 1011 can be used to detect the components of the gravitational acceleration on the three coordinate axes. The processor 1001 can control the display screen 1005 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 1011. The acceleration sensor 1011 can also be used for game or collection of the user's motion data.

[0225] The gyroscope sensor 1012 can detect the body direction and rotation angle of the terminal 1000. The gyroscope sensor 1012 can cooperate with the acceleration sensor 1011 to collect the 3D actions of the user on the terminal 1000. Based on the data collected by the gyroscope sensor 1012, the processor 1001 can implement the following functions: motion sensing (such as changing the UI according to the user's tilting operation), image stabilization during shooting, game control, and inertial navigation.

[0226] The pressure sensor 1013 can be disposed on the side frame of the terminal 1000 and / or the lower layer of the display screen 1005. When the pressure sensor 1013 is disposed on the side frame of the terminal 1000, it can detect the holding signal of the user on the terminal 1000, and the processor 1001 can perform left / right hand recognition or quick operation according to the holding signal collected by the pressure sensor 1013. When the pressure sensor 1013 is disposed on the lower layer of the display screen 1005, the processor 1001 can control the operable controls on the UI interface according to the pressure operation of the user on the display screen 1005. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0227] The fingerprint sensor 1014 is used to collect the fingerprint of the user. The processor 1001 can identify the user's identity according to the fingerprint collected by the fingerprint sensor 1014, or the fingerprint sensor 1014 can identify the user's identity according to the collected fingerprint. When the identified user identity is a trusted identity, the processor 1001 authorizes the user to perform relevant sensitive operations, and the sensitive operations include unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings, etc. The fingerprint sensor 1014 can be disposed on the front, back, or side of the terminal 1000. When there are physical buttons or manufacturer logos on the terminal 1000, the fingerprint sensor 1014 can be integrated with the physical buttons or manufacturer logos.

[0228] The optical sensor 1015 is used to collect the ambient light intensity. In one embodiment, the processor 1001 can control the display brightness of the display screen 1005 according to the ambient light intensity collected by the optical sensor 1015. Specifically, when the ambient light intensity is high, the display brightness of the display screen 1005 is increased; when the ambient light intensity is low, the display brightness of the display screen 1005 is decreased. In another embodiment, the processor 1001 can also dynamically adjust the shooting parameters of the camera module 1006 according to the ambient light intensity collected by the optical sensor 1015.

[0229] The proximity sensor 1016, also known as the distance sensor, is usually disposed on the front panel of the terminal 1000. The proximity sensor 1016 is used to collect the distance between the user and the front of the terminal 1000. In one embodiment, when the proximity sensor 1016 detects that the distance between the user and the front of the terminal 1000 is gradually decreasing, the processor 1001 controls the display screen 1005 to switch from the lit state to the off state; when the proximity sensor 1016 detects that the distance between the user and the front of the terminal 1000 is gradually increasing, the processor 1001 controls the display screen 1005 to switch from the off state to the lit state.

[0230] Those skilled in the art can understand that Figure 10 the structure shown in does not constitute a limitation on the terminal 1000, and may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0231] Figure 11 is a schematic structural diagram of a server provided by an embodiment of the present application. The server 1100 may vary greatly due to different configurations or performances, and may include one or more processors 1101 and one or more memories 1102. Among them, at least one instruction is stored in the memory 1102, and the at least one instruction is loaded and executed by the processor 1101 to implement the methods provided by the above various method embodiments. Of course, the server may also have components such as a wired or wireless network interface, a keyboard, and an input / output interface for input / output. The server may also include other components for implementing the functions of the device, which will not be elaborated here.

[0232] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including instructions, and the above instructions can be executed by a processor in the terminal to complete the shale gas production capacity prediction method in the above embodiment. The computer-readable storage medium may be non-transitory. For example, the computer-readable storage medium may be a ROM (read-only memory), a RAM (random access memory), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0233] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above embodiments can be completed by hardware, or can be completed by a program instructing relevant hardware. The program can be stored in a computer-readable storage medium, and the above-mentioned storage medium can be a read-only memory, a magnetic disk, or an optical disc, etc.

[0234] The above are only alternative embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

Claims

1. A method for predicting shale gas production capacity, characterized in that, the method includes: Based on the well logging interpretation data and microseismic data corresponding to the target shale gas well, determine the initial value range of fracture property parameters and the initial value range of matrix property parameters, wherein the fracture property parameters include natural fracture property parameters and artificial fracture property parameters; Based on the initial value range of the fracture property parameters and the initial value range of the matrix property parameters, establish the first preset number of initial geological models of the target shale gas well; Respectively input the first preset number of initial geological models into the reservoir simulator to obtain the predicted production data for the first preset period corresponding to each initial geological model; Based on the predicted production data for the first preset period corresponding to each initial geological model and the actual production data for the first preset period of the target shale gas well, calculate the global error value between the predicted production data and the actual production data corresponding to each initial geological model; Based on the global error value corresponding to each initial geological model and a preset global error threshold, determine a plurality of target geological models; Respectively input the plurality of target geological models into the reservoir simulator to obtain the predicted production data for the second preset period corresponding to each target geological model; Based on the predicted production data for the second preset period corresponding to each target geological model, determine the production capacity statistical data of the target shale gas well.

2. The method according to claim 1, characterized in that, the natural fracture property parameters include at least one of the lengths, widths, heights and conductivities of a plurality of natural fractures, the artificial fracture property parameters include at least one of the lengths, widths, heights and conductivities of a plurality of artificial fractures, and the matrix property parameters include at least one of matrix porosity, matrix water saturation and matrix permeability.

3. The method according to claim 1, characterized in that, the establishing the first preset number of initial geological models of the target shale gas well based on the initial value range of the fracture property parameters and the initial value range of the matrix property parameters includes: Respectively perform multiple random value-taking processes within the initial value range of the fracture property parameters and the initial value range of the matrix property parameters to obtain multiple groups of parameter value combinations composed of fracture property parameter values and matrix property parameter values, wherein the number of times of the random value-taking process and the number of groups of parameter value combinations are both the same as the first preset number; Based on multiple groups of parameter value combinations, determine the first preset number of initial geological models.

4. The method according to claim 1, characterized in that, the predicted production data for the first preset period includes the predicted daily gas production, predicted daily water production, predicted gas-water ratio and predicted bottom-hole flowing pressure for the first preset period, and the actual production data for the first preset period includes the actual daily gas production, actual daily water production, actual gas-water ratio and actual bottom-hole flowing pressure for the first preset period.

5. The method according to claim 1, characterized in that, the first preset period is composed of a plurality of consecutive unit periods; Calculating the global error value between the predicted production data corresponding to each initial geological model and the actual production data of the target shale gas well during the first preset period based on the predicted production data corresponding to each initial geological model during the first preset period and the actual production data of the target shale gas well during the first preset period, includes: Based on the actual production data during the first preset period, determining a plurality of target unit periods in the first preset period, and obtaining the actual production data of the plurality of target unit periods; Respectively obtaining the predicted production data of the plurality of target unit periods from the predicted production data corresponding to each initial geological model during the first preset period; For each initial geological model, based on the predicted production data of the plurality of target unit periods corresponding to the geological model and the actual production data of the plurality of target unit periods, determining the global error value of each target unit period corresponding to the initial geological model.

6. The method according to claim 5, wherein, Determining a plurality of target geological models based on the global error value corresponding to each initial geological model and a preset global error threshold, includes: Determining a plurality of first reference geological models as those initial geological models for which the global error value of each corresponding target unit period is less than the preset global error threshold; Determining the fracture attribute parameter values of the fracture attribute parameters corresponding to each first reference geological model among the plurality of first reference geological models and the matrix attribute parameter values of the matrix attribute parameters corresponding to each first reference geological model; Respectively adjusting the initial value range of the fracture attribute parameters and the initial value range of the matrix attribute parameters based on the fracture attribute parameter values and the matrix attribute parameter values corresponding to each first reference geological model, to obtain a reference value range of the fracture attribute parameters and a reference value range of the matrix attribute parameters; Based on the reference value range of the fracture attribute parameters and the reference value range of the matrix attribute parameters, establishing a second preset number of second reference geological models; Inputting the second preset number of second reference geological models into the reservoir simulator to obtain the predicted production data of each second reference geological model during the first preset period; Based on the predicted production data of each second reference geological model during the first preset period and the actual production data of the first preset period, determining the plurality of target geological models.

7. The method according to claim 6, wherein, Determining the plurality of target geological models based on the predicted production data of each second reference geological model during the first preset period and the actual production data of the first preset period, includes: Based on the predicted production data of each second reference geological model during the first preset period and the actual production data of the first preset period, determining the global error value of each target unit period corresponding to each second reference geological model; If the global error value of each target unit period corresponding to each second reference geological model is less than the preset global error threshold, then determining the plurality of second reference geological models as the plurality of target geological models; If there is a global error value greater than or equal to the preset global error threshold among the global error values of each target unit time period corresponding to each of the second reference geological models, then determine the multiple second reference geological models in which the global error value of each target unit time period is less than the preset global error threshold as multiple first reference geological models, and go to execute determining the fracture attribute parameter values of the fracture attribute parameters corresponding to each first reference geological model among the multiple first reference geological models and the matrix attribute parameter values of the matrix attribute parameters corresponding to each first reference geological model.

8. The method according to claim 1, wherein, the determining the production capacity statistical data of the target shale gas well based on the predicted production data of the second preset time period corresponding to each target geological model includes: determining the cumulative production data of the second preset time period corresponding to each target geological model based on the predicted production data of the second preset time period corresponding to each target geological model; determining the shale gas production capacity statistical data of the target shale gas well based on the cumulative production data of the second preset time period corresponding to each target geological model, wherein the shale gas production capacity statistical data includes the P10 value, P50 value, and P90 value of multiple cumulative production data.

9. A shale gas production capacity prediction device, wherein, the device includes: a parameter determination module, configured to determine the initial value range of the fracture attribute parameters and the initial value range of the matrix attribute parameters based on the well logging interpretation data and microseismic data corresponding to the target shale gas well, wherein the fracture attribute reference includes natural fracture attribute parameters and artificial fracture attribute parameters; a modeling module, configured to establish a first preset number of initial geological models of the target shale gas well based on the initial value range of the fracture attribute parameters and the initial value range of the matrix attribute parameters; a first numerical simulation module, configured to input the first preset number of initial geological models into a reservoir simulator respectively to obtain the predicted production data of the first preset time period corresponding to each initial geological model; a model determination module, configured to calculate the global error value between the predicted production data and the actual production data corresponding to each initial geological model based on the predicted production data of the first preset time period corresponding to each initial geological model and the actual production data of the first preset time period of the target shale gas well; determine multiple target geological models based on the global error value corresponding to each initial geological model and the preset global error threshold; a second numerical simulation module, configured to input the multiple target geological models into the reservoir simulator respectively to obtain the predicted production data of the second preset time period corresponding to each target geological model; a production capacity statistics module, configured to determine the production capacity statistical data of the target shale gas well based on the predicted production data of the second preset time period corresponding to each target geological model.

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