Prediction method for fracture-controlled reserves of shale oil reservoir

Abstract

The invention relates to a method for predicting shale oil reservoir fracture-controlled reserves, which comprises the following steps of: collecting geomechanical parameters and fracturing construction parameters which are similar to a shale oil target reservoir in background, calculating a correlation factor between each parameter and a microseism monitoring fracture volume, and determining a main control parameter which affects the fracture volume; calculating a fracture control factor of each fracturing section according to the fracture volume main control parameter, establishing a relational expression of the fracture volume of the horizontal well and the fracture control factors, and quantitatively predicting the fracture volume of each fracturing section of the target horizontal well; and establishing a geological model of a reservoir where the target horizontal well is located, correcting the fracture volume, and further predicting the fracture-controlled reserves in combination with the physical property parameters of each fracturing section. According to the method, rapid prediction of any shale oil horizontal well volume fracturing fracture volume mine field scale is achieved, fracture-controlled reserves are quantitatively represented, microseism monitoring is not needed, and the problems that mine field construction is large in risk, long in test period and high in cost are solved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas development, and in particular to a method for predicting fracture-controlled reserves in shale oil reservoirs. Background Technology

[0002] Shale oil, as a strategic alternative to petroleum, is the main force driving my country's crude oil reserve growth and production, and an important resource for ensuring national energy security. The core of unconventional volumetric fracturing is the construction of artificial oil reservoirs through large-scale, long horizontal well slickwater volumetric fracturing. Fracture-controlled reserves (Fractured Reserves) are a core indicator for evaluating the effectiveness of volumetric fracturing in shale oil and gas reservoirs. However, compared to North America, my country's shale oil reservoirs are highly heterogeneous, with vertically interbedded thin composite lithologies and well-developed natural fractures. The occurrence and distribution of these fractures lack effective characterization methods, making it difficult to understand the interaction between hydraulic and natural fractures. This poses a significant challenge to characterizing and evaluating fracture-controlled reserves formed by volumetric fracturing, and makes it impossible to quantitatively evaluate fracturing effectiveness and single-well productivity.

[0003] Domestic and foreign scholars have conducted preliminary research on the calculation of the swept volume and fracture-controlled reserves of multi-fracture networks. Currently, there are mainly the following methods: (1) Li Zhiqiang et al. (Li Zhiqiang, Qi Zhilin, Yan Wende et al. A calculation method for unconventional reservoir fracturing and production enhancement zone, patent number: CN201910985860.8) This method treats fractured reservoirs as dual continuous medium 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 and 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. However, unconventional reservoirs are extremely heterogeneous, and the propagation law of multiple fractures is still a bottleneck problem in the field, 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. (2) Le Hong et al. (Le Hong, Fang Hongming, Zhou Changlin et al. Evaluation method and system for reservoir utilization efficiency of horizontal well-fracturing in tight sandstone gas reservoirs, patent number: CN202111028352.4). This method includes obtaining reservoir parameters of tight sandstone and regional seismic and logging data, sand fracturing construction data and field detection data, establishing a three-dimensional geostress model, simulating the propagation of artificial fractures during fracturing, and embedding the artificial fracture morphology into the three-dimensional geological model. By comparing and fitting the post-fracturing production prediction results with the tracer interpretation and post-fracturing test results, the utilization efficiency of artificial fracture reservoirs after fracturing is evaluated, and the degree of balance of multi-fracture propagation is evaluated. (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 No.: 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.

[0004] The above methods primarily rely on mainstream downhole microseismic monitoring technology and reservoir numerical simulation methods to determine the fracture control volume, and further calculate fracture-controlled reserves based on reservoir physical parameters. Downhole microseismic monitoring technology obtains the fracture control volume based on the parameters and volume of fractures responding to event points during fracturing. However, numerous studies have confirmed that the fracture volume monitored by microseismic monitoring is far larger than the actual volume of reservoir stimulation, making it impossible to accurately calculate fracture control reserves. Furthermore, microseismic monitoring is time-consuming and expensive, making it difficult for oilfields to conduct monitoring for every well to obtain fracture volume. Current reservoir numerical simulation methods can only establish experimental-scale models, revealing fracture propagation patterns, and the differences between these models and actual large-scale reservoir conditions are significant. Therefore, to accelerate the efficient development of shale oil and establish a suitable volumetric fracturing technology model for shale oil, a reliable, convenient, and economical method for predicting fracture control reserves is urgently needed to improve the accuracy of fracturing effect evaluation and further advance technological progress. Summary of the Invention

[0005] The purpose of this invention is to provide a method for predicting fracture-controlled reserves in shale oil reservoirs. This method first collects geomechanical parameters and fracturing parameters of horizontal wells at the same platform and stratigraphic level in the shale oil reservoir. Then, using grey theory, it calculates the correlation factors between each parameter and the fracture volume monitored by downhole microseismic monitoring, identifying the main controlling parameters affecting fracture volume. Next, based on the main controlling parameters of fracture volume, it calculates the fracture control factors for each fracturing section, further establishing a correlation chart between the horizontal well fracture volume and the fracture control factors, and quantitatively predicting the fracture volume of each fracturing section in the predicted horizontal well. Finally, it establishes a geological model of the reservoir where the predicted horizontal well is located, corrects the fracture volume, and further combines the physical properties of each fracturing section to predict fracture-controlled reserves, thereby achieving the purpose of quantitative characterization.

[0006] To achieve the above technical objectives, this invention provides a method for predicting fracture-controlled reserves in shale oil reservoirs, comprising the following steps:

[0007] A method for predicting fracture-controlled reserves in shale oil reservoirs includes the following steps:

[0008] (1) Collect the geomechanical parameters and fracturing construction parameters of horizontal wells on the same platform and in the same layer as the target shale oil reservoir, and use grey theory to calculate the correlation factors between each parameter and the fracture volume monitored by downhole microseismic monitoring, and identify the main control parameters affecting the fracture volume;

[0009] (2) Calculate the fracture control factor of each fracture section based on the main control parameters of fracture volume, and further establish a correlation chart between fracture volume and fracture control factor of horizontal well to quantitatively predict the fracture volume of each fracture section of the target horizontal well.

[0010] (3) Establish a geological model of the reservoir where the target horizontal well is located, correct the fracture volume, and further combine the physical property parameters of each fracture section to predict the fracture-controlled reserves.

[0011] Furthermore, in step (1), the geomechanical parameters and fracturing parameters of horizontal wells on the same platform and at the same stratigraphic level as the target shale oil reservoir are collected. Grey theory is used to calculate the correlation factors between each parameter and the fracture volume monitored by downhole microseismic monitoring, thus identifying the main controlling parameters affecting fracture volume. This also includes:

[0012] (11) Collect geomechanical parameters and fracturing construction parameters of horizontal wells on the same platform and in the same layer as the target shale oil reservoir. The geomechanical parameters include: porosity, permeability, oil saturation, oil layer thickness, clay content, brittleness index, and horizontal stress difference of each fracturing section of the horizontal well. The fracturing construction parameters include the number of fracture clusters, construction flow rate, fluid injection rate, and sand addition rate of each fracturing section of the horizontal well.

[0013] (12) Obtain the volume of fractures in each fractured section of the measured horizontal well through microseismic monitoring;

[0014] (13) Establishment of multi-factor comprehensive evaluation matrix: Based on the geomechanical parameters and fracturing construction parameters collected in step (11), an evaluation matrix X is established, in which the elements of the evaluation matrix are the geomechanical parameters and volumetric fracturing parameters of each fracturing section of the measured horizontal well.

[0015]

[0016] In the formula: X is the evaluation matrix; X i (j) represents the evaluation matrix element; m represents the number of horizontal well volumetric fracturing stages; n represents the number of fracture volumetric influence parameters;

[0017] (14) Establishment of multi-factor comprehensive evaluation reference column: Based on the measured fracture volume of each fractured section of the horizontal well, an evaluation reference column X0 is established;

[0018] X0=(X1(0),L,X i (0),L,X m (0)) T i = 1, 2, L, m (2)

[0019] In the formula: X0 is the evaluation reference column;

[0020] (15) Standardize the evaluation matrix and evaluation reference columns;

[0021] (16) Calculation of multi-factor correlation factors: Calculate the multi-factor correlation between different influencing parameters and microseismic monitoring based on the multi-factor comprehensive evaluation matrix and the multi-factor comprehensive evaluation reference column.

[0022] (17) Determination of main control parameters: The multi-factor correlation factors affecting fracture volume in the measured horizontal well are sorted. The larger the value, the greater the influence on fracture volume.

[0023] Furthermore, the standardization process in step (15) employs the maximum method, and the calculation formula is as follows:

[0024]

[0025] In the formula: To evaluate the standardized elements of the matrix; (X i (j)) max It represents the maximum value in the parameter set of the j-th influencing factor.

[0026] Furthermore, step (16) also includes:

[0027] (161) Calculate the standard deviation based on the standardized data of the evaluation matrix and the standardized data of the reference column. The calculation formula is as follows:

[0028]

[0029] In the formula: Δ i (j) represents the standard deviation between the standardized data of the evaluation matrix and the standardized data of the reference column; To evaluate the standardized data in the reference column; To evaluate the data after the matrix elements have been standardized;

[0030] (162) After standardizing the data of each evaluation index, the correlation factor between each crack volume influencing factor and the main factor of the evaluation index is calculated. The calculation expression is as follows:

[0031]

[0032] In the formula: ξ j For relevance; in The data are standardized from the main factors of the evaluation indicators; The data represents the standardized parameters affecting the volumetric properties of the crack; ρ is the resolution coefficient.

[0033] Furthermore, step (2) involves calculating the fracture control factor for each fractured section based on the fracture volume master control parameter, establishing a correlation chart between fracture volume and fracture control factor in the horizontal well, and quantitatively predicting the fracture volume of each fractured section in the target horizontal well. This also includes:

[0034] (21) Calculation of weighting factors: Based on the main control parameters of the volume influence of the horizontal well microseismic monitoring measured in step (1), calculate the weighting factors of each parameter.

[0035]

[0036] In the formula: r j The weighting factor for the main control parameter of the volume influence of microseismic monitoring in horizontal wells is dimensionless.

[0037] (22) Fracture control factor calculation: Based on the weighting factors of each parameter and the standardized data of the evaluation matrix, the fracture volume control factor of each fracture section of the horizontal well is calculated. The higher the value, the better the reservoir stimulation effect.

[0038]

[0039] In the formula: EF is the fracture control factor, which is dimensionless; k is the number of key control parameters for the measured horizontal well shadow fracture volume;

[0040] (23) Establishment of prediction fitting formula: Based on the calculation results of fracture control factors of each fractured section in step (22), establish a prediction chart with correlation between fracture volume and microseismic monitoring of each fractured section of the measured horizontal well, and obtain the prediction fitting formula;

[0041] (24) Fracture control volume prediction: Based on the main control parameters that affect the fracture volume of each fractured section of the horizontal well, the fracture control factor is calculated using formula (7), and the fracture control volume of each fractured section of the horizontal well is calculated using the prediction fitting formula obtained in step (23).

[0042] Furthermore, step (3) of establishing a geological model of the reservoir where the target horizontal well is located, correcting the fracture volume, and further predicting the fracture-controlled reserves by combining the physical properties of each fracturing section, also includes:

[0043] (31) Collect the basic parameters of the predicted horizontal well and the geological parameters of the shale oil reservoir. The basic parameters of the predicted horizontal well include the length of the horizontal section, the number of fractured sections, the cumulative oil production, and the average daily oil production. The geological parameters of the shale oil reservoir include the reservoir depth, the porosity of each fractured section, the permeability, the oil saturation, the reservoir thickness, and the reservoir fluid parameters.

[0044] (32) Establish a geological model based on the predicted basic parameter data of the horizontal well, including the reservoir porosity distribution field, permeability distribution field, oil saturation distribution field, and formation pressure distribution field, and import the fracture volume of each section of the predicted horizontal well into the geological model;

[0045] (33) The cumulative oil production and average daily oil production were predicted using the predicted horizontal well geological model. The fracture volume of each fracturing section was corrected by combining the actual production data.

[0046] (34) Calculate the fracture-controlled reserves based on the predicted reservoir porosity and oil saturation of each fractured section of the shale oil reservoir where the horizontal well is located.

[0047] Furthermore, in step (33), the fracture volume of each fracturing section is corrected, and the calculation formula is as follows:

[0048]

[0049] In the formula: F l To predict the corrected fracture volume of each fracturing section in a horizontal well, 10 4 m 3 E l To predict the fracture volume of each fractured section in a horizontal well, 10 4 m 3 C h C p These represent the predicted actual cumulative oil production and the model-predicted cumulative oil production of horizontal wells, respectively, in tons; V h V p These represent the actual average daily oil production and the model-predicted average daily oil production of the horizontal well, respectively, in tons / day; w is the correction weighting coefficient, with a value of 0.5; l is the predicted fracturing section number of the horizontal well.

[0050] Further, in step (34), the seam control reserve is calculated using the following formula:

[0051]

[0052] In the formula: RQ represents the predicted horizontal well fracture controlled reserves, in tons; To predict the reservoir porosity (%) for each fractured section of a horizontal well; S ol Predict the reservoir oil saturation corresponding to each fractured section of the horizontal well; ρ o The density of crude oil in shale oil reservoirs, kg / m³ 3 h represents the fracturing section of the water well being evaluated.

[0053] On the other hand, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, when the processor executes the computer program, it implements the steps of the method for predicting fracture-controlled reserves in shale oil reservoirs as described in any of the preceding claims.

[0054] On the other hand, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the method for predicting fracture-controlled reserves in shale oil reservoirs as described in any of the preceding claims.

[0055] This invention offers the following advantages: It proposes a method for predicting fracture-controlled reserves in shale oil reservoirs. By integrating the analysis of key controlling factors and fracture volume correction, fracture control factors are obtained, enabling rapid prediction of fracture volume at the field scale for any shale oil horizontal well with volumetric fracturing, and quantitatively characterizing fracture-controlled reserves. The calculation method is accurate and reliable, eliminating the need for extensive microseismic monitoring at the mine site, thus solving the problems of high construction risks, long testing cycles, and high costs at the mine site. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, 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 drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a ranking diagram of the correlation factors between different influencing parameters and crack volume in this invention.

[0058] Figure 2 This is a ranking diagram of the weighting factors of the main control parameters affecting the volume of microseismic monitoring in horizontal wells according to the present invention.

[0059] Figure 3 This is a diagram showing the calculation of fracture volume control factors for each fractured section of a horizontal well as measured by the present invention.

[0060] Figure 4 This is a prediction chart showing the correlation between fracture factors and fracture volume in each fractured section of a horizontal well as measured in this invention.

[0061] Figure 5 This is a graph showing the calculation results of the fracture factor for each fractured section of a horizontal well predicted by this invention.

[0062] Figure 6 This is a diagram showing the calculation results of the predicted fracture volume in each fractured section of a horizontal well according to the present invention.

[0063] Figure 7 This is a diagram showing the corrected fracture volume prediction results for each fractured section of a horizontal well according to the present invention. Detailed Implementation

[0064] 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.

[0065] The specific implementation of the present invention will be described in detail below with reference to the accompanying drawings and geological data of a shale oil reservoir in a certain block.

[0066] This example provides a method for predicting fracture-controlled reserves in shale oil reservoirs, as detailed below:

[0067] This example requires predicting fractured reserves in horizontal well H1-2, with a horizontal section length of 1250m and a design for 15 fracturing stages. Horizontal well H1-1, already measured on the same platform, has a horizontal section length of 1620m, a design for 20 fracturing stages, and is equipped with 20 downhole microseismic monitoring stages.

[0068] (1) Collect geomechanical parameters and fracturing operation parameters of horizontal wells at the same platform and layer in shale oil reservoirs, and use grey theory to calculate the weight of each parameter with the fracture volume monitored by downhole microseismic monitoring, and identify the main controlling parameters affecting the fracture volume. The specific contents are as follows:

[0069] (11) Geomechanical and fracturing parameters of horizontal wells in the same platform and layer of shale oil reservoirs. The geomechanical parameters include: porosity, permeability, oil saturation, oil layer thickness, clay content, brittleness index, and horizontal stress difference of each fracturing section of the horizontal well. The fracturing parameters include: number of fracture clusters, fracturing flow rate, fluid injection rate, and proppant injection rate of each fracturing section of the horizontal well, as shown in Tables 1 to 4.

[0070] (12) Collect the fracture volume of each fractured section of the horizontal well measured by downhole microseismic monitoring, as shown in Table 2.

[0071] Table 1. Geomechanical parameters of each fractured section in horizontal well H1-1 as measured.

[0072]

[0073]

[0074] Table 2. Fracturing parameters and downhole microseismic monitoring fracture volumes for each fractured section of horizontal well H1-1.

[0075]

[0076] Table 3. Predicted geomechanical parameters of each fractured section in horizontal well H1-2

[0077]

[0078] Table 4 Evaluation of fracturing parameters for each fracturing section of water well H1-2

[0079]

[0080]

[0081] (13) Establishment of multi-factor comprehensive evaluation matrix: Based on the geomechanical and fracturing construction parameters collected in step (11), an evaluation matrix X is established, in which the elements of the evaluation matrix are the geomechanical parameters and volumetric fracturing transformation parameters of each fracturing section of the measured well.

[0082] (14) Establishment of multi-factor comprehensive evaluation reference column: Based on the measured fracture volume of each fractured section of the horizontal well in Table 2, establish evaluation reference column X0.

[0083] (15) Standardization of multi-factor comprehensive evaluation matrix: Since the dimensions and physical meanings of different influencing parameters are quite different, the evaluation matrix and evaluation reference column should be standardized using formula (3).

[0084] (16) Calculation of multi-factor correlation factors: Based on the multi-factor comprehensive evaluation matrix and the multi-factor comprehensive evaluation reference column, the multi-factor correlation factors between different influencing parameters and the volume of microseismic monitoring cracks are calculated using formulas (3) and (5). See Appendix Figure 1 .

[0085] (17) Determination of main control parameters: The multi-factor correlation factors affecting fracture volume in the measured horizontal wells are sorted, see Appendix. Figure 1 The larger the value, the greater the influence on the size of the crack. In this embodiment, the main control parameters affecting the crack volume are the amount of liquid entering the ground, the number of crack clusters, the brittleness index, the construction discharge rate, the horizontal stress difference, the clay content, the amount of sand added, and the permeability.

[0086] (2) Based on the main control parameters of fracture volume, calculate the fracture control factor for each fractured section, and further establish a correlation chart between fracture volume and fracture control factor in horizontal wells to quantitatively predict the fracture volume of each fractured section in the horizontal well. The specific details are as follows:

[0087] (21) Weighting factor calculation: Based on the main control parameters of the influence of the horizontal well microseismic monitoring volume measured in step (1), the weighting factors of each parameter are further calculated using formula (6), see Figure 2 .

[0088] (22) Fracture control factor calculation: Based on the weighting factors of each parameter and the standardized data of the evaluation matrix, the fracture volume control factor of each fractured section of the horizontal well is calculated using formula (7), as shown in the appendix. Figure 3 .

[0089] (23) Establishment of Prediction Fitting Formula: Based on the calculation results of fracture control factors in each fractured section of the steps, establish a prediction chart for the correlation between fracture volume and microseismic monitoring of each fractured section of the measured horizontal well. (See Appendix) Figure 4 The predicted fitting formula is obtained as y = 619.97x - 6.931, where y is the crack volume and x is the crack factor.

[0090] (24) Fracture control volume prediction: Based on the main control parameters affecting the fracture volume of each fractured section of the horizontal well in microseismic monitoring, the fracture control factor is calculated using formula (7), see Appendix. Figure 5 Furthermore, using the prediction fitting formula obtained in step (23), the predicted fracture control volume of each fractured section of the horizontal well is calculated, as shown in the appendix. Figure 6 .

[0091] (3) Establish a geological model of the reservoir where the predicted horizontal well is located, correct the fracture volume, and further calculate the fracture-controlled volume by combining the physical properties of each fractured section. Specific details are as follows:

[0092] (31) Collect the basic parameters of the predicted horizontal well and the geological parameters of the shale oil reservoir, including the basic horizontal section length, number of fractured sections, cumulative oil production, and average daily oil production of the predicted horizontal well H1-2. The basic parameters of the shale oil reservoir include reservoir depth, reservoir porosity, permeability, oil saturation, reservoir thickness, and reservoir fluid parameters.

[0093] Table 5. Basic parameters for predicted horizontal well H1-2

[0094]

[0095]

[0096] (32) Establish a geological model based on the predicted basic parameters of the horizontal well, including the reservoir porosity distribution field, permeability distribution field, oil saturation distribution field, and formation pressure distribution field. Then import the fracture volume of each section of the predicted horizontal well into the geological model.

[0097] (33) The predicted cumulative oil production for the first phase was 11,360 tons and the average daily oil production was 22.7 tons, based on the predicted horizontal well geological model. The actual production data was then used to correct the fracture volume of each fracturing section using formula (8), as shown in the appendix. Figure 7 .

[0098] (34) Based on Table 3, which predicts the reservoir porosity and oil saturation of each fractured section of the shale oil reservoir where horizontal well H1-2 is located, the fractured reserves are calculated to be 58.596×104 tons using formula (9).

[0099] This invention first collects geomechanical parameters and fracturing parameters of horizontal wells at the same platform and formation in shale oil reservoirs. Using grey theory, it calculates the correlation factors between each parameter and the fracture volume monitored by downhole microseismic monitoring, identifying the main controlling parameters affecting fracture volume. Second, based on the main controlling parameters of fracture volume, it calculates the fracture control factors for each fracturing section, further establishing a correlation chart between horizontal well fracture volume and fracture control factors to quantitatively predict the fracture volume of each fracturing section in the predicted horizontal well. Finally, it establishes a geological model of the reservoir where the predicted horizontal well is located, corrects the fracture volume, and further combines the physical properties of each fracturing section to predict fracture-controlled reserves, thereby achieving the purpose of quantitative characterization.

[0100] This invention obtains fracture control factors through an integrated approach of controlling factor analysis and fracture volume correction, enabling rapid prediction of fracture volume at the field scale in any shale oil horizontal well volumetric fracturing, and quantitatively characterizing fracture-controlled reserves. Its calculation method is accurate and reliable, eliminating the need for extensive microseismic monitoring at the mine site, and solving the problems of high construction risks, long testing cycles, and high costs at the mine site.

[0101] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for predicting fracture-controlled reserves in shale oil reservoirs, characterized in that, The steps are as follows: (1) Collect the geomechanical parameters and fracturing construction parameters of horizontal wells on the same platform and in the same layer as the target shale oil reservoir, and use grey theory to calculate the correlation factors between each parameter and the fracture volume monitored by downhole microseismic monitoring, and identify the main control parameters affecting the fracture volume; (2) Calculate the fracture control factor of each fracture section based on the main control parameters of fracture volume, establish the relationship between fracture volume and fracture control factor of horizontal well, and make quantitative prediction of fracture volume of each fracture section of target horizontal well. (3) Establish a geological model of the reservoir where the target horizontal well is located, correct the fracture volume, and predict the fracture-controlled reserves by combining the physical property parameters of each fracture section.

2. The method for predicting fracture-controlled reserves in shale oil reservoirs according to claim 1, wherein step (1) involves collecting geomechanical parameters and fracturing parameters of horizontal wells at the same platform and stratigraphic level as the target shale oil reservoir, calculating the correlation factors between each parameter and the fracture volume monitored by downhole microseismic monitoring using grey theory, and identifying the main controlling parameters affecting the fracture volume, further includes: (11) Collect geomechanical parameters and fracturing construction parameters of horizontal wells on the same platform and in the same layer as the target shale oil reservoir. The geomechanical parameters include: porosity, permeability, oil saturation, oil layer thickness, clay content, brittleness index, and horizontal stress difference of each fracturing section of the horizontal well. The fracturing construction parameters include the number of fracture clusters, construction flow rate, fluid injection rate, and sand addition rate of each fracturing section of the horizontal well. (12) Obtain the volume of fractures in each fractured section of the measured horizontal well through microseismic monitoring; (13) Establishment of multi-factor comprehensive evaluation matrix: Based on the geomechanical parameters and fracturing construction parameters collected in step (11), an evaluation matrix X is established, in which the elements of the evaluation matrix are the geomechanical parameters and volumetric fracturing parameters of each fracturing section of the measured horizontal well. In the formula: X is the evaluation matrix; X i (j) represents the evaluation matrix element; m represents the number of horizontal well volumetric fracturing stages; n represents the number of fracture volumetric influence parameters; (14) Establishment of multi-factor comprehensive evaluation reference column: Based on the measured fracture volume of each fractured section of the horizontal well, an evaluation reference column X0 is established; X0=(X1(0),L,X i (0),L,X m (0) T i=1,2,L,m (2) In the formula: X0 is the evaluation reference column; (15) Standardize the evaluation matrix and evaluation reference columns; (16) Calculation of multi-factor correlation factors: Calculate the multi-factor correlation between different influencing parameters and microseismic monitoring based on the multi-factor comprehensive evaluation matrix and the multi-factor comprehensive evaluation reference column. (17) Determination of main control parameters: The multi-factor correlation factors affecting fracture volume in the measured horizontal well are sorted. The larger the value, the greater the influence on fracture volume.

3. The method for predicting fractured reserves in shale oil reservoirs according to claim 2, wherein the standardization process in step (15) adopts the maximum value method, and the calculation formula is as follows: In the formula: To evaluate the standardized elements of the matrix; (X i (j)) max It represents the maximum value in the parameter set of the j-th influencing factor.

4. The method for predicting fracture-controlled reserves in shale oil reservoirs according to claim 2, wherein step (16) further includes: (161) Calculate the standard deviation based on the standardized data of the evaluation matrix and the standardized data of the reference column. The calculation formula is as follows: In the formula: Δ i (j) represents the standard deviation between the standardized data of the evaluation matrix and the standardized data of the reference column; To evaluate the standardized data in the reference column; To evaluate the data after the matrix elements have been standardized; (162) After standardizing the data of each evaluation index, the correlation factor between each crack volume influencing factor and the main factor of the evaluation index is calculated. The calculation expression is as follows: In the formula: ξ j For relevance; in The data are standardized from the main factors of the evaluation indicators; The data represents the standardized parameters affecting the volumetric properties of the crack; ρ is the resolution coefficient.

5. The method for predicting fracture-controlled reserves in shale oil reservoirs according to claim 1, wherein step (2) involves calculating the fracture control factor of each fractured section based on the main fracture volume control parameters, establishing the relationship between fracture volume and fracture control factor in a horizontal well, and quantitatively predicting the fracture volume of each fractured section in the target horizontal well, further comprising: (21) Calculation of weighting factors: Based on the main control parameters of the volume influence of the horizontal well microseismic monitoring measured in step (1), calculate the weighting factors of each parameter. In the formula: r j The weighting factor for the main control parameter of the volume influence of microseismic monitoring in horizontal wells is dimensionless. (22) Fracture control factor calculation: Based on the weighting factors of each parameter and the standardized data of the evaluation matrix, the fracture volume control factor of each fracture section of the horizontal well is calculated. The higher the value, the better the reservoir stimulation effect. In the formula: EF is the fracture control factor, which is dimensionless; k is the number of key control parameters for the measured horizontal well shadow fracture volume; (23) Establishment of prediction fitting formula: Based on the calculation results of fracture control factors of each fractured section in step (22), establish a prediction chart with correlation between fracture volume and microseismic monitoring of each fractured section of the measured horizontal well, and obtain the prediction fitting formula; (24) Fracture control volume prediction: Based on the main control parameters that affect the fracture volume of each fractured section of the horizontal well, the fracture control factor is calculated using formula (7), and the fracture control volume of each fractured section of the horizontal well is calculated using the prediction fitting formula obtained in step (23).

6. The method for predicting fracture-controlled reserves in shale oil reservoirs according to claim 1, wherein step (3) involves establishing a geological model of the reservoir where the target horizontal well is located, correcting the fracture volume, and predicting the fracture-controlled reserves by combining the physical properties of each fractured section, further comprising: (31) Collect the basic parameters of the predicted horizontal well and the geological parameters of the shale oil reservoir. The basic parameters of the predicted horizontal well include the length of the horizontal section, the number of fractured sections, the cumulative oil production, and the average daily oil production. The geological parameters of the shale oil reservoir include the reservoir depth, the porosity of each fractured section, the permeability, the oil saturation, the reservoir thickness, and the reservoir fluid parameters. (32) Establish a geological model based on the predicted basic parameter data of the horizontal well, including the reservoir porosity distribution field, permeability distribution field, oil saturation distribution field, and formation pressure distribution field, and import the fracture volume of each section of the predicted horizontal well into the geological model; (33) The cumulative oil production and average daily oil production were predicted using the predicted horizontal well geological model. The fracture volume of each fracturing section was corrected by combining the actual production data. (34) Calculate the fracture-controlled reserves based on the predicted reservoir porosity and oil saturation of each fractured section of the shale oil reservoir where the horizontal well is located.

7. The method for predicting fracture-controlled reserves in shale oil reservoirs according to claim 6, wherein the fracture volume of each fracturing section is corrected in step (33), and the calculation formula is as follows: In the formula: F l To predict the corrected fracture volume of each fracturing section in a horizontal well, 10 4 m 3 E l To predict the fracture volume of each fractured section in a horizontal well, 10 4 m 3 C h C p These represent the predicted actual cumulative oil production and the model-predicted cumulative oil production of horizontal wells, respectively, in tons; V h V p These represent the actual average daily oil production and the model-predicted average daily oil production of the horizontal well, respectively, in tons / day; w is the correction weighting coefficient, with a value of 0.5; l is the predicted fracturing section number of the horizontal well.

8. The method for predicting fracture-controlled reserves in shale oil reservoirs according to claim 6, wherein the fracture-controlled reserves are calculated in step (34) using the following formula: In the formula: RQ represents the predicted horizontal well fracture controlled reserves, in tons; To predict the reservoir porosity (%) for each fractured section of a horizontal well; S ol Predict the reservoir oil saturation corresponding to each fractured section of the horizontal well; ρ o The density of crude oil in shale oil reservoirs, kg / m³ 3 h represents the fracturing section of the water well being evaluated.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for predicting fractured reserves in shale oil reservoirs as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for predicting fractured reserves in shale oil reservoirs as described in any one of claims 1-8.

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