A shale oil horizontal well volume fracturing maximum recoverable reserve prediction method

Abstract

The present application provides a kind of shale oil horizontal well volume fracturing ultimate recoverable reserves prediction method, first based on the reservoir geological parameters of the horizontal well to be measured and adjacent horizontal well in the same block, establish productivity prediction model, calculate effective fracture network sweep efficiency using reservoir numerical simulation method, and establish effective fracture network sweep efficiency and maximum recoverable reserves correlation prediction chart;Secondly, the correlation coefficient between the geology and volume fracturing reconstruction parameters of the horizontal well to be measured and the fracture network sweep volume is calculated using grey relational analysis method, and the key control parameters affecting fracture network sweep volume are determined;Finally, the fracture network sweep volume prediction model coupled with key control parameters is established, the fracture network sweep volume of the horizontal well to be measured is obtained, the effective fracture network sweep efficiency is obtained, and the maximum recoverable reserves of the horizontal well to be measured is obtained using the correlation prediction chart. The method can quickly predict the maximum recoverable reserves of any horizontal well by establishing the correlation chart of effective fracture network sweep efficiency and maximum recoverable reserves.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas development, specifically relating to a method for predicting the maximum recoverable reserves of shale oil horizontal wells through volumetric fracturing. Background Technology

[0002] China's technically recoverable shale oil resources could reach 145 million tons. 8 Shale oil and gas are mainly distributed in large basins such as the Ordos Basin, Junggar Basin, Bohai Bay Basin, and Songliao Basin, with huge development potential. Horizontal well volumetric fracturing technology has enabled the commercial development of shale oil and gas.

[0003] The ultimate recoverable reserves of a single horizontal well are a direct evaluation indicator for assessing the compatibility of volumetric fracturing technology with unconventional shale oil reservoirs, and a crucial foundation for optimizing volumetric fracturing process parameters, matching fracturing equipment, and building production capacity. However, numerous factors influence the maximum recoverable reserves, and these factors exhibit complex nonlinear relationships, affecting the evaluation results to varying degrees. Currently, the main evaluation methods include the following:

[0004] (1) Li Fang et al. (Li Fang, Wu Juan, Jiang Xin et al. Method, apparatus, equipment and storage medium for determining EUR of shale gas wells, patent application number: CN202110002077.2) This method establishes a production prediction model for each sample gas well by fitting the first historical production data of multiple sample gas wells; predicts the shale gas production of each sample gas well in the subsequent target time period through the production prediction model of each sample gas well; determines the EUR of the sample gas well by summing the total historical production of each sample gas well with the shale gas production in the target time period; establishes a mapping relationship between the estimated cumulative production and EUR based on the estimated cumulative production and EUR of multiple sample gas wells; for any target gas well that has not reached the boundary flow pattern, the EUR of the target gas well is determined based on the estimated cumulative production and mapping relationship of the target gas well. This method only establishes an empirical model for single-well production prediction through production dynamic fitting, which is a commonly used prediction method. It requires the collection of a large amount of historical production data and is time-consuming.

[0005] (2) Hou Lianhua et al. (Hou Lianhua, Yu Zhichao, Luo Xia et al. Geological controlling factors of shale oil and gas final recovery - a case study of Eagle Beach Shale in the Gulf Basin, USA [J]. Petroleum Exploration and Development, 2021, 48(3):654-6664). This method first analyzes the key geological controlling factors affecting the final recoverable amount, and then uses a normalization method to establish a prediction model of the final recoverable amount and key geological parameters of a single well, thereby achieving the prediction of the final recovery amount of a single shale gas well. This method only analyzes the influence of key geological parameters on the final recovery amount of horizontal wells, and lacks consideration of the influence of volumetric fracturing engineering parameters. The analysis and consideration of factors are not comprehensive, making it difficult to achieve accurate prediction of the final recoverable amount of shale oil and gas. Summary of the Invention

[0006] To overcome the shortcomings of existing technologies, this invention provides a method for predicting the maximum recoverable reserves of shale oil horizontal wells through volumetric fracturing.

[0007] To achieve the above technical objectives, the present invention provides the following technical solutions:

[0008] A method for predicting the maximum recoverable reserves of shale oil horizontal wells via volumetric fracturing includes the following steps:

[0009] (4) Based on the geological parameters of the reservoir where the horizontal well to be tested and the adjacent horizontal well in the same block are located, a production capacity prediction model is established. Combined with the reservoir numerical simulation method, the effective fracture network sweep efficiency is calculated by using the production capacity comparison method, and a correlation prediction chart between the effective fracture network sweep efficiency and the maximum recoverable reserves is established.

[0010] (5) Use grey relational analysis to calculate the correlation coefficients between the geological parameters and volumetric fracturing parameters of each fracturing section of the horizontal well to be tested and the fracture network swept volume, and identify the key control parameters that affect the fracture network swept volume.

[0011] (6) Based on the determination of the key control parameters of the fracture network sweep volume, a fracture network sweep volume prediction model coupled with the key control parameters is established to predict the fracture network sweep volume of the horizontal well to be tested, and the effective fracture network sweep coefficient is obtained. Then, the maximum recoverable reserves of the horizontal well to be tested are predicted using the correlation prediction chart of step (1).

[0012] Further, step (1) establishes a production capacity prediction model based on the reservoir geological parameters of the horizontal well to be tested and adjacent horizontal wells in the same block as the horizontal well to be tested. Combining the reservoir numerical simulation method, the effective fracture network sweep efficiency is calculated using the production capacity comparison method, and a correlation prediction chart between the effective fracture network sweep efficiency and the maximum recoverable reserves is established. Specifically, this includes:

[0013] Step (101), Basic Database Establishment: This includes obtaining the basic parameters of adjacent horizontal wells and the horizontal well to be measured within the same block.

[0014] Step (102): Based on the basic parameters of adjacent horizontal wells in the same block obtained in step (101), a geological model of the horizontal well is established using the reservoir numerical simulation software Eclipse. The single-segment fracture network swept volume in the basic parameters of adjacent horizontal wells in the same block obtained in step (101) is imported into the geological model of the horizontal well to obtain the production capacity prediction model.

[0015] Step (103): Using the production capacity prediction model established in step (102), predict the cumulative oil production of adjacent horizontal wells in the same block in the first year, and combine the cumulative oil production in the first year in the basic parameters of adjacent horizontal wells in the same block obtained in step (101), and calculate the difference coefficient by using the production capacity comparison method.

[0016] Step (104): Based on the difference coefficient obtained in step (103), and combined with the fracture network sweep volume of the entire well section in the basic parameters of adjacent horizontal wells in the same block obtained in step (101), calculate the effective fracture network sweep volume.

[0017] Step (105): Calculate the reservoir volume controlled by the horizontal well using the basic parameters of the adjacent horizontal wells in the same block obtained in step (101). At the same time, combine the effective fracture network sweep volume obtained in step (104) to calculate the effective fracture network sweep coefficient of the adjacent horizontal wells on the same platform.

[0018] Step (106): Based on the maximum recoverable reserves of the entire well section in the basic parameters of adjacent horizontal wells in the same block obtained in step (101), establish a correlation prediction chart between the maximum recoverable reserves of a single well and the effective fracture network sweep efficiency obtained in step (105), and obtain the prediction fitting formula.

[0019] Furthermore, step (101), establishing the basic database, specifically includes:

[0020] Step (1011) obtains the basic parameters of adjacent horizontal wells in the same block, including two parts. The first part consists of parameters used to calculate the difference coefficient, including: reservoir depth, reservoir thickness, reservoir pressure, reservoir temperature, reservoir fluid parameters, average porosity, average permeability, average oil saturation of the reservoir, horizontal section length, number of fractured sections, cumulative oil production in the first year, and data of each fractured section of a single well monitored by microseismic monitoring. The data of each fractured section of a single well monitored by microseismic monitoring includes single-section fracture length, single-section fracture width, single-section fracture height, and single-section fracture network swept volume. The second part consists of parameters used for the effective fracture network swept coefficient, including: horizontal section length, reservoir thickness, horizontal well spacing, fracture network swept volume of the entire well section, and maximum recoverable reserves of the entire well section.

[0021] Step (1012) Obtain the basic parameters of the horizontal well to be tested, including the geological parameters and volumetric fracturing parameters of each fracturing section. The geological parameters include: porosity, permeability, oil saturation, clay content, brittleness index, horizontal stress difference, and fracturing pressure. The volumetric fracturing parameters include: fracture density, construction flow rate, fracturing fluid volume and sand volume of a single section.

[0022] Furthermore, in step (103), the formula for calculating the difference coefficient is as follows:

[0023]

[0024] In the formula: FI is the difference coefficient, which has no dimension;

[0025] Q H The cumulative oil production in the first year of the historical production of adjacent horizontal wells in the same block, in tons;

[0026] Q P The first-year cumulative oil production (t) is the predicted production of adjacent horizontal wells in the same block.

[0027] Furthermore, in step (104), the formula for calculating the effective ripple volume of the seam mesh is as follows:

[0028] ESRV = FI·SRV

[0029] In the formula: ESRV is the effective suture mesh sweep volume, 10 4 m 3 ;

[0030] FI is the coefficient of difference, which is dimensionless;

[0031] SRV is the sweep volume of the entire well section with fractured network, 10 4 m 3 .

[0032] Furthermore, in step (105),

[0033] The formula for calculating the reservoir volume controlled by a horizontal well is: Vr = Lhd;

[0034] The formula for calculating the effective fracture network sweep efficiency of adjacent horizontal wells on the same platform is:

[0035]

[0036] In the formula: V r To control reservoir volume in horizontal wells, m 3 ;

[0037] L is the length of the horizontal segment, in meters;

[0038] h is the reservoir thickness, in meters;

[0039] d represents the horizontal well spacing, in meters.

[0040] E represents the effective mesh sweep efficiency, %;

[0041] ESRV is the effective suture mesh sweep volume, 10 4 m 3 .

[0042] Furthermore, in step (106), the fitting formula is:

[0043] EUR = 2.0656ln(E) - 6.4729

[0044] In the formula, EUR represents the maximum recoverable reserves of a single well, 10 4 t;

[0045] E is the effective mesh sweep efficiency, %.

[0046] Further, step (2) uses grey relational analysis to calculate the correlation coefficients between the geological parameters and volumetric fracturing parameters of each fracturing section of the horizontal well to be tested and the fracture network swept volume of the horizontal well to be tested as monitored by microseismic analysis, thereby clarifying the key control parameters affecting the fracture network swept volume, specifically including,

[0047] Step (201), Establishment of Multi-Factor Comprehensive Evaluation Matrix: Based on the basic parameters of the horizontal well to be predicted obtained in step (101), establish a multi-factor comprehensive evaluation matrix X, where the elements of the multi-factor comprehensive evaluation matrix are the geological parameters and volumetric fracturing stimulation parameters of the measured fracturing section of the horizontal well to be tested. The expression of the multi-factor comprehensive evaluation matrix X is as follows:

[0048]

[0049] In the formula: X is a multi-factor comprehensive evaluation matrix;

[0050] X i (j) represents the elements of the multi-factor comprehensive evaluation matrix;

[0051] m represents the number of volumetric fracturing stages in the horizontal well to be tested;

[0052] n represents the number of factors affecting the swept volume of the mesh;

[0053] Step (202), Evaluation reference column establishment: The evaluation reference column X0 is established based on the fracture network swept volume of the measured fractured section of the horizontal well to be tested obtained from microseismic monitoring;

[0054] X0 = [X1(0), ... X i (0), ...X m (0)] T i = 1, 2, ..., m

[0055] In the formula: X0 is the evaluation reference column; T is the transpose symbol;

[0056] Step (203), Standardization: The multi-factor comprehensive evaluation matrix X and the evaluation reference column X0 are standardized using the maximum value method. The standardization formula is as follows:

[0057]

[0058] In the formula: Standardized elements for multi-factor comprehensive evaluation matrix or evaluation reference columns;

[0059] Xi (j) represents elements of a multi-factor comprehensive evaluation matrix or evaluation reference column;

[0060] (X i (j)) max It represents the maximum value in the parameter set of the j-th influencing factor;

[0061] Step (204), Correlation coefficient calculation: Based on step (203), calculate the correlation coefficient between different influencing factors and the fracture network swept volume of the entire well section of the microseismic monitoring of volumetric fracturing of adjacent horizontal wells in the same block;

[0062] Step (205), Determination of key control parameters: Sort the correlation coefficient calculation results of step (204), and define the value of the coefficient as greater than 0.5 as the key control parameter affecting the volumetric pressure fracture network wave and volume of the horizontal well to be tested.

[0063] Furthermore, step (204), the specific content of calculating the correlation coefficient, includes:

[0064] Step (2041): 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:

[0065]

[0066] 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;

[0067] To evaluate the standardized data of the reference column elements;

[0068] This refers to the standardized data of the multi-factor comprehensive evaluation matrix elements;

[0069] Step (2042): Based on the standard deviation calculated in step (2041), further calculate the correlation coefficient between different influencing factors and the swept volume of the seam mesh. The calculation formula is as follows:

[0070]

[0071] In the formula: r j ρ is the correlation coefficient; ρ is the resolution coefficient, usually taken as 0.5.

[0072] m represents the number of volumetric fracturing stages in the horizontal well to be tested.

[0073] Further, in step (2), based on the determination of the key control parameters of the fracture network sweep volume, a fracture network sweep volume prediction model coupled with the key control parameters is established to predict the fracture network sweep volume of the horizontal well, obtain the effective fracture network sweep coefficient, and then use the correlation prediction chart to predict the maximum recoverable reserves of the horizontal well. The fracture network sweep volume prediction model includes prediction matrix establishment, prediction matrix standardization, and similarity factor calculation, specifically including…

[0074] Step (301), Establishment of the suture mesh sweep volume prediction matrix: Based on the key control parameters affecting the suture mesh sweep volume determined in step (2), establish the suture mesh sweep volume prediction matrix. The matrix formula is as follows:

[0075]

[0076] In the formula: B is the seam mesh sweep volume prediction matrix;

[0077] B i (j) represents the elements of the seam mesh sweep ripple prediction matrix, i = 0, 1, 2, ..., h; j = 1, 2, ..., k;

[0078] 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;

[0079] k represents the number of key parameters affecting the swept volume of the seam mesh;

[0080] Step (302), prediction matrix standardization: Based on the distribution range of the key control parameters affecting the seam mesh wave and volume, the prediction matrix of the seam mesh wave and volume is standardized.

[0081] Step (303), similarity factor calculation: Based on the normalized matrix of fracture network sweep volume prediction, calculate the similarity factor between the measured fractured section and the predicted fractured section of the horizontal well. The calculation formula is as follows:

[0082]

[0083] Where: ID i The similarity factor between the measured segment and the predicted segment;

[0084] Standardized elements for the seam mesh wave volume prediction matrix;

[0085] Standardized elements for predicting fracturing segments;

[0086] Step (304) sorts the similarity factors between the predicted segment and the measured segment, assigns the microseismic monitoring fracture network swept volume of the measured segment corresponding to the largest similarity factor to the predicted segment, and thus obtains the predicted microseismic monitoring fracture network swept volume of the entire horizontal well section. The calculation formula is as follows:

[0087]

[0088] In the formula: k is the number of fracturing stages in a horizontal well;

[0089] Step (305): Based on step (304), the effective fracture network sweep volume, the reservoir volume controlled by the horizontal well, and the effective fracture network sweep coefficient of adjacent horizontal wells on the same platform are calculated quantitatively using the formulas in step (1). The maximum recoverable reserves of the horizontal well are predicted using the prediction fitting formula in step (1).

[0090] Furthermore, step (302), the specific content of prediction matrix standardization, includes:

[0091] Step (3021): Based on the distribution range of key parameters affecting the wave and volume of the seam mesh, the elements of the prediction matrix are standardized. The calculation formula is as follows:

[0092]

[0093]

[0094] In the formula: The mean of each parameter set in the column vector of the seam mesh wave and volume prediction matrix;

[0095] B i (j) represents the elements of the seam mesh sweep ripple prediction matrix, i = 0, 1, 2, ..., h; j = 1, 2, ..., k;

[0096] Standardized elements for the seam mesh wave volume prediction matrix;

[0097] k represents the number of key parameters affecting the swept volume of the seam mesh;

[0098] Step (3021) involves standardizing the seam mesh sweep volume prediction matrix using steps (Ⅰ) and (Ⅱ). The formula for the standardized matrix is ​​as follows:

[0099]

[0100] In the formula: B * The normalized matrix for predicting the wave and volume of the seam mesh.

[0101] Compared with the prior art, the present invention has the following beneficial effects:

[0102] 1. This invention proposes a novel method for predicting the maximum recoverable reserves of shale oil horizontal wells through volumetric fracturing. This method can quickly predict the maximum recoverable reserves of any horizontal well by establishing a correlation chart between the effective fracture network sweep efficiency and the maximum recoverable reserves.

[0103] 2. This invention, through quantitative calculation of similarity factors, can rapidly predict the effective fracture network swept volume affecting the maximum recoverable reserves of horizontal wells, eliminating the need for full-section microseismic monitoring of the horizontal well, thus significantly saving on field microseismic monitoring costs. It also solves the problem of inflated reservoir numerical simulation results caused by overestimating fracture volumes in microseismic monitoring. This prediction method is also applicable to predicting the maximum recoverable reserves of horizontal wells in similar unconventional reservoirs through volumetric fracturing, demonstrating broad application prospects and providing strong support for evaluating the effectiveness of horizontal well volumetric fracturing and optimizing fracturing parameters.

[0104] 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

[0105] 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.

[0106] Figure 1 This is a graph showing the correlation between the maximum recoverable reserves and the effective sweep efficiency of the welded mesh in this invention.

[0107] Figure 2 This is a bar chart of the St19 similarity factor for the volumetric fracturing prediction section of shale oil horizontal wells in this invention;

[0108] Figure 3 This is a bar chart of the St20 similarity factor for the volumetric fracturing prediction section of shale oil horizontal wells in this invention;

[0109] Figure 4 This is a bar chart of the similarity factor of St21 in the volumetric fracturing prediction section of shale oil horizontal wells according to the present invention.

[0110] 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

[0111] 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.

[0112] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0113] A method for predicting the maximum recoverable reserves of shale oil horizontal wells via volumetric fracturing includes the following steps:

[0114] (1) Based on the geological parameters of the reservoir where the horizontal well to be tested and the adjacent horizontal well in the same block are located, a production capacity prediction model is established. Combined with the reservoir numerical simulation method, the effective fracture network sweep efficiency is calculated by using the production capacity comparison method, and a correlation prediction chart between the effective fracture network sweep efficiency and the maximum recoverable reserves is established.

[0115] (2) The correlation coefficients between geological parameters and volumetric fracturing parameters of each fractured section of the horizontal well to be tested and the fracture network swept volume were calculated by using grey relational analysis, and the key control parameters affecting the fracture network swept volume were identified.

[0116] (3) Based on the determination of the key control parameters of the fracture network sweep volume, a fracture network sweep volume prediction model coupled with the key control parameters is established to predict the fracture network sweep volume of the horizontal well to be tested, and the effective fracture network sweep coefficient is obtained. Then, the maximum recoverable reserves of the horizontal well to be tested are predicted using the correlation prediction chart of step (1).

[0117] Further, step (1) establishes a production capacity prediction model based on the reservoir geological parameters of the horizontal well to be tested and adjacent horizontal wells in the same block as the horizontal well to be tested. Combining the reservoir numerical simulation method, the effective fracture network sweep efficiency is calculated using the production capacity comparison method, and a correlation prediction chart between the effective fracture network sweep efficiency and the maximum recoverable reserves is established. Specifically, this includes:

[0118] Step (101), Basic Database Establishment: This includes obtaining the basic parameters of adjacent horizontal wells and the horizontal well to be measured in the same block. More specifically, it involves:

[0119] Step (1011) obtains the basic parameters of adjacent horizontal wells in the same block, including two parts. The first part consists of parameters used to calculate the difference coefficient, including: reservoir depth, reservoir thickness, reservoir pressure, reservoir temperature, reservoir fluid parameters, average porosity, average permeability, average oil saturation of the reservoir, horizontal section length, number of fractured sections, cumulative oil production in the first year, and data of each fractured section of a single well monitored by microseismic monitoring. The data of each fractured section of a single well monitored by microseismic monitoring includes single-section fracture length, single-section fracture width, single-section fracture height, and single-section fracture network swept volume. The second part consists of parameters used for the effective fracture network swept coefficient, including: horizontal section length, reservoir thickness, horizontal well spacing, fracture network swept volume of the entire well section, and maximum recoverable reserves of the entire well section.

[0120] Step (1012) Obtain the basic parameters of the horizontal well to be tested, including the geological parameters and volumetric fracturing parameters of each fracturing section. The geological parameters include: porosity, permeability, oil saturation, clay content, brittleness index, horizontal stress difference, and fracturing pressure. The volumetric fracturing parameters include: fracture density, construction flow rate, fracturing fluid volume and sand volume of a single section.

[0121] Step (102): Based on the basic parameters of adjacent horizontal wells in the same block obtained in step (101), a geological model of the horizontal well is established using the reservoir numerical simulation software Eclipse. The single-segment fracture network swept volume in the basic parameters of adjacent horizontal wells in the same block obtained in step (101) is imported into the geological model of the horizontal well to obtain the production capacity prediction model.

[0122] Step (103): Using the production capacity prediction model established in step (102), predict the cumulative oil production of adjacent horizontal wells in the same block in the first year, and combine the cumulative oil production in the first year in the basic parameters of adjacent horizontal wells in the same block obtained in step (101), use the production capacity comparison method to calculate the difference coefficient using formula (1).

[0123]

[0124] In the formula: FI is the difference coefficient, which has no dimension;

[0125] Q H The cumulative oil production in the first year of the historical production of adjacent horizontal wells in the same block, in tons;

[0126] Q P The first-year cumulative oil production (t) is the predicted total oil production of adjacent horizontal wells in the same block.

[0127] Step (104): Based on the difference coefficient obtained in step (103), and combined with the fracture network sweep volume of the entire well section obtained in the basic parameters of adjacent horizontal wells in the same block in step (101), the effective fracture network sweep volume is calculated using formula (2).

[0128] ESRV = FI·SRV (2)

[0129] In the formula: ESRV is the effective suture mesh sweep volume, 10 4 m 3 ;

[0130] FI is the coefficient of difference, which is dimensionless;

[0131] SRV is the sweep volume of the entire well section with fractured network, 10 4 m 3 ;

[0132] Step (105): Using the basic parameters of adjacent horizontal wells in the same block obtained in step (101), the reservoir volume controlled by the horizontal well is calculated using formula (3). Simultaneously, combining the effective fracture network sweep volume obtained in step (104), the effective fracture network sweep coefficient of adjacent horizontal wells on the same platform is calculated using formula (4). Specifically,

[0133] Vr=Lhd (3)

[0134]

[0135] In the formula: V r To control reservoir volume in horizontal wells, m 3 ;

[0136] L is the length of the horizontal segment, in meters;

[0137] h is the reservoir thickness, in meters;

[0138] d represents the horizontal well spacing, in meters.

[0139] E represents the effective mesh sweep efficiency, %;

[0140] ESRV is the effective suture mesh sweep volume, 10 4 m 3 ;

[0141] Step (106): Based on the maximum recoverable reserves of the entire well section in the basic parameters of adjacent horizontal wells in the same block obtained in step (101), establish a correlation prediction chart between the maximum recoverable reserves of a single well and the effective fracture network sweep efficiency obtained in step (105), and obtain the prediction fitting formula: EUR=2.0656ln(E)-6.4729;

[0142] In the formula, EUR represents the maximum recoverable reserves of a single well, 10 4 t;

[0143] E is the effective mesh sweep efficiency, %.

[0144] Further, step (2) uses grey relational analysis to calculate the correlation coefficients between the geological parameters and volumetric fracturing parameters of each fracturing section of the horizontal well to be tested and the fracture network swept volume of the horizontal well to be tested as monitored by microseismic analysis, thereby clarifying the key control parameters affecting the fracture network swept volume, specifically including,

[0145] Step (201), Establishment of multi-factor comprehensive evaluation matrix: Based on the basic parameters of the horizontal well to be predicted obtained in step (101), establish a multi-factor comprehensive evaluation matrix X, where the elements of the multi-factor comprehensive evaluation matrix are the geological parameters and volumetric fracturing stimulation parameters of the measured fracturing section of the horizontal well to be tested. The expression of the multi-factor comprehensive evaluation matrix X is as follows (5):

[0146]

[0147] In the formula: X is a multi-factor comprehensive evaluation matrix;

[0148] X i (j) represents the elements of the multi-factor comprehensive evaluation matrix;

[0149] m represents the number of volumetric fracturing stages in the horizontal well to be tested;

[0150] n represents the number of factors affecting the swept volume of the mesh;

[0151] Step (202), Evaluation reference column establishment: Based on the fracture network swept volume of the measured fractured section of the horizontal well to be tested obtained from microseismic monitoring, the evaluation reference column X0 is established as follows (6);

[0152] X0 = [X1(0), ... X i (0), ...X m (0)] T i = 1, 2, ..., m (6)

[0153] In the formula: X0 is the evaluation reference column; T is the transpose symbol;

[0154] Step (203), standardization: The multi-factor comprehensive evaluation matrix X and the evaluation reference column X0 are standardized using the maximum value method. The standardization formula is as follows (7):

[0155]

[0156] In the formula: Standardized elements for multi-factor comprehensive evaluation matrix or evaluation reference columns;

[0157] X i (j) represents elements of a multi-factor comprehensive evaluation matrix or evaluation reference column;

[0158] (X i (j)) maxIt represents the maximum value in the parameter set of the j-th influencing factor;

[0159] Step (204), Correlation Coefficient Calculation: Based on step (203), calculate the correlation coefficient between different influencing factors and the fracture network swept volume of the entire well section monitored by microseismic fracturing in adjacent horizontal wells within the same block; specific content includes,

[0160] Step (2041): 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 (8):

[0161]

[0162] 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;

[0163] To evaluate the standardized data of the reference column elements;

[0164] This refers to the standardized data of the multi-factor comprehensive evaluation matrix elements;

[0165] Step (2042): Based on the standard deviation calculated in step (2041), further calculate the correlation coefficient between different influencing factors and the swept volume of the seam mesh. The calculation formula is as follows (9):

[0166]

[0167] In the formula: r j ρ is the correlation coefficient; ρ is the resolution coefficient, usually taken as 0.5.

[0168] m represents the number of volumetric fracturing stages in the horizontal well to be tested;

[0169] Step (205), Determination of key control parameters: Sort the correlation coefficient calculation results of step (204), and define the value of the coefficient as greater than 0.5 as the key control parameter affecting the volumetric pressure fracture network wave and volume of the horizontal well to be tested.

[0170] Further, in step (2), based on the determination of the key control parameters of the fracture network sweep volume, a fracture network sweep volume prediction model coupled with the key control parameters is established to predict the fracture network sweep volume of the horizontal well, obtain the effective fracture network sweep coefficient, and then use the correlation prediction chart to predict the maximum recoverable reserves of the horizontal well. The fracture network sweep volume prediction model includes prediction matrix establishment, prediction matrix standardization, and similarity factor calculation, specifically including…

[0171] Step (301), Establishment of the suture mesh sweep volume prediction matrix: Based on the key control parameters affecting the suture mesh sweep volume determined in step (2), establish the suture mesh sweep volume prediction matrix. The matrix formula is as follows (10):

[0172]

[0173] In the formula: B is the seam mesh sweep volume prediction matrix;

[0174] B i (j) represents the elements of the seam mesh sweep ripple prediction matrix, i = 0, 1, 2, ..., h; j = 1, 2, ..., k;

[0175] 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;

[0176] k represents the number of key parameters affecting the swept volume of the seam mesh;

[0177] Step (302), prediction matrix standardization: Based on the distribution range of key control parameters affecting the seam-net sweep volume, the seam-net sweep volume prediction matrix is ​​standardized; specifically, this includes...

[0178] Step (3021): Based on the distribution range of key parameters affecting the volume of the seam mesh, the elements of the prediction matrix are standardized, and the calculation formulas are as follows (11) and (12):

[0179]

[0180]

[0181] In the formula: The mean of each parameter set in the column vector of the seam mesh wave and volume prediction matrix;

[0182] B i (j) represents the elements of the seam mesh sweep ripple prediction matrix, i = 0, 1, 2, ..., h; j = 1, 2, ..., k;

[0183] Standardized elements for the seam mesh wave volume prediction matrix;

[0184] k represents the number of key parameters affecting the swept volume of the seam mesh;

[0185] Step (3021) involves standardizing the seam mesh sweep volume prediction matrix using (11) and (12). The standardized matrix formula is as follows (13):

[0186]

[0187] In the formula: B *Normalized matrix for predicting wave volume of suture mesh

[0188] Step (303), similarity factor calculation: Based on the normalized matrix of fracture network sweep volume prediction, calculate the similarity factor between the measured fractured section and the predicted fractured section of the horizontal well. The calculation formula is as follows (14):

[0189]

[0190] Where: ID i The similarity factor between the measured segment and the predicted segment;

[0191] Standardized elements for the seam mesh wave volume prediction matrix;

[0192] Standardized elements for predicting fracturing segments;

[0193] Step (304) sorts the similarity factors between the predicted segment and the measured segment, assigns the microseismic monitoring fracture network swept volume of the measured segment corresponding to the largest similarity factor to the predicted segment, and then obtains the predicted microseismic monitoring fracture network swept volume of the entire horizontal well section. The calculation formula is as follows (15):

[0194]

[0195] In the formula: k is the number of fracturing stages in a horizontal well;

[0196] Step (305): Based on step (304), the effective fracture network sweep volume, the reservoir volume controlled by the horizontal well, and the effective fracture network sweep coefficient of adjacent horizontal wells on the same platform are calculated quantitatively using the formulas in step (1). The maximum recoverable reserves of the horizontal well are then predicted using the prediction fitting formula in step (106).

[0197] 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.

[0198] Example 1:

[0199] Shale oil in the basin is characterized by low rock brittleness index, low reservoir pressure coefficient, and low single-well production. Long horizontal wells with advanced energy storage and fine-grained volumetric fracturing technology are key technologies for the efficient development of shale oil. Maximum recoverable reserves (MRRP) is a direct evaluation indicator of the effectiveness of volumetric fracturing. The case study, horizontal well H1-1, is located in the main development test area of ​​the basin's shale oil, with a reservoir depth of 1985m, a completed well depth of 4034m, a horizontal section length of 1980m, a reservoir thickness of 14.2m, and a well spacing of 500m. Its strong heterogeneity makes predicting the MRP after volumetric fracturing of horizontal wells a significant challenge.

[0200] This embodiment provides a complete method for predicting the maximum recoverable reserves of shale oil horizontal wells via volumetric fracturing, as detailed below:

[0201] 1. Based on the reservoir geological parameters of adjacent horizontal wells in the same block as the predicted horizontal wells, a production capacity prediction model is established. Combining reservoir numerical simulation methods, a production capacity comparison method is used to calculate the effective fracture network sweep efficiency, and a correlation prediction chart between the effective fracture network sweep efficiency and the maximum recoverable reserves is established. Specifically, this includes the following:

[0202] (1) Establishment of basic database: This includes two parts: basic parameters of horizontal wells on adjacent platforms in the same block and predicted horizontal wells. The specific contents are as follows:

[0203] ① The basic parameters of adjacent horizontal wells in the same block include two parts. The first part consists of parameters used to calculate the difference coefficient, including reservoir depth, reservoir thickness, reservoir pressure, reservoir temperature, reservoir fluid parameters, average porosity, average permeability, average oil saturation, horizontal section length, number of fractured sections, microseismic monitoring of the entire well section's fracture network swept volume, and first-year cumulative oil production, as shown in Tables 1 and 2. The second part consists of parameters used for the effective fracture network swept volume, including horizontal section length, reservoir thickness, well spacing, microseismic monitoring of the entire well section's fracture network swept volume, and maximum recoverable reserves, as shown in Table 3.

[0204] Table 1. Basic geological parameters of the reservoir where adjacent horizontal well H1 is located in the same block.

[0205] parameter numerical values parameter numerical values Reservoir depth (m) 1985 Average oil saturation (%) 56.2 Reservoir thickness (m) 11.2 Formation crude oil volume factor ( / ) 1.28 Reservoir pressure (MPa) 18.7 Crude oil viscosity (mPa.s) 1.52 Reservoir temperature (°C) 66.5 Horizontal segment length (m) 1543 Average porosity (%) 10.1 Number of fracturing stages (stages) 13 Average permeability (mD) 0.16 Cumulative oil production in Year 1 (t) 2078

[0206] Table 2 Microseismic monitoring data of adjacent horizontal wells H1 in the same block

[0207]

[0208]

[0209] Table 3 Basic parameters of adjacent horizontal wells H2 to H12 in the same block

[0210]

[0211] ② The predicted basic parameters for horizontal wells include geological parameters of each fractured section and volumetric fracturing parameters. Geological parameters include: porosity, permeability, oil saturation, clay content, brittleness index, horizontal stress difference, and fracturing pressure of each fractured section. Volumetric fracturing parameters include: fracture density, fracturing flow rate, fracturing fluid volume and sand volume per section, as shown in Tables 4 and 5.

[0212] Table 4. Predicted geological parameters of each fractured section in horizontal well H1-1

[0213]

[0214]

[0215] Table 5. Predicted volumetric fracturing parameters and microseismic monitoring data for each fracturing section of horizontal well H1-1.

[0216]

[0217]

[0218] (2) Based on the reservoir geological parameters of the adjacent horizontal well H1 in the same block, the geological model of the horizontal well was established using the reservoir numerical simulation software Eclipse, and its microseismic monitoring fracture network swept volume (Table 2) was imported into the water well geological model to obtain the production capacity prediction model.

[0219] (3) Using the production capacity prediction model established in step (2), the cumulative oil production of adjacent horizontal well H1 in the same block in the first year is predicted to be 9856t. Combined with the cumulative oil production of horizontal well H1 in the first year of historical production of 2078t (Table 1), the difference coefficient is calculated to be 0.21 using formula (1).

[0220] (4) Combined with the swept volume of the microseismic monitoring of the entire well section of adjacent horizontal wells in the same block (Table 3), the effective swept volume of the fracture network was calculated using formula (2), and the calculation results are shown in Table 6.

[0221] Table 6. Calculation of Effective Fracture Network Sweep Volume and Coefficient for Adjacent Horizontal Wells H2-H12 in the Same Block

[0222]

[0223]

[0224] (5) Based on the calculation results in Table 6, the effective fracture network sweep efficiency of adjacent horizontal wells on the same platform is calculated using formula (4). The calculation results are shown in Table (6).

[0225] (6) Using the data in Table (6), a correlation prediction chart was established between the maximum recoverable reserves EUR of a single well and the effective fracture network sweep efficiency E. The prediction fitting formula is EUR = 2.0656ln(E) - 6.4729, see Appendix. Figure 1 .

[0226] 2. The grey relational analysis method was used to calculate the correlation coefficients between the geological and volumetric fracturing parameters of each fracturing section in the predicted horizontal well and the fracture network swept volume, thus identifying the key control parameters affecting the fracture network swept volume. Specifically, this includes the following:

[0227] (1) Based on the geological parameters and volumetric fracturing parameters of the measured section of the predicted horizontal well volumetric fracturing (Tables 4 and 5), a multi-factor comprehensive evaluation matrix is ​​established, as shown in expression (16).

[0228]

[0229] (2) The multi-factor comprehensive evaluation matrix is ​​standardized using formula (7), and the correlation coefficients between different influencing parameters and the multi-factor evaluation reference column are calculated using formulas (8) and (9), as shown in Table 7.

[0230] Table 7. Calculation of Correlation Coefficients between Different Influencing Parameters and Sweep Volume of Microseismic Monitoring Suspension Network

[0231]

[0232] (3) The correlation coefficient calculation results are sorted as shown in Table 7. A value greater than 0.5 is defined as a key control parameter affecting the volumetric fracture network and volumetric fracture of horizontal wells, in the following order: fracturing fluid volume, fracture density, brittleness index, sand volume, construction discharge rate, horizontal stress difference, fracture pressure, permeability, and clay content.

[0233] 2. Based on the determination of key influencing parameters of fracture network sweep volume, a fracture network sweep volume prediction model coupled with key control parameters is established to predict the fracture network sweep volume of horizontal wells, obtain the effective fracture network sweep coefficient, and then use correlation prediction charts to predict the maximum recoverable reserves of horizontal wells. The fracture network sweep volume prediction model includes prediction matrix establishment, prediction matrix standardization, and similarity factor calculation, specifically including the following:

[0234] (1) Establishment of the prediction matrix for the ripple volume of the suture mesh. Based on the key control parameters affecting the ripple volume of the suture mesh determined in step 1, a prediction matrix is ​​established. Taking the unmeasured segment St19 as an example, it is shown in expression (17).

[0235]

[0236] (2) Prediction matrix standardization. The elements of the seam sweep volume matrix are standardized using formulas (11) and (12) to obtain the seam sweep volume prediction standardization matrix.

[0237] (3) Similarity factor calculation. Based on the normalized matrix of the fracture network swept volume prediction, the similarity factor between the predicted section St19 and the measured section is calculated using formula (11). The similarity factor of the measured section St8 is the largest, which is 1.0. According to the method determined in step (7) of step 3), the fracture network swept volume of St19 is the same as that of St8. Using the same method, repeat steps (1) to (3) of step 2) to calculate the similarity factors between St20 and S21 and the measured sections St1 to St18, as shown in Table 8 and Appendix. Figure 2 ~Attached Figure 4 As shown.

[0238] Table 8. Calculation of similarity factors between predicted segment St19~St21 and measured segment St1~St18

[0239]

[0240]

[0241] (4) Based on the similarity factor between the predicted segments St19~St21 and the measured segments St1~St18 in Table 8, the swept volumes of the microseismic monitoring network for predicted segments St19, St20, and St21 are 240.3×10⁻⁶. 4 m 3 252.2×10 4 m 3 207.7×10 4 m 3 The swept volume of the microseismic monitoring fracture network throughout the predicted well section was calculated using formula (15) to be 4534.7 × 10⁻⁶. 4 m 3 .

[0242] (5) Based on step (4), the effective fracture network sweep efficiency E of the horizontal well is quantitatively calculated using formulas (2) to (4) in step 1), which is 67.7%. The maximum recoverable reserves EUR of the horizontal well H1-1 are predicted to be 2.234 × 10⁻⁶ using the prediction fitting formula obtained in step (6) of step 1. 4 t.

[0243] 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. Furthermore, any 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 predicting the maximum recoverable reserves of shale oil horizontal wells through volumetric fracturing, characterized in that... Includes the following steps: (1) Based on the geological parameters of the horizontal well to be tested and the reservoirs of adjacent horizontal wells in the same block, a production capacity prediction model is established. Combining the reservoir numerical simulation method, the effective fracture network sweep efficiency is calculated using the production capacity comparison method, and a correlation prediction chart between the effective fracture network sweep efficiency and the maximum recoverable reserves is established. The effective fracture network sweep efficiency E is calculated according to the following formula: E=ESRV / V r In the formula, ESRV is the effective fracture network swept volume, which is calculated by the following formula: ESRV=FI·SRV, where FI is the difference coefficient and SRV is the swept volume of the fracture network in the whole well section; V r To control the reservoir volume in a horizontal well, the following formula is used: V r =L·h·d, where L is the length of the horizontal section, h is the reservoir thickness, and d is the horizontal well spacing; (2) The correlation coefficient between the geological parameters and volumetric fracturing parameters of each fracturing section of the horizontal well to be tested and the fracture network swept volume was calculated by using the grey relational analysis method, so as to identify the key control parameters affecting the fracture network swept volume. (3) Based on the determination of the key control parameters of the fracture network sweep volume, a fracture network sweep volume prediction model coupled with the key control parameters is established to predict the fracture network sweep volume of the horizontal well to be tested, and the effective fracture network sweep coefficient is obtained. Then, the maximum recoverable reserves of the horizontal well to be tested are predicted using the correlation prediction chart of step (1).

2. The method for predicting the maximum recoverable reserves of shale oil horizontal wells by volumetric fracturing as described in claim 1, characterized in that, Step (1) establishes a production capacity prediction model based on the reservoir geological parameters of the horizontal well to be tested and adjacent horizontal wells in the same block as the horizontal well to be tested. Combining reservoir numerical simulation methods, the effective fracture network sweep efficiency is calculated using a production capacity comparison method, and a correlation prediction chart between the effective fracture network sweep efficiency and the maximum recoverable reserves is established. Specifically, this includes: Step (101), Basic Database Establishment: This includes obtaining the basic parameters of adjacent horizontal wells and the horizontal well to be measured within the same block. Step (102): Based on the basic parameters of adjacent horizontal wells in the same block obtained in step (101), a geological model of the horizontal well is established using the reservoir numerical simulation software Eclipse. The single-segment fracture network sweep volume in the basic parameters of adjacent horizontal wells in the same block obtained in step (101) is imported into the geological model of the horizontal well to obtain the production capacity prediction model. Step (103): Using the production capacity prediction model established in step (102), predict the cumulative oil production of adjacent horizontal wells in the same block in the first year, and combine the cumulative oil production in the first year in the basic parameters of adjacent horizontal wells in the same block obtained in step (101), and calculate the difference coefficient FI by using the production capacity comparison method. Step (104): Based on the difference coefficient FI obtained in step (103), and combined with the full-section fracture network sweep volume SRV in the basic parameters of adjacent horizontal wells in the same block obtained in step (101), calculate the effective fracture network sweep volume ESRV. Step (105): Calculate the reservoir volume controlled by the horizontal well using the basic parameters of the adjacent horizontal wells in the same block obtained in step (101). At the same time, combine the effective fracture network sweep volume (ESRV) obtained in step (104) to calculate the effective fracture network sweep coefficient (E) of the adjacent horizontal wells on the same platform. Step (106): Based on the maximum recoverable reserves EUR of the entire well section in the basic parameters of adjacent horizontal wells in the same block obtained in step (101), establish a correlation prediction chart between the maximum recoverable reserves EUR of a single well and the effective fracture network sweep efficiency E obtained in step (105), and obtain the prediction fitting formula.

3. The method for predicting the maximum recoverable reserves of shale oil horizontal wells by volumetric fracturing as described in claim 2, characterized in that, Step (101), establishing the basic database, specifically includes: Step (1011) obtains the basic parameters of adjacent horizontal wells in the same block, including two parts. The first part consists of parameters used to calculate the difference coefficient, including: reservoir depth, reservoir thickness, reservoir pressure, reservoir temperature, reservoir fluid parameters, average porosity, average permeability, average oil saturation of the reservoir, horizontal section length, number of fractured sections, cumulative oil production in the first year, and data of each fractured section of a single well monitored by microseismic monitoring. The data of each fractured section of a single well monitored by microseismic monitoring includes single-section fracture length, single-section fracture width, single-section fracture height, and single-section fracture network swept volume. The second part consists of parameters used for the effective fracture network swept coefficient, including: horizontal section length, reservoir thickness, horizontal well spacing, fracture network swept volume of the entire well section, and maximum recoverable reserves of the entire well section. Step (1012) Obtain the basic parameters of the horizontal well to be tested, including the geological parameters and volumetric fracturing parameters of each fracturing section. The geological parameters include: porosity, permeability, oil saturation, clay content, brittleness index, horizontal stress difference, and fracturing pressure. The volumetric fracturing parameters include: fracture density, construction flow rate, fracturing fluid volume and sand volume of a single section.

4. The method for predicting the maximum recoverable reserves of shale oil horizontal wells by volumetric fracturing as described in claim 2, characterized in that, In step (103), the formula for calculating the difference coefficient is as follows: In the formula: The coefficient of variation is dimensionless. The cumulative oil production in the first year of the historical production of adjacent horizontal wells in the same block, in tons; The first-year cumulative oil production (t) is the predicted production of adjacent horizontal wells in the same block.

5. The method for predicting the maximum recoverable reserves of shale oil horizontal wells by volumetric fracturing as described in claim 2, characterized in that: In step (106), the fitting formula is: EUR =2.0656ln( E )-6.4729 In the formula, EUR is Maximum recoverable reserves per well, 10 4 t; E The effective sweep efficiency of the mesh is %.

6. The method for predicting the maximum recoverable reserves of shale oil horizontal wells by volumetric fracturing as described in claim 2, characterized in that, Step (2) uses grey relational analysis to calculate the correlation coefficients between the geological parameters and volumetric fracturing parameters of each fracturing section of the horizontal well to be tested and the fracture network swept volume of the horizontal well to be tested as monitored by microseismic analysis, and clarifies the key control parameters affecting the fracture network swept volume, specifically including, Step (201), Establishment of multi-factor comprehensive evaluation matrix: Based on the basic parameters of the horizontal well to be measured obtained in step (101), establish a multi-factor comprehensive evaluation matrix. The multi-factor comprehensive evaluation matrix elements are the geological parameters and volumetric fracturing stimulation parameters of the measured fracturing section of the horizontal well to be tested. The expression is as follows In the formula: A multi-factor comprehensive evaluation matrix; Elements of a multi-factor comprehensive evaluation matrix; This represents the number of volumetric fracturing stages in the horizontal well to be tested. The number of factors affecting the swept volume of the mesh; Step (202), Evaluation reference column establishment: An evaluation reference column is established based on the fracture network swept volume of the measured fractured section of the horizontal well to be tested obtained from microseismic monitoring. ; In the formula: This is the reference column for evaluation; T is the transpose symbol; Step (203), Standardization: Using the maximum value method, the multi-factor comprehensive evaluation matrix X and the evaluation reference column are standardized. Each part is then standardized. The standardization formula is as follows: 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; For the first The maximum value in the set of influencing factor parameters; Step (204), Correlation coefficient calculation: Based on step (203), calculate the correlation coefficient between different influencing factors and the fracture network swept volume of the entire well section of the microseismic monitoring of volumetric fracturing of adjacent horizontal wells in the same block; Step (205), Determination of key control parameters: Sort the correlation coefficient calculation results of step (204) and define its value as greater than 0.5 as the key control parameter affecting the volumetric pressure fracture network wave and volume of the horizontal well to be tested.

7. The method for predicting the maximum recoverable reserves of shale oil horizontal wells by volumetric fracturing as described in claim 6, characterized in that, Step (204), the specific content of the correlation coefficient calculation includes, Step (2041): 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: 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 (2042): Based on the standard deviation calculated in step (2041), further calculate the correlation coefficient between different influencing factors and the swept volume of the seam mesh. The calculation formula is as follows: In the formula: The correlation coefficient; The resolution factor is set to 0.5; m represents the number of volumetric fracturing sections in the horizontal well to be tested.

8. The method for predicting the maximum recoverable reserves of shale oil horizontal wells by volumetric fracturing as described in claim 2, characterized in that, Step (3) establishes a fracture network sweep volume prediction model coupled with the key control parameters based on the determined key control parameters, predicts the fracture network sweep volume of the horizontal well, obtains the effective fracture network sweep coefficient, and then uses the correlation prediction chart to predict the maximum recoverable reserves of the horizontal well. The fracture network sweep volume prediction model includes prediction matrix establishment, prediction matrix standardization, and similarity factor calculation, specifically including... Step (301), Establishment of the seam mesh sweep volume prediction matrix: Based on the key control parameters affecting the seam mesh sweep volume determined in step (2), establish the seam mesh sweep volume prediction matrix. The matrix formula is as follows: In the formula: This is the prediction matrix for the wave volume of the seam mesh; 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 =1, 2, ..., h Numbering of the measured fracturing sections. The number of key parameters affecting the swept volume of the seam mesh; Step (302), prediction matrix standardization: Based on the distribution range of the key control parameters affecting the wave and volume of the seam, the prediction matrix of the wave and volume of the seam is standardized; Step (303), similarity factor calculation: Based on the normalized matrix of fracture network sweep volume prediction, calculate the similarity factor between the measured fractured section and the predicted fractured section of the horizontal well. The calculation formula is as follows: In the formula: The similarity factor between the measured segment and the predicted segment; Standardized elements for the seam mesh wave volume prediction matrix; Standardized elements for predicting fracturing segments; Step (304): Sort the similarity factors between the predicted segment and the measured segment, and assign the microseismic monitoring fracture network swept volume of the measured segment corresponding to the largest similarity factor to the predicted segment, thereby obtaining the predicted microseismic monitoring fracture network swept volume of the entire horizontal well section. The calculation formula is as follows: In the formula: h This refers to the number of fracturing stages in a horizontal well. Step (305): Based on step (304), further utilize the formulas in step (1) for calculating the effective fracture network sweep volume, the reservoir volume controlled by the horizontal well, and the effective fracture network sweep coefficient of adjacent horizontal wells on the same platform to quantitatively calculate the effective fracture network sweep coefficient of the horizontal well, and use the prediction fitting formula in step (1) to predict the maximum recoverable reserves of the horizontal well.

9. The method for predicting the maximum recoverable reserves of shale oil horizontal wells by volumetric fracturing as described in claim 8, characterized in that, The specific content of the prediction matrix standardization in step (302) includes: Step (3021): Based on the distribution range of key parameters affecting the wave and volume of the seam mesh, the elements of the prediction matrix are standardized. The calculation formula is as follows: （Ⅰ） （Ⅱ） In the formula: The mean of each parameter set in the column vector of the seam mesh wave and volume prediction matrix; For the elements of the stitched mesh wave and prediction matrix, ; Standardized elements for the seam mesh wave volume prediction matrix; The number of key parameters affecting the swept volume of the seam mesh; Step (3022) involves standardizing the seam mesh wave and volume prediction matrix using (Ⅰ) and (Ⅱ). The formula for the standardized matrix is ​​as follows: In the formula: The normalized matrix for predicting the wave and volume of the seam mesh.

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