Evaluation method of plugging ability of volumetric fracturing temporary plugging agent based on mine big data

Abstract

The present invention relates to an evaluation method for the plugging capacity of volumetric fracturing temporary plugging agents based on mine big data, comprising the following steps: S1, establishing a mine big database of factors affecting the plugging capacity of temporary plugging agents for horizontal wells that have been subjected to fracturing, establishing a comprehensive evaluation model for plugging capacity, calculating the correlation between different influencing factors and plugging capacity, and determining the main control parameters of geological engineering; S2, establishing a plugging boost multivariate regression prediction model coupled with the big data of the main control parameters of geological engineering, obtaining the mine plugging boost of each fracturing section of the evaluation horizontal well; S3, combining the construction pressure before the temporary plugging agent is added to each fracturing section of the evaluation horizontal well, obtaining the plugging capacity evaluation factor, and dividing the plugging capacity level according to the plugging capacity evaluation factor. The present invention can quickly realize the quantitative prediction of the mine-scale plugging boost of each fracturing section of any well, solves the problems of previous experimental evaluation errors and the difficulty of monitoring the plugging boost on-site in the mine, and at the same time realizes the quantitative characterization of the plugging capacity through the plugging capacity evaluation factor.

Description

Technical Field

[0001] The present invention relates to the field of petroleum and natural gas engineering, and in particular to a method for evaluating the plugging ability of a volumetric fracturing temporary plugging agent based on mine field big data. Background Art

[0002] Adding temporary plugging agents to plug the opened perforations during the fracturing process, so that the cracks in the remaining perforations are opened and the purpose of uniform expansion is achieved. This is the core means of achieving the maximum volume of fracture network in volume development. Its key indicator is the plugging ability of the temporary plugging agent to reach the perforations. At present, various oil fields still evaluate the temporary plugging effect through thousands of construction experiences, and the implementation effects vary greatly. There is an urgent need for a systematic and fast evaluation method suitable for mines to guide mine practices and further optimize and improve volume fracturing technology. The survey shows that there are currently several main evaluation methods for the plugging ability of temporary plugging agents:

[0003] (1) Patent CN202010802695.0 discloses a device and method for evaluating the pressure bearing performance of temporary plugging materials for fracturing and acidizing. The device includes a liquid storage tank, a displacement pump, a feeding bin, a buffer tank, a core clamp and a confining pressure pump. The discharge end of the liquid storage tank is connected to the feed end of the displacement pump through a pipeline, the discharge end of the feeding bin is connected to the feed end of the buffer tank through a pipeline, the discharge end of the buffer tank is collected with the discharge end of the displacement pump and connected to the feed end of the core clamp, the steel core is fixed in the core clamp, and the confining pressure pump is connected to the core clamp, which is used to study the pressure bearing performance of temporary plugging materials under dynamic conditions.

[0004] (2) Patent CN202210165687.9 discloses a device and method for evaluating the plugging ability of temporary plugging diverting agents in non-uniform cracks. The device includes a simulated crack system, and an annular rubber cylinder and a stainless steel outer cylinder are sequentially arranged outside the simulated crack system; the characteristics are: the simulated crack system includes a fourth simulated crack and a crack module arranged in sequence; wherein the fourth simulated crack is a bell-mouth module, and the crack module includes three simulated cracks of different crack widths arranged in sequence; the front end of the bell-mouth module is connected to an inlet pipe, and the end is connected to the crack module; it also includes a pumping system, an annular pressure system and a monitoring system. Through two systems and two methods, two plugging methods, static and dynamic plugging, are simulated.

[0005] (3) Patent CN201821879734.1 discloses a device for evaluating the performance of a temporary plugging ball, the device comprising: a pumping module, a first fluid manifold, a temporary plugging steering level test module, a slit orifice temporary plugging steering simulation module, and a data acquisition module; the output end of the pumping module is connected to the temporary plugging steering level test module through the first fluid manifold; a plurality of holes are arranged on the side wall of the temporary plugging steering level test module, and the temporary plugging steering level test module is connected to an input end of the slit orifice temporary plugging steering simulation module through the plurality of holes; the output end of the slit orifice temporary plugging steering simulation module is connected to the data acquisition module, and the data acquisition module is used to collect a first flow rate of the fluid output by the slit orifice temporary plugging steering simulation module, and evaluate the steering performance of the temporary plugging ball according to the first flow rate. The device provided by the embodiment of the utility model can obtain the blocking ability and steering performance of the temporary plugging ball during the slit temporary plugging steering process.

[0006] The above three methods are all based on the experimental perspective. Through the research and development and design of relevant evaluation devices to test the plugging pressure of temporary plugging agents at the blasthole, the experimental conditions are very different from the actual mine field test and are difficult to accurately characterize. The temporary plugging cracks in the mine field are usually large segments and multiple clusters, and the migration law in the horizontal wellbore is extremely complex. It is difficult for indoor experimental evaluation to accurately represent the plugging capacity in the practical fracturing process. Therefore, it is necessary to propose a new evaluation method for the plugging capacity of temporary plugging agents that is suitable for mines and can be quickly and easily operated, providing important theoretical support for the efficient development of unconventional resources. Summary of the invention

[0007] In order to overcome the problems in the prior art, the present invention provides a method for evaluating the plugging capacity of volumetric fracturing temporary plugging agents based on mine big data. The method first uses the analytic hierarchy process to establish a mine big database of factors affecting the plugging capacity of temporary plugging agents for horizontal wells that have been fracturing. On this basis, a comprehensive evaluation model for plugging capacity is established, the correlation between different influencing factors and plugging capacity is calculated, and the main control parameters of geological engineering are clarified. Secondly, a plugging boost multivariate regression prediction model coupled with the big data of geological engineering main control parameters is established to quantitatively obtain the mine plugging boost of each fracturing section of the evaluation horizontal well. Finally, combined with the construction pressure before the addition of the temporary plugging agent in each fracturing section of the evaluation horizontal well, the plugging capacity evaluation factor is defined, and the plugging capacity level is divided according to its size, so as to achieve the purpose of quantitative evaluation. To achieve the above purpose, the present invention provides the following scheme:

[0008] A method for evaluating the plugging capacity of a volumetric fracturing temporary plugging agent based on mine field big data comprises the following steps:

[0009] S1. Establish a large database of factors affecting the plugging capacity of temporary plugging agents in horizontal wells that have been subjected to fracturing, establish a comprehensive evaluation model for plugging capacity, calculate the correlation between different influencing factors and plugging capacity, and determine the main control parameters of geological engineering;

[0010] S2. Establish a plugging and pressure-raising multivariate regression prediction model coupled with the big data of geological engineering main control parameters to obtain the plugging and pressure-raising of each fracturing section of the evaluation horizontal well;

[0011] S3. Based on the construction pressure of each fracturing section of the evaluated horizontal well before the temporary plugging agent is added, the plugging capacity evaluation factor is obtained, and the plugging capacity level is divided according to the plugging capacity evaluation factor.

[0012] A further technical solution is to use the analytic hierarchy process in step S1 to establish a large mine database of factors affecting the plugging capacity of temporary plugging agents in horizontal wells that have undergone fracturing.

[0013] A further technical solution is to use the analytic hierarchy process in step S1 to establish a large database of factors affecting the plugging capacity of temporary plugging agents in horizontal wells that have undergone fracturing, and also include:

[0014] The factors affecting the plugging ability of temporary plugging agents are divided into target layer, decision layer and index layer by using the analytic hierarchy process. The target layer is the plugging pressure after the temporary plugging agent is applied in each fracturing section of the horizontal well. The decision layer is the reservoir physical properties, rock mechanics parameters and fracturing transformation parameters. The reservoir physical properties in the index layer are the porosity, permeability, oil saturation and mud content of the reservoir where each fracturing section is located.

[0015] A further technical solution is that the rock mechanics parameters include one or more parameters of the brittleness index of the reservoir where each fracturing stage is located and the biaxial stress difference, and the fracturing transformation parameters include one or more parameters of the number of single-stage clusters, the number of perforation holes, the construction displacement, the single-stage liquid volume, the single-stage sand volume, and the temporary plugging agent dosage.

[0016] A further technical solution is that the comprehensive evaluation model for plugging capacity is established in step S1, further comprising:

[0017] S11. Establish a plugging capacity evaluation matrix M based on the mine big database, where the elements of the evaluation matrix M are the reservoir physical property parameters, rock mechanics parameters and fracturing transformation parameter sets of each fracturing section of the horizontal wells that have been fractured;

[0018] S12, establishing a prediction model matrix reference column M0 based on the plugging and pressure-raising of each fracturing stage of the horizontal well where temporary plugging agent has been implemented;

[0019] S13, standardizing the plugging and boosting evaluation matrix and the reference column, and calculating the correlation between different influencing factors and the evaluation reference column according to the standardized evaluation matrix and the evaluation standard column;

[0020] S14. Determine the parameters with a correlation greater than a set threshold as the main control parameters of the geological engineering that affect the plugging capacity.

[0021] A further technical solution is that in step S2, when establishing a plugging pressure increase multiple regression prediction model that couples big data of main control parameters of geological engineering, it further includes:

[0022] S21. According to the corresponding data of the main control parameters of geological engineering, establish a multiple linear equation system between the plugging pressure increase of each fracturing stage of the temporarily plugged horizontal well and the main control parameters of geological engineering;

[0023] S22. Solve the coefficients of the multiple regression equation according to the multiple linear equation system;

[0024] S23. Establish a plugging pressure increase multiple regression prediction model that couples the main control parameters of geological engineering, and conduct quantitative prediction of the plugging pressure increase of each fracturing stage.

[0025] A further technical solution is that in step S3, when obtaining the plugging ability evaluation factor, it further includes:

[0026]

[0027] In the formula: T is the plugging ability evaluation factor, dimensionless; y is the plugging pressure increase of each fracturing stage, MPa; P l is the construction pressure before plugging, MPa; l is the number of the fracturing stage of the evaluation horizontal well; h is the number of fracturing stages of the evaluation horizontal well, stage.

[0028] A further technical solution is that the greater the plugging ability evaluation factor, the stronger the plugging ability.

[0029] A further technical solution is that when 0.75 < T ≤ 1, the plugging ability is grade I; when 0.5 < T ≤ 0.75, the plugging ability is grade II; when 0.25 < T ≤ 0.5, the plugging ability is grade III; when 0 < T ≤ 0.25, the plugging ability is grade IV, and the plugging ability ranking is I > II > III > IV.

[0030] On the other hand, the present invention provides an electronic device, including a memory and a processor. A computer program that can run on the processor is stored in the memory. It is characterized in that when the processor executes the computer program, the steps of the method described in any one of the above claims are implemented.

[0031] The present invention has the following advantages: The present invention proposes a method for evaluating the plugging capacity of volumetric fracturing temporary plugging agents based on mine field big data. The method establishes a plugging boost prediction model that couples the mine field geological engineering parameter big data that affects the plugging capacity, and can quickly realize the quantitative prediction of the mine field scale plugging boost of each fracturing section of any well, solving the previous experimental evaluation errors and the difficulty of plugging boost in field monitoring of mines. At the same time, the plugging capacity is quantitatively characterized through the plugging capacity evaluation factor, and the different levels of plugging capacity are further realized, which can greatly reduce the cost of mine field testing. The calculation method is simple and feasible, and is also applicable to the application and effect evaluation of volumetric fracturing temporary plugging agents for similar unconventional reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0033] Figure 1 A graph showing the correlation between different influencing factors and plugging and pressure increase in one embodiment of the present invention;

[0034] Figure 2 A plugging and pressure-raising diagram for evaluating the implementation of each fracturing stage of a horizontal well in a mine field in one embodiment of the present invention;

[0035] Figure 3 It is a construction pressure diagram for evaluating each fracturing stage of a horizontal well before temporary plugging in one embodiment of the present invention;

[0036] Figure 4 This is a factor diagram for evaluating the plugging ability of the fracturing stages St1 to St10 of the horizontal wells in one embodiment of the present invention;

[0037] Figure 5 This is a factor diagram for evaluating the plugging capacity of the fracturing stages St11 to St20 of horizontal wells in one embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] The purpose of the present invention is to provide a method for evaluating the plugging capacity of volumetric fracturing temporary plugging agents based on mine big data. In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0040] The present invention provides a method for evaluating the plugging ability of a volumetric fracturing temporary plugging agent based on mine field big data, comprising the following steps:

[0041] S1. Establish a large database of factors affecting the plugging capacity of temporary plugging agents in horizontal wells that have been subjected to fracturing, establish a comprehensive evaluation model for plugging capacity, calculate the correlation between different influencing factors and plugging capacity, and determine the main control parameters of geological engineering;

[0042] S2. Establish a plugging and pressure-raising multivariate regression prediction model coupled with the big data of geological engineering main control parameters to obtain the plugging and pressure-raising of each fracturing section of the evaluation horizontal well;

[0043] S3. Based on the construction pressure of each fracturing section of the evaluated horizontal well before the temporary plugging agent is added, the plugging capacity evaluation factor is obtained, and the plugging capacity level is divided according to the plugging capacity evaluation factor.

[0044] In the present invention, the step S1 uses the analytic hierarchy process to establish a large database of factors affecting the plugging capacity of temporary plugging agents in horizontal wells that have been subjected to fracturing. On this basis, a comprehensive evaluation model for plugging capacity is established to calculate the correlation between different influencing factors and plugging capacity, and to clarify the main control parameters of geological engineering. The specific contents are as follows:

[0045] (11) The factors affecting the plugging ability of temporary plugging agents are divided into target layer, decision layer and index layer by using the analytic hierarchy process. The target layer is the plugging pressure after the temporary plugging agent is applied to each fracturing stage of the horizontal well. The decision layer is the reservoir physical property parameters, rock mechanical parameters and fracturing transformation parameters. The reservoir physical property parameters in the index layer include the porosity, permeability, oil saturation and mud content of the reservoir where each fracturing stage is located. The rock mechanical parameters include the brittleness index and biaxial stress difference of the reservoir where each fracturing stage is located. The fracturing transformation parameters include the number of single-stage clusters, the number of perforations, the construction displacement, the single-stage liquid volume, the single-stage sand volume and the amount of temporary plugging agent used.

[0046] (12) Establish a comprehensive evaluation model for plugging capacity based on the plugging capacity influencing factor database in step S1, calculate the correlation between different influencing factors and plugging capacity, and clarify the main control parameters of geological engineering. The specific contents are as follows:

[0047] ① Establish a plugging capacity evaluation matrix M based on the database, where the evaluation matrix elements are the reservoir physical property parameters, rock mechanics parameters and fracturing transformation parameter sets of each fracturing section of the horizontal well that has been fractured.

[0048]

[0049] Where: M is the evaluation matrix; M i (j) is the evaluation matrix element; m is the number of fracturing stages for horizontal well reservoir transformation using temporary plugging agents; and n is the number of parameters affecting the plugging capacity.

[0050] ② According to the plugging and pressure-raising of each fracturing section of the horizontal well where temporary plugging agent has been implemented, the reference column M0 of the prediction model matrix is ​​established.

[0051] M0=(M1(0),…,M i (0),…,M m (0)) T i=1,2,…,m (2)

[0052] Where: M0 is the reference column of the evaluation matrix.

[0053] ③ Since the dimensions and physical meanings of different influencing parameters are quite different, it is necessary to standardize the plugging and boosting evaluation matrix and reference column. The invention of this application adopts the maximum value method, and the calculation formula is as follows:

[0054]

[0055] Where: Standardized elements for the evaluation matrix; (M i (j)) max is the maximum value in the jth influencing factor parameter set.

[0056] ④ According to the standardized evaluation matrix and evaluation standard column, the correlation between different influencing factors and the evaluation reference column is calculated. The calculation expression is as follows:

[0057]

[0058] Where: r j It is the degree of association and has no dimension; in The data were normalized for the evaluation reference column; is the data after the evaluation matrix elements are standardized; ρ is the resolution coefficient, which is usually taken as 0.5.

[0059] ⑤ According to the calculation results of the correlation between different influencing factors and plugging capacity, they are ranked. The larger the value, the higher the degree of influence. The correlation greater than 0.5 is defined as the main control parameter of geological engineering affecting the plugging capacity.

[0060] In the present invention, the step S2 establishes a plugging and pressure-boosting multivariate regression prediction model coupled with the geological engineering main control parameter big data to obtain the plugging and pressure-boosting data of each fracturing section of the evaluation horizontal well. The specific contents are as follows:

[0061] (21) According to the corresponding data of the geological engineering master control parameters of the plugging capacity determined in step S1, a multivariate linear equation group of the plugging pressure increase and the geological engineering master control parameters of each fracturing section of the horizontal well where the temporary plugging agent has been implemented is established, and the calculation expression is as follows:

[0062]

[0063] Where: y is the plugging and pressure increase of each fracturing stage in the implemented horizontal well, MPa; x is the main control parameter of geological engineering affecting the temporary plugging capacity of each fracturing stage in the implemented horizontal well; m is the number of fracturing stages in the horizontal well; k is the number of main control parameters of geological engineering affecting the plugging capacity; β is the coefficient of the multiple regression equation.

[0064] (22) According to the multivariate linear equation group of the plugging and pressure-boosting of each fracturing section of the horizontal well implemented by the temporary plugging agent in step (21) and the main control parameters of geological engineering, the coefficients of the multivariate regression equation are solved, and the calculation formula is shown in (6), wherein the expressions of β, X, and Y are shown in formulas (7) to (9), respectively.

[0065] β=(X T X) -1 X T Y (6)

[0066] β=(β1,β2,…,β m ) T (7)

[0067]

[0068] Y=(y1,y2,…,y m ) T (9)

[0069] (23) Based on the solution of the coefficients of the multivariate regression equation in step (22), a plugging and pressure-boosting multivariate regression prediction model coupled with key geological engineering parameters is established, as shown in formula (10). The key geological engineering parameters affecting the plugging and pressure-boosting of each fracturing section of the horizontal well are further collected and evaluated, and the plugging and pressure-boosting of each fracturing section is quantitatively predicted using formula (10).

[0070] y=β1+β2x 12 +…+β k x mk (10)

[0071] In the present invention, in step S3, the construction pressure before adding the temporary plugging agent for each fracturing stage of the evaluation horizontal well is combined to obtain a plugging ability evaluation factor, and the plugging ability level is divided according to the plugging ability evaluation factor. The calculation formula is shown in (11). According to its magnitude, the plugging ability level is divided into four levels: I, II, III, and IV. The plugging ability ranking is I > II > III > IV. Among them, when 0.75 < T ≤ 1, the plugging ability is at level I; when 0.5 < T ≤ 0.75, the plugging ability is at level II; when 0.25 < T ≤ 0.5, the plugging ability is at level III; when 0 < T ≤ 0.25, the plugging ability is at level IV.

[0072]

[0073] In the formula: T is the plugging ability evaluation factor, dimensionless; P l is the construction pressure before temporary plugging, MPa; l is the fracturing stage number of the evaluation horizontal well; h is the number of fracturing stages of the evaluation horizontal well, in sections.

[0074] Application Example

[0075] The following describes in detail the specific implementation manner of the present invention with reference to the drawings and the evaluation of the plugging ability of the temporary plugging agent for a tight oil horizontal well in a certain block as an example to illustrate the practicability of the method.

[0076] The evaluation horizontal well MH1 is a tight oil horizontal well in the main development area of a certain block. The main body adopts the horizontal well large-section multi-cluster + temporary plugging and diversion technology. At present, the efficient development of the block has been achieved. However, due to the differences in reservoir physical properties, the microseismic monitoring fracture network sweep volume varies greatly. Among them, the plugging ability of the temporary plugging agent is the core of the uniform initiation and propagation of the large-section multi-cluster, and is the key to further affecting the fracture network sweep volume. In order to evaluate the plugging ability of the temporary plugging agent and select a suitable temporary plugging agent, the horizontal section of this well is 1760 m, and 20 fracturing stages are designed. The temporary plugging agent WY-1 and the temporary plugging agent WY-2 are respectively tested. Among them, the temporary plugging agent WY-1 is tested in the fracturing stages St1 to St10, and the temporary plugging agent WY-2 is tested in the fracturing stages St11 to St20. Through the method of the invention patent of this application, the plugging ability of the two temporary plugging agents in the field test is evaluated, and a temporary plugging agent suitable for this block is selected.

[0077] S1. Establish a large database of the influencing factors of the plugging ability of the temporary plugging agent for the fracturing horizontal wells that have been implemented, establish a comprehensive evaluation model of the plugging ability, calculate the correlation degree between different influencing factors and the plugging ability, and determine the main control parameters of geology and engineering. The specific content is as follows:

[0078] (11) The reservoir physical parameters, rock mechanical parameters and fracturing transformation parameters of 30 horizontal wells with 215 fracturing stages in the same block and layer of the horizontal wells were collected and evaluated. The factors affecting the plugging ability of the temporary plugging agent were divided into target layer, decision layer and index layer by using the analytic hierarchy process. The target layer is the plugging pressure after the temporary plugging agent is applied to each fracturing stage of the horizontal well. The decision layer is the reservoir physical parameters, rock mechanical parameters and fracturing transformation parameters. The reservoir physical parameters in the index layer include the porosity, permeability, oil saturation and mud content of the reservoir where each fracturing stage is located; the rock mechanical parameters include the brittleness index and biaxial stress difference of the reservoir where each fracturing stage is located; the fracturing transformation parameters include the number of single-stage clusters, the number of perforations, the operation displacement, the single-stage liquid volume, the single-stage sand volume and the temporary plugging agent dosage. Among them, the basic parameters of each fracturing stage of the evaluated horizontal well MH1 are shown in Tables 1 and 2.

[0079] Table 1 Reservoir physical properties and rock mechanics parameters of the evaluated horizontal well MH1

[0080]

[0081] Table 2 Evaluation of fracturing parameters of horizontal well MH1

[0082]

[0083] (12) Establish a comprehensive evaluation model for plugging capacity based on the database of factors affecting the plugging capacity of the 30-well 215 fracturing stage in step (11), calculate the correlation between different influencing factors and plugging capacity, and clarify the main control parameters of geological engineering. The specific contents are as follows:

[0084] ① Based on the big data database, the plugging capacity evaluation matrix is ​​established using formula (1), where the evaluation matrix elements are the reservoir physical property parameters, rock mechanics parameters and fracturing transformation parameter sets of each fracturing section of the horizontal well that has been fractured.

[0085] ② Based on the plugging and pressure-raising of each fracturing section of the horizontal well where temporary plugging agents have been implemented, the reference column of the prediction model matrix is ​​established using formula (2).

[0086] ③ Due to the large differences in the dimensions and physical meanings of different influencing parameters, formula (3) is used to standardize the plugging and boosting evaluation matrix and reference column.

[0087] ④ According to the standardized evaluation matrix and evaluation criteria column, the correlation between different influencing factors and the evaluation reference column is calculated using formula (4), see Appendix Figure 1 .

[0088] ⑤ According to the calculation results of the correlation between different influencing factors and plugging capacity, they are ranked. The larger the value, the higher the degree of influence. The main control parameters of geological engineering affecting the plugging capacity are biaxial stress difference, number of perforations, amount of temporary plugging agent, number of single-stage clusters, amount of sand in a single stage, amount of liquid in a single stage, and clay content.

[0089] S2. Establish a plugging and pressure-boosting multivariate regression prediction model that couples the big data of geological engineering master control parameters, and quantitatively obtain and evaluate the plugging and pressure-boosting of each fracturing section of the horizontal well. The specific contents are as follows:

[0090] (21) Based on the data of the main control parameters of geological engineering for plugging capacity determined in step S1, a multivariate linear equation group of the plugging pressure increase and the main control parameters of geological engineering for each fracturing section of the horizontal well where the temporary plugging agent has been implemented is established using formula (5).

[0091] (22) According to the multivariate linear equation group of the plugging and pressure-raising of each fracturing section of the horizontal well implemented by the temporary plugging agent in step (21) and the main control parameters of geological engineering, the coefficients of the multivariate regression equation are solved using (6) to (9).

[0092] (23) Based on the solution of the multivariate regression equation coefficients in step (22), a plugging pressure increase multivariate regression prediction model coupled with key geological engineering parameters is established, as shown in expression (12). Further, according to the key geological engineering parameters affecting the plugging pressure increase of each fracturing section of the horizontal well (Table 1-Table 2), the plugging pressure increase of each fracturing section is quantitatively predicted using formula (10), see Appendix Figure 2 .

[0093] y=28.272-0.637S H +0.04△P-0.277S-0.05N P -0.003V L -0.03V P +0.013V D (12)

[0094] Where: y is the plugging pressure, MPa; S H is the mud content, %; △P is the biaxial stress difference, MPa; S is the number of clusters in a single segment, cluster; N P is the number of perforation holes, holes; V L is the single-stage liquid volume, square; V P is the sand volume in a single stage, cubic; V D is the dosage of temporary plugging agent, kg.

[0095] S3. Combined with the evaluation of the construction pressure of each fracturing section of the horizontal well before the temporary plugging agent is added, see the attached Figure 3 , the plugging capacity evaluation factor of temporary plugging agent WY-1 in fracturing stages S1 to S10 is calculated using formula (11) as 0.36, see Appendix Figure 4According to the plugging capacity classification standard, it is grade III. The plugging capacity evaluation factor of temporary plugging agent WY-2 in fracturing stage S11~S20 is 0.47. Figure 5 The plugging ability level is III, so the plugging ability of temporary plugging agent WY-2 is higher than that of temporary plugging agent WY-1. The recommended temporary plugging agent in this tight oil area is WY-2.

[0096] The above is a specific description of the present invention through embodiments. It is necessary to point out that the embodiments are only preferred embodiments of the present invention and do not limit the present invention in any way. They are not limited to the forms disclosed herein and should not be regarded as excluding other embodiments. The modifications and simple changes made by those skilled in the art do not deviate from the technical concept and scope of the present invention and are within the protection scope of the technical solution of the present invention.

Claims

1. A method for evaluating the plugging capacity of volumetric fracturing temporary plugging agents based on mine field big data, comprising the following steps: S1. Establish a large database of factors affecting the plugging capacity of temporary plugging agents in horizontal wells that have been subjected to fracturing, establish a comprehensive evaluation model for plugging capacity, calculate the correlation between different influencing factors and plugging capacity, and determine the main control parameters of geological engineering; S2. Establish a plugging and pressure-raising multivariate regression prediction model coupled with the big data of geological engineering main control parameters to obtain the plugging and pressure-raising of each fracturing section of the evaluation horizontal well; S3. Based on the construction pressure of each fracturing section of the evaluated horizontal well before the temporary plugging agent is added, the plugging capacity evaluation factor is obtained, and the plugging capacity level is divided according to the plugging capacity evaluation factor.

2. According to the method for evaluating the plugging capacity of volumetric fracturing temporary plugging agents based on mine big data in claim 1, in step S1, a mine big database of factors affecting the plugging capacity of temporary plugging agents for horizontal wells that have undergone fracturing is established using the hierarchical analysis method.

3. According to the method for evaluating the plugging capacity of volumetric fracturing temporary plugging agents based on mine big data in claim 2, in step S1, a mine big database of factors affecting the plugging capacity of temporary plugging agents for horizontal wells that have undergone fracturing is established by using the analytic hierarchy process, and further comprising: The factors affecting the plugging ability of temporary plugging agents are divided into target layer, decision layer and index layer by using the analytic hierarchy process. The target layer is the plugging pressure after the temporary plugging agent is applied in each fracturing section of the horizontal well. The decision layer is the reservoir physical properties, rock mechanics parameters and fracturing transformation parameters. The reservoir physical properties in the index layer are the porosity, permeability, oil saturation and mud content of the reservoir where each fracturing section is located.

4. According to the method for evaluating the plugging capacity of volumetric fracturing temporary plugging agents based on mine big data in claim 3, the rock mechanics parameters include one or more parameters of the brittleness index of the reservoir where each fracturing stage is located and the biaxial stress difference, and the fracturing transformation parameters include one or more parameters of the number of single-stage clusters, the number of perforation holes, the construction displacement, the single-stage liquid volume, the single-stage sand volume, and the temporary plugging agent dosage.

5. According to the method for evaluating the plugging capacity of volumetric fracturing temporary plugging agents based on mine field big data in claim 1, the step S1 of establishing a comprehensive evaluation model for plugging capacity further comprises: S11. Establish a plugging capacity evaluation matrix M based on the mine big database, where the elements of the evaluation matrix M are the reservoir physical property parameters, rock mechanics parameters and fracturing transformation parameter sets of each fracturing section of the horizontal wells that have been fractured; S12, establishing a prediction model matrix reference column M0 based on the plugging and pressure-raising of each fracturing stage of the horizontal well where temporary plugging agent has been implemented; S13, standardizing the plugging and boosting evaluation matrix and the reference column, and calculating the correlation between different influencing factors and the evaluation reference column according to the standardized evaluation matrix and the evaluation standard column; S14. Determine the parameters with a correlation greater than a set threshold as the main control parameters of the geological engineering that affect the plugging capacity.

6. According to the method for evaluating the plugging ability of volumetric fracturing temporary plugging agents based on mine field big data in claim 1, the step S2 of establishing a plugging and boosting multivariate regression prediction model coupled with the big data of geological engineering main control parameters also includes: S21. Establish a multiple linear equation system between the plugging pressure increase of each fracturing stage of the temporary plugging agent in the horizontal wells where it has been implemented and the main geological engineering control parameters according to the corresponding data of the main geological engineering control parameters; S22. Solve the coefficients of the multiple regression equation according to the multiple linear equation system; S23. Establish a multiple regression prediction model for plugging pressure increase coupling with the main geological engineering control parameters, and conduct quantitative prediction of the plugging pressure increase of each fracturing stage.

7. According to the evaluation method for the plugging ability of the volume fracturing temporary plugging agent based on field big data described in claim 1, the step of obtaining the plugging ability evaluation factor in step S3 further includes: Where: T is the plugging capacity evaluation factor, dimensionless; y is the plugging pressure increase of each fracturing stage, MPa; P l is the construction pressure before temporary plugging, MPa; l is the fracturing section number of the evaluated horizontal well; h is the fracturing section number of the evaluated horizontal well, section.

8. According to the evaluation method for the plugging ability of the volume fracturing temporary plugging agent based on field big data described in claim 1, the greater the plugging ability evaluation factor, the stronger the plugging ability.

9. According to the evaluation method for the plugging ability of the volume fracturing temporary plugging agent based on field big data described in claim 1, when 0.75 < T ≤ 1, the plugging ability is at grade I; when 0.5 < T ≤ 0.75, the plugging ability is at grade II; when 0.25 < T ≤ 0.5, the plugging ability is at grade III; when 0 < T ≤ 0.25, the plugging ability is at grade IV. The plugging ability ranking is I > II > III > IV.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 - 9 above.

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