CO2 pre-fracturing parameter optimization design method, processor and storage medium

Abstract

The embodiment of the invention provides a CO2 pre-fracturing parameter optimization design method, a processor and a storage medium. The CO2 pre-fracturing parameter optimization design method comprises the following steps: S1, determining an evaluation index of a fracturing effect; s2, main control fracturing engineering parameters are selected; s3, analyzing a key parameter single variable; s4, carrying out orthogonal test design and evaluation; according to the method, the influence of a plurality of fracturing engineering parameters on a CO2 preposed fracturing crack propagation rule can be analyzed through a plurality of research means such as physical experiments, numerical simulation and theoretical analysis; the problem of insufficient design maturity of the construction scheme of the on-site CO2 pre-fracturing technology at the present stage due to less research on CO2 pre-fracturing on-site application effect analysis and fracturing construction parameter optimization is effectively solved, the reservoir recovery degree can be improved, the yield decline speed is slowed down, and meanwhile, the dependence and consumption on water resources are reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of oil and gas fracturing, and particularly relates to a CO2 pre-positioning fracturing parameter optimization design method, a processor and a storage medium. BACKGROUND

[0002] There are three main factors affecting the fracturing effect of the reservoir: geological factors, engineering factors and production systems. Due to the high heterogeneity of shale oil reservoirs, geological factors such as reservoir temperature, stress distribution, permeability and porosity are objective factors affecting the fracturing effect. The injection mode, injection rate and injection volume of the fracturing fluid and other fracturing engineering parameters are subjective factors. Reasonable design of fracturing engineering parameters can increase the stimulated volume of shale oil reservoirs, optimize the flow conditions of the formation fluid, thereby improving the reservoir recovery efficiency and increasing the oil well production. Therefore, for target shale oil reservoir blocks with relatively mature geological conditions, the fracturing engineering parameters are closely related to the recoverable reserves of the reservoir and are an important content of field fracturing research.

[0003] The working principle of the CO2 pre-positioning fracturing technology is to first perform preliminary CO2 fracturing by using the fracture forming performance of CO2. Then, a water-based fracturing fluid is used to inject sand to expand and support the initial fractures. The significant advantage of this technology is that it effectively addresses the technical challenges in CO2 injection fracturing, simplifies the operation process and reduces the production cost of oilfield development. In addition, CO2 pre-positioning fracturing can effectively open microfractures in the reservoir, change the expansion direction of artificial fractures and increase the complexity of the fracture network structure. The water-based sand-carrying fluid can further extend and effectively support the fractures, thereby achieving energy enhancement and large-scale reconstruction of the reservoir. SUMMARY

[0004] The purpose of the embodiments of the application is to provide a CO2 pre-positioning fracturing parameter optimization design method, a processor and a storage medium To achieve the above purpose, the first aspect of the application provides a CO2 pre-positioning fracturing parameter optimization design method, comprising the following steps: Step S1: determining the evaluation index of the fracturing effect; Step S2: selecting the main control fracturing engineering parameter; Step S3: analyzing the single variable of the key parameter; Step S4: orthogonal test design and evaluation; The step S1 comprises: using the three-dimensional seepage area S evaluating the engineering parameters to assess the hydraulic fracturing effect, wherein the engineering parameters include at least one of the fracturing fluid pumping sequence, the CO2 pre-positioning injection mode, the CO2 injection volume and the CO2 injection rate; The step S2 comprises: selecting the main control fracturing engineering parameter by using the Pearson correlation coefficient; Step S3 includes: Arrange the Pearson correlation coefficients of the main control fracturing engineering parameters from high to low to determine the importance ranking of the parameters affecting the fracturing effect; According to the determined ranking, use the fracture fractal dimension D , seepage area S and the proportion of the main fracture to conduct single-factor analysis for each key main control fracturing engineering parameter in sequence to study the influence of different parameters on the hydraulic fracturing effect, and thus determine the optimal value range of each main control fracturing engineering parameter.

[0005] In the embodiment of the present invention, the three-dimensional seepage area in Step S1 S is defined as: (1) where, is the seepage area of the three-dimensional fracture, and n represents dividing the fracture end face into n independent small regions.

[0006] In the embodiment of the present invention, the Pearson correlation coefficient in Step S2 is defined as: (2) where r is the Pearson correlation coefficient, and its value range is -1 ≤ r ≤ 1. When 0 < r ≤ 1, it shows a positive correlation; when -1 ≤ r < 0, it shows a negative correlation; when r = 0, there is no correlation; n is the number of samples taken; X and Y are random variables; and [[ID= thirty-five]]are the average values of the random variables.

[0007] In the embodiment of the present invention, the fracture fractal dimension in Step S3 D is defined as: (3) , where is the measurement scale of the box covering the entire rough fracture surface F, is the number of boxes required to cover the entire rough fracture surface F; for the fracture surface conforming to the rough law, and The relationship between them is expressed as: (4) In the formula, is the fractal dimension of the rough surface.

[0008] In the embodiment of the present invention, the proportion of the main fracture in Step S3 is defined as: (5) where, The seepage area of ​​the three-dimensional main seam. S The seepage area of ​​the three-dimensional crack.

[0009] In this embodiment of the invention, step S4 includes: Based on the optimal value range obtained in step S3, the key control fracturing engineering parameters are analyzed for the combined effect of multiple factors based on the orthogonal test principle.

[0010] A second aspect of the present invention provides a processor configured to execute the above-described CO2 pre-fracturing parameter optimization design method.

[0011] A third aspect of the present invention provides a machine-readable storage medium storing instructions for causing a machine to execute the above-described CO2 pre-fracturing parameter optimization design method.

[0012] Through the above technical solutions, various research methods such as physical experiments, numerical simulations, and theoretical analysis can be used to analyze the influence of multiple fracturing engineering parameters on the propagation law of CO2 pre-fracturing fractures. This effectively solves the problem of insufficient maturity in the design of on-site CO2 pre-fracturing technology construction schemes caused by the lack of analysis of the field application effect of CO2 pre-fracturing and the optimization of fracturing construction parameters. It is conducive to improving reservoir recovery, slowing down the rate of production decline, and reducing dependence on and consumption of water resources.

[0013] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0014] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram illustrating the steps of the CO2 pre-fracturing parameter optimization design method according to an embodiment of the present invention; Figure 2 This is a flowchart of the CO2 pre-fracturing parameter optimization design method according to an embodiment of the present invention. Detailed Implementation

[0015] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0016] The purpose of this invention is to achieve three-dimensional percolation area based on target parameters. SBy using Pearson correlation coefficient to conduct correlation analysis on the fracturing effect of different engineering parameters, the priority research order of each parameter is clarified, and univariate analysis is carried out on each parameter to determine its reasonable range. Then, the fracturing parameters are comprehensively evaluated and optimized through orthogonal experimental principle, and finally the best CO2 pre-fracturing engineering parameter design scheme is selected.

[0017] Figure 1 This is a schematic diagram illustrating the steps of the CO2 pre-fracturing parameter optimization design method according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the CO2 pre-fracturing parameter optimization design method according to an embodiment of the present invention. (Reference) Figure 1 and Figure 2 The CO2 pre-fracturing parameter optimization design method provided in this embodiment of the invention may include the following steps.

[0018] Step S1: Determine the evaluation indicators for fracturing effect.

[0019] Specifically, step S1 may include: Utilizing three-dimensional permeation area S The engineering parameters are evaluated to assess the hydraulic fracturing effect, wherein the engineering parameters include at least one of the following: fracturing fluid injection sequence, CO2 pre-injection method, CO2 injection volume, and CO2 injection discharge volume.

[0020] In step S1, when determining the evaluation index for fracturing effect, the three-dimensional percolation area can be used. S To evaluate the hydraulic fracturing effect, four key parameters were assessed: fracturing fluid injection sequence, CO2 pre-injection method, CO2 injection rate, and CO2 injection displacement. Among these, the three-dimensional permeation area was also evaluated. S It can be described as: (1) In the formula, The seepage area of ​​the three-dimensional crack (e.g., in meters). 2 ); n represents dividing the crack end face into n independent small regions, and Λ represents the omitted terms.

[0021] Step S2: Select the main control fracturing engineering parameters.

[0022] Step 2 uses the Pearson correlation coefficient to select the main fracturing engineering parameters. Specifically, when selecting the main fracturing engineering parameters in step S2, the Pearson correlation coefficient can be used as the main control engineering parameter to provide a basis for the selection and research order design of the fracturing fluid injection sequence, CO2 pre-injection method, CO2 injection volume, CO2 injection displacement, etc. By carrying out an analysis of the main engineering factors affecting the fracturing effect, this step can prioritize the study of the main factors and exclude irrelevant factors, thereby simplifying the analysis process. And by selecting factors with a relatively high correlation with the target parameters, the accuracy of the optimization scheme can be effectively enhanced. The Pearson correlation coefficient in the selection of the main fracturing engineering parameters can be described as follows: (2) In the formula, r is the Pearson correlation coefficient, and its value range is -1 ≤ r ≤ 1. When 0 < r ≤ 1, it shows a positive correlation; when -1 ≤ r < 0, it shows a negative correlation; when r = 0, there is no correlation; n is the number of samples taken in the study; X and Y are random variables; and are the average values of the random variables.

[0023] Step S3: Analyze the univariate of the key parameters.

[0024] Specifically, step S3 includes: Arrange the Pearson correlation coefficients of the main fracturing engineering parameters from high to low to determine the importance ranking of the parameters affecting the fracturing effect; According to the determined ranking, use the fracture fractal dimension D , filtration area S and the proportion of the main fracture to conduct univariate analysis for each key main fracturing engineering parameter in turn to study the influence of different parameters on the hydraulic fracturing effect, and thus determine the optimal value range of each main fracturing engineering parameter.

[0025] More specifically, when step S3 conducts an analysis of the univariate of the key parameters, the Pearson correlation coefficients of each parameter can be arranged from high to low to determine the importance ranking of the parameters affecting the fracturing effect. Through the order generated by the key parameters, use the fracture fractal dimension D , filtration area S and the proportion of the main fracture to conduct univariate analysis for each key engineering parameter in turn to further explore the influence of different parameters on the hydraulic fracturing effect, and accordingly determine the optimal value range of each engineering parameter.

[0026] The surface fractal dimension can be described as: (3) The theoretical fractal dimension of rough surfaces ranges from 1 to 3. A fractal dimension of 2 means the rough surface is planar, and a fractal dimension close to 3 means the rough surface occupies almost the entire space. The measurement scale used is... The boxes cover the entire rough fracture surface F, and the required number of boxes is expressed as... For fracture surfaces that conform to the roughness law, and The relationship between them can be represented as: (4) In the formula, This represents the fractal dimension of the rough surface. When using a measurement scale... When the box, This indicates the number of boxes required to cover the entire rough fracture surface F.

[0027] Main seam ratio It can be represented as: (5) In the formula, The infiltration area of ​​the three-dimensional main seam (e.g., in m²) 2 Main seam ratio The larger the value, the more uneven the crack propagation between clusters and the greater the degree of stress interference.

[0028] Step S4: Orthogonal Experimental Design and Evaluation. Specifically, this step, based on the optimal value range obtained in Step S3, conducts a multi-factor interaction analysis of key controlling fracturing engineering parameters based on the orthogonal experimental principle. In the orthogonal experimental design and evaluation, the optimal parameter value range results obtained from single-factor analysis can be used as a basis to further conduct a multi-factor interaction analysis based on the orthogonal experimental principle. This step determines the optimal combination of CO2 pre-fracturing engineering parameters by comparing fracturing effect evaluation indicators. The orthogonal experimental principle can be a method known to those skilled in the art.

[0029] Example Based on the on-site work situation, taking a certain block as an example, the specific steps for optimizing the CO2 pre-fracturing parameters are as follows: I. Determine the evaluation indicators for fracturing effect.

[0030] 1. Select representative values ​​for key parameters to develop a simplified simulation scheme; 2. Conduct simulations of the simplified plan; 3. Analyze the permeation area of ​​key parameters such as fracturing fluid injection sequence, CO2 pre-injection method, CO2 injection volume, and CO2 injection displacement. S Results data; II. Selection of Main Control Fracturing Engineering Parameters 1. Based on the infiltration areaS Based on the statistical data, the dominant factors affecting the fracturing effect of key parameters such as fracturing fluid injection sequence, CO2 pre-injection method, CO2 injection volume and CO2 injection displacement were analyzed using Pearson correlation coefficient. 2. Sort the analysis results.

[0031] III. Univariate Analysis of Key Parameters 1. Determine the order of importance of parameters affecting fracturing effect based on the order of Pearson correlation coefficients of each parameter; 2. Based on this order, utilize the fractal dimension of the crack. D filtration area S and the proportion of main seam Single-factor analyses were conducted sequentially for each key engineering parameter. 3. Obtain the approximate optimal optimization data range for the four key statistical parameters.

[0032] IV. Orthogonal Experimental Design and Evaluation 1. Based on the results of the optimal range of parameter values ​​obtained from the single-factor analysis, further analysis of the combined effects of multiple factors is carried out based on the principle of orthogonal experiment. 2. Utilizing the fractal dimension of cracks D filtration area S and the proportion of main seam The optimal combination of CO2 pre-fracturing engineering parameters was determined by comparing the evaluation indicators of fracturing effect.

[0033] This invention also provides a processor configured to execute the CO2 pre-fracturing parameter optimization design method of any of the above embodiments.

[0034] This invention also provides a machine-readable storage medium storing instructions that cause a machine to execute the CO2 pre-fracturing parameter optimization design method of any of the above embodiments.

[0035] The method of this invention utilizes GOHFER to determine the three-dimensional infiltration area based on the target parameter. SThis study utilizes Pearson correlation coefficients to analyze the correlation between different engineering parameters and fracturing effects, clarifying the priority of each parameter for research. Univariate analysis is then conducted on each parameter to determine its reasonable range. Finally, orthogonal experimental design is used to comprehensively evaluate and optimize fracturing parameters, ultimately selecting the optimal CO2 pre-fracturing engineering parameter design scheme. This technology addresses the current issue of insufficient maturity in the design of CO2 pre-fracturing technology construction schemes due to a lack of research on the field application effects and optimization of fracturing construction parameters. It helps determine suitable key fracturing process parameters in the field, effectively increasing fracture size and reservoir stimulation volume, improving fracture network structure characteristics and near-wellbore seepage, thereby further increasing the fracture drainage area. This plays a crucial role in improving the hydraulic fracturing development effect of reservoirs. Furthermore, the application of CO2 pre-fracturing calculations helps improve reservoir recovery, slow down the rate of production decline, and reduce dependence on and consumption of water resources.

[0036] Those skilled in the art will understand that embodiments of this application 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 embodied 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.

[0037] 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.

[0038] 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 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0039] 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.

[0040] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0041] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0042] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0043] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0044] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A CO2 pre-pad frac parameter optimization design method, characterized in that, The method comprises the following steps: Step S1: determining an evaluation index of fracturing effect; Step S2: selecting a main control fracturing engineering parameter; Step S3: analyzing a key parameter single variable; Step S4: orthogonal test design and evaluation; The step S1 comprises: Utilizing three-dimensional percolation area S evaluating engineering parameters to assess the hydraulic fracturing effect, wherein the engineering parameters include at least one of a fracturing fluid pumping sequence, a CO2 preflush injection method, a CO2 injection volume, and a CO2 injection rate The step S2 comprises: selecting the main control fracturing engineering parameter by using a Pearson correlation coefficient; The step S3 comprises: Pearson correlation coefficients of the main control fracturing engineering parameters are arranged from high to low to determine an importance order of the parameters affecting the fracturing effect; According to the determined sequence, the crack fractal dimension is used D , the filtration area S and the main crack proportion The single factor analysis is carried out for each key main control fracturing engineering parameter in turn, so as to study the influence of different parameters on the hydraulic fracturing effect, and thus the optimal value range of each main control fracturing engineering parameter is determined.

2. The CO2 pre-pad frac parameter optimization design method of claim 1, wherein, The three-dimensional percolation area in step S1 S is defined as: （1） wherein, is the filtration area of the three-dimensional fracture, and n represents the division of the fracture end face into n independent small areas.

3. The CO2 pre-pad frac parameter optimization design method of claim 1, wherein, The Pearson correlation coefficient in the step S2 is defined as: （2） Wherein, r is Pearson correlation coefficient, the value range is -1≤r≤1, when 0 and are random variable average values.

4. The CO2 pre-pad frac parameter optimization design method of claim 1, wherein, Fractal dimension of the cracks in step S3 D is defined as: （3） wherein is the measurement scale of the box covering the entire rough fracture surface F, is the number of boxes needed to cover the entire rough fracture surface F; for fracture surfaces complying with the roughness law, and the relationship between them is expressed as: （4） In the formula, Df is the fractal dimension of the rough surface.

5. The CO2 pre-pad frac parameter optimization design method of claim 1, wherein, Main stitch ratio in step S3 is defined as: （5） wherein is the filtration area of the three-dimensional main fracture, S is the filtration area of the three-dimensional fracture.

6. The CO2 pre-pad frac parameter optimization design method of claim 1, wherein, The step S4 comprises: Based on the optimal value range obtained in the step S3, a multi-factor joint action analysis is performed on the key main control fracturing engineering parameters based on an orthogonal test principle.

7. A processor, comprising:

8. A machine readable storage medium having stored thereon instructions for causing a machine to perform the CO2 pre-frac parameter optimization design method according to any one of claims 1 to 6.

8. A machine readable storage medium having stored thereon instructions for causing a machine to perform the CO2 pre-frac parameter optimization design method according to any one of claims 1 to 6.

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