Fracturing parameter optimization method and system based on reservoir transformation index and related equipment

Abstract

The invention provides a fracturing parameter optimization method and system based on a reservoir transformation index and related equipment. The method comprises the steps that a reservoir three-dimensional geologic model is constructed; based on the reservoir three-dimensional geologic model and the original data of the fracturing parameters, establishing a fracture expansion model to obtain the reservoir transformation volume and the fracture complex index; determining a fracture stress difference change volume and a horizontal stress difference coefficient by using a fracture expansion model; integrating the reservoir transformation volume, the crack complex index, the fracturing stress difference change volume and the horizontal stress difference coefficient to obtain an entropy weight of the reservoir transformation index; based on the entropy weight of the reservoir transformation index and the original data of the fracturing parameters, a regression relational expression between the reservoir transformation index and the fracturing parameters is determined, the regression relational expression is used for determining the optimal value of the fracturing parameters capable of evaluating the transformation degree in advance, and the fracturing parameters are optimized by using the multi-dimensional index, so that the fracturing efficiency is improved. And the optimization precision of the fracturing parameters is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field development, and particularly relates to a method, system and related equipment for optimizing fracturing parameters based on a reservoir transformation index. Background Art

[0002] Deep coal reservoirs are characterized by strong compactness and significant heterogeneity. Hydraulic fracturing technology is a key means for the development of deep coal seams, and optimizing fracturing parameters is an important link to achieve efficient development of deep coal seams.

[0003] Currently, fracturing parameters are optimized by evaluating single indexes such as reservoir transformation volume and fracture size. However, the reservoir transformation effect cannot be effectively represented by a single index alone, resulting in a low optimization accuracy of fracturing parameters. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method, system and related equipment for optimizing fracturing parameters based on a reservoir transformation index to solve the problem of low optimization accuracy existing in the method of optimizing fracturing parameters only through a single index.

[0005] To achieve the above object, embodiments of the present invention provide the following technical solutions:

[0006] A first aspect of an embodiment of the present invention discloses a method for optimizing fracturing parameters based on a reservoir transformation index, the method comprising:

[0007] Constructing a three-dimensional geological model of the reservoir;

[0008] Based on the three-dimensional geological model of the reservoir and the original data of fracturing parameters, establishing a fracture propagation model to obtain the reservoir transformation volume and the fracture complexity index;

[0009] Using the fracture propagation model, determining the fracturing stress difference change volume and the horizontal stress difference coefficient;

[0010] Integrating the reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume and the horizontal stress difference coefficient to obtain the entropy weight value of the reservoir transformation index;

[0011] Based on the entropy weight value of the reservoir transformation index and the original data of the fracturing parameters, determining a regression relationship between the reservoir transformation index and the fracturing parameters;

[0012] Wherein, the regression relationship is used to determine the optimized value of the fracturing parameters that can evaluate the transformation degree in advance.

[0013] Preferably, integrating the reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume and the horizontal stress difference coefficient to obtain the entropy weight value of the reservoir transformation index, includes:

[0014] Normalize the reservoir stimulation volume, the fracture complexity index, the volume of change in fracturing stress difference, and the horizontal stress difference coefficient;

[0015] Determine the first weights of the reservoir stimulation volume, the fracture complexity index, the volume of change in fracturing stress difference, and the horizontal stress difference coefficient by the entropy weight method;

[0016] Based on the first weights, perform non - negative translation and weighted summation on the normalized reservoir stimulation volume, the fracture complexity index, the volume of change in fracturing stress difference, and the horizontal stress difference coefficient to integrally obtain the entropy weight value of the reservoir stimulation index.

[0017] Preferably, based on the entropy weight value of the reservoir stimulation index and the original data of the fracturing parameters, determine the regression relationship between the reservoir stimulation index and the fracturing parameters, including:

[0018] Normalize the original data of the fracturing parameters, where the fracturing parameters at least include construction displacement, fracturing fluid viscosity, proppant quantity, perforation density, and fracturing fluid volume;

[0019] Perform multiple linear regression on the entropy weight value of the reservoir stimulation index and the normalized original data of the fracturing parameters to determine the regression relationship between the reservoir stimulation index and the fracturing parameters.

[0020] Preferably, construct a three - dimensional reservoir geological model, including:

[0021] Construct a three - dimensional reservoir geological model based on well location data, reservoir physical property parameters, rock mechanics parameters, geomechanical models, and natural fracture models.

[0022] Preferably, based on the three - dimensional reservoir geological model and the original data of the fracturing parameters, establish a fracture propagation model to obtain the reservoir stimulation volume and the fracture complexity index, including:

[0023] Input the original data of the fracturing parameters on the basis of the three - dimensional reservoir geological model, and use an unconventional fracture network model to establish a fracture propagation model to obtain the reservoir stimulation volume and the fracture complexity index.

[0024] Preferably, use the fracture propagation model to determine the volume of change in fracturing stress difference and the horizontal stress difference coefficient, including:

[0025] On the basis of the fracture propagation model, through unstructured grid division and the data of pore pressure change during the fracturing process, combined with the geomechanical finite - element model, determine the volume of change in fracturing stress difference and the horizontal stress difference coefficient.

[0026] In a second aspect of the embodiments of the present invention, a fracturing parameter optimization system based on a reservoir transformation index is disclosed. The system includes:

[0027] A first construction unit for constructing a three-dimensional geological model of the reservoir;

[0028] A second construction unit for establishing a fracture propagation model based on the three-dimensional geological model of the reservoir and the original data of the fracturing parameters to obtain the reservoir transformation volume and the fracture complexity index;

[0029] A first determination unit for using the fracture propagation model to determine the fracturing stress difference change volume and the horizontal stress difference coefficient;

[0030] An integration unit for integrating the reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume, and the horizontal stress difference coefficient to obtain the entropy weight value of the reservoir transformation index;

[0031] A second determination unit for determining the regression relationship between the reservoir transformation index and the fracturing parameters based on the entropy weight value of the reservoir transformation index and the original data of the fracturing parameters;

[0032] Wherein, the regression relationship is used to determine the optimized value of the fracturing parameters that can evaluate the transformation degree in advance.

[0033] Preferably, the integration unit includes:

[0034] A normalization module for normalizing the reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume, and the horizontal stress difference coefficient;

[0035] A determination module for determining the first weights of the reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume, and the horizontal stress difference coefficient by the entropy weight method;

[0036] An integration module for performing non-negative translation and weighted summation on the normalized reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume, and the horizontal stress difference coefficient based on the first weights to integrally obtain the entropy weight value of the reservoir transformation index.

[0037] In a third aspect of the embodiments of the present invention, an electronic device is disclosed, including: a processor and a memory, and the processor and the memory are connected through a communication bus; wherein, the processor is used to call and execute a program stored in the memory; the memory is used to store a program, and the program is used to implement the fracturing parameter optimization method based on the reservoir transformation index disclosed in the first aspect of the embodiments of the present invention.

[0038] A fourth aspect of the embodiments of the present invention discloses a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, it implements the fracturing parameter optimization method based on the reservoir transformation index disclosed in the first aspect of the embodiments of the present invention.

[0039] Based on the fracturing parameter optimization method, system and related equipment provided by the above embodiments of the present invention, the method is as follows: constructing a three-dimensional geological model of the reservoir; based on the three-dimensional geological model of the reservoir and the original data of the fracturing parameters, establishing a fracture propagation model to obtain the reservoir transformation volume and the fracture complexity index; using the fracture propagation model, determining the fracturing stress difference change volume and the horizontal stress difference coefficient; integrating the reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume and the horizontal stress difference coefficient to obtain the entropy weight value of the reservoir transformation index; based on the entropy weight value of the reservoir transformation index and the original data of the fracturing parameters, determining the regression relationship between the reservoir transformation index and the fracturing parameters, and the regression relationship is used to determine the optimized value of the fracturing parameters that can evaluate the transformation degree in advance. This solution determines the reservoir transformation index by integrating the reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume and the horizontal stress difference coefficient. Based on the reservoir transformation index and the fracturing parameters, a regression relationship is determined, and the regression relationship is used to determine the optimized value of the fracturing parameters that can evaluate the transformation degree in advance, and the fracturing parameters are optimized by using multi-dimensional indexes, so as to improve the optimization accuracy of the fracturing parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings according to the provided drawings without creative efforts.

[0041] Figure 1 It is a flowchart of a fracturing parameter optimization method based on the reservoir transformation index provided by the embodiments of the present invention;

[0042] Figure 2 It is a schematic diagram of the SRV range in the UFM provided by the embodiments of the present invention;

[0043] Figure 3 It is an overall architecture diagram of a fracturing parameter optimization method based on the reservoir transformation index provided by the embodiments of the present invention;

[0044] Figure 4 It is a structural block diagram of a fracturing parameter optimization system based on the reservoir transformation index provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0046] In this application, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0047] Deep coalbed methane resources are abundant and have great exploitation potential, making it one of the current major resource development hotspots. Deep coal reservoirs are characterized by strong compactness and significant heterogeneity. Hydraulic fracturing technology is the key means for deep coal seam development, and fracturing parameter optimization is an important link to achieve efficient development of deep coal seams.

[0048] It has been found through research that currently, fracturing parameters are optimized by evaluating single indices such as reservoir stimulation volume (SRV) and fracture size. However, a single index alone cannot effectively represent the reservoir stimulation effect, and there is a lack of systematic research on multi-factor comprehensive evaluation, which restricts the in-depth understanding of deep coal seam fracturing parameter optimization, makes it difficult to effectively predict the fracturing effect, and results in a low optimization accuracy of fracturing parameters.

[0049] To solve the above problems, the embodiments of the present invention provide a fracturing parameter optimization method, system and related equipment based on a reservoir stimulation index. By integrating reservoir stimulation volume, fracture complexity index, fracturing stress difference change volume and horizontal stress difference coefficient, the reservoir stimulation index is determined. Based on the reservoir stimulation index and fracturing parameters, a regression relationship is determined, and this regression relationship is used to determine the optimized value of fracturing parameters that can evaluate the stimulation degree in advance. The fracturing parameters are optimized using multi-dimensional indices, thereby improving the optimization accuracy of fracturing parameters.

[0050] See Figure 1 , which shows a flowchart of a fracturing parameter optimization method based on a reservoir stimulation index provided by the embodiments of the present invention. The fracturing parameter optimization method includes:

[0051] Step S101: Construct a three-dimensional geological model of the reservoir.

[0052] In the process of specifically implementing step S101, a three-dimensional reservoir geological model is constructed based on well location data, reservoir physical property parameters, rock mechanics parameters, geomechanical models, and natural fracture models.

[0053] Step S102: Based on the three-dimensional reservoir geological model and the original data of fracturing parameters, establish a fracture propagation model to obtain the reservoir stimulation volume and fracture complexity index.

[0054] In the process of specifically implementing step S102, on the basis of the three-dimensional reservoir geological model, input the original data of fracturing parameters such as fracturing fluid, proppant, and pumping program, and use the Unconventional Fracture Network Model (UFM) to establish a fracture propagation model to obtain the reservoir stimulation volume (SRV) and fracture complexity index (FCI, also known as fracture complexity).

[0055] In some specific embodiments, the fracturing parameters at least include: construction displacement (also known as injection displacement), fracturing fluid viscosity, proppant quantity, perforation density, and fracturing fluid volume, etc.

[0056] That is to say, on the basis of the three-dimensional geological model, input the original data of fracturing parameters, use the Unconventional Fracture Network Model (UFM) to establish a fracture propagation model, and obtain the reservoir stimulation volume (SRV) and fracture complexity index (FCI).

[0057] Among them, the specific contents of the reservoir stimulation volume (SRV) and fracture complexity index (FCI) are as follows:

[0058] In the Unconventional Fracture Network Model (UFM), the reservoir stimulation volume (SRV) refers to the relative position of the grids in the fractures, and is represented by defining the different deformation degrees of the grids after fracturing.

[0059] As Figure 2 It can be seen from the schematic diagram of the SRV range in the UFM shown, in order to unify the SRV of different simulation cases, the numbers "0 - 5" can be defined as the SRV range.

[0060] The fracture complexity index (FCI) is one of the important indicators to measure the reservoir stimulation effect, and the fracture complexity index (FCI) is determined by formula (1).

[0061] (1);

[0062] In formula (1), FCI is the fracture complexity index, dimensionless; is the total bandwidth of the fracture, with the unit of m; is the total length of the fracture, with the unit of m; 0 < FCI < 1, the larger the FCI, the greater the fracture complexity, and the smaller the FCI, the smaller the fracture complexity.

[0063] Step S103: Using the fracture propagation model, determine the stress difference change volume and the horizontal stress difference coefficient during fracturing.

[0064] In the specific implementation process of step S103, based on the fracture propagation model, through unstructured grid division and the pore pressure change data during the fracturing process, combined with the geomechanics finite element model, determine the stress difference change volume (SSRV) and the horizontal stress difference coefficient (HSDR) during fracturing.

[0065] It should be noted that the stress difference change volume (SSRV) during fracturing represents the volume where the stress difference decreases during the fracturing process.

[0066] The specific content of the horizontal stress difference coefficient (HSDR) is shown in the following formula (2).

[0067] (2);

[0068] In formula (2), is the maximum horizontal in-situ stress, with the unit of MPa; is the minimum horizontal in-situ stress, with the unit of MPa.

[0069] Through the above steps S101 to S103, the reservoir stimulation volume (SRV), fracture complexity index (FCI), stress difference change volume (SSRV) and horizontal stress difference coefficient (HSDR) can be obtained. SRV, FCI, SSRV and HSDR are the fracturing evaluation parameters (or fracturing evaluation indicators).

[0070] Step S104: Integrate the reservoir stimulation volume, fracture complexity index, stress difference change volume and horizontal stress difference coefficient to obtain the entropy weight value of the reservoir stimulation index.

[0071] In the specific implementation process of step S104, normalize the reservoir stimulation volume (SRV), fracture complexity index (FCI), stress difference change volume (SSRV) and horizontal stress difference coefficient (HSDR).

[0072] Specifically, in order to eliminate the influence brought by different dimensions, it is necessary to normalize the obtained SRV, FCI, SSRV and HSDR above, and the normalization process can be carried out through formula (3).

[0073] (3);

[0074] In formula (3), is the normalized data, X is the original data, and are the minimum and maximum values of the data respectively.

[0075] Determine the first weights of the reservoir stimulation volume, fracture complexity index, volume of change in fracturing stress difference, and horizontal stress difference coefficient through the entropy weight method.

[0076] For example: Through the entropy weight method, the first weights of the reservoir stimulation volume (SRV), fracture complexity index (FCI), volume of change in fracturing stress difference (SSRV), and horizontal stress difference coefficient (HSDR) are shown in Table 1.

[0077] Table 1:

[0078]

[0079] Based on the first weights, perform non - negative translation and weighted summation on the normalized reservoir stimulation volume, fracture complexity index, volume of change in fracturing stress difference, and horizontal stress difference coefficient to integrate and obtain the entropy weight value of the reservoir stimulation index (denoted as ξ).

[0080] That is to say, integrate the normalized SRV, FCI, SSRV, and HSDR into a comprehensive evaluation index through non - negative translation and weighted summation. This comprehensive evaluation index can be denoted as the reservoir stimulation index (ξ), thereby obtaining the entropy weight value of the reservoir stimulation index (ξ).

[0081] For example: Based on the first weights of the four parameters in Table 1, perform non - negative translation and weighted summation on the normalized SRV, FCI, SSRV, and HSDR of the simulation case to obtain the entropy weight value of the reservoir stimulation index (ξ).

[0082] Step S105: Based on the entropy weight value of the reservoir stimulation index and the original data of the fracturing parameters, determine the regression relationship between the reservoir stimulation index and the fracturing parameters. The regression relationship is used to determine the optimized values of the fracturing parameters that can evaluate the stimulation degree in advance.

[0083] In the specific process of implementing step S105, normalize the original data of the fracturing parameters. The fracturing parameters at least include the construction displacement, fracturing fluid viscosity, proppant quantity, perforation density, and fracturing fluid volume.

[0084] Specifically, the original data of the fracturing parameters can be normalized through the above formula (3).

[0085] Perform multiple linear regression on the entropy weight value of the reservoir stimulation index and the normalized original data of the fracturing parameters to determine the regression relationship between the reservoir stimulation index and the fracturing parameters.

[0086] That is to say, based on the entropy weight value of the reservoir stimulation index and the original data of the normalized fracturing parameters, the relationship between the reservoir stimulation index (ξ) and the fracturing parameters is established by using the multiple linear regression method, and the regression equation between the reservoir stimulation index (ξ) and the fracturing parameters is obtained. Among them, the second weight of the fracturing parameters can be determined by the entropy weight method.

[0087] For example: Using the multiple linear regression method, the relationship between the reservoir stimulation index (ξ) and the fracturing parameters is established, and the regression equation shown in formula (4) is obtained.

[0088] (4);

[0089] In formula (4), F is the construction displacement, with the unit of m 3 / min; μ is the viscosity of the fracturing fluid, with the unit of mPa·s; P is the amount of proppant, with the unit of m 3 ; D is the perforation density, with the unit of holes / m; V is the volume of the fracturing fluid, with the unit of m 3 .

[0090] The coefficient of determination R 2 (also known as the goodness of fit) of the regression equation shown in the above formula (4) is 0.93, indicating that the regression accuracy is good and can be used for parameter optimization. Among them, the value of R 2 ranges between [0, 1], and the closer it is to 1, the better the regression effect.

[0091] In practical applications, the regression equation between the reservoir stimulation index and the fracturing parameters determined by the above method can be used to: determine the optimized value of the fracturing parameters that can evaluate the stimulation degree in advance.

[0092] It should be noted that the value of the reservoir stimulation index (ξ) in different preset ranges can reflect different stimulation degrees.

[0093] When conducting fracturing design, design a set of values of fracturing parameters (construction displacement F, viscosity of fracturing fluid μ, amount of proppant P, perforation density D, and volume of fracturing fluid V), substitute the designed values into the "regression equation between the reservoir stimulation index and the fracturing parameters", and the value of the reservoir stimulation index (ξ) can be calculated. The value of the reservoir stimulation index (ξ) can be used to evaluate the stimulation degree in advance; adjust the values of the fracturing parameters in the above-mentioned manner to obtain the optimized value of the fracturing parameters, and this optimized value makes the value of the reservoir stimulation index (calculated by the regression equation) fall within the "desired preset range", and the "desired preset range" reflects the desired stimulation degree.

[0094] For example: The corresponding relationship between different preset ranges and different stimulation degrees is shown in Table 2.

[0095] Table 2:

[0096]

[0097] Combined with the content shown in Table 2, the accuracy of this solution is illustrated by the following example:

[0098] Apply this solution at the site of Well JSP-A. Well JSP-A uses large-scale volume fracturing technology, with a total of 8 stages. Considering the stimulation effect and economy comprehensively, the optimization goal is that the reservoir stimulation index ζ is between 0.6 and 0.7, and the designed pumping rate is 20 m 3 / min. Medium-viscosity and high-viscosity slickwater are continuously pumped, and the average net liquid volume per stage is 3200 m 3 , the amount of proppant per stage is 550 - 560 m 3 , and the perforation density is 12 holes / m. When the previous fracturing design of Well JSP-B was still mainly based on fracture size and experience, the designed fracturing stage was 9 stages, the pumping rate was 16 m 3 / min, low-viscosity and medium-viscosity slickwater are continuously pumped, the average net liquid volume per stage is 2700 m 3 , the amount of proppant per stage is 300 - 400 m 3 , and the perforation density is 12 holes / m.

[0099] During the fracturing process of the two wells, downhole microseismic monitoring was carried out. The monitoring results in Table 3 show that the average fracture lengths of JSP-A and JSP-B are 345.5 m and 324.6 m respectively; the fracture network widths are 192 m and 158 m; the total SRV of a single well are 14.498 million m 3 、11.504 million m 3 respectively, and the average reservoir stimulation indices ζ are 0.632 (the reservoir stimulation effect is relatively sufficient) and 0.468 (the reservoir stimulation effect is average) respectively. The production performance shows that within the same production time, the cumulative gas production per stage of JSP-A and JSP-B are 1.638 million m 3 、1.3779 million m 3 respectively. Compared with JSP-B, the stimulation of JSP-A is more sufficient. The fracture morphology monitored by microseismic also shows that a complex fracture network has been formed, with less blank area between wells, and the production has increased by 15.9%, which is relatively consistent with the model prediction results, verifying the accuracy of this solution.

[0100] Table 3:

[0101]

[0102] As can be seen from the above examples, when this solution is applied at the JSP-A well site, the on-site microseismic monitoring and MAPS gas production profile test results show that the optimization method of deep coal seam fracturing parameters based on the reservoir stimulation index in this solution can significantly increase the production compared with the single parameter optimization method, and has good applicability and accuracy, which can provide a reference for supporting the efficient development of deep coalbed methane.

[0103] In the embodiment of the present invention, the reservoir stimulation index is determined by integrating the reservoir stimulation volume, fracture complexity index, differential volume of fracturing stress change, and horizontal stress difference coefficient. Based on the reservoir stimulation index and fracturing parameters, a regression relationship is determined, and this regression relationship is used to determine the optimized value of the fracturing parameters that can evaluate the stimulation degree in advance, and the fracturing parameters are optimized by using multi-dimensional indexes to improve the optimization accuracy of the fracturing parameters.

[0104] Overall, as Figure 3 can be seen from the overall architecture diagram of a fracturing parameter optimization method based on the reservoir stimulation index shown, this solution can be generally divided into three major parts: "3D geological modeling", "fracture propagation simulation", and "fracturing stress calculation".

[0105] For the "3D geological modeling" part, a reservoir 3D geological model is constructed based on well location data, reservoir physical property parameters, rock mechanics parameters, geomechanical model, and natural fracture model.

[0106] For the "fracture propagation simulation" part, a fracture propagation model is established on the basis of the 3D geological model; the reservoir stimulation volume (SRV) and fracture complexity index (FCI) are determined through fracturing parameters, the established fracture propagation model, fracturing fluid, and proppant system.

[0107] For the "fracturing stress calculation", the differential volume of fracturing stress change (SSRV) and horizontal stress difference coefficient (HSDR) are determined through unstructured grid division, pore pressure change data during the fracturing process, and geomechanical finite element model.

[0108] SRV, FCI, SSRV, and HSDR are integrated into the reservoir stimulation index (ξ), and then the regression relationship between the reservoir stimulation index (ξ) and fracturing parameters is determined.

[0109] It should be noted that Figure 3 the execution principles of each part can be referred to the relevant content in the above embodiments of the present invention Figure 1 and will not be elaborated here.

[0110] Corresponding to the fracturing parameter optimization method based on the reservoir stimulation index provided by the above embodiments of the present invention, see Figure 4, an embodiment of the present invention further provides a structural block diagram of a fracturing parameter optimization system based on a reservoir transformation index. The fracturing parameter optimization system includes: a first construction unit 100, a second construction unit 200, a first determination unit 300, an integration unit 400, and a second determination unit 500;

[0111] The first construction unit 100 is used to construct a three-dimensional reservoir geological model.

[0112] In specific implementation, the first construction unit 100 is specifically used to: construct a three-dimensional reservoir geological model based on well location data, reservoir physical property parameters, rock mechanics parameters, geomechanical models, and natural fracture models.

[0113] The second construction unit 200 is used to establish a fracture propagation model based on the three-dimensional reservoir geological model and the original data of fracturing parameters to obtain the reservoir transformation volume and the fracture complexity index.

[0114] In specific implementation, the second construction unit 200 is specifically used to: input the original data of fracturing parameters on the basis of the three-dimensional reservoir geological model, and establish a fracture propagation model using an unconventional fracture network model to obtain the reservoir transformation volume and the fracture complexity index.

[0115] The first determination unit 300 is used to determine the fracturing stress difference change volume and the horizontal stress difference coefficient by using the fracture propagation model.

[0116] In specific implementation, the first determination unit 300 is specifically used to: on the basis of the fracture propagation model, determine the fracturing stress difference change volume and the horizontal stress difference coefficient through unstructured grid division and pore pressure change data during the fracturing process, in combination with the geomechanical finite element model.

[0117] The integration unit 400 is used to integrate the reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume, and the horizontal stress difference coefficient to obtain the entropy weight value of the reservoir transformation index.

[0118] The second determination unit 500 is used to determine the regression relationship between the reservoir transformation index and the fracturing parameters based on the entropy weight value of the reservoir transformation index and the original data of the fracturing parameters;

[0119] Among them, the regression relationship is used to determine the optimized value of the fracturing parameters that can evaluate the transformation degree in advance.

[0120] In the embodiment of the present invention, by integrating the reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume, and the horizontal stress difference coefficient, the reservoir transformation index is determined. Based on the reservoir transformation index and the fracturing parameters, a regression relationship is determined. The regression relationship is used to determine the optimized value of the fracturing parameters that can evaluate the transformation degree in advance, and the fracturing parameters are optimized by using multi-dimensional indexes to improve the optimization accuracy of the fracturing parameters.

[0121] Preferably, in combination with Figure 4 the content shown, the integration unit 400 includes a normalization module, a determination module, and an integration module. The execution principles of each module are as follows:

[0122] The normalization module is used to normalize the reservoir stimulation volume, fracture complexity index, change volume of fracturing stress difference, and horizontal stress difference coefficient.

[0123] The determination module is used to determine the first weights of the reservoir stimulation volume, fracture complexity index, change volume of fracturing stress difference, and horizontal stress difference coefficient by the entropy weight method.

[0124] The integration module is used to perform non - negative translation and weighted summation on the normalized reservoir stimulation volume, fracture complexity index, change volume of fracturing stress difference, and horizontal stress difference coefficient based on the first weights to integrally obtain the entropy weight value of the reservoir stimulation index.

[0125] Preferably, in combination with Figure 4 the content shown, the second determination unit 500 includes a normalization module and a linear regression module. The execution principles of each module are as follows:

[0126] The normalization module is used to normalize the original data of fracturing parameters, and the fracturing parameters at least include construction displacement, fracturing fluid viscosity, proppant quantity, perforation density, and fracturing fluid volume;

[0127] The linear regression module is used to perform multiple linear regression on the entropy weight value of the reservoir stimulation index and the normalized original data of fracturing parameters to determine the regression relationship between the reservoir stimulation index and the fracturing parameters.

[0128] Preferably, an embodiment of the present invention further provides an electronic device, including: a processor and a memory, and the processor and the memory are connected by a communication bus; wherein, the processor is used to call and execute a program stored in the memory; the memory is used to store a program, and the program is used to implement the fracturing parameter optimization method based on the reservoir stimulation index provided in the above - mentioned method embodiment.

[0129] Preferably, an embodiment of the present invention further provides a computer - readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, it implements the fracturing parameter optimization method based on the reservoir stimulation index provided in the above - mentioned method embodiment.

[0130] In summary, the embodiments of the present invention provide a method, a system and related devices for optimizing fracturing parameters based on a reservoir stimulation index. By integrating the reservoir stimulation volume, the fracture complexity index, the volume of change in fracturing stress difference, and the horizontal stress difference coefficient, the reservoir stimulation index is determined. Based on the reservoir stimulation index and the fracturing parameters, a regression relationship is determined, and this regression relationship is used to determine the optimized values of the fracturing parameters that can evaluate the stimulation degree in advance. The fracturing parameters are optimized using multi-dimensional indices, improving the optimization accuracy of the fracturing parameters.

[0131] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiments, since they are basically similar to the method embodiments, they are described relatively simply. For the relevant parts, reference can be made to the partial description of the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0132] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0133] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing fracturing parameters based on reservoir transformation index, characterized in that: The method comprises: Construct three-dimensional reservoir geological model; Based on the three-dimensional geological model of the reservoir and the original data of the fracturing parameters, a fracture extension model is established to obtain the reservoir transformation volume and the fracture complexity index; Using the fracture propagation model, determining the fracturing stress difference change volume and the horizontal stress difference coefficient; Integrating the reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume and the horizontal stress difference coefficient to obtain an entropy weight of a reservoir transformation index; Determining a regression relationship between the reservoir transformation index and the fracturing parameter based on the entropy weight of the reservoir transformation index and the original data of the fracturing parameter; The regression equation is used to determine the optimal value of the fracturing parameter that can evaluate the degree of transformation in advance.

2. The method according to claim 1, characterized in that Integrating the reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume and the horizontal stress difference coefficient to obtain the entropy weight of the reservoir transformation index includes: Normalizing the reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume, and the horizontal stress difference coefficient; Determine the first weight of the reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume and the horizontal stress difference coefficient by an entropy weight method; Based on the first weight, the normalized reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume and the horizontal stress difference coefficient are non-negatively translated and weighted summed to integrate and obtain the entropy weight of the reservoir transformation index.

3. The method according to claim 1, characterized in that Based on the entropy weight of the reservoir transformation index and the original data of the fracturing parameters, a regression relationship between the reservoir transformation index and the fracturing parameters is determined, including: Normalizing the raw data of the fracturing parameters, wherein the fracturing parameters at least include operation displacement, fracturing fluid viscosity, proppant quantity, perforation density and fracturing fluid volume; A multivariate linear regression is performed on the entropy weight of the reservoir transformation index and the normalized original data of the fracturing parameters to determine a regression relationship between the reservoir transformation index and the fracturing parameters.

4. The method according to claim 1, characterized in that: Construct a 3D reservoir geological model, including: A three-dimensional reservoir geological model is constructed based on well location data, reservoir physical property parameters, rock mechanics parameters, geomechanical model and natural fracture model.

5. The method according to claim 1, characterized in that Based on the three-dimensional geological model of the reservoir and the original data of the fracturing parameters, a fracture extension model is established to obtain the reservoir transformation volume and the fracture complexity index, including: The original data of the fracturing parameters are inputted on the basis of the three-dimensional geological model of the reservoir, and a fracture extension model is established by using an unconventional fracture network model to obtain the reservoir transformation volume and the fracture complexity index.

6. The method according to claim 1, characterized in that Using the fracture propagation model, the fracturing stress difference change volume and the horizontal stress difference coefficient are determined, including: On the basis of the fracture propagation model, the fracturing stress difference variation volume and the horizontal stress difference coefficient are determined by unstructured grid division and pore pressure variation data during fracturing, combined with the geomechanical finite element model.

7. A fracturing parameter optimization system based on reservoir transformation index, characterized in that: The system comprises: A first construction unit is used to construct a three-dimensional reservoir geological model; A second construction unit is used to establish a fracture extension model based on the three-dimensional geological model of the reservoir and the original data of the fracturing parameters to obtain the reservoir transformation volume and the fracture complexity index; A first determination unit is used to determine the fracturing stress difference change volume and the horizontal stress difference coefficient by using the fracture extension model; An integration unit, used for integrating the reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume and the horizontal stress difference coefficient to obtain an entropy weight of the reservoir transformation index; A second determination unit is used to determine a regression relationship between the reservoir transformation index and the fracturing parameter based on the entropy weight of the reservoir transformation index and the original data of the fracturing parameter; The regression equation is used to determine the optimal value of the fracturing parameter that can evaluate the degree of transformation in advance.

8. The system according to claim 7, characterized in that The integration unit comprises: A normalization module, used for normalizing the reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume and the horizontal stress difference coefficient; A determination module, used to determine the first weight of the reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume and the horizontal stress difference coefficient by an entropy weight method; An integration module is used to perform non-negative translation and weighted summation on the normalized reservoir transformation volume, the fracture complexity index, the fracturing stress difference change volume and the horizontal stress difference coefficient based on the first weight, so as to integrate and obtain the entropy weight of the reservoir transformation index.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the processor and the memory are connected via a communication bus; wherein the processor is used to call and execute a program stored in the memory; The memory is used to store a program, and the program is used to implement the fracturing parameter optimization method based on the reservoir transformation index as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for optimizing fracturing parameters based on a reservoir transformation index as described in any one of claims 1 to 6 is implemented.

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