A shale oil horizontal well volume fracturing fracture volume quantitative characterization method

Abstract

The present application provides a kind of shale oil horizontal well volume fracturing fracture volume quantitative characterization method, comprising the following steps: S1 utilizes grey correlation analysis method to calculate the correlation coefficient between different geological engineering factors and the fracture volume of each fracturing section of horizontal well microseismic monitoring, determines the key influencing factors of fracture volume according to its size;S2 is based on the step S1, establishes the fracture volume prediction model considering the key factors of geology engineering simultaneously, calculates the fracture volume of horizontal well whole section microseismic monitoring;S3 according to the fracture volume of horizontal well whole section microseismic monitoring and the reservoir physical property parameter of horizontal well, establishes horizontal well volume fracturing productivity prediction model, obtains the relationship between microseismic monitoring fracture volume and effective fracture volume, and then quantitatively characterizes fracture volume.The method is simple and feasible, and greatly saves the problems of long test period and high cost of field microseismic monitoring, and is also applicable to the prediction of effective fracture volume of volume fracturing of other similar horizontal wells in oil reservoirs.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas development, and specifically relates to a quantitative characterization method for fracture volume in shale oil horizontal well volumetric fracturing. Background Technology

[0002] China's technically recoverable shale oil resources could reach 145 million tons. 8 Shale oil resources, mainly distributed in large basins such as the Ordos Basin, Junggar Basin, Bohai Bay Basin, and Songliao Basin, have enormous development potential. Horizontal well volumetric fracturing technology has enabled the commercial development of shale oil and gas. Among them, the Changqing Oilfield in the Ordos Basin is rich in shale oil resources, mainly developed in the Chang 7 Member of the Yanchang Formation. Before 2010, due to technical limitations, the post-fracturing trial production was less than 4 tons / day. Since 2011, it has taken the lead in carrying out horizontal well volumetric fracturing tests in China. Since 2018, it has promoted the construction of a national-level shale oil development demonstration project, and by the end of 2020, shale oil production reached 1.43 million tons.

[0003] The success of unconventional oil and gas volumetric fracturing is marked by the formation of complex fracture networks in the reservoir through multi-stage, multi-cluster, high-volume fracturing in horizontal wells. This shortens the seepage distance; the more complex the fracture network, the lower the oil and gas seepage resistance, thus solving the bottleneck technical problem of difficult seepage in unconventional oil and gas. Fracture volume refers to the effective volume contributing to production capacity and is an important indicator for evaluating the complexity of the fracture network; the larger the fracture volume, the higher the complexity of the fracture network. Therefore, accurate quantitative characterization of fracture volumetric fracturing is crucial for evaluating fracturing effectiveness and optimizing fracturing design. However, numerous factors influence fracture volume in horizontal well volumetric fracturing, and their relationships are complex. Different factors affect the complexity of the fracture network to varying degrees, exhibiting complex nonlinear relationships. Currently, the main evaluation methods include the following:

[0004] (1) Li Zhiqiang et al. (Li Zhiqiang, Qi Zhilin, Yan Wende et al. A calculation method for fracturing and enhancing production zones in unconventional reservoirs, patent application number: CN201910985860.8) This method treats fractured reservoirs as dual continuous media reservoirs, establishes the fluid mass conservation equation in the fracture system, and establishes a two-dimensional equivalent mathematical model for calculating the enhanced volume based on the natural fracture activation criterion, fracture width equation, full tensor permeability conversion equation, cubic law, and fracture porosity calculation method. Combining the initial and internal / external boundary conditions of the mathematical model, the fluid mass conservation equation and fracture width equation in the fracture system are iteratively coupled to obtain the fracture fluid pressure and average fracture width in each grid block, thereby obtaining the fracture enhanced volume. This method quantitatively calculates the fracture volume by establishing a mathematical model, but unconventional reservoirs are extremely heterogeneous, and the multi-fracture propagation law is still a bottleneck problem in the field of unconventional reservoirs, making fracture volume prediction even more difficult. This method is a numerical simulation method with many assumptions, and the predicted results differ significantly from the actual values.

[0005] (2) Zeng Bin et al. (Zeng Bin, Ao Ke, Liu Xiangping et al. A method for testing fracture volume using chemical tracers, patent application number: CN201811455278.2). This method injects a phase-separated chemical tracer into the formation along with the hydraulic fracturing fluid; if it is multi-stage fracturing, a specific phase-separated chemical tracer is injected into each stage; after the well is bleed-out for oil or gas testing, oil or gas samples and water samples are taken periodically at the wellhead, and the cumulative production of oil or gas and water at the wellhead is recorded; the concentration of the chemical tracer in all samples is detected; the detected chemical tracer concentration data is plotted with the corresponding cumulative production data of the phase fluid to obtain the swept volume of the tracer in the oil phase, gas phase, and water phase, and then the effective volume of the fracturing fracture is calculated. This method predicts the fracture volume using tracers, but the amount of tracer after fracturing is related to many factors such as the production system, sampling system, and sampling time of the horizontal well, and cannot obtain the actual effective volume of the fracture.

[0006] (3) Tang Dongyang et al. (Tang Dongyang, Pang Rui, Zhao Deming et al. Method and system for calculating the fracturing volume of a microseismic fracturing fracture model, patent application number: CN201710835626.8). This method equates the microseismic fracturing fracture model to a triangular polyhedron model; projects the triangular faces in the triangular polyhedron model to obtain multiple convex pentahedrons; and obtains the fracturing volume of the microseismic fracturing fracture model based on the volume of the multiple convex pentahedrons. The microseismic monitoring event points are related to the mechanical properties of the rock and the monitoring accuracy of the equipment. Extensive practice has shown that the monitoring range of microseismic events is much larger than that of hydraulic fractures, thus this method overestimates the fracture volume.

[0007] In summary, the main methods for obtaining fracture volume currently include reservoir numerical simulation and microseismic monitoring technology, with microseismic monitoring technology being a commonly used method in major oilfields. However, unconventional reservoirs typically employ multi-stage and multi-cluster fracturing, making it impossible to utilize microseismic monitoring technology for every stage. The testing cycle is long, the testing cost is high, and it is difficult to test and apply across the entire well section in oilfields. Furthermore, it cannot obtain the true fracture volume of horizontal well volumetric fracturing. Summary of the Invention

[0008] The purpose of this invention is to provide a quantitative characterization method for fracture volume in shale oil horizontal well volumetric fracturing. This method overcomes the shortcomings of existing technologies. First, grey relational analysis is used to calculate the correlation coefficients between different geological engineering factors and the fracture volume monitored by microseismic monitoring in each fracturing section of the horizontal well, identifying the key factors affecting fracture volume. Based on this, a fracture volume prediction model that simultaneously considers key geological engineering factors is established to calculate the fracture volume monitored by microseismic monitoring throughout the horizontal well. Finally, based on fracture volume and reservoir physical parameters, a horizontal well volumetric fracturing productivity prediction model is established to obtain the relationship between the microseismic monitored fracture volume and the effective fracture volume, thereby achieving the goal of quantitative characterization of fracture volume in shale oil horizontal well volumetric fracturing.

[0009] To achieve the above technical objectives, the present invention provides the following technical solutions:

[0010] This invention provides a method for quantitatively characterizing the volumetric fracture volume in shale oil horizontal wells, comprising the following steps:

[0011] S1. The correlation coefficient between different geological engineering factors and the fracture volume of each fractured section of the horizontal well was calculated using the grey relational analysis method. Based on the size of the correlation coefficient, the key influencing factors of fracture volume were determined.

[0012] S2. Based on the determination of key influencing factors of fracture volume, a fracture volume prediction model that simultaneously considers key geological engineering factors is established to calculate the fracture volume of the entire horizontal well section under microseismic monitoring.

[0013] S3. Based on the fracture volume monitored by microseismic monitoring throughout the horizontal well and the reservoir properties of the horizontal well, a volumetric fracturing production prediction model for the horizontal well is established. The relationship between the fracture volume monitored by microseismic monitoring and the effective fracture volume is obtained, and then the fracture volume is quantitatively characterized.

[0014] Further, step S1 uses grey relational analysis to calculate the correlation coefficient between different geological engineering factors and the fracture volume monitored by microseismic monitoring in each fractured section of the horizontal well. Based on the magnitude of the correlation coefficient, the key influencing factors of fracture volume are determined, specifically including:

[0015] Step S101: Collect and organize big data from the mining area to establish a database of factors affecting the volumetric fracturing fracture volume of horizontal wells, including geomechanical parameters and volumetric fracturing stimulation parameters. The geomechanical parameters include: porosity, permeability, oil saturation, clay content, brittleness index, horizontal stress difference, and fracturing pressure of each fracturing section of the horizontal well. The volumetric fracturing stimulation parameters include: fracture density, fracturing flow rate, fracturing fluid volume, and proppant usage of each fracturing section of the horizontal well.

[0016] Step S102: Collect microseismic monitoring fracture volume data of the predicted horizontal well fractured section;

[0017] Step S103, Establishment of Multi-Factor Comprehensive Evaluation Matrix: Based on the fracture volume influencing factor database obtained in step S101, a multi-factor comprehensive evaluation matrix is ​​established. The elements of the multi-factor comprehensive evaluation matrix are the predicted geomechanical parameters and volumetric fracturing parameters of the measured fractured section of the horizontal well. The expression of the multi-factor comprehensive evaluation matrix is ​​as follows (I):

[0018]

[0019] In the formula: X is a multi-factor comprehensive evaluation matrix;

[0020] X i (j) represents the elements of the multi-factor comprehensive evaluation matrix;

[0021] m represents the number of volumetric fracturing stages in a horizontal well;

[0022] n is the number of factors affecting crack volume;

[0023] Step S104, Establishment of Multi-Factor Comprehensive Evaluation Reference Column: Based on the predicted fracture volume of the measured fractured section of the horizontal well, an evaluation reference column is established. The expression for the multi-factor comprehensive evaluation reference column is as follows (II):

[0024] X0 = [X1(0), ... X i (0), ...X m (0)] T i = 1, 2, ..., m (Ⅱ)

[0025] In the formula: X0 is the reference column for multi-factor comprehensive evaluation; T is the transpose symbol;

[0026] Step S105, Standardization: The multi-factor comprehensive evaluation matrix X and the multi-factor comprehensive evaluation reference column X0 are standardized using the maximum value method. The standardization formula is as follows (III):

[0027]

[0028] In the formula: Standardized elements for multi-factor comprehensive evaluation matrix or evaluation reference columns;

[0029] X i (j) represents elements of a multi-factor comprehensive evaluation matrix or evaluation reference column elements;

[0030] (X i (j)) max It represents the maximum value in the parameter set of the j-th influencing factor;

[0031] Step S106, Correlation Coefficient Calculation: Calculate the correlation coefficients between different influencing factors and the fracture volume in horizontal well volumetric fracturing microseismic monitoring based on a standardized database. Specifically, this includes:

[0032] Step S1061: Calculate the standard deviation based on the standardized data of the multi-factor comprehensive evaluation matrix and the standardized data of the evaluation reference column. The calculation formula is as follows: (IV):

[0033]

[0034] In the formula: Δ i (j) represents the standard deviation between the standardized data of the multi-factor comprehensive evaluation matrix and the standardized data of the evaluation reference column;

[0035] To evaluate the standardized data of the reference column elements;

[0036] This refers to the standardized data of the multi-factor comprehensive evaluation matrix elements;

[0037] Step S1062: Based on the standard deviation calculated in step S1061, further calculate the correlation coefficient between different influencing factors and the volume of cracks in microseismic monitoring. The calculation formula is as follows: (V):

[0038] In the formula: r j The correlation coefficient;

[0039] ρ is the resolution coefficient, which is usually taken as 0.5;

[0040] Δ i (j) represents the standard deviation between the standardized data of the multi-factor comprehensive evaluation matrix and the standardized data of the evaluation reference column;

[0041] m represents the number of volumetric fracturing stages in a horizontal well.

[0042] Step S107, Determination of key influencing factors: Sort the correlation coefficient calculation results from step S106, and define a value greater than 0.5 as a key influencing factor affecting the volume of fractures in horizontal well volumetric fracturing.

[0043] Furthermore, based on the determination of key influencing factors of fracture volume, step S2 establishes a fracture volume prediction model that simultaneously considers key geological engineering factors, and calculates the fracture volume of the entire horizontal well section using microseismic monitoring. Specifically, this includes:

[0044] Step 201: Based on the key influencing factors of crack volume determined in step S1, establish a crack volume prediction matrix.

[0045] Step 202: Based on the establishment of the fracture volume prediction matrix, the elements of the microseismic monitoring fracture volume prediction matrix are further standardized.

[0046] Step 203, Euclidean distance calculation: Based on the standardized microseismic monitoring fracture volume prediction matrix elements, calculate the Euclidean distance between the predicted fracture segment and the measured fracture segment, and normalize it.

[0047] Step 204: Based on the Euclidean distance calculation results between the predicted fracturing section and the measured fracturing section obtained in step S203, the microseismic monitoring fracture volume of the measured fracturing section corresponding to the minimum Euclidean distance is assigned to the predicted fracturing section, thereby obtaining the microseismic monitoring fracture volume of the entire horizontal well section.

[0048] Furthermore, in step S201, the expression for the microseismic monitoring crack volume prediction matrix is ​​as follows: (VI):

[0049]

[0050] In the formula: Y is the seam mesh sweep volume prediction matrix;

[0051] Y i (j) represents the elements of the seam mesh sweep ripple prediction matrix, i = 0, 1, 2, ..., h; j = 1, 2, ..., k;

[0052] h represents the number of fracturing sections in the horizontal well, where i = 0 is the predicted fracturing section number, and i = 2 to h is the measured fracturing section number;

[0053] k represents the number of parameters that affect crack volume.

[0054] Furthermore, in step S202, the standardization process for the elements of the prediction matrix includes:

[0055] Step 2021: Based on the elements of the crack volume evaluation matrix, calculate the mean and standard deviation of the column vectors of the matrix, using the following formulas (Ⅶ) and (Ⅷ):

[0056]

[0057]

[0058] In the formula: The mean of the column vectors of the crack volume prediction matrix;

[0059] n represents the number of fracturing segments that have been measured;

[0060] y ij Elements of the crack volume prediction matrix;

[0061] S jLet be the standard deviation of the column vectors of the crack volume prediction matrix.

[0062] Step 2022: Based on the mean and standard deviation of the column vectors of the crack volume prediction matrix elements, the prediction matrix elements are standardized using the following formula (IX):

[0063]

[0064] In the formula: These are the standardized elements of the crack volume prediction matrix;

[0065] Step 2023: Standardize the elements of the microseismic monitoring fracture volume prediction matrix using formulas (VII) to (IX) to obtain the standardized matrix for microseismic monitoring fracture volume prediction, which is expressed as follows:

[0066]

[0067] In the formula: Y * This is a standardized matrix for predicting crack volume in microseismic monitoring.

[0068] Furthermore, in step S203, the Euclidean distance between the predicted fracturing section and the measured fracturing section is calculated using formula (XI), and the Euclidean distance between the predicted fracturing section and the measured fracturing section is normalized to the interval of 0 to 1 using formula (XII):

[0069]

[0070]

[0071] In the formula: ED i To predict the Euclidean distance between the fractured section and the measured fractured section, dimensionless;

[0072] To predict the normalized Euclidean distance between the fractured section and the measured fractured section, which is dimensionless;

[0073] Standardized elements for the crack volume prediction matrix;

[0074] These are the standardized elements of the crack volume prediction matrix.

[0075] Furthermore, in step S204, the formula for calculating the fracture volume of the entire horizontal well section using microseismic monitoring is as follows (XIII):

[0076]

[0077] In the formula: SRV is the fracture volume monitored by microseismic monitoring throughout the horizontal well section, 10 4 m3 ;

[0078] SRV i For single-segment microseismic monitoring of fracture volume in horizontal wells, 10 4 m 3 ;

[0079] k represents the number of fracturing stages in a horizontal well.

[0080] Further, step S3 establishes a volumetric fracturing production prediction model for the horizontal well based on the fracture volume monitored by microseismic monitoring throughout the horizontal well and the reservoir physical parameters where the horizontal well is located. This model yields the relationship between the fracture volume monitored by microseismic monitoring and the effective fracture volume, thereby quantitatively characterizing the fracture volume. Specifically, this includes:

[0081] Step S302: Using the horizontal well production capacity prediction model from step S301, predict the cumulative oil production in the first year, and further calculate the effective fracture volume index by combining the actual cumulative oil production in the first year of the horizontal well.

[0082] Step S303: Based on the effective fracture volume index calculated in step S302 and the fracture volume predicted by microseismic monitoring of the entire horizontal well section in step S2, the effective fracture volume of shale oil horizontal well volume fracturing is quantitatively characterized.

[0083] Furthermore, in step S301, based on the basic parameters of the horizontal well and the reservoir, a volumetric fracturing production prediction model for shale oil horizontal wells is established using the reservoir numerical simulation software Eclipse. This specifically includes:

[0084] Step S3011, Database Establishment: Obtain basic parameters of the horizontal well and basic parameters of the reservoir where the horizontal well is located. The basic parameters of the horizontal well include the length of the horizontal section, the number of fracturing sections, and the cumulative oil production in the first year. The geological parameters of the reservoir where the horizontal well is located include reservoir depth, reservoir thickness, formation pressure, reservoir fluid parameters such as water, average porosity of the entire well section, average permeability, average oil saturation, and formation temperature.

[0085] Step S3012: Based on the database established in step 3011, a horizontal well geological model is established using the reservoir numerical simulation software Eclipse, including the reservoir porosity distribution field, permeability distribution field, oil saturation distribution field, and formation pressure distribution field.

[0086] Step S3013: The fracture volume predicted in step S2 from the microseismic monitoring of the entire horizontal well section is imported into the geological model to form a production capacity prediction model.

[0087] Furthermore, the formula for calculating the effective crack volume index is as follows (XIV):

[0088]

[0089] In the formula: EI is the effective crack volume index, which is dimensionless;

[0090] Q H The actual cumulative oil production of the horizontal well in the first year, in tons;

[0091] Q P The cumulative oil production for the first year of a horizontal well is predicted in tons.

[0092] Furthermore, the formula for calculating the effective crack volume is as follows (XV):

[0093]

[0094] In the formula: ESRV is the effective crack volume, 10 4 m 3 ;

[0095] SRV stands for microseismic monitoring of fracture volume, 10 4 m 3

[0096] SRV i For single-segment microseismic monitoring of fracture volume in horizontal wells, 10 4 m 3 ;

[0097] i represents the fracturing stage number;

[0098] k represents the number of fracturing stages.

[0099] Compared with the prior art, the present invention has the following beneficial effects:

[0100] 1. This invention innovatively proposes a method for predicting fracture volume in the entire horizontal well section using microseismic monitoring, which simultaneously considers the combined effects of geomechanics and volumetric fracturing parameters. This method greatly saves on the problems of long testing cycles and high costs associated with microseismic monitoring in mining areas, and its calculation method is simple and feasible.

[0101] 2. This invention can reflect the effective fracture volume of horizontal well volumetric fracturing. By calculating the effective fracture volume index, the fracture volume is quantitatively characterized. It can be widely applied to the same shale oil block without requiring extensive microseismic monitoring at the mining site, greatly saving testing costs. This method is also applicable to predicting the effective fracture volume of horizontal well volumetric fracturing in other similar reservoirs.

[0102] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0103] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other design solutions and drawings can be obtained based on these drawings without creative effort.

[0104] Figure 1 This is a ranking diagram of factors affecting fracture volume in horizontal well volumetric fracturing according to the present invention;

[0105] Figure 2 This is a European distance diagram between the predicted fracturing section S19 and the measured fracturing section in the horizontal well according to the present invention.

[0106] Figure 3 This is a European distance diagram between the predicted fracturing section S20 and the measured fracturing section in the horizontal well according to the present invention.

[0107] Figure 4 This is a European distance diagram between the predicted fracturing section S21 and the measured fracturing section in the horizontal well according to the present invention.

[0108] To more clearly illustrate the present invention, the following description, in conjunction with preferred embodiments, further clarifies the invention. Those skilled in the art should understand that the specific descriptions below are illustrative rather than restrictive, and should not be construed as limiting the scope of protection of the present invention. Detailed Implementation

[0109] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0110] This invention provides a method for quantitatively characterizing the volumetric fracture volume in shale oil horizontal wells, comprising the following steps:

[0111] S1. The correlation coefficient between different geological engineering factors and the fracture volume of each fractured section of the horizontal well was calculated using the grey relational analysis method. Based on the size of the correlation coefficient, the key influencing factors of fracture volume were determined.

[0112] S2. Based on the determination of key influencing factors of fracture volume, a fracture volume prediction model that simultaneously considers key geological engineering factors is established to calculate the fracture volume of the entire horizontal well section under microseismic monitoring.

[0113] S3. Based on the fracture volume monitored by microseismic monitoring throughout the horizontal well and the reservoir properties of the horizontal well, a volumetric fracturing production prediction model for the horizontal well is established. The relationship between the fracture volume monitored by microseismic monitoring and the effective fracture volume is obtained, and then the fracture volume is quantitatively characterized.

[0114] Further, step S1 uses grey relational analysis to calculate the correlation coefficient between different geological engineering factors and the fracture volume monitored by microseismic monitoring in each fractured section of the horizontal well. Based on the magnitude of the correlation coefficient, the key influencing factors of fracture volume are determined, specifically including:

[0115] Step S101: Collect and organize big data from the mining area to establish a database of factors affecting the volumetric fracturing fracture volume of horizontal wells, including geomechanical parameters and volumetric fracturing stimulation parameters. The geomechanical parameters include: porosity, permeability, oil saturation, clay content, brittleness index, horizontal stress difference, and fracturing pressure of each fracturing section of the horizontal well. The volumetric fracturing stimulation parameters include: fracture density, fracturing flow rate, fracturing fluid volume, and proppant usage of each fracturing section of the horizontal well.

[0116] Step S102: Collect microseismic monitoring fracture volume data of the predicted horizontal well fractured section;

[0117] Step S103, Establishment of Multi-Factor Comprehensive Evaluation Matrix: Based on the fracture volume influencing factor database obtained in step S101, a multi-factor comprehensive evaluation matrix is ​​established. The elements of the multi-factor comprehensive evaluation matrix are the predicted geomechanical parameters and volumetric fracturing parameters of the measured fractured section of the horizontal well. The expression of the multi-factor comprehensive evaluation matrix is ​​as follows (I):

[0118]

[0119] In the formula: X is a multi-factor comprehensive evaluation matrix;

[0120] X i (j) represents the elements of the multi-factor comprehensive evaluation matrix;

[0121] m represents the number of volumetric fracturing stages in a horizontal well;

[0122] n is the number of factors affecting crack volume;

[0123] Step S104, Establishment of Multi-Factor Comprehensive Evaluation Reference Column: Based on the predicted fracture volume of the measured fractured section of the horizontal well, an evaluation reference column is established. The expression for the multi-factor comprehensive evaluation reference column is as follows (II):

[0124] X0 = [X1(0), ... X i (0), ...X m (0)] T i = 1, 2, ..., m (Ⅱ)

[0125] In the formula: X0 is the reference column for multi-factor comprehensive evaluation; T is the transpose symbol;

[0126] Step S105, Standardization Processing: Due to the significant differences in the dimensions and physical meanings of parameters from different influencing factors, it is necessary to standardize the evaluation matrix and evaluation reference columns. Specifically, this invention employs the maximum value method to standardize the multi-factor comprehensive evaluation matrix X and the multi-factor comprehensive evaluation reference column X0, respectively. The standardization formula is as follows (III):

[0127]

[0128] In the formula: Standardized elements for multi-factor comprehensive evaluation matrix or evaluation reference columns;

[0129] X i (j) represents elements of a multi-factor comprehensive evaluation matrix or evaluation reference column elements;

[0130] (X i (j)) max It represents the maximum value in the parameter set of the j-th influencing factor;

[0131] Step S106, Correlation Coefficient Calculation: Calculate the correlation coefficients between different influencing factors and the fracture volume in horizontal well volumetric fracturing microseismic monitoring based on a standardized database. Specifically, this includes:

[0132] Step S1061: Calculate the standard deviation based on the standardized data of the multi-factor comprehensive evaluation matrix and the standardized data of the evaluation reference column. The calculation formula is as follows: (IV):

[0133]

[0134] In the formula: Δ i (j) represents the standard deviation between the standardized data of the multi-factor comprehensive evaluation matrix and the standardized data of the evaluation reference column;

[0135] To evaluate the standardized data of the reference column elements;

[0136] This refers to the standardized data of the multi-factor comprehensive evaluation matrix elements;

[0137] Step S1062: Based on the standard deviation calculated in step S1061, further calculate the correlation coefficient between different influencing factors and the volume of cracks in microseismic monitoring. The calculation formula is as follows: (V):

[0138] In the formula: r j The correlation coefficient;

[0139] ρ is the resolution coefficient, which is usually taken as 0.5;

[0140] Δ i (j) represents the standard deviation between the standardized data of the multi-factor comprehensive evaluation matrix and the standardized data of the evaluation reference column;

[0141] m represents the number of volumetric fracturing stages in a horizontal well.

[0142] Step S107, Determination of key influencing factors: Sort the correlation coefficient calculation results from step S106, and define a value greater than 0.5 as a key influencing factor affecting the volume of fractures in horizontal well volumetric fracturing.

[0143] Furthermore, based on the determination of key influencing factors of fracture volume, step S2 establishes a fracture volume prediction model that simultaneously considers key geological engineering factors, and calculates the fracture volume of the entire horizontal well section using microseismic monitoring. Specifically, this includes:

[0144] Step 201: Based on the key influencing factors of fracture volume determined in step S1, establish a fracture volume prediction matrix, where the elements of the fracture volume prediction matrix are the key influencing factors affecting the volumetric fracturing fracture volume of the horizontal well; the expression of the fracture volume prediction matrix is ​​as follows (VI):

[0145]

[0146] In the formula: Y is the crack volume prediction matrix;

[0147] Y i (j) represents the elements of the seam mesh sweep ripple prediction matrix, i = 0, 1, 2, ..., h; j = 1, 2, ..., k;

[0148] h represents the number of fracturing sections in the horizontal well, where i = 0 is the predicted fracturing section number, and i = 2 to h is the measured fracturing section number;

[0149] k represents the number of parameters that affect crack volume;

[0150] Step 202, based on the established fracture volume prediction matrix, further standardizes the elements of the microseismic monitoring fracture volume prediction matrix, specifically including:

[0151] Step 2021: Based on the elements of the crack volume evaluation matrix, calculate the mean and standard deviation of the column vectors of the matrix, using the following formulas (Ⅶ) and (Ⅷ):

[0152]

[0153]

[0154] In the formula: The mean of the column vectors of the crack volume prediction matrix;

[0155] n represents the number of fracturing segments that have been measured;

[0156] y ij Elements of the crack volume prediction matrix;

[0157] S j Let be the standard deviation of the column vectors of the crack volume prediction matrix.

[0158] Step 2022: Based on the mean and standard deviation of the column vectors of the crack volume prediction matrix elements, the prediction matrix elements are standardized using the following formula (IX):

[0159]

[0160] In the formula: These are the standardized elements of the crack volume prediction matrix;

[0161] Step 2023: Standardize the elements of the microseismic monitoring fracture volume prediction matrix using formulas (VII) to (IX) to obtain the standardized matrix for microseismic monitoring fracture volume prediction, which is expressed as follows:

[0162]

[0163] In the formula: Y * A standardized matrix for predicting crack volume in microseismic monitoring;

[0164] Step 203, Euclidean distance calculation: Based on the standardized microseismic monitoring fracture volume prediction matrix elements, calculate the Euclidean distance between the predicted fracturing segment and the measured fracturing segment. The smaller the value, the higher the comprehensive similarity of the geomechanical parameters and fracturing modification parameters between the predicted and measured fracturing segments, and the closer the fracturing modification effects, i.e., the similarity of the microseismic monitoring fracture volumes. For ease of comparison, the Euclidean distance between the predicted and measured fracturing segments needs to be normalized to the 0-1 range. Furthermore, the Euclidean distance between the predicted and measured fracturing segments is calculated using formula (XI), and normalized to the 0-1 range using formula (XII):

[0165]

[0166]

[0167] In the formula: ED i To predict the Euclidean distance between the fractured section and the measured fractured section, dimensionless;

[0168] To predict the normalized Euclidean distance between the fractured section and the measured fractured section, which is dimensionless;

[0169] Standardized elements for the crack volume prediction matrix;

[0170] These are the standardized elements of the crack volume prediction matrix.

[0171] Step 204: Based on the Euclidean distance calculation results between the predicted fracturing section and the measured fracturing section obtained in step S203, the microseismic monitoring fracture volume of the measured fracturing section corresponding to the minimum Euclidean distance is assigned to the predicted fracturing section, thereby obtaining the microseismic monitoring fracture volume of the entire horizontal well section; furthermore, the formula for calculating the microseismic monitoring fracture volume of the entire horizontal well section is as follows (XIII):

[0172]

[0173] In the formula: SRV is the fracture volume monitored by microseismic monitoring throughout the horizontal well section, 10 4 m 3 ;

[0174] SRV i For single-segment microseismic monitoring of fracture volume in horizontal wells, 10 4 m 3 ;

[0175] k represents the number of fracturing stages in a horizontal well.

[0176] Further, step S3 establishes a volumetric fracturing production prediction model for the horizontal well based on the fracture volume monitored by microseismic monitoring throughout the horizontal well and the reservoir physical parameters where the horizontal well is located. This model yields the relationship between the fracture volume monitored by microseismic monitoring and the effective fracture volume, thereby quantitatively characterizing the fracture volume. Specifically, this includes:

[0177] Step S301, based on the basic parameters of the horizontal well and the reservoir, establishes a shale oil horizontal well volumetric fracturing production prediction model using the reservoir numerical simulation software Eclipse, specifically including:

[0178] Step S3011, Database Establishment: Obtain basic parameters of the horizontal well and basic parameters of the reservoir where the horizontal well is located. The basic parameters of the horizontal well include the length of the horizontal section, the number of fracturing sections, and the cumulative oil production in the first year. The geological parameters of the reservoir where the horizontal well is located include reservoir depth, reservoir thickness, formation pressure, reservoir fluid parameters such as water, average porosity of the entire well section, average permeability, average oil saturation, and formation temperature.

[0179] Step S3012: Based on the database established in step 3011, a horizontal well geological model is established using the reservoir numerical simulation software Eclipse, including the reservoir porosity distribution field, permeability distribution field, oil saturation distribution field, and formation pressure distribution field.

[0180] Step S3013: The fracture volume predicted in step S2 from the microseismic monitoring of the entire horizontal well section is imported into the geological model to form a production capacity prediction model.

[0181] Step S302: Using the horizontal well production prediction model from step S301, predict the cumulative oil production for the first year, and further calculate the effective fracture volume index based on the actual cumulative oil production for the first year of the horizontal well; more specifically, the formula for calculating the effective fracture volume index is as follows (XIV):

[0182]

[0183] In the formula: EI is the effective crack volume index, which is dimensionless;

[0184] Q H The actual cumulative oil production of the horizontal well in the first year, in tons;

[0185] Q P For the predicted cumulative oil production in the first year for horizontal wells, in tons;

[0186] Step S303: Based on the effective fracture volume index calculated in step S302 and the fracture volume predicted by microseismic monitoring of the entire horizontal well section in step S2, the effective fracture volume of shale oil horizontal well volumetric fracturing is quantitatively characterized. Furthermore, the formula for calculating the effective fracture volume is as follows (XV):

[0187]

[0188] In the formula: ESRV is the effective crack volume, 10 4 m 3 ;

[0189] SRV stands for microseismic monitoring of fracture volume, 10 4 m 3

[0190] SRV i For single-segment microseismic monitoring of fracture volume in horizontal wells, 10 4 m 3 ;

[0191] i represents the fracturing stage number;

[0192] k represents the number of fracturing stages.

[0193] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples of horizontal shale oil wells in the Ordos Basin, illustrating the practicality of the method.

[0194] After years of technological breakthroughs and field practice, horizontal well volumetric fracturing technology has become a key technology for the efficient development of shale oil in basins. However, due to significant differences in reservoir properties and highly complex fracture propagation patterns, the matching of volumetric fracturing parameters with reservoir conditions still faces considerable challenges. Specifically, this manifests as large differences in single-well productivity after volumetric fracturing, necessitating urgent evaluation of the fracturing effect and optimization of fracturing parameters to pursue higher productivity targets. Effective fracture volume is a crucial and primary indicator for directly evaluating the effectiveness of volumetric fracturing.

[0195] In the example, horizontal well JPH1 is located in the main development test area of ​​shale oil in the basin, with a reservoir depth of 2135m, a horizontal section length of 1980m, and a drilling rate of 64.6%. It has strong heterogeneity, which makes the evaluation of the effect of volumetric fracturing of horizontal wells face great challenges.

[0196] This embodiment provides a complete quantitative characterization method for fracture volume in shale oil horizontal well volumetric fracturing, specifically including the following steps:

[0197] Step S1: Calculate the correlation coefficients between different geological engineering factors and the fracture volume monitored by microseismic monitoring in each fractured section of the horizontal well using grey relational analysis. Based on these coefficients, determine the key factors affecting fracture volume. Specifically, this includes the following:

[0198] S101. Collect and organize big data from the mining area to establish a database of factors affecting the volumetric fracturing fracture volume in horizontal wells, including geomechanical parameters and volumetric fracturing stimulation parameters. The geomechanical parameters include: porosity, permeability, oil saturation, clay content, brittleness index, horizontal stress difference, and fracturing pressure of each fracturing segment in the horizontal well, as shown in Table 1. The volumetric fracturing stimulation parameters include: fracture density, fracturing flow rate, fracturing fluid volume, and proppant dosage of each fracturing segment in the horizontal well, as shown in Table 2.

[0199] Table 1 Geomechanical parameters of each fractured section in horizontal well JPH1

[0200]

[0201]

[0202] Table 2. Volumetric fracturing parameters and microseismic monitoring fracture volumes for each fracturing section in horizontal well JPH1.

[0203]

[0204] S102. Based on the geomechanical parameters and volumetric fracturing parameters of the measured fracturing section of the horizontal well, a multi-factor comprehensive evaluation matrix X is established, as shown in expression (1).

[0205]

[0206] S103. Based on the predicted fracture volume of the measured fractured section of the horizontal well, establish an evaluation reference column X0, as shown in expression (2).

[0207] X0=(133.0 229.3 146.0 290.7 252.5 310.3 199.8 240.3 211.3 192.6207.2 173.7 266.6 207.7 208.6 231.5 140.9 192.2) (2)

[0208] S103. The multi-factor comprehensive evaluation matrix and the measured microseismic fracture volume data of the fractured section were standardized using formula (Ⅲ). The correlation coefficients between different influencing parameters and the multi-factor evaluation reference column (microseismic monitoring fracture volume) were calculated using formulas (Ⅳ) and (Ⅴ). The calculation results are shown in Table 3.

[0209] Table 3. Calculation of Correlation Coefficients Between Different Influencing Factors and Crack Volume in Microseismic Monitoring

[0210]

[0211]

[0212] S104. Sort the correlation coefficient calculation results, see appendix. Figure 1 A value greater than 0.5 is defined as a key factor affecting the volume of fractures in horizontal well volumetric fracturing. The factors, ordered from largest to smallest, are fracturing fluid volume, fracture density, brittleness index, proppant dosage, construction displacement, horizontal stress difference, fracture pressure, permeability, and clay content.

[0213] Step 2: Based on the determination of key influencing factors of fracture volume, establish a fracture volume prediction model that simultaneously considers key geological engineering factors, and calculate the fracture volume of the entire horizontal well section using microseismic monitoring. This includes the following:

[0214] S201. Based on the key influencing factors of the fracture volume determined in step S1, establish a microseismic monitoring fracture volume prediction matrix. Taking the prediction of the fractured segment S19 as an example, the expression of the microseismic monitoring fracture volume prediction matrix Y is shown in the following formula (3).

[0215]

[0216] S202. Standardize the elements of the fracture volume prediction matrix using formulas (VII) to (IX), and calculate the Euclidean distance between the predicted fracture segment S19 and the measured fracture segments S1 to S18 using formulas (XI) and (XII). Repeat the above steps using the same method to calculate the Euclidean distances between the predicted fracture segment S20, the predicted fracture segment S21, and the measured fracture segments S1 to S18 respectively. The results are shown in Table 4. Figure 2 , Figure 3 and Figure 4 As shown.

[0217] Table 4. Calculation of Euclidean distance between predicted fracturing segments S19-S21 and measured fracturing segments S1-S18

[0218]

[0219]

[0220] S203. According to Table 4, the predicted Euclidean distances between fracturing segments S19–S21 and the measured fracturing segments S1–S18 show that the predicted fracturing segments S19–S21 have the smallest Euclidean distances to the measured fracturing segments S8, S5, and S10, respectively, indicating similar microseismic monitoring fracture volumes. The predicted microseismic monitoring fracture volumes for S19–S21 are 240.3 × 10⁻⁶, respectively. 4 m 3 252.2×10 4 m 3 207.7×10 4 m 3 .

[0221] S204. Based on the predicted fracture volume in step S203 and the measured fracture volume in Table 2, the total microseismic fracture volume of the horizontal well is calculated using formula (XIII) to be 4534.5 × 10⁻⁶. 4 m 3 .

[0222] Step 3: Based on the fracture volume monitored by microseismic monitoring throughout the horizontal well and the reservoir properties of the horizontal well, establish a volumetric fracturing productivity prediction model for the horizontal well. Obtain the relationship between the microseismic monitored fracture volume and the effective fracture volume, and then quantitatively characterize the effective fracture volume. Specifically, this includes the following:

[0223] S301. Basic data collection: Geological parameters of the reservoir where the horizontal well is located include reservoir depth, reservoir thickness, formation pressure, reservoir fluid parameters, average porosity, average permeability, average oil saturation, and formation temperature of the entire horizontal well section. Basic parameters of the horizontal well include horizontal section length, number of fracturing sections, and cumulative oil production in the first year, as shown in Table 5.

[0224] Table 5. Basic geological parameters of the reservoir where horizontal well JPH1 is located.

[0225] parameter numerical values parameter numerical values Reservoir depth (m) 2135 Average oil saturation (%) 55.2 Reservoir thickness (m) 14.2 Formation crude oil volume factor ( / ) 1.30 Formation pressure (MPa) 19.7 Crude oil viscosity (mPa.s) 1.50 Formation temperature (°C) 68.2 Horizontal segment length (m) 1980 Average porosity (%) 12.1 Number of fracturing stages (stages) 21 Average permeability (mD) 0.13 Cumulative oil production in Year 1 (t) 3560

[0226] S302. Based on the basic parameters of the reservoir where the horizontal well is located, establish a geological model of the horizontal well using the reservoir numerical simulation software Eclipse, including the reservoir porosity distribution field, permeability distribution field, oil saturation distribution field, formation pressure distribution field, etc.

[0227] S303. Import the fracture volume predicted in step 2 from the microseismic monitoring of the entire horizontal well section into the horizontal well geological model to form a production capacity prediction model.

[0228] S304. Using the horizontal well production capacity prediction model, the cumulative oil production in the first year is predicted to be 13,692 t. Further, combined with the cumulative oil production of horizontal wells in the first year of Table 5 (3,560 t), the effective fracture control volume index of 0.26 is calculated using formula (XIV).

[0229] S305. Based on the effective fracture volume index of 0.26 calculated in step S304 and the predicted fracture volume of the entire horizontal well section from microseismic monitoring in step 2, the fracture volume is 4534.5 × 10⁻⁶. 4 m 3 The effective fracture volume of shale oil horizontal well volumetric fracturing, quantitatively characterized by formula (XV), is 1088.3 × 10⁻⁶. 4 m 3 .

[0230] 3. In summary, this invention first utilizes grey relational analysis to calculate the correlation coefficients between different geological engineering factors and the fracture volumes monitored by microseismic monitoring in each fractured section of a horizontal well, thus identifying the key factors affecting fracture volume. Based on this, a fracture volume prediction model considering key geological engineering factors is established to calculate the fracture volume monitored by microseismic monitoring throughout the horizontal well. Finally, based on fracture volume and reservoir physical parameters, a horizontal well volumetric fracturing productivity prediction model is established, deriving the relationship between the microseismic monitored fracture volume and the effective fracture volume, thereby achieving the goal of quantitatively characterizing the fracture volume of shale oil horizontal wells. This method can be scaled up and applied to the same shale oil block without requiring extensive microseismic monitoring at the mine, significantly saving testing costs. This method is also applicable to predicting the effective fracture volume of horizontal wells in other similar reservoirs.

[0231] The present invention has been specifically described above through embodiments. It should be noted that these embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way, nor are they limited to the forms disclosed herein, and should not be construed as excluding other embodiments. Modifications and simple variations made by those skilled in the art that do not depart from the technical concept and scope of the present invention are all within the protection scope of the present invention.

Claims

1. A method for quantitatively characterizing the volume of fractures in a shale oil horizontal well via volumetric fracturing, characterized in that, Includes the following steps: S1. The correlation coefficient between different geological engineering factors and the fracture volume of each fractured section of the horizontal well was calculated using the grey relational analysis method. Based on the size of the correlation coefficient, the key influencing factors of fracture volume were determined. S2, based on the determination of key influencing factors of fracture volume, establishes a fracture volume prediction model that simultaneously considers key geological engineering factors, and calculates the fracture volume of the entire horizontal well section using microseismic monitoring, specifically including: Step 201: Based on the key influencing factors of crack volume determined in step S1, establish a crack volume prediction matrix. Step 202: Based on the establishment of the fracture volume prediction matrix, the elements of the microseismic monitoring fracture volume prediction matrix are further standardized. Step 203: Based on the standardized microseismic monitoring fracture volume prediction matrix elements, calculate the Euclidean distance between the predicted fracture segment and the measured fracture segment, and normalize it. Step 204: Based on the Euclidean distance calculation results between the predicted fracturing section and the measured fracturing section obtained in step S203, the microseismic monitoring fracture volume of the measured fracturing section corresponding to the minimum Euclidean distance is assigned to the predicted fracturing section, thereby obtaining the microseismic monitoring fracture volume of the entire horizontal well section. S3. Based on the fracture volume monitored by microseismic monitoring throughout the horizontal well and the reservoir properties of the horizontal well, a volumetric fracturing production prediction model for the horizontal well is established. The relationship between the fracture volume monitored by microseismic monitoring and the effective fracture volume is obtained, and then the fracture volume is quantitatively characterized.

2. The method for quantitatively characterizing the volume of fractures in shale oil horizontal well volumetric fracturing as described in claim 1, characterized in that, Step S1 uses grey relational analysis to calculate the correlation coefficient between different geological engineering factors and the fracture volume of each fractured section of the horizontal well in microseismic monitoring. Based on the magnitude of the correlation coefficient, the key influencing factors of fracture volume are determined, specifically including: Step S101: Collect and organize big data from the mine, and establish a database of factors affecting the volumetric fracturing fracture volume of horizontal wells, including geomechanical parameters and volumetric fracturing stimulation parameters. Step S102: Collect microseismic monitoring fracture volume data of the predicted horizontal well fractured section; Step S103, Establishment of Multi-Factor Comprehensive Evaluation Matrix: Based on the fracture volume influencing factor database obtained in step S101, a multi-factor comprehensive evaluation matrix is ​​established, wherein the elements of the multi-factor comprehensive evaluation matrix are the predicted geomechanical parameters and volumetric fracturing parameters of the measured fractured section of the horizontal well. The expression of the multi-factor comprehensive evaluation matrix is ​​as follows (I): （Ⅰ） In the formula: A multi-factor comprehensive evaluation matrix; These are the elements of a multi-factor comprehensive evaluation matrix; j=1,2,……n; This refers to the number of volumetric fracturing stages in a horizontal well. The number of factors affecting crack volume; Step S104, Establishment of Multi-Factor Comprehensive Evaluation Reference Column: Based on the predicted fracture volume of the measured fractured section of the horizontal well, an evaluation reference column is established. The expression for the multi-factor comprehensive evaluation reference column is as follows (II): （Ⅱ） In the formula: This is a reference column for multi-factor comprehensive evaluation; T is the transpose symbol; Step S105, Standardization: The multi-factor comprehensive evaluation matrix X and the multi-factor comprehensive evaluation reference column X0 are standardized using the maximum value method. The standardization formula is as follows (III): （Ⅲ） In the formula: Standardized elements for multi-factor comprehensive evaluation matrix or evaluation reference columns; These are elements of a multi-factor comprehensive evaluation matrix or elements of an evaluation reference column; It represents the maximum value in the parameter set of the j-th influencing factor; Step S106, Correlation coefficient calculation: Calculate the correlation coefficient between different influencing factors and the fracture volume of horizontal well volumetric fracturing microseismic monitoring based on the standardized database; Step S107, Determination of key influencing factors: Sort the correlation coefficient calculation results from step S106, and define a value greater than 0.5 as a key influencing factor affecting the volume of fractures in horizontal well volumetric fracturing.

3. The method for quantitatively characterizing the volume of fractures in shale oil horizontal well volumetric fracturing as described in claim 2, characterized in that, In step S101, the geomechanical parameters include: porosity, permeability, oil saturation, clay content, brittleness index, horizontal stress difference, and fracturing pressure of each fracturing section of the horizontal well; the volumetric fracturing parameters include: fracture density, fracturing flow rate, fracturing fluid volume, and proppant usage of each fracturing section of the horizontal well.

4. The method for quantitatively characterizing the volume of fractures in shale oil horizontal well volumetric fracturing as described in claim 2, characterized in that, In step S106, the correlation coefficient calculation includes: Step S1061: Calculate the standard deviation based on the standardized data of the multi-factor comprehensive evaluation matrix and the standardized data of the evaluation reference column. The calculation formula is as follows (IV): （Ⅳ） In the formula: The standard deviation between the standardized data of the multi-factor comprehensive evaluation matrix and the standardized data of the evaluation reference column; To evaluate the standardized data of the reference column elements; This refers to the standardized data of the multi-factor comprehensive evaluation matrix elements; Step S1062: Based on the standard deviation calculated in step S1061, further calculate the correlation coefficient between different influencing factors and the volume of cracks in microseismic monitoring. The calculation formula is as follows (V): （Ⅴ） In the formula: The correlation coefficient; The resolution coefficient is set to 0.

5. The standard deviation between the standardized data of the multi-factor comprehensive evaluation matrix and the standardized data of the evaluation reference column; m represents the number of volumetric fracturing stages in a horizontal well.

5. The method for quantitatively characterizing the volume of fractures in a shale oil horizontal well volumetric fracturing as described in claim 1, characterized in that, In step S201, the expression for the crack volume prediction matrix is ​​as follows (VI): （Ⅵ） In the formula: Y is the crack volume prediction matrix; For the elements of the stitched mesh wave and prediction matrix, ; This refers to the number of fracturing stages in a horizontal well, where... i =0 indicates the predicted fracturing segment number. i =2~ h Number the measured fracturing sections; The number of parameters affecting crack volume.

6. The method for quantitatively characterizing the volume of fractures in a shale oil horizontal well volumetric fracturing as described in claim 1, characterized in that, In step S202, the standardization process for the elements of the prediction matrix includes: Step 2021: Based on the elements of the crack volume evaluation matrix, calculate the mean and standard deviation of the matrix column vectors, using the following formulas (Ⅶ) and (Ⅷ): （Ⅶ） （Ⅷ） In the formula: The mean of the column vectors of the crack volume prediction matrix; n represents the number of fracturing segments that have been measured; Elements of the crack volume prediction matrix; The standard deviation of the column vectors of the crack volume prediction matrix; Step 2022: Based on the mean and standard deviation of the column vectors of the crack volume prediction matrix elements, the prediction matrix elements are standardized using the following formula (IX): （Ⅸ） In the formula: These are the standardized elements of the crack volume prediction matrix; Step 2023: Standardize the elements of the microseismic monitoring fracture volume prediction matrix using formulas (VII) to (IX) to obtain the standardized matrix for microseismic monitoring fracture volume prediction, whose expression is as follows: (X): （Ⅹ） In the formula: This is a standardized matrix for predicting crack volume in microseismic monitoring.

7. The method for quantitatively characterizing the volume of fractures in a shale oil horizontal well volumetric fracturing as described in claim 1, characterized in that, In step S203, the Euclidean distance between the predicted fracturing section and the measured fracturing section is calculated using formula (XI), and the Euclidean distance between the predicted fracturing section and the measured fracturing section is normalized to the 0~1 interval using formula (XII): （Ⅺ） （Ⅻ） In the formula: To predict the Euclidean distance between the fractured section and the measured fractured section, dimensionless; To predict the normalized Euclidean distance between the fractured section and the measured fractured section, which is dimensionless; Standardized elements for the crack volume prediction matrix; These are the standardized elements of the crack volume prediction matrix; In step S204, the formula for calculating the fracture volume of the entire horizontal well section using microseismic monitoring is as follows (XIII): (XIII) In the formula: For microseismic monitoring of fracture volume throughout the horizontal well section, 10 4 m 3 ; For single-segment microseismic monitoring of fracture volume in horizontal wells, 10 4 m 3 ; k represents the number of fracturing stages in a horizontal well.

8. The method for quantitative characterizing the volume of fractures in a shale oil horizontal well volumetric fracturing as described in claim 1, characterized in that, S3 establishes a volumetric fracturing productivity prediction model for horizontal wells based on the fracture volume monitored by microseismic monitoring throughout the horizontal well and the reservoir physical parameters where the horizontal well is located. This model derives the relationship between the monitored fracture volume and the effective fracture volume, and then quantitatively characterizes the fracture volume, specifically including: Step S301: Based on the basic parameters of the horizontal well and the reservoir, a shale oil horizontal well volumetric fracturing production capacity prediction model is established using the reservoir numerical simulation software Eclipse. Step S302: Using the horizontal well production capacity prediction model from step S301, predict the cumulative oil production in the first year, and further calculate the effective fracture volume index by combining the actual cumulative oil production in the first year of the horizontal well. Step S303: Based on the effective fracture volume index calculated in step S302 and the fracture volume predicted by microseismic monitoring of the entire horizontal well section in step S2, the effective fracture volume of shale oil horizontal well volume fracturing is quantitatively characterized.

9. The method for quantitatively characterizing the volume of fractures in a shale oil horizontal well volumetric fracturing as described in claim 8, characterized in that, In step S301, the step of establishing a shale oil horizontal well volumetric fracturing production capacity prediction model based on the basic parameters of the horizontal well and the reservoir, using the reservoir numerical simulation software Eclipse, specifically includes: Step S3011, Database Establishment: Obtain basic parameters of the horizontal well and basic parameters of the reservoir where the horizontal well is located. The basic parameters of the horizontal well include the length of the horizontal section, the number of fracturing sections, and the cumulative oil production in the first year. The geological parameters of the reservoir where the horizontal well is located include the reservoir depth, reservoir thickness, formation pressure, reservoir fluid parameters (water), average porosity of the entire well section, average permeability, average oil saturation, and formation temperature. Step S3012: Establish a horizontal well geological model using the database, including reservoir porosity distribution field, permeability distribution field, oil saturation distribution field, and formation pressure distribution field; Step S3013: The fracture volume predicted in step S2 from the microseismic monitoring of the entire horizontal well section is imported into the geological model to form a production capacity prediction model.

10. The method for quantitative characterizing the volume of fractures in a shale oil horizontal well volumetric fracturing as described in claim 8, characterized in that, In step S302, the formula for calculating the effective crack volume index is as follows (XIV): (XIV) In the formula: The effective crack volume index is dimensionless. Q A The actual cumulative oil production of the horizontal well in the first year, in tons; For the predicted cumulative oil production in the first year for horizontal wells, in tons; In step S303, the formula for calculating the effective crack volume is as follows (XV): (XV) In the formula: For the effective crack volume, 10 4 m 3 ; For single-segment microseismic monitoring of fracture volume in horizontal wells, 10 4 m 3 ; i represents the fracturing stage number; This represents the number of fracturing stages.

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