Method, device, equipment and medium for analyzing influencing factors of repeated fracturing effect

Abstract

The application provides an analysis method and device for influencing factors of repeated fracturing effect, equipment and medium, and belongs to the technical field of oil and gas reservoir reconstruction. In the method, the initial data column corresponding to a plurality of data analysis methods can determine the ranking of the main control factor in each type of parameter, and the influence level of each main control factor can be determined through the corresponding graph and parameter change data of each type of parameter. Since the ranking of any main control factor can reflect the importance of the main control factor in the plurality of main control factors of each type of parameter, and the influence level of the main control factor can reflect the influence strength of the main control factor on the repeated fracturing effect, the ranking and influence level of each main control factor can be determined through the comprehensive analysis method combining a plurality of data analysis methods, the influence of each main control factor on the repeated fracturing effect is more comprehensively and accurately displayed, effective data reference can be provided for subsequent development work, and the data processing efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of oil and gas field reservoir stimulation technology, and in particular to a method, apparatus, equipment and medium for analyzing the influencing factors of repeated fracturing effect. Background Technology

[0002] Low-permeability sandstone reservoirs are characterized by low natural productivity, requiring fracturing to stimulate the reservoir and improve well production and lifespan. However, after a period of production, these fracturing wells gradually experience a decline in production and oil recovery rate, severely impacting development effectiveness. Repeated fracturing is an effective means of managing these low-yield and inefficient wells.

[0003] Currently, the analysis of factors influencing the effectiveness of retrieval fracturing mainly relies on single methods. However, these methods are relatively simplistic and lack a comprehensive analysis of key factors before and after retrieval fracturing, thus failing to accurately reflect the impact of the main controlling factors on the effectiveness of retrieval fracturing. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, and medium for analyzing the influencing factors of repeated fracturing effects. This comprehensive analysis method more intuitively and accurately demonstrates the degree of influence of each major controlling factor on the repeated fracturing effect, improving the comprehensiveness and richness of the analysis of major controlling factors. It can provide effective data references for subsequent development work, thereby improving data processing efficiency. The technical solution is as follows:

[0005] On the one hand, a method for analyzing the influencing factors of repeated fracturing effect is provided, the method comprising:

[0006] Data corresponding to each type of parameter is obtained from multiple types of parameters, including geological parameters, primary fracturing engineering parameters, and repeated fracturing engineering parameters. Each type of parameter includes multiple main controlling factors that affect the effect of repeated fracturing.

[0007] For any type of parameter, based on multiple initial data columns corresponding to the type of parameter, the ranking of each main control factor in the type of parameter is determined. Different initial data columns correspond to different data analysis methods. Any initial data column is used to represent the initial weight value of multiple main control factors in the type of parameter, and the ranking of any main control factor is the ranking of the total weight value of the main control factor.

[0008] For any type of parameter, based on the parameter change data of each main control factor in the type of parameter, the influence level of each main control factor in the type of parameter is determined in the chart corresponding to the type of parameter. The chart is used to indicate the relationship between the parameter change ratio and the capacity growth ratio. The influence level of any main control factor is used to indicate the degree of influence of the main control factor on the repeated fracturing effect.

[0009] On the other hand, an analytical device for the influencing factors of repeated fracturing effect is provided, the device comprising:

[0010] The acquisition module is used to acquire data corresponding to each type of parameter among multiple types of parameters. The multiple types of parameters include geological parameters, primary fracturing engineering parameters, and repeated fracturing engineering parameters. Each type of parameter includes multiple main control factors that affect the repeated fracturing effect.

[0011] The first determining module is used to determine the ranking of each main control factor in any class parameter based on multiple initial data columns corresponding to the class parameter. Different initial data columns correspond to different data analysis methods. Any initial data column is used to represent the initial weight value of multiple main control factors in the class parameter, and the ranking of any main control factor is the ranking of the total weight value of the main control factor.

[0012] The second determining module is used to determine the influence level of each major control factor in the class of parameters based on the parameter change data of each major control factor in the class of parameters in the chart corresponding to the class of parameters. The chart is used to indicate the relationship between the parameter change ratio and the capacity growth ratio. The influence level of any major control factor is used to indicate the degree of influence of the major control factor on the repeated fracturing effect.

[0013] In some embodiments, the first determining module is configured to, for any class parameter, standardize multiple initial data columns corresponding to the class parameter to obtain multiple standardized data columns corresponding to the class parameter, wherein the multiple standardized data columns correspond one-to-one with the multiple initial data columns; based on the multiple standardized data columns, determine the total weight value of each controlling factor in the class parameter, wherein the total weight value of any controlling factor is the sum of the initial weight values ​​of the controlling factors in the multiple standardized data columns; and, when the multiple controlling factors in the class parameter are sorted in descending order of total weight value, determine the ranking of each controlling factor in the class parameter.

[0014] In some embodiments, the plurality of initial data columns include a first data column and a second data column. The first data column includes the Pearson correlation coefficient of each controlling factor in the class parameters. The Pearson correlation coefficient of any controlling factor is used to indicate the linear relationship between the controlling factor and the production capacity. The second data column includes the production capacity growth multiple under changing conditions for each controlling factor in the class parameters.

[0015] In some embodiments, the second determining module is configured to, for any class of parameters, project the parameter change data of each major control factor in the class of parameters onto the chart corresponding to the class of parameters to obtain the parameter change curve of each major control factor in the class of parameters. The parameter change curve of any major control factor is used to indicate the relationship between the parameter change ratio of the major control factor and the capacity growth multiple. Based on the region to which the parameter change curve of each major control factor in the class of parameters belongs in the chart, the influence level of each major control factor in the class of parameters is determined. Multiple regions in the chart correspond one-to-one with multiple reference influence levels. The influence level of each major control factor includes at least one reference influence level.

[0016] In some embodiments, the chart includes a first region, a second region, a third region, a fourth region, and a fifth region. The first region is the region where the parameter change ratio is greater than a first threshold and the capacity growth ratio is less than a second threshold. The second region is the region where the parameter change ratio is less than the first threshold and the capacity growth ratio is less than the second threshold. The third region and the fourth region are both regions where the parameter change ratio is greater than the first threshold and the capacity growth ratio is greater than the second threshold. The third region is located below the fourth region. The fifth region is the region where the parameter change ratio is less than the first threshold and the capacity growth ratio is greater than the second threshold. The degree of influence on the repeated fracturing effect indicated by the reference influence level corresponding to each region increases sequentially from the first region, the second region, the third region, the fourth region to the fifth region.

[0017] In some embodiments, the geological parameters correspond to a first chart, which is used to represent the relationship between the parameter variation ratio of the main controlling factors in the geological parameters and the production capacity growth ratio; the primary fracturing engineering parameters and the repeated fracturing engineering parameters both correspond to a second chart, which is used to represent the relationship between the parameter variation ratio of at least one main controlling factor in the primary fracturing engineering parameters and the repeated fracturing engineering parameters and the production capacity growth ratio.

[0018] In some embodiments, the apparatus further includes:

[0019] The preprocessing module is used to standardize the data of each class of parameters in the multiple classes of parameters to obtain standardized data for each class of parameters; for any class of parameters, based on the standardized data of the class of parameters, the feature value corresponding to each influencing factor in the class of parameters is determined; based on the feature value corresponding to each influencing factor in the class of parameters, multiple controlling factors in the class of parameters are determined, and the feature values ​​corresponding to the multiple controlling factors are greater than the feature values ​​corresponding to other influencing factors in the class of parameters.

[0020] On the other hand, a computer device is provided, the computer device including a processor and a memory, the memory being used to store at least one computer program, the at least one computer program being loaded and executed by the processor to implement the method for analyzing the influencing factors of repeated fracturing effect in the embodiments of this application.

[0021] On the other hand, a computer-readable storage medium is provided, wherein at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor to implement the method for analyzing the influencing factors of repeated fracturing effect in the embodiments of this application.

[0022] On the other hand, a computer program product is provided, including a computer program that is executed by a processor to implement the method for analyzing the influencing factors of repeated fracturing effect in the embodiments of this application.

[0023] This application provides an analysis method for factors influencing the effectiveness of repeated fracturing. In this method, using initial data columns corresponding to multiple data analysis methods, the main control factors can be sorted according to their total weight value, thus obtaining the ranking of the main control factors in each parameter category. Furthermore, by using the corresponding charts and parameter change data for each parameter category, the influence level of each main control factor can be determined. Since the ranking of any main control factor reflects its importance among multiple main control factors in each parameter category, and the influence level of the main control factor reflects the strength of its impact on the repeated fracturing effect, this comprehensive analysis method combining multiple data analysis methods can determine the ranking and influence level of each main control factor. This provides a more comprehensive and accurate view of the impact of each main control factor on the repeated fracturing effect, offering effective data references for subsequent development work and improving data processing efficiency. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of an implementation environment provided according to an embodiment of this application;

[0026] Figure 2 This is a flowchart illustrating a method for analyzing the influencing factors of repeated fracturing effects according to an embodiment of this application;

[0027] Figure 3This is a flowchart of an analysis method for the influencing factors of repeated fracturing effect according to an embodiment of this application;

[0028] Figure 4 This is a schematic diagram of a drawing provided according to an embodiment of this application;

[0029] Figure 5 This is a schematic diagram of a graph including a parameter variation curve provided according to an embodiment of this application;

[0030] Figure 6 This is a block diagram of an analysis device for factors influencing the effectiveness of repeated fracturing, according to an embodiment of this application.

[0031] Figure 7 This is a block diagram of an analysis device for influencing factors of repeated fracturing effect according to an embodiment of this application;

[0032] Figure 8 This is a schematic diagram of the structure of a terminal according to an embodiment of this application;

[0033] Figure 9 This is a schematic diagram of the structure of a server according to an embodiment of this application. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0035] In this application, the terms "first," "second," etc., are used to distinguish identical or similar items with essentially the same function. It should be understood that there is no logical or temporal dependency between "first," "second," and "nth," nor are there any restrictions on quantity or execution order.

[0036] In this application, the term "at least one" means one or more, and "multiple" means two or more.

[0037] The following is a brief introduction to the terminology used in this application.

[0038] Low-permeability sandstone reservoirs refer to oil resources stored in sandstone formations with low permeability. These reservoirs are typically characterized by strong heterogeneity, numerous thin layers, and poor overall permeability. Due to the low rock permeability, oil flow is difficult, resulting in low production capacity.

[0039] Fracturing is a method to improve reservoir permeability. By injecting high-pressure fluid into the formation, fractures are created in the rock, increasing the channels for oil flow and thus increasing production. The fractures created by fracturing are called artificial fractures. Low-permeability sandstone reservoirs require fracturing to stimulate the reservoir and improve single-well production and the duration of stable production. After a period of production, these fracturing wells will gradually experience a decrease in production and oil recovery rate due to the long-term conductivity decline characteristic of artificial fractures. Long-term conductivity decline refers to the phenomenon that the conductivity of fractures gradually decreases over time. Conductivity refers to the ability of fluid to pass through fractures. This characteristic of artificial fractures is usually caused by factors such as the breakage, embedding, or loss of proppant within the fracture, as well as the closure or deformation of the fracture surface.

[0040] Repeat fracturing refers to performing fracturing operations again on oil wells that have already undergone fracturing treatment, in order to further improve permeability and production. This is usually done after the effects of the initial fracturing have gradually diminished.

[0041] Pearson correlation coefficient: This is a statistic that measures the degree of linear correlation between two variables. The Pearson correlation coefficient ranges from -1 to 1. The closer the absolute value of the Pearson correlation coefficient is to 1, the stronger the linear relationship between the two variables; the closer the absolute value is to 0, the weaker the linear relationship. In this embodiment, the Pearson correlation coefficient corresponding to the controlling factor is used to indicate the degree of linear correlation between the controlling factor and production capacity.

[0042] Capacity growth ratio: This is an indicator used to quantify the extent of capacity increase. It assesses the effect of implementing a measure or changing a parameter on capacity improvement. Specifically, the capacity growth ratio refers to the multiple by which adjusted capacity increases compared to the baseline capacity before adjustment. Adjustments can be implemented through measures or changes to parameters. The formula for calculating the capacity growth ratio is typically expressed as: Capacity Growth Ratio = (Adjusted Capacity - Base Capacity) / Base Capacity. A larger capacity growth ratio indicates a more significant effect of the adjustment on capacity improvement; a smaller ratio indicates a less significant effect. It should be noted that the capacity growth ratio can also be called the output increase ratio.

[0043] A reservoir numerical model is a model used in petroleum engineering to describe and predict the dynamic distribution of underground oil reservoirs, demonstrating the flow and distribution of fluids within the reservoir. Typically, based on relevant reservoir simulation software, the dynamic behavior of the reservoir is simulated using input geological parameters, fluid parameters, wellbore parameters, and production operation parameters. Reservoir numerical models can also simulate the production rate of oil wells or reservoirs. For field-scale reservoir numerical models, the field scale indicates the size and extent of the actual reservoir or oilfield.

[0044] Principal component analysis (PCA) is a statistical analysis method used for dimensionality reduction and feature extraction. In this method, multiple variables in the original data are transformed into a set of independent principal components. The principal components are ranked according to their contribution to the variance of the original data, thus allowing for the selection and retention of important principal components, thereby achieving dimensionality reduction. PCA typically includes several steps: data standardization, finding eigenvectors and eigenvalues, selecting principal components, calculating principal component coefficients, and interpreting the principal components.

[0045] Engineering control methods refer to the methods used to control or optimize the results of an engineering project during construction or modification. In repeated fracturing stimulation, engineering control methods may include selecting appropriate fracturing fluids and optimizing fracturing parameters.

[0046] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, many of the parameters and multiple graphics involved in this application were obtained with full authorization.

[0047] Figure 1 This is a schematic diagram of an implementation environment provided according to an embodiment of this application. See also... Figure 1 The implementation environment includes terminal 101 and server 102. Terminal 101 and server 102 can be connected directly or indirectly via wired or wireless communication, which is not limited herein.

[0048] Among them, terminal 101 can be various types of terminals such as mobile phones, desktop computers, laptops, and tablets. Server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0049] Optionally, the method for analyzing the influencing factors of repeated fracturing effect provided in the embodiments of this application can be executed by the terminal 101 alone, by the server 102 alone, or by the terminal 101 and the server 102 interacting.

[0050] In some embodiments, when the terminal 101 executes the method alone, the terminal 101 can obtain data corresponding to multiple types of parameters and determine the ranking and influence level of the main controlling factor in each type of parameter.

[0051] In some embodiments, when the server 102 executes the method alone, the server 102 can perform data calculations independently. It analyzes data corresponding to multiple types of parameters uploaded to the server by other devices to determine the ranking and influence level of the controlling factors in each type of parameter. After obtaining the above results, the server 102 can store the results on the server, send the results to other devices for display to relevant personnel, or continue to perform other data calculations based on the results; there are no restrictions on this.

[0052] In some embodiments, when the terminal 101 and server 102 interactively execute the method, the terminal 101 and server 102 are associated, and the server 102 provides background services to the terminal 101. Optionally, the server 102 undertakes the main computing work, and the terminal 101 undertakes the secondary computing work; or, the server 102 undertakes the secondary computing work, and the terminal 101 undertakes the main computing work; or, the server 102 and the terminal 101 use a distributed computing architecture for collaborative computing.

[0053] In other words, some steps in this method are executed by terminal 101, while others are executed by server 102. For example, terminal 101 obtains data corresponding to multiple types of parameters and sends this data to server 102; server 102 performs data analysis based on the data to determine the ranking and influence level of the controlling factors in each type of parameter, and sends the result back to terminal 101; terminal 101 receives the result and displays it.

[0054] It should be noted that in the following embodiments, the analysis method for the influencing factors of repeated fracturing effect proposed in the embodiments of this application, which is executed by the terminal alone, will be used as an example. That is, the data acquisition and data processing processes in this method are all executed by the terminal.

[0055] Figure 2 This is a flowchart of an analysis method for the influencing factors of repeated fracturing effect according to an embodiment of this application. This method is applied to a terminal device. (See attached diagram.) Figure 2 The method includes the following steps:

[0056] 201. The terminal acquires data corresponding to each type of parameter among multiple types of parameters, including geological parameters, primary fracturing engineering parameters, and repeated fracturing engineering parameters. Each type of parameter includes multiple main control factors that affect the effect of repeated fracturing.

[0057] In the embodiments of this application, the data corresponding to each type of parameter includes the original data of multiple treatment layers in multiple repeated fracturing wells in the target block, and may also include multiple initial data columns corresponding to each type of parameter and parameter change data of each type of main control factor obtained by data processing of the original data.

[0058] For example, the raw data corresponding to geological parameters includes the permeability, porosity, oil saturation, formation resistivity, and production level of the formation corresponding to the treatment well. The raw data corresponding to the primary fracturing engineering parameters includes the perforation thickness of the treatment well corresponding to the treatment layer, fracturing process, injection rate, and production data after the primary fracturing. The raw data corresponding to the retracement fracturing engineering parameters includes the perforation thickness of the treatment well corresponding to the treatment layer, retracement process, injection rate, fluid volume, and production data after retracement fracturing. The above data is for illustrative purposes only and is not intended to be limiting.

[0059] 202. For any type of parameter, the terminal determines the ranking of each controlling factor in that type of parameter based on multiple initial data columns corresponding to that type of parameter. Different initial data columns correspond to different data analysis methods. Any initial data column is used to represent the initial weight value of multiple controlling factors in that type of parameter, and the ranking of any controlling factor is the ranking of the total weight value of the controlling factors.

[0060] In this embodiment, since different initial data columns correspond to different data analysis methods, and multiple initial data columns are used to determine the total weight value corresponding to each main control factor, the final ranking of each main control factor combines multiple data analysis methods, making it more accurate and reliable.

[0061] The data analysis methods used to determine the initial data columns can be big data analysis methods or numerical simulation methods, etc., and this application embodiment does not impose any restrictions on this. In this solution, a comprehensive analysis is performed based on the initial weight values ​​of different data analysis methods to comprehensively determine the final ranking of each main control factor, so as to select the common factors that rank higher after comprehensive analysis as the main control factor results.

[0062] 203. For any type of parameter, the terminal determines the influence level of each major control factor in the corresponding chart based on the parameter change data of each major control factor in that type of parameter. The chart is used to indicate the relationship between the parameter change ratio and the capacity growth ratio. The influence level of any major control factor is used to indicate the degree of influence of the major control factor on the repeated fracturing effect.

[0063] In this embodiment, the controlling factors in the primary fracturing engineering parameters and the refractory fracturing engineering parameters are quite similar, while the controlling factors in the geological parameters differ significantly from both. Therefore, the geological parameters correspond to one chart, which represents the relationship between the percentage change of the controlling factors in the geological parameters and the production capacity growth ratio. The primary fracturing engineering parameters and the refractory fracturing engineering parameters correspond to another chart, which represents the relationship between the percentage change of at least one controlling factor in the primary fracturing engineering parameters and the production capacity growth ratio.

[0064] The parameter variation data of any controlling factor is used to indicate the change in production capacity under different adjustments to that controlling factor. The parameter variation data of the controlling factors in geological parameters can be projected onto the corresponding geological parameter charts, and the parameter variation data of the controlling factors in primary fracturing engineering parameters and refractory fracturing engineering parameters can be projected onto the corresponding engineering parameter charts. Since different areas on the charts correspond to different levels of influence, the influence level of the controlling factor can be determined based on the projection location of the parameter variation data.

[0065] This application provides a method for analyzing the influencing factors of repeated fracturing effects. In this method, by using initial data columns corresponding to multiple data analysis methods, the main control factors can be sorted according to their total weight values, thereby obtaining the ranking of the main control factors in each parameter category. Furthermore, by using the corresponding charts and parameter change data for each parameter category, the influence level of each main control factor can be determined. Since the ranking of any main control factor reflects its importance among multiple main control factors in each parameter category, and the influence level of the main control factor reflects the strength of its influence on repeated fracturing effects, this comprehensive analysis method combining multiple data analysis methods can determine the ranking and influence level of each main control factor. This provides a more comprehensive and accurate representation of the impact of each main control factor on repeated fracturing effects, offering effective data references for subsequent development work and improving data processing efficiency.

[0066] The above Figure 2 This paper introduces a simplified flowchart of the analytical method for analyzing the influencing factors of repeated fracturing effectiveness. See below. Figure 3 As shown, the analysis method for the influencing factors of repeated fracturing effect is described in detail. Figure 3 This is a flowchart of another method for analyzing the influencing factors of repeated fracturing effects according to an embodiment of this application. The method is applied to a terminal and includes the following steps:

[0067] 301. Based on the production capacity data, geological data, and engineering data of the repeatedly fractured wells in the target block, the terminal establishes a database for each type of parameter in multiple parameters, including geological parameters, primary fracturing engineering parameters, and repeated fracturing engineering parameters.

[0068] In this embodiment of the application, data of the corresponding treatment layer of the repeated fracturing well in the target block are obtained, and databases of geological parameters, primary fracturing engineering parameters and repeated fracturing engineering parameters are established respectively.

[0069] The database of geological parameters mainly includes n parameters corresponding to the treatment layer of the treatment well. These parameters can include permeability, porosity, oil saturation, formation resistivity, principal stress, sandstone thickness, clay content, sonic transit time, bulk density, compensated neutrons, bound water saturation, wettability, and productivity level. The database of primary fracturing parameters mainly includes x parameters corresponding to the treatment layer of the treatment well. These parameters can include perforation thickness, fracturing process, injection rate, fluid volume, fluid strength, proppant volume, proppant strength, fracturing pressure, pump shutdown pressure, extension pressure, maximum proppant ratio, average proppant ratio, number of temporary plugging attempts, and productivity data after primary fracturing. The database of repeated fracturing parameters mainly includes y parameters corresponding to the treatment layer of the treatment well. These parameters can include perforation thickness, repeated fracturing process, injection rate, fluid volume, fluid strength, proppant volume, proppant strength, fracturing pressure, pump shutdown pressure, extension pressure, maximum proppant ratio, average proppant ratio, number of temporary plugging attempts, and productivity data after repeated fracturing.

[0070] The above n parameters, x parameters, and y parameters correspond to the n influencing factors in geological parameters, the x influencing factors in primary fracturing engineering parameters, and the y influencing factors in repeated fracturing engineering parameters, respectively.

[0071] 302. The terminal selects multiple controlling factors from the multiple influencing factors in each type of parameter. The influence of the multiple controlling factors in each type of parameter on the repeated fracturing effect is greater than the influence of other influencing factors in that type of parameter on the repeated fracturing effect.

[0072] In this embodiment, principal component analysis (PCA) is used to reduce the dimensionality of data in the databases of geological parameters, primary fracturing parameters, and repeated fracturing parameters. This allows for the selection of the controlling factors that significantly impact production capacity before and after repeated fracturing from multiple influencing factors corresponding to each parameter type, while filtering out other factors with minimal impact. PCA is a multivariate statistical analysis method that uses linear transformations to summarize and determine the main characteristics of the data, thereby identifying the controlling factors.

[0073] In some embodiments, the process of determining multiple controlling factors in each type of parameter includes: standardizing the data of each type of parameter in the multiple types of parameters to obtain standardized data for each type of parameter; for any type of parameter, determining the characteristic value corresponding to each influencing factor in that type of parameter based on the standardized data of that type of parameter; and determining multiple controlling factors in that type of parameter based on the characteristic value corresponding to each influencing factor in that type of parameter, wherein the characteristic value corresponding to the multiple controlling factors is greater than the characteristic value corresponding to other influencing factors in that type of parameter.

[0074] In other words, the data from the databases of geological parameters, primary fracturing parameters, and repeated fracturing parameters are standardized to obtain standardized data for each type of parameter. Then, the characteristic values ​​corresponding to the influencing factors in each type of parameter are obtained through correlation coefficient matrix calculation. Finally, the main controlling factors with larger characteristic values ​​and their corresponding data are selected.

[0075] Among them, m main controlling factors were initially selected from the geological parameters, 0 < m ≤ n; g main controlling factors were initially selected from the initial fracturing engineering parameters, 0 < g ≤ x; and s main controlling factors were initially selected from the initial fracturing engineering parameters, 0 < s ≤ y.

[0076] It should be noted that, having identified multiple controlling factors within each parameter category, various data analysis methods can be employed to process the corresponding parameters. The controlling factors within each parameter category can then be ranked according to their importance, resulting in multiple initial data columns. For example, the big data analysis method shown in step 303 and the numerical simulation method shown in step 304 below can be used for processing. It should be noted that steps 303 and 304 below are merely illustrative examples. This application embodiment supports the use of other data analysis methods to determine and rank the importance of the controlling factors within each parameter category, thereby enabling their participation in the comprehensive analysis process shown in step 305.

[0077] 303. Based on the data corresponding to the main control factors in each type of parameter, the terminal determines the Pearson correlation coefficient corresponding to each main control factor in each type of parameter.

[0078] In this embodiment, based on the parameters corresponding to each controlling factor, the Pearson correlation coefficient is determined using the Pearson correlation coefficient method. The Pearson correlation coefficient method can describe the uniform trend of change in two sets of data and accurately reflect the strength of the linear correlation between the two variables. Accordingly, for the initial fracturing engineering parameters, the Pearson correlation coefficient between the data corresponding to each controlling factor and the production capacity data after the initial fracturing is determined; for the repeated fracturing engineering parameters, the Pearson correlation coefficient between the data corresponding to each controlling factor and the production capacity data after repeated fracturing is determined.

[0079] Since the Pearson correlation coefficient ranges from -1 to 1, the absolute value of the Pearson correlation coefficient is positively correlated with the strength of the linear relationship between the variables. Therefore, the larger the absolute value of the Pearson correlation coefficient for any controlling factor, the greater the influence of that controlling factor on the repeated fracturing effect, and the higher the importance of that controlling factor.

[0080] Based on the Pearson correlation coefficients corresponding to the controlling factors, the importance of multiple controlling factors in each type of parameter can be determined and ranked. Specifically, the controlling factors in the geological parameters, ranked by importance, yield A. p1 ≥A p2 ≥A p3 ≥....≥A pm The main controlling factors in the initial fracturing engineering parameters are ranked in order of importance to obtain B. p1 ≥B p2 ≥B p3 ≥....≥B pg The main controlling factors in repeated fracturing engineering parameters are ranked in order of importance to obtain C. p1 ≥C p2 ≥C p3 ≥....≥C ps .

[0081] For example, the order of the main controlling factors in geological parameters is: sand body thickness > oil saturation > permeability > principal stress > porosity; the order of the main controlling factors in primary fracturing parameters is: perforation thickness > sand addition amount ≥ cumulative fluid volume > discharge rate; the order of the main controlling factors in repeated fracturing parameters is: discharge rate > sand addition amount > cumulative fluid volume > perforation thickness > sand and fluid addition intensity.

[0082] It should be noted that after obtaining the Pearson correlation coefficients for each major controlling factor and ranking the major controlling factors through step 303, further screening and summarization of the major controlling factors are still possible. For example, the main geological controlling factors may be sand body thickness and reservoir properties, while the main engineering controlling factors may be fracturing scale and fracturing displacement.

[0083] 304. The terminal obtains the capacity growth ratio under each controlling factor as a condition of change by adjusting the main control factors in each type of parameter and simulating the capacity after adjusting the main control factors.

[0084] In this embodiment, the production capacity growth ratio is determined by the time-production capacity curve obtained from the reservoir numerical model simulation. In the time-production capacity curve, the horizontal axis represents time, and the vertical axis represents production capacity. Accordingly, based on the geological parameters of the target block, a field-scale reservoir numerical model is established, coupled with the long-term conductivity decline characteristics of artificial fractures, thereby simulating the failure process of the conductivity capacity of fracturing fractures. For the reduced geological and engineering parameters, production capacity is simulated by adjusting the parameters corresponding to each main control factor individually, thereby further analyzing the importance of parameter changes corresponding to different main control factors on the production capacity after repeated fracturing. Specifically, using the production capacity before and after repeated fracturing as the baseline production capacity value, a time-production capacity curve is established based on the simulation data of each main control factor, further obtaining the production capacity growth ratio of each main control factor under changing conditions. The production capacity growth ratio of the main control factors can reflect the degree of influence of the main control factors on the repeated fracturing effect, as well as the importance of the main control factors.

[0085] Based on the production capacity growth ratio of each controlling factor under changing conditions, simulation analysis can identify and rank the controlling factors that are strongly correlated with production before and after repeated fracturing. Among them, the controlling factors in the geological parameters are ranked according to importance to obtain A. m1 ≥A m2 ≥A m3 ≥....≥A mm The main controlling factors in the initial fracturing engineering parameters are ranked in order of importance to obtain B. m1 ≥B m2 ≥B m3 ≥....≥B mg The main controlling factors in repeated fracturing engineering parameters are ranked in order of importance to obtain C. m1 ≥C m2 ≥C m3 ≥....≥C ms .

[0086] For example, among geological parameters, reservoir thickness, permeability, oil saturation, and formation pressure are all strongly positively correlated with production, but the degree of influence varies. Among engineering parameters, construction scale, number of fractures, and conductivity are all positively correlated with production, but the positive correlation is slightly weaker than that of the main controlling factors in geological parameters.

[0087] 305. For any type of parameter, the terminal determines the ranking of each main control factor in that type of parameter based on multiple initial data columns corresponding to that type of parameter. The data in any initial data column is used to indicate the Pearson correlation coefficient or the capacity growth ratio. The ranking of any main control factor is the ranking of the total weight value of the main control factor.

[0088] In this embodiment of the application, based on the main control factors and their ranking obtained by the big data analysis method in step 303 and the numerical simulation method in step 304, a comprehensive analysis is performed according to their respective ranking weights, thereby comprehensively determining the final ranking of each main control factor, so as to select the common factors that rank higher after comprehensive analysis as the main control factor results.

[0089] In some embodiments, the multiple initial data columns include a first data column and a second data column. The first data column includes the Pearson correlation coefficient for each controlling factor in this class of parameters. The first data column is used to determine the ranking weight corresponding to the big data analysis method in step 303. The second data column includes the capacity growth multiple under changing conditions for each controlling factor in this class of parameters. The second data column is used to determine the ranking weight corresponding to the numerical simulation method in step 304.

[0090] In some embodiments, the total weight value is determined by multiple standardized data columns to rank the main control factors. Accordingly, for any type of parameter, multiple initial data columns corresponding to that type of parameter are standardized to obtain multiple standardized data columns corresponding to that type of parameter, with each standardized data column corresponding to one of the multiple initial data columns. Based on the multiple standardized data columns, the total weight value of each main control factor in that type of parameter is determined, where the total weight value of any main control factor is the sum of the weight values ​​of the main control factors in the multiple standardized data columns. When the multiple main control factors in that type of parameter are sorted in descending order of their total weight values, the ranking of each main control factor in that type of parameter is determined.

[0091] When multiple initial data columns include a first data column and a second data column, multiple standardized data columns include a first standardized data column and a second standardized data column. Accordingly, the first data column corresponding to each type of parameter is standardized to obtain the first standardized data column corresponding to each type of parameter after standardization. Among them, the first standardized data column corresponding to the geological parameters is A. spi , 0 < i ≤ m; the first standardized data column corresponding to the initial fracturing engineering parameters is B. spi , 0 < i ≤ g; the first standardized data column corresponding to the repeated fracturing engineering parameters is C. spi , 0 < i ≤ s. Standardize the second data column corresponding to each type of parameter to obtain the standardized second data column corresponding to each type of parameter after standardization. Among them, the standardized second data column corresponding to the geological parameters is A. smi , 0 < i ≤ m; the second standardized data column corresponding to the initial fracturing engineering parameters is B. smi , 0 < i ≤ g; the second standardized data column corresponding to the repeated fracturing engineering parameters is C. smi , 0 < i ≤ s.

[0092] Optionally, the standardization process can use methods such as MIN-MAX standardization, Z-SCORE standardization, or mean-variance standardization. It should be noted that the standardization methods used for the first and second data columns must be consistent.

[0093] Based on the first and second standardized data columns corresponding to each type of parameter, the total weight value of each controlling factor in each type of parameter is determined. The total weight of the controlling factor in the geological parameters is A. i =A spi+ A smi The total weight of the controlling factors in the initial fracturing engineering parameters is B. i =B spi+ B smi The total weight of the controlling factors in the parameters of repeated fracturing engineering is C. i =C spi+ C smi .

[0094] Therefore, the controlling factors in geological parameters, ranked according to their total weight values, yield A1≥A2≥A3≥....≥A m The controlling factors in the initial fracturing engineering parameters, ranked according to their total weight values, yield B1≥B2≥B3≥....≥B g The controlling factors in repeated fracturing engineering parameters, ranked according to their total weight values, yield C1≥C2≥C3≥....≥C s .

[0095] 306. For any type of parameter, the terminal determines the influence level of each major control factor in the corresponding chart based on the parameter change data of each major control factor in that type of parameter. The chart is used to indicate the relationship between the parameter change ratio and the capacity growth ratio. The influence level of any major control factor is used to indicate the degree of influence of the major control factor on the repeated fracturing effect.

[0096] In this embodiment, geological parameters correspond to a first chart, which represents the relationship between the percentage change of the main controlling factor among the geological parameters and the production capacity growth ratio. The first chart can also be called the chart showing the impact of geological parameter changes on production capacity. Primary fracturing engineering parameters and refractory fracturing engineering parameters both correspond to a second chart, which represents the relationship between the percentage change of at least one main controlling factor among the primary and refractory fracturing engineering parameters and the production capacity growth ratio. The second chart can also be called the chart showing the impact of engineering parameter changes on production capacity. The percentage change of parameters reflects the degree of parameter change.

[0097] See Figure 4 As shown, Figure 4This is a schematic diagram of a plate provided according to an embodiment of this application. Wherein, Figure 4 Figure (a) is the first plot. The horizontal axis of the first plot represents the percentage change in geological parameters, and the vertical axis of the first plot represents the production capacity growth ratio. Figure 4 Figure (b) is the second chart. The horizontal axis of the second chart represents the percentage change of engineering parameters, and the vertical axis of the second chart represents the ratio of production capacity growth.

[0098] In some embodiments, the diagram includes a first region, a second region, a third region, a fourth region, and a fifth region. The first region is a region where the parameter change ratio is greater than a first threshold and the capacity growth ratio is less than a second threshold. The second region is a region where the parameter change ratio is less than the first threshold and the capacity growth ratio is less than the second threshold. The third and fourth regions are both regions where the parameter change ratio is greater than the first threshold and the capacity growth ratio is greater than the second threshold. The third region is located below the fourth region. The fifth region is a region where the parameter change ratio is less than the first threshold and the capacity growth ratio is greater than the second threshold.

[0099] Following the order of Zone 1, Zone 2, Zone 3, Zone 4, and finally Zone 5, the degree of influence on the repeated fracturing effect indicated by the reference influence level corresponding to each zone increases sequentially. For ease of description, Zones 1 through 5 can be respectively referred to as the zone with weak controlling factor, the zone with slightly weak controlling factor, the zone with moderate controlling factor, the zone with slightly strong controlling factor, and the zone with strong controlling factor.

[0100] For the first chart, we will use an example with a first threshold of 50% and a second threshold of 0.5. See [link / reference]. Figure 4 As shown in Figure (a), Region 1 represents the weak controlling factor, where the parameter change rate is between 50% and 150%, and the capacity growth ratio is between 0.0 and 0.5; Region 2 represents the slightly weak controlling factor, where the parameter change rate is between 0% and 50%, and the capacity growth ratio is between 0.0 and 0.5; Region 3 represents the medium controlling factor, where the parameter change rate is between 50% and 150%, and the capacity growth ratio is between 0.5 and 1.5; Region 4 represents the slightly strong controlling factor, where the parameter change rate is between 50% and 150%, and the capacity growth ratio is between 0.5 and 1.5; and Region 5 represents the strong controlling factor, where the parameter change rate is between 0% and 50%, and the capacity growth ratio is between 0.5 and 1.5.

[0101] For the second chart, we will use an example with a first threshold of 100% and a second threshold of 0.5. See [link / reference]. Figure 4As shown in Figure (b), the region with weak controlling factors is region 6, where the parameter variation is within the range of 100%-250% and the output increase factor is within the range of 0.0-0.5; the region with slightly weak controlling factors is region 7, where the parameter variation is within the range of 0%-100% and the output increase factor is within the range of 0.0-0.5; the region with moderate controlling factors is region 8, where the parameter variation is within the range of 100%-250% and the output increase factor is within the range of 0.5-1.5; the region with slightly strong controlling factors is region 9, where the parameter variation is within the range of 100%-250% and the output increase factor is within the range of 0.5-1.5; and the region with strong controlling factors is region 10, where the parameter variation is within the range of 0%-100% and the output increase factor is within the range of 0.5-1.5.

[0102] Using the above method, with the changes in geological parameters and engineering parameters as the horizontal axis and the production increase factor as the vertical axis, we established charts showing the impact of the changes in geological parameters and engineering parameters on the production of low-permeability reservoirs after repeated fracturing. We also proposed a regional division method corresponding to the influence levels of weak, slightly weak, moderate, slightly strong, and strong control. By dividing the region according to different regional characteristics, we can intuitively and clearly identify the key influencing factors of repeated fracturing in the target block.

[0103] In some embodiments, for any type of parameter, the parameter change data of each main control factor in the type of parameter is projected onto the corresponding chart of the type of parameter to obtain the parameter change curve of each main control factor in the type of parameter. The parameter change curve of any main control factor is used to indicate the relationship between the parameter change ratio of the main control factor and the capacity growth ratio. Based on the region to which the parameter change curve of each main control factor in the type of parameter belongs in the chart, the influence level of each main control factor in the type of parameter is determined. Multiple regions in the chart correspond one-to-one with multiple reference influence levels. The influence level of each main control factor includes at least one reference influence level.

[0104] By analyzing the parameter variation data of each major controlling factor in the geological and engineering parameters, and observing the parameter variation curves on the corresponding charts, the influence level of each major controlling factor can be determined, thereby identifying the degree of influence of the major controlling factors on the repeated fracturing effect. For ease of description, see [link to relevant documentation]. Figure 5 As shown, Figure 5 This is a schematic diagram of a graph containing parameter variation curves provided according to an embodiment of this application. Figure 5 Figure (a) shows the parameter variation curves corresponding to the first plate and the main controlling factors such as oil saturation, permeability, reservoir thickness, and pressure coefficient. According to the area traversed by each curve, at this time, formation pressure is the strong controlling factor among geological parameters, while reservoir thickness, permeability, and oil saturation are the relatively strong controlling factors, with different degrees of influence. Figure 5Figure (b) shows the parameter variation curves corresponding to the second chart and the construction scale, flow conductivity, and number of fractures. Based on the regions traversed by each curve, it can be seen that the construction scale is a moderately strong controlling factor, the number of fractures is a moderately strong controlling factor, and the flow conductivity is a weakly strong controlling factor. A comprehensive comparison shows that the influence of each parameter varies, with the positive correlation between engineering parameters and geological parameters being slightly weaker.

[0105] For ease of description, the overall flow of the analysis method in this application is introduced below. First, the geological and engineering parameters of the repeated fracturing wells in the target block are obtained and a database is established, as shown in step 301. Then, the database parameters are reduced in dimensionality to initially screen out the key factors that have a significant impact before and after repeated fracturing, as shown in step 302. Next, big data analysis methods are used to determine the main geological and engineering control factors, as shown in step 303. At the same time, numerical simulation analysis methods are used to determine the main geological and engineering control factors, as shown in step 304. Then, the weights of each factor are comprehensively analyzed to obtain and rank the main geological and engineering control factors, providing basic data for the establishment and correction of the chart template, as shown in step 305. A chart showing the influence of changes in geological and engineering parameters on the production after repeated fracturing is established, as shown in step 306. After the above steps, based on the chart analysis of the effects of repeated fracturing production, engineering control methods to improve the effect of repeated fracturing can be further established, thereby improving the effect of repeated fracturing in the target block and enhancing the potential of remaining oil.

[0106] This application provides an analysis method for factors influencing the effectiveness of repeated fracturing. Combining big data analysis and numerical simulation analysis, based on the previous stimulation effect, it quantifies the importance of different geological and engineering factors on the production capacity before and after repeated fracturing. It establishes a chart showing the influence of different parameter change ratios on the production capacity of low-permeability reservoirs after repeated fracturing. It proposes a regional division method corresponding to multiple influence levels, such as weak main control, slightly weak main control, medium main control, slightly strong main control, and strong main control, to facilitate the establishment of engineering control methods to improve the effect of repeated fracturing stimulation, thereby guiding the repeated fracturing stimulation work of old wells.

[0107] More specifically, this method, using initial data columns corresponding to various data analysis methods, allows for the sorting of key control factors according to their total weight values, thus obtaining the ranking of key control factors within each parameter category. Furthermore, by analyzing the corresponding charts and parameter change data for each parameter category, the influence level of each key control factor can be determined. Since the ranking of any key control factor reflects its importance among multiple key control factors in each parameter category, and the influence level of a key control factor specifically reflects its strength in influencing the repeated fracturing effect, this comprehensive analysis method combining multiple data analysis techniques more intuitively and accurately demonstrates the degree of influence of each key control factor on the repeated fracturing effect. This improves the comprehensiveness and richness of the key control factor analysis, enhances the targeting of repeated fracturing, provides data support for adjusting subsequent block development plans and specifying repeated fracturing schemes, and offers effective data references for subsequent development work, thereby improving data processing efficiency.

[0108] Figure 6 This is a block diagram of an apparatus for analyzing the influencing factors of repeated fracturing effects according to an embodiment of this application. The apparatus is used to execute the steps of the method for analyzing the influencing factors of repeated fracturing effects described above, see [link to relevant documentation]. Figure 6 The device for analyzing the influencing factors of the repeated fracturing effect includes: an acquisition module 601, a first determination module 602, and a second determination module 603.

[0109] The acquisition module 601 is used to acquire data corresponding to each type of parameter among multiple types of parameters. The multiple types of parameters include geological parameters, primary fracturing engineering parameters and repeated fracturing engineering parameters. Each type of parameter includes multiple main control factors that affect the repeated fracturing effect.

[0110] The first determining module 602 is used to determine the ranking of each main control factor in any type of parameter based on multiple initial data columns corresponding to that type of parameter. Different initial data columns correspond to different data analysis methods. Any initial data column is used to represent the initial weight value of multiple main control factors in that type of parameter, and the ranking of any main control factor is the ranking of the total weight value of the main control factors.

[0111] The second determining module 603 is used to determine the influence level of each main control factor in the corresponding chart for any type of parameter based on the parameter change data of each main control factor in the type of parameter. The chart is used to indicate the relationship between the parameter change ratio and the capacity growth ratio. The influence level of any main control factor is used to indicate the degree of influence of the main control factor on the repeated fracturing effect.

[0112] In some embodiments, the first determining module 602 is configured to, for any type of parameter, perform standardization processing on multiple initial data columns corresponding to that type of parameter to obtain multiple standardized data columns corresponding to that type of parameter, wherein the multiple standardized data columns correspond one-to-one with the multiple initial data columns; based on the multiple standardized data columns, determine the total weight value of each controlling factor in that type of parameter, wherein the total weight value of any controlling factor is the sum of the initial weight values ​​of the controlling factors in the multiple standardized data columns; and, when the multiple controlling factors in that type of parameter are sorted in descending order of their total weight values, determine the ranking of each controlling factor in that type of parameter.

[0113] In some embodiments, the plurality of initial data columns include a first data column and a second data column. The first data column includes the Pearson correlation coefficient of each controlling factor in the class of parameters. The Pearson correlation coefficient of any controlling factor is used to indicate the linear relationship between the controlling factor and the production capacity. The second data column includes the production capacity growth multiple under changing conditions for each controlling factor in the class of parameters.

[0114] In some embodiments, the second determining module 602 is used to project the parameter change data of each main control factor in any type of parameter onto the corresponding chart of the type of parameter to obtain the parameter change curve of each main control factor in the type of parameter. The parameter change curve of any main control factor is used to indicate the relationship between the parameter change ratio of the main control factor and the capacity growth ratio. Based on the region to which the parameter change curve of each main control factor in the type of parameter belongs in the chart, the influence level of each main control factor in the type of parameter is determined. Multiple regions in the chart correspond one-to-one with multiple reference influence levels. The influence level of each main control factor includes at least one reference influence level.

[0115] In some embodiments, the chart includes a first region, a second region, a third region, a fourth region, and a fifth region. The first region is a region where the parameter change ratio is greater than a first threshold and the capacity growth ratio is less than a second threshold. The second region is a region where the parameter change ratio is less than the first threshold and the capacity growth ratio is less than the second threshold. The third and fourth regions are both regions where the parameter change ratio is greater than the first threshold and the capacity growth ratio is greater than the second threshold. The third region is located below the fourth region. The fifth region is a region where the parameter change ratio is less than the first threshold and the capacity growth ratio is greater than the second threshold. The degree of influence on the repeated fracturing effect indicated by the reference influence level corresponding to each region increases sequentially from the first region, the second region, the third region, the fourth region to the fifth region.

[0116] In some embodiments, geological parameters correspond to a first chart, which is used to represent the relationship between the parameter variation ratio of the main controlling factors in the geological parameters and the production capacity growth ratio; primary fracturing engineering parameters and repeated fracturing engineering parameters both correspond to a second chart, which is used to represent the relationship between the parameter variation ratio of at least one main controlling factor in the primary fracturing engineering parameters and repeated fracturing engineering parameters and the production capacity growth ratio.

[0117] In some embodiments, Figure 7 This is a block diagram of an analysis device for factors influencing the effect of repeated fracturing, according to an embodiment of this application. See also... Figure 7 The device also includes:

[0118] The preprocessing module 604 is used to standardize the data of each class of parameters in the multi-class parameters to obtain standardized data for each class of parameters; for any class of parameters, based on the standardized data of that class of parameters, the characteristic value corresponding to each influencing factor in that class of parameters is determined; based on the characteristic value corresponding to each influencing factor in that class of parameters, multiple main controlling factors in that class of parameters are determined, and the characteristic values ​​corresponding to the multiple main controlling factors are greater than the characteristic values ​​corresponding to other influencing factors in that class of parameters.

[0119] This application provides an analysis device for factors influencing the effectiveness of repeated fracturing. Using initial data columns corresponding to multiple data analysis methods, it can sort the main control factors according to their total weight value, thereby obtaining the ranking of the main control factors in each parameter category. Through the corresponding charts and parameter change data for each parameter category, the influence level of each main control factor can be determined. Since the ranking of any main control factor reflects its importance among multiple main control factors in each parameter category, and the influence level of the main control factor reflects the strength of its influence on the repeated fracturing effect, this comprehensive analysis method combining multiple data analysis methods can determine the ranking and influence level of each main control factor. This provides a more comprehensive and accurate display of the impact of each main control factor on the repeated fracturing effect, offering effective data references for subsequent development work and improving data processing efficiency.

[0120] It should be noted that the analysis device for the influencing factors of repeated fracturing effect provided in the above embodiments is only illustrated by the division of the above functional modules when running the application. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the terminal can be divided into different functional modules to complete all or part of the functions described above. In addition, the analysis device for the influencing factors of repeated fracturing effect and the method embodiment for the analysis of the influencing factors of repeated fracturing effect provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiment, which will not be repeated here.

[0121] Figure 8This is a schematic diagram of a terminal according to an embodiment of this application. The terminal 800 can be a portable mobile terminal, such as a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The terminal 800 may also be referred to as a user device, portable terminal, laptop terminal, desktop terminal, or other names.

[0122] Typically, terminal 800 includes a processor 801 and a memory 802.

[0123] Processor 801 may include one or more processing cores, such as a quad-core processor or an octa-core processor. Processor 801 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 801 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 801 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 801 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0124] The memory 802 may include one or more computer-readable storage media, which may be non-transitory. The memory 802 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 802 are used to store at least one computer program, which is executed by the processor 801 to implement the method for analyzing the influencing factors of repeated fracturing effects provided in the method embodiments of this application.

[0125] In some embodiments, the terminal 800 may also optionally include a peripheral device interface 803 and at least one peripheral device. The processor 801, memory 802, and peripheral device interface 803 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 803 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 804, a display screen 805, a camera assembly 806, an audio circuit 807, and a power supply 808.

[0126] Peripheral device interface 803 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 801 and memory 802. In some embodiments, processor 801, memory 802 and peripheral device interface 803 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 801, memory 802 and peripheral device interface 803 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0127] The radio frequency (RF) circuit 804 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 804 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 804 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. In some embodiments, the RF circuit 804 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 804 can communicate with other terminals through at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 804 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0128] Display screen 805 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 805 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 801 for processing. In this case, display screen 805 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 805, disposed on the front panel of terminal 800; in other embodiments, there may be at least two display screens, disposed on different surfaces of terminal 800 or in a folded design; in other embodiments, display screen 805 may be a flexible display screen, disposed on a curved or folded surface of terminal 800. Furthermore, display screen 805 may be configured as a non-rectangular irregular shape, i.e., a non-rectangular screen. Display screen 805 may be made of materials such as LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

[0129] The camera assembly 806 is used to acquire images or videos. In some embodiments, the camera assembly 806 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the terminal, and the rear-facing camera is located on the back of the terminal. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 806 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash is a combination of a warm-light flash and a cool-light flash, which can be used for light compensation at different color temperatures.

[0130] The audio circuit 807 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 801 for processing, or input to the radio frequency circuit 804 to achieve voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each located at a different part of the terminal 800. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert the electrical signals from the processor 801 or the radio frequency circuit 804 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 807 may also include a headphone jack.

[0131] Power supply 808 is used to supply power to the various components in terminal 800. Power supply 808 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 808 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0132] Those skilled in the art will understand that Figure 8 The structure shown does not constitute a limitation on terminal 800 and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0133] Figure 9 This is a schematic diagram of a server structure according to an embodiment of this application. The server 900 can vary significantly due to different configurations or performance. It may include one or more Central Processing Units (CPUs) 901 and one or more memories 902. The memory 902 stores at least one computer program, which is loaded and executed by the processor 901 to implement the analysis method for the influencing factors of repeated fracturing effects provided in the above-described method embodiments. Of course, the server may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The server may also include other components for implementing device functions, which will not be elaborated here.

[0134] This application also provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to implement the method for analyzing the influencing factors of repeated fracturing effects in the above embodiments. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, or optical data storage device, etc.

[0135] This application also provides a computer program product, including a computer program that is executed by a processor to implement the method for analyzing the influencing factors of repeated fracturing effects in this application embodiment.

[0136] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0137] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for analyzing the influencing factors of repeated fracturing effect, characterized in that, The method includes: Data corresponding to each type of parameter is obtained from multiple types of parameters, including geological parameters, primary fracturing engineering parameters, and repeated fracturing engineering parameters. Each type of parameter includes multiple main controlling factors that affect the effect of repeated fracturing. For any type of parameter, based on multiple initial data columns corresponding to the type of parameter, the ranking of each main control factor in the type of parameter is determined. Different initial data columns correspond to different data analysis methods. Any initial data column is used to represent the initial weight value of multiple main control factors in the type of parameter, and the ranking of any main control factor is the ranking of the total weight value of the main control factor. For any type of parameter, based on the parameter change data of each main control factor in the type of parameter, the influence level of each main control factor in the type of parameter is determined in the chart corresponding to the type of parameter. The chart is used to indicate the relationship between the parameter change ratio and the capacity growth ratio. The influence level of any main control factor is used to indicate the degree of influence of the main control factor on the repeated fracturing effect.

2. The method according to claim 1, characterized in that, For any type of parameter, determining the ranking of each controlling factor in the type of parameter based on multiple initial data columns corresponding to the type of parameter includes: For any type of parameter, the multiple initial data columns corresponding to the type of parameter are standardized to obtain multiple standardized data columns corresponding to the type of parameter, and the multiple standardized data columns correspond one-to-one with the multiple initial data columns. Based on the multiple standardized data columns, the total weight value of each controlling factor in the class parameter is determined, and the total weight value of any controlling factor is the sum of the initial weight values ​​of the controlling factors in the multiple standardized data columns; When multiple controlling factors in the class parameters are sorted in descending order of total weight value, the ranking of each controlling factor in the class parameters is determined.

3. The method according to claim 1, characterized in that, The plurality of initial data columns include a first data column and a second data column. The first data column includes the Pearson correlation coefficient of each controlling factor in the class parameters. The Pearson correlation coefficient of any controlling factor is used to indicate the linear relationship between the controlling factor and the production capacity. The second data column includes the production capacity growth multiple under changing conditions for each controlling factor in the class parameters.

4. The method according to claim 1, characterized in that, For any type of parameter, based on the parameter change data of each controlling factor in the type of parameter, the influence level of each controlling factor in the corresponding chart of the type of parameter is determined, including: For any type of parameter, the parameter change data of each main control factor in the type of parameter is projected onto the chart corresponding to the type of parameter to obtain the parameter change curve of each main control factor in the type of parameter. The parameter change curve of any main control factor is used to indicate the relationship between the parameter change ratio of the main control factor and the capacity growth ratio. Based on the region to which the parameter change curve of each major control factor in the class parameters belongs in the chart, the influence level of each major control factor in the class parameters is determined. Multiple regions in the chart correspond one-to-one with multiple reference influence levels, and the influence level of each major control factor includes at least one reference influence level.

5. The method according to claim 1, characterized in that, The diagram includes a first region, a second region, a third region, a fourth region, and a fifth region. The first region is the region where the parameter change ratio is greater than a first threshold and the capacity growth ratio is less than a second threshold. The second region is the region where the parameter change ratio is less than the first threshold and the capacity growth ratio is less than the second threshold. The third region and the fourth region are both regions where the parameter change ratio is greater than the first threshold and the capacity growth ratio is greater than the second threshold. The third region is located below the fourth region. The fifth region is the region where the parameter change ratio is less than the first threshold and the capacity growth ratio is greater than the second threshold. The degree of influence on repeated fracturing effects indicated by the reference influence level for each region increases sequentially, from the first region, the second region, the third region, the fourth region up to the fifth region.

6. The method according to claim 1, characterized in that, The geological parameters correspond to the first chart, which is used to represent the relationship between the parameter change ratio of the main controlling factor in the geological parameters and the production capacity growth ratio. The primary fracturing engineering parameters and the repeated fracturing engineering parameters both correspond to the second chart, which is used to show the relationship between the parameter change ratio and the production capacity growth ratio of at least one of the primary fracturing engineering parameters and the repeated fracturing engineering parameters.

7. The method according to claim 1, characterized in that, The process of determining multiple controlling factors in each type of parameter includes: The data of each type of parameter in the multiple types of parameters are standardized to obtain standardized data for each type of parameter; For any class of parameters, based on the standardized data of the class of parameters, determine the characteristic value corresponding to each influencing factor in the class of parameters; Based on the feature value corresponding to each influencing factor in the class parameters, multiple controlling factors in the class parameters are determined, and the feature values ​​corresponding to the multiple controlling factors are greater than the feature values ​​corresponding to other influencing factors in the class parameters.

8. An analytical device for analyzing factors influencing the effectiveness of repeated fracturing, characterized in that, The device includes: The acquisition module is used to acquire data corresponding to each type of parameter among multiple types of parameters. The multiple types of parameters include geological parameters, primary fracturing engineering parameters, and repeated fracturing engineering parameters. Each type of parameter includes multiple main control factors that affect the repeated fracturing effect. The first determining module is used to determine the ranking of each main control factor in any class parameter based on multiple initial data columns corresponding to the class parameter. Different initial data columns correspond to different data analysis methods. Any initial data column is used to represent the initial weight value of multiple main control factors in the class parameter, and the ranking of any main control factor is the ranking of the total weight value of the main control factor. The second determining module is used to determine the influence level of each major control factor in the class of parameters based on the parameter change data of each major control factor in the class of parameters in the chart corresponding to the class of parameters. The chart is used to indicate the relationship between the parameter change ratio and the capacity growth ratio. The influence level of any major control factor is used to indicate the degree of influence of the major control factor on the repeated fracturing effect.

9. A computer device, characterized in that, The computer device includes a processor and a memory, the memory being used to store at least one computer program, the at least one computer program being loaded by the processor and executed by the processor to analyze the influencing factors of repeated fracturing effects as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store at least one computer program, which is used to execute the method for analyzing the influencing factors of repeated fracturing effects as described in any one of claims 1 to 7.

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