Tight oil and gas reservoir horizontal well volume fracturing network wave and volume prediction method

Abstract

The invention discloses a tight oil and gas reservoir horizontal well volume fracturing fracture network wave and volume prediction method, which comprises the following steps of: based on geological engineering parameters of a fractured horizontal well and microseism monitoring fracture volume data, calculating weight factors of each geological and engineering parameter by establishing a multi-factor evaluation model; aiming at a to-be-predicted fracturing section, combining the solved weight factor and the geological parameters of the perforation positions of the fracturing clusters in the fracturing section, and calculating a fracturing crack-making efficiency coefficient representing the distribution uniformity of fracturing fluid; multiplying the original micro-seismic monitoring fracture volume of the fractured section by the calculated fracture forming efficiency coefficient to obtain a corrected effective fracture network wave and volume; according to the method, the fracture forming efficiency coefficient is introduced to correct the original microseismic monitoring volume, the problem that prediction results of an existing method are generally large is solved, accurate quantitative prediction of the effective fracture network waves and the volume is achieved, and a scientific basis is provided for fracturing effect evaluation and scheme optimization.

Description

Technical Field

[0001] This invention relates to the field of oil and gas development technology, and in particular to a method for predicting volumetric pressure fracture network and volumetric volume in horizontal wells of tight oil and gas reservoirs. Background Technology

[0002] Tight oil and gas reservoirs, as an important component of unconventional oil and gas resources, are of strategic significance for ensuring national energy security. Horizontal well volumetric fracturing technology is a core means to achieve the economical and effective development of tight oil and gas reservoirs, aiming to form a complex network of fractures within the reservoir to increase the flow channels for oil and gas. Therefore, accurate evaluation of the stimulated reservoir volume (SRV) formed after fracturing is crucial for optimizing fracturing design, predicting production capacity, and guiding adjustments to subsequent development plans. Currently, methods for evaluating SRV mainly include numerical simulation, physical simulation, and microseismic monitoring. Numerical simulation methods predict fracture initiation and propagation by constructing complex mathematical and physical models; however, the inherent strong heterogeneity of tight reservoirs leads to excessive simplification assumptions in the models, resulting in significant deviations between simulation results and actual field conditions. While physical simulation methods can visually reveal fracture propagation mechanisms in the laboratory, their results are limited by size effects, making it difficult to effectively generalize to the field scale.

[0003] Microseismic monitoring, a geophysical method capable of real-time and dynamic monitoring of fracture propagation, has been widely used in evaluating fracturing effectiveness in the field. This technology delineates the fracture-affected area by capturing tiny seismic events generated by rock fracturing, thereby estimating the fracture network volume. However, extensive field practice shows that the volume represented by the microseismic event cloud is usually significantly larger than the actual fracture network volume that generates effective conductivity. This is because microseismic events not only reflect the propagation of the main fracture but also include signals generated by non-effective seepage channels such as stress adjustment and micro-fractures in the rock matrix. Furthermore, the monitoring results are affected by the accuracy of the monitoring instruments and the mechanical properties of the formation rocks. Therefore, the fracture network volume calculated directly from microseismic monitoring results is generally overestimated, failing to accurately reflect the effective reservoir stimulation effect. This leads to overly optimistic evaluations of fracturing effectiveness, consequently affecting the continuous optimization of fracturing schemes and the scientific decision-making in reservoir development.

[0004] CN110738001B discloses a method for calculating the fracturing and production enhancement zone of unconventional reservoirs. This method establishes a two-dimensional equivalent mathematical model for calculating the enhanced volume, and iteratively solves the fluid mass conservation equation and fracture width equation in the fracture system by combining the initial and internal / external boundary conditions of the mathematical model to obtain the fracture fluid pressure and average fracture width in each grid block, thereby obtaining the fracture enhanced volume. Although this method quantitatively calculates the fracture volume by establishing a mathematical model, unconventional reservoirs are extremely heterogeneous, and the multi-fracture propagation law remains a major challenge in the field, making fracture volume prediction even more difficult. In addition, since this method is a numerical simulation method, it makes many assumptions, resulting in a significant deviation between the predicted results and the actual values.

[0005] CN109507723A discloses a method and system for calculating the fracturing volume of a microseismic fracturing fracture model. This method equates the microseismic fracturing fracture model to a triangular polyhedron model, projects the triangular faces of the polyhedron model, and obtains the fracturing volume of the microseismic fracturing fracture model. However, in reality, the mechanical properties of rock at microseismic monitoring event points are usually related to the monitoring accuracy of the equipment, and extensive practical experience has shown that the monitoring range of microseismic events is much larger than that of hydraulic fractures. Therefore, this method tends to overestimate the fracture volume calculated and is not ideal. Summary of the Invention

[0006] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0007] In view of the aforementioned existing problems, this invention is proposed. Therefore, this invention provides a method for predicting volumetric fracture network waves and volume in horizontal wells of tight oil and gas reservoirs, to solve the problems mentioned in the background art.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for predicting the volumetric pressure fracture network and volumetric volume in horizontal wells of tight oil and gas reservoirs, comprising: Based on the geological and engineering parameters and microseismic monitoring fracture volume data of at least one horizontal well that has been fractured, a multi-factor evaluation model is established, and the weighting factors of the influence of various geological parameters and engineering parameters on the microseismic monitoring fracture volume are calculated. For any fracturing section of the horizontal well to be predicted, the fracturing efficiency coefficient of the fracturing section is calculated based on the geological parameters of the fracturing cluster perforation locations within the fracturing section and the weighting factor. Based on the microseismic monitoring fracture volume of the fracturing section and the fracturing efficiency coefficient, the effective fracture network swept volume of the fracturing section is calculated.

[0009] As a preferred embodiment of the volumetric pressure fracture network and volumetric prediction method for horizontal wells in tight oil and gas reservoirs described in this invention, the geological engineering parameters include: Reservoir geological parameters, geomechanical parameters, and fracturing parameters; The reservoir geological parameters include porosity, permeability, and oil saturation; the geomechanical parameters include the brittleness index; and the fracturing parameters include fracturing fluid volume and sand volume.

[0010] As a preferred embodiment of the volumetric pressure fracture network and volumetric prediction method for horizontal wells in tight oil and gas reservoirs according to the present invention, the step of calculating the weighting factor includes: Establish a multi-factor evaluation model matrix composed of the aforementioned geological engineering parameters; Establish an evaluation matrix reference column composed of the microseismic monitoring fracture volumes corresponding to each fracturing segment; The evaluation model matrix and the evaluation matrix reference columns are standardized. Based on the standardized data, the correlation coefficients between various geological engineering parameters and the volume of cracks monitored by microseismic monitoring were calculated; Based on the correlation coefficient, the weighting factors of each geological engineering parameter are obtained through normalization.

[0011] As a preferred embodiment of the volumetric pressure fracture network and volumetric prediction method for horizontal wells in tight oil and gas reservoirs described in this invention, the step of calculating the correlation coefficient includes: The absolute difference between each element in the standardized evaluation model matrix and the corresponding element in the reference column of the evaluation matrix is ​​determined, and the correlation coefficient is determined based on the maximum and minimum values ​​of the absolute difference and the preset calculation coefficient.

[0012] As a preferred embodiment of the volumetric fracture network and volume prediction method for horizontal wells in tight oil and gas reservoirs described in this invention, the step of calculating the fracture creation efficiency coefficient includes: Geological parameters of the location of each fracturing cluster perforation in the section to be predicted for fracturing are obtained and the geological parameters are standardized. The standardized geological parameters are multiplied by the weighting factors of the corresponding geological parameters and summed to obtain the geological quality index of each fracturing cluster. Based on the geological quality index of each region, the fracturing efficiency coefficient of the fracturing modification section is calculated.

[0013] As a preferred embodiment of the volumetric fracture network and volume prediction method for horizontal wells in tight oil and gas reservoirs described in this invention, the step of calculating the fracture creation efficiency coefficient based on local geological quality indices includes: The fluid inlet coefficient of a single fracturing cluster is calculated by dividing the geological quality index of a single fracturing cluster by the sum of the geological quality indices of all fracturing clusters within the fracturing section.

[0014] As a preferred embodiment of the volumetric fracture network and volume prediction method for horizontal wells in tight oil and gas reservoirs described in this invention, the step of calculating the fracture creation efficiency coefficient based on local geological quality indices further includes: The total fracturing fluid volume of the fracturing modification section is multiplied by the fluid inlet coefficient of each fracturing cluster to obtain the fluid inlet volume of each fracturing cluster.

[0015] As a preferred embodiment of the volumetric fracture network and volume prediction method for horizontal wells in tight oil and gas reservoirs described in this invention, the step of calculating the fracture creation efficiency coefficient based on local geological quality indices further includes: Calculate the standard deviation of the fluid injection volume of each fracturing cluster within the fracturing modification section, and determine the fracturing efficiency coefficient based on the standard deviation; The more uneven the fluid inflow distribution of each fracturing cluster, the larger the standard deviation, and the smaller the fracturing efficiency coefficient.

[0016] As a preferred embodiment of the volumetric pressure fracture network sweep volume prediction method for horizontal wells in tight oil and gas reservoirs according to the present invention, the step of calculating the effective fracture network sweep volume includes: The effective fracture network swept volume is obtained by multiplying the volume of the microseismic monitoring fracture in the fracturing section by the fracturing efficiency coefficient corresponding to the fracturing section.

[0017] As a preferred embodiment of the method for predicting the volumetric fracture network sweep volume of a horizontal well in a tight oil and gas reservoir according to the present invention, the steps of repeatedly calculating the fracture efficiency coefficient of the fractured section and calculating the effective fracture network sweep volume are performed to obtain the effective fracture network sweep volume of all fractured sections of the horizontal well to be predicted.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Based on the geological engineering parameters of the well and the large volume data of microseismic monitoring, the effective fracture network swept volume of each fractured section of the horizontal well is quantitatively evaluated by calculating the fracture efficiency coefficient of different fractured sections, thus overcoming the problem of the fracture network swept volume results of the field microseismic monitoring being too large. 2. By establishing a multi-factor evaluation model, the weight factors of different geological engineering parameters are calculated. Based on the geological parameters at the perforation location of the fracturing section, the geological quality index is calculated. Furthermore, the fluid injection index of each cluster in the horizontal well fracturing section is calculated. This achieves quantitative evaluation of the fluid injection volume and fracture creation efficiency of different fracturing clusters in the fracturing section, overcoming the shortcomings of the previous general evaluation methods. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of 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. Wherein: Figure 1 This is a flowchart illustrating the overall process of predicting volumetric pressure fracture network and volumetric volume in a horizontal well of a tight oil and gas reservoir according to an embodiment of the present invention. Figure 2 This is a comparison chart of the correlation coefficients of geological engineering parameters of different fractured sections in a horizontal well of a tight oil and gas reservoir according to an embodiment of the present invention, which describes the volumetric fracture network wave and volume prediction method. Figure 3 This is a comparison chart showing the calculation of weighting factors for geological engineering parameters of different fractured sections in a horizontal well of a tight oil and gas reservoir volumetric fracture network and volume prediction method according to an embodiment of the present invention. Figure 4 This is a comparison chart of geological quality indices of different clusters in the F1 fractured section of a horizontal well in a tight oil and gas reservoir, as described in an embodiment of the present invention, using a method for predicting volumetric fracture networks and volumes in a horizontal well. Figure 5 This is a comparison chart of different cluster fluid indices in the F1 fractured section of a horizontal well, based on the volumetric fracture network and volumetric prediction method for tight oil and gas reservoirs according to an embodiment of the present invention. Figure 6 This is a comparison chart of the correlation coefficients of geological engineering parameters in different fracturing sections of an example well in this invention, and a comparison chart of different cluster injection volumes in the F1 fracturing section of a horizontal well. Detailed Implementation

[0020] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0024] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0025] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0026] Example 1 Reference Figures 1 to 6 This is the first embodiment of the present invention, which provides a method for predicting the volumetric pressure fracture network and volumetric volume in a horizontal well of a tight oil and gas reservoir, including: In this embodiment, a typical tight oil reservoir (QC) in China is used, with a reservoir depth of 2000m. The main development layers are C6-C8 sub-layers. Horizontal well volumetric fracturing is used for development. QC1 is a typical horizontal well in the block, with the development layer being C6 sub-layer. The well spacing is 350m, the horizontal well section is 1700m long, and the oil layer penetration rate is 79.4%. 18 fracturing stages are designed, numbered F1 to F18. To evaluate the reservoir stimulation fracturing effect, 18 downhole microseismic tests are performed. Taking the basic data of stages F1 to F5 as an example, the effective fracture network swept volume of stage F1 is calculated to provide a basis for fracturing scheme optimization and effect evaluation. S1. Based on the geological and engineering parameters and microseismic monitoring fracture volume data of at least one horizontal well that has been fractured, establish a multi-factor evaluation model and calculate the weighting factors of the influence of various geological parameters and engineering parameters on the microseismic monitoring fracture volume. It should be noted that this step aims to quantitatively identify the key geological and engineering factors affecting the volume of the fracture network and their importance by analyzing historical data; Furthermore, at least one horizontal well with complete fracturing operation data and microseismic monitoring data in the study area was selected as a sample well. A three-dimensional geological model was established based on the well logging interpretation results of the sample well, and the average reservoir geological parameters and geomechanical parameters of each fracturing section were extracted from it. At the same time, the fracturing parameters and fracture volume data obtained from microseismic monitoring for each fracturing section were collected, as shown in Table 1. Among them, the reservoir geological parameters, geomechanical parameters and fracturing parameters together constitute the geological engineering parameters. Specifically, reservoir geological parameters include porosity, permeability, and oil saturation. Specifically, geomechanical parameters include the brittleness index; Specifically, fracturing parameters include the amount of fracturing fluid and the amount of sand in a single stage; Table 1. Basic parameters of the F1~F5 fracturing sections of horizontal well QC1. Furthermore, all the above parameters are mapped one-to-one with the microseismic monitoring fracture volume of each fracturing segment to form a complete basic dataset. Furthermore, based on the collected basic dataset, an evaluation model matrix and reference columns for correlation analysis are constructed, and a multi-factor evaluation model matrix composed of various geological engineering parameters is established: in, A model for evaluating crack volume in microseismic monitoring; This refers to the number of fracturing stages in a horizontal well. Basic parameters corresponding to horizontal well fracturing stimulation; Furthermore, a reference column for the evaluation matrix is ​​established, consisting of the microseismic monitoring fracture volumes corresponding to each fracturing segment: in, The volume of fractures in each fracturing section of a horizontal well, measured in cubic meters (m³). 3 ); In addition, in order to eliminate the influence of differences in the dimensions and numerical ranges of different geological engineering parameters on the analysis results, it is necessary to standardize the evaluation model matrix and the evaluation matrix reference column. Methods such as mean normalization, initial value normalization or range normalization can be used to map all data into a comparable interval (such as [0, 1]). Furthermore, based on the standardized data, grey relational analysis was used to calculate the correlation coefficient between various geological engineering parameters and the volume of microseismic monitoring cracks (reference). Figure 2 This correlation coefficient characterizes the degree of correlation between the sequence of influencing factors and the reference sequence at various time points (i.e., different fracturing stages): in, is the correlation coefficient, and is a dimensionless quantity; The coefficient is set to 0.5. These are the standardized elements of the evaluation matrix for geological and engineering parameters of each fracturing section in a horizontal well. Elements after standardization of fracture volume for microseismic monitoring of each fractured section of a horizontal well; Furthermore, based on the correlation coefficients of each fracturing segment and parameter calculated above, the average correlation coefficient of each parameter across all fracturing segments is calculated and normalized to obtain the weighting factor of the influence of various geological engineering parameters on the fracture volume of microseismic monitoring (see reference). Figure 3 ): in, Let be the weighting factors for different parameters, be a dimensionless quantity, and the sum of the weighting factors for all parameters is 1. It should be noted that this weighting factor quantitatively reflects the contribution of different parameters to the formation of the fracture network volume, providing a basis for subsequent calculation of the fracturing fracture efficiency coefficient; S2. For any fracturing section of the horizontal well to be predicted, calculate the fracturing efficiency coefficient of the fracturing section based on the geological parameters and weighting factors of each fracturing cluster perforation location within the fracturing section. It should be noted that the core of this step is to evaluate the uniformity of the distribution of fracturing fluid among different fracturing clusters within a single fracturing section. It should be explained that the more uniform the distribution, the more ideal the fracturing effect and the higher the fracture creation efficiency. This invention introduces a "fracturing fracture creation efficiency coefficient" to quantitatively characterize this efficiency. This coefficient will be used to correct the usually overestimated fracture network swept volume obtained directly from microseismic monitoring. Furthermore, taking any fracturing section of the horizontal well to be predicted as an example, its fracturing efficiency coefficient is calculated. Specifically, detailed geological parameters are obtained at the perforation locations of each fracturing cluster within the fracturing section to be predicted. Unlike the average parameters obtained in step S1, more refined data corresponding to each cluster location is needed here. These geological parameters mainly include porosity, permeability, oil saturation, and brittleness index. Subsequently, the obtained geological parameters of each cluster are standardized to eliminate the influence of dimensions. Finally, the standardized geological parameter values ​​of each fracturing cluster are multiplied by the weighting factors of the corresponding geological parameters calculated in step S1 and summed to obtain the geological quality index of each fracturing cluster (see reference). Figure 4 This index comprehensively reflects the quality of the geological conditions at the location of the cluster, and its calculation formula is as follows: in, The geological quality index for different perforation locations in the fracturing section is a dimensionless quantity. The value of the standardized geological parameter of the k-th reservoir is a dimensionless quantity. Let be the weighting factor for the k-th geological parameter, which is a dimensionless quantity; It should be noted that fracturing clusters with higher geological quality indices have rock physical and mechanical properties more conducive to fracture formation and propagation, and therefore theoretically will absorb more fracturing fluid. Based on this, the fluid inflow coefficient of a fracturing cluster can be calculated by normalizing the geological quality index of a single fracturing cluster by dividing it by the sum of the geological quality indices of all fracturing clusters within the fracturing section (see reference). Figure 5 This coefficient represents the proportion of the total liquid volume that the cluster is expected to receive: in, is the fluid injection coefficient of the lth cluster in the horizontal well fracturing section, and is a dimensionless quantity; L represents the cluster number of the fracturing section in the horizontal well; L is the number of clusters in the fracturing section of the horizontal well, in units of clusters. Furthermore, by multiplying the total planned fracturing fluid volume for the fracturing stimulation section by the fluid injection coefficient of each fracturing cluster calculated in the previous step, the specific fracturing fluid volume allocated to each fracturing cluster can be predicted (see reference). Figure 6 ): in, For the horizontal well fracturing and stimulation section Cluster injection volume, in cubic meters; Q is the total fracturing fluid volume of the horizontal well fracturing section, in cubic meters; It should be noted that, since ideal volumetric fracturing requires fracturing fluid to enter uniformly among the clusters in order to form a balanced fracture network, the dispersion of the predicted fluid inflow of each cluster reflects the efficiency of the fracturing operation. Based on this, we also need to calculate the standard deviation of the predicted fluid inflow of each fracturing cluster within the fracturing stimulation section. The larger the standard deviation, the more uneven the fluid inflow of each cluster. Specifically, the formula for calculating this standard deviation is as follows: Where D is the standard deviation of the predicted fluid inflow of each fracturing cluster within the fracturing modification section; Furthermore, the final fracturing efficiency coefficient is determined based on this standard deviation. It should be noted that the more uneven the fluid injection distribution among the fracturing clusters, the larger the standard deviation, and the smaller the corresponding fracturing efficiency coefficient. in, is the fracturing efficiency coefficient, and is a dimensionless quantity; It should be noted that the fracturing efficiency coefficient directly quantifies the degree to which the fracture network within a segment is effectively and evenly modified. S3. Based on the fracture volume and fracture creation efficiency coefficient of the fracturing section, the effective fracture network swept volume of the fracturing section is calculated. It should be noted that the purpose of this step is to solve the core problem of the generally large microseismic monitoring volume mentioned in the background technology. By using the "fracture efficiency coefficient" calculated in step S2, which can reflect the uniformity of the segment modification, as a correction factor, the original fracture network swept volume obtained by field monitoring is corrected, so as to obtain an effective fracture network swept volume that is closer to the actual oil and gas production contribution. Furthermore, the effective fracture network swept volume of a single fracturing section is calculated; Specifically, the volume of microseismic monitoring fractures in the fracturing section to be predicted is multiplied by the fracturing efficiency coefficient calculated in step S2 for that fracturing section. The product is the effective fracture network swept volume of the fracturing section. in, The volumetric pressure fracture network sweep volume in a horizontal well is expressed in 10,000 cubic meters. The volume of fractures monitored by microseismic monitoring in the t-th fracturing segment of the horizontal well is expressed in 10,000 cubic meters; T is the number of the fracturing segments in the horizontal well, expressed in segments; t is the number of the fracturing and modified segment in the horizontal well. It should be noted that the effective fracture network swept volume obtained through the above calculation eliminates the loss of stimulation efficiency caused by the uneven distribution of fracturing fluid among clusters within the segment. The result is more practically physical than the original microseismic monitoring volume and can more accurately reflect the reservoir stimulation area with effective conductivity. In addition, to obtain an evaluation of the overall fracturing effect of the horizontal well to be predicted, steps S2 and S3 can be repeated to calculate all fracturing sections of the horizontal well in sequence. The effective fracture network sweep volume can ultimately yield the effective alteration volume profile of each section distributed along the horizontal wellbore, providing a more scientific and quantitative basis for the well's production capacity prediction, subsequent optimization of fracturing schemes, and adjustment of the development strategy for the entire block.

[0027] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0028] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0029] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0030] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0031] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0032] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for predicting the volumetric pressure fracture network swept volume in horizontal wells of tight oil and gas reservoirs, characterized in that, include: Based on the geological and engineering parameters and microseismic monitoring fracture volume data of at least one horizontal well that has been fractured, a multi-factor evaluation model is established, and the weighting factors of the influence of various geological parameters and engineering parameters on the microseismic monitoring fracture volume are calculated. For any fracturing section of the horizontal well to be predicted, the fracturing efficiency coefficient of the fracturing section is calculated based on the geological parameters of the fracturing cluster perforation locations within the fracturing section and the weighting factor. Based on the microseismic monitoring fracture volume of the fracturing section and the fracturing efficiency coefficient, the effective fracture network swept volume of the fracturing section is calculated.

2. The method for predicting volumetric pressure fracture network sweep volume in horizontal wells of tight oil and gas reservoirs as described in claim 1, characterized in that, The geological engineering parameters include: Reservoir geological parameters, geomechanical parameters, and fracturing parameters; The reservoir geological parameters include porosity, permeability, and oil saturation; the geomechanical parameters include the brittleness index; and the fracturing parameters include fracturing fluid volume and sand volume.

3. The method for predicting volumetric pressure fracture network sweep volume in horizontal wells of tight oil and gas reservoirs as described in claim 1, characterized in that, The steps for calculating the weighting factors include: Establish a multi-factor evaluation model matrix composed of the aforementioned geological engineering parameters; Establish an evaluation matrix reference column composed of the microseismic monitoring fracture volumes corresponding to each fracturing segment; The evaluation model matrix and the evaluation matrix reference columns are standardized. Based on the standardized data, the correlation coefficients between various geological engineering parameters and the volume of cracks monitored by microseismic monitoring were calculated; Based on the correlation coefficient, the weighting factors of each geological engineering parameter are obtained through normalization.

4. The method for predicting volumetric pressure fracture network sweep volume in horizontal wells of tight oil and gas reservoirs as described in claim 3, characterized in that, The step of calculating the correlation coefficient includes: The absolute difference between each element in the standardized evaluation model matrix and the corresponding element in the reference column of the evaluation matrix is ​​determined, and the correlation coefficient is determined based on the maximum and minimum values ​​of the absolute difference and the preset calculation coefficient.

5. The method for predicting volumetric pressure fracture network sweep volume in horizontal wells of tight oil and gas reservoirs as described in claim 1, characterized in that, The steps for calculating the fracturing efficiency coefficient include: Geological parameters of the location of each fracturing cluster perforation in the section to be predicted for fracturing are obtained and the geological parameters are standardized. The standardized geological parameters are multiplied by the weighting factors of the corresponding geological parameters and summed to obtain the geological quality index of each fracturing cluster. Based on the geological quality index of each region, the fracturing efficiency coefficient of the fracturing modification section is calculated.

6. The method for predicting volumetric pressure fracture network sweep volume in horizontal wells of tight oil and gas reservoirs as described in claim 5, characterized in that, The steps for calculating the fracturing efficiency coefficient based on local geological quality indices include: The fluid inlet coefficient of a single fracturing cluster is calculated by dividing the geological quality index of a single fracturing cluster by the sum of the geological quality indices of all fracturing clusters within the fracturing section.

7. The method for predicting volumetric pressure fracture network sweep volume in horizontal wells of tight oil and gas reservoirs as described in claim 6, characterized in that, The step of calculating the fracturing efficiency coefficient based on local geological quality indices further includes: The total fracturing fluid volume of the fracturing modification section is multiplied by the fluid inlet coefficient of each fracturing cluster to obtain the fluid inlet volume of each fracturing cluster.

8. The method for predicting volumetric pressure fracture network sweep volume in horizontal wells of tight oil and gas reservoirs as described in claim 7, characterized in that, The step of calculating the fracturing efficiency coefficient based on local geological quality indices further includes: Calculate the standard deviation of the fluid injection volume of each fracturing cluster within the fracturing modification section, and determine the fracturing efficiency coefficient based on the standard deviation; The more uneven the fluid inflow distribution of each fracturing cluster, the larger the standard deviation, and the smaller the fracturing efficiency coefficient.

9. The method for predicting volumetric pressure fracture network sweep volume in horizontal wells of tight oil and gas reservoirs as described in claim 1, characterized in that, The step of calculating the effective mesh sweep volume includes: The effective fracture network swept volume is obtained by multiplying the volume of the microseismic monitoring fracture in the fracturing section by the fracturing efficiency coefficient corresponding to the fracturing section.

10. The method for predicting volumetric pressure fracture network sweep volume in horizontal wells of tight oil and gas reservoirs as described in claim 1, characterized in that, Repeat the steps of calculating the fracturing efficiency coefficient of the fracturing section and calculating the effective fracture network swept volume to obtain the effective fracture network swept volume of all fracturing sections of the horizontal well to be predicted.

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