Shale oil fracturing effect evaluation method and device, computer equipment and storage medium

Abstract

The invention provides a shale oil fracturing effect evaluation method and device, a terminal and a storage medium, and belongs to the technical field of petroleum engineering. According to the method provided by the embodiment of the invention, the fuzzy comprehensive evaluation method is utilized to perform fuzzy processing on the basic parameters such as the target reservoir parameters and the target engineering parameters, so that the values of the basic parameters are more representative and are not certain values, and therefore, an evaluation result close to the reality can be obtained, and the accuracy of a calculation result is improved; and optimizing the shale oil fracturing effect evaluation method. Moreover, by fitting the function model of the main control factors and the yield and utilizing the function model to evaluate the fracturing effect, the model construction process is simplified, and the evaluation efficiency is higher.

Description

Technical Field

[0001] The present application relates to the field of petroleum engineering technology, and in particular to a method, device, computer equipment and storage medium for evaluating the effect of shale oil fracturing horizontal wells. Background Art

[0002] Shale oil resources are abundant and show good exploration and development potential. In order to increase production capacity, fracturing technology can usually be used. The fracturing effect of shale oil mainly depends on the matching of engineering parameters and reservoir parameters. How to establish an effective shale oil fracturing effect evaluation method to optimize fracturing technology is an important research topic to improve shale oil production capacity.

[0003] At present, the commonly used method for evaluating the effect of fracturing is mainly through machine learning methods. That is, the collected engineering parameters and reservoir parameters are processed using the random forest algorithm to obtain the main controlling factors of post-fracturing capacity. Based on the above main controlling factors of post-fracturing capacity, a back propagation algorithm neural network capacity prediction model optimized by genetic algorithm is established, and the fracturing effect is evaluated based on this.

[0004] However, there are also many shortcomings in the machine learning method. For example, the values ​​of each reservoir parameter are taken from deterministic values ​​such as limit values ​​and average values, which makes the final evaluation result biased, thus affecting the evaluation of the production increase effect. Therefore, the above method will lead to the problem of inaccurate evaluation results. Summary of the invention

[0005] The embodiment of the present application provides a shale oil fracturing effect evaluation method, device, computer equipment and storage medium, which can be used to optimize the shale oil fracturing effect evaluation method and improve the shale oil production capacity. The technical solution is as follows:

[0006] In one aspect, a method for evaluating shale oil fracturing effect is provided, the method comprising:

[0007] Obtain reservoir data and engineering data for multiple horizontal wells in the target block;

[0008] Based on the random forest algorithm, the reservoir data and engineering data of the plurality of horizontal wells are analyzed to obtain e main control factors affecting the fracturing effect, wherein the e main control factors include target reservoir parameters and target engineering parameters, wherein e is an integer greater than 1;

[0009] Based on the principal component analysis method, the e main control factors are reduced in dimension to obtain n principal components that affect the fracturing effect, wherein e is greater than n, and e and n are integers greater than 1;

[0010] Based on the fuzzy comprehensive evaluation method, scoring the n principal components of the multiple horizontal wells to obtain fuzzy comprehensive scores of the multiple horizontal wells;

[0011] Fitting the relationship between the fuzzy comprehensive scores and the production of the multiple horizontal wells to obtain a function model between the main controlling factors and the production;

[0012] For any horizontal well, the predicted production of the multiple horizontal wells is determined based on the e main controlling factors of the multiple horizontal wells and the function model.

[0013] In some embodiments, the reservoir data and engineering data of the plurality of horizontal wells are analyzed based on the random forest algorithm to obtain e main control factors affecting the fracturing effect, including:

[0014] Based on the reservoir data and the engineering data of the multiple horizontal wells and the random forest algorithm, the importance of the reservoir parameters and the engineering parameters of the multiple horizontal wells is obtained, the importance is sorted, and the parameters ranked in the top e positions are screened out.

[0015] In some embodiments, the principal component analysis method is used to reduce the dimensions of the e main control factors to obtain n principal components that affect the fracturing effect, including:

[0016] Based on the e main controlling factors and the plurality of horizontal wells, a correlation coefficient matrix is ​​established, wherein the correlation coefficient matrix indicates the correlation between different main controlling factors;

[0017] Based on the correlation coefficient matrix, a normalized principal component matrix of the plurality of horizontal wells is obtained, wherein the principal component matrix includes principal component values ​​of the n principal components.

[0018] In some embodiments, the establishing of a correlation coefficient matrix based on the e main controlling factors and the plurality of horizontal wells comprises:

[0019] Based on the e main controlling factors and the multiple horizontal wells, a sample matrix is ​​established, wherein the sample matrix includes the e main controlling factors of the multiple horizontal wells;

[0020] According to the sample matrix, applying a standardized data method to obtain a standardized matrix;

[0021] The correlation coefficient matrix is ​​established according to the standardized matrix.

[0022] In some embodiments, obtaining a normalized principal component matrix of the plurality of horizontal wells based on the correlation coefficient matrix includes:

[0023] Calculate based on the correlation coefficient matrix to obtain corresponding eigenvalues ​​and eigenvectors;

[0024] Calculating based on the eigenvector and the standardized matrix to obtain the n principal component values, wherein the number n of the n principal components is determined by the variance contribution rate and the cumulative contribution rate, and the variance contribution rate and the cumulative contribution rate are calculated from the eigenvalue and the eigenvector;

[0025] Based on the principal component values, a principal component matrix is ​​established, and the principal component matrix is ​​normalized to obtain a normalized principal component matrix of the plurality of horizontal wells.

[0026] In some embodiments, the scoring of the n principal components of the plurality of horizontal wells based on the fuzzy comprehensive evaluation method to obtain the fuzzy comprehensive scores of the plurality of horizontal wells includes:

[0027] Establishing an evaluation set for grading the fracturing effects of the plurality of horizontal wells;

[0028] Based on the n principal components of the horizontal wells, an evaluation factor set is established to participate in the evaluation of the fracturing effects of the plurality of horizontal wells;

[0029] Based on the evaluation factor set, configuring fuzzy weights for the n principal components in the evaluation factor set;

[0030] Based on the fuzzy weights and membership degrees of the n principal components, an evaluation fuzzy set of the multiple horizontal wells is obtained, wherein the evaluation fuzzy set of the multiple horizontal wells is established based on the evaluation values ​​of the n principal components of the multiple horizontal wells, and the membership degrees are calculated based on membership functions;

[0031] Based on the evaluation fuzzy sets of the plurality of horizontal wells and the evaluation set, fuzzy comprehensive scores of the plurality of horizontal wells are determined.

[0032] In some embodiments, configuring fuzzy weights for the n principal components in the evaluation factor set based on the evaluation factor set includes:

[0033] Obtaining importance scores of the n principal components, wherein the importance scores are calculated based on a random forest algorithm;

[0034] Based on the importance scores of the n principal components, different fuzzy weights are configured for the n principal components, and the fuzzy weights represent the importance of the n principal components to the evaluation result.

[0035] In some embodiments, obtaining the evaluation fuzzy sets of the plurality of horizontal wells based on the fuzzy weights of the n principal components and the membership degrees of the n principal components includes:

[0036] Determining a membership function based on a fitting relationship between the n principal components and the production rates of the plurality of horizontal wells;

[0037] Based on the membership function and the n principal components, obtaining the membership degrees of the n principal components of the plurality of horizontal wells;

[0038] Based on the fuzzy weights of the n principal components and the membership degrees of the n principal components, an evaluation fuzzy set of the multiple horizontal wells is obtained, and the evaluation fuzzy set of the multiple horizontal wells includes evaluation values ​​of different principal components of the multiple horizontal wells.

[0039] On the one hand, a shale oil fracturing effect evaluation device is provided, the device comprising:

[0040] A data acquisition module is used to acquire reservoir data and engineering data of multiple horizontal wells in the target block;

[0041] A data analysis module, for analyzing the reservoir data and engineering data of the plurality of horizontal wells based on a random forest algorithm, to obtain e main control factors affecting the fracturing effect, wherein the e main control factors include target reservoir parameters and target engineering parameters, wherein e is an integer greater than 1;

[0042] A data dimension reduction module, used for reducing the dimensions of the e main control factors based on principal component analysis to obtain n principal components affecting the fracturing effect, wherein e is greater than n, and e and n are integers greater than 1;

[0043] A model generation module is used to score the n principal components of the multiple horizontal wells based on a fuzzy comprehensive evaluation method to obtain fuzzy comprehensive scores of the multiple horizontal wells, fit the relationship between the fuzzy comprehensive scores of the multiple horizontal wells and production, and obtain a function model between the main control factors and production;

[0044] A prediction module is used to determine the predicted production of the multiple horizontal wells for any horizontal well based on the e main controlling factors of the multiple horizontal wells and the function model.

[0045] In some embodiments, the data analysis module is used to:

[0046] Based on the reservoir data and the engineering data of the multiple horizontal wells and the random forest algorithm, the importance of the reservoir parameters and the engineering parameters of the multiple horizontal wells is obtained, the importance is sorted, and the parameters ranked in the top e positions are screened out.

[0047] In some embodiments, the data dimension reduction module includes:

[0048] A correlation coefficient matrix acquisition unit, used to establish a correlation coefficient matrix based on the e main control factors and the plurality of horizontal wells, wherein the correlation coefficient matrix indicates the correlation between different main control factors;

[0049] A principal component matrix acquisition unit is used to obtain a normalized principal component matrix of the plurality of horizontal wells based on the correlation coefficient matrix, wherein the principal component matrix includes principal component values ​​of the n principal components.

[0050] In some embodiments, the correlation coefficient matrix acquisition unit is used to:

[0051] Based on the e main controlling factors and the multiple horizontal wells, a sample matrix is ​​established, wherein the sample matrix includes the e main controlling factors of the multiple horizontal wells;

[0052] According to the sample matrix, applying a standardized data method to obtain a standardized matrix;

[0053] The correlation coefficient matrix is ​​established according to the standardized matrix.

[0054] In some embodiments, the principal component matrix acquisition unit is used to:

[0055] Based on the correlation coefficient matrix, corresponding eigenvalues ​​and eigenvectors are calculated;

[0056] Based on the eigenvector and the standardized matrix, the n principal component values ​​are calculated, where the number n of the n principal components is determined by the variance contribution rate and the cumulative contribution rate, and the variance contribution rate and the cumulative contribution rate are calculated by the eigenvalue and the eigenvector;

[0057] Based on the principal component values, a principal component matrix is ​​established, and the principal component matrix is ​​normalized to obtain a normalized principal component matrix of the plurality of horizontal wells.

[0058] In some embodiments, the model generation module includes:

[0059] An evaluation set establishing unit, used for classifying the fracturing effects of the plurality of horizontal wells;

[0060] An evaluation factor set establishing unit, used for participating in the evaluation of the fracturing effects of the plurality of horizontal wells;

[0061] A fuzzy weight configuration unit, used for configuring fuzzy weights for the n principal components in the evaluation factor set;

[0062] An evaluation fuzzy set establishing unit, configured to obtain the evaluation fuzzy sets of the plurality of horizontal wells based on the fuzzy weights and the membership degrees of the n principal components, wherein the evaluation fuzzy sets of the plurality of horizontal wells are established based on the evaluation values ​​of the n principal components of the plurality of horizontal wells, and the membership degrees are calculated based on membership functions;

[0063] A scoring unit is used to determine the fuzzy comprehensive scores of the multiple horizontal wells based on the evaluation fuzzy sets of the multiple horizontal wells and the evaluation set.

[0064] In some embodiments, the fuzzy weight configuration unit is used to:

[0065] Obtaining importance scores of the n principal components, wherein the importance scores are calculated based on a random forest algorithm;

[0066] Based on the importance scores of the n principal components, different fuzzy weights are configured for the n principal components, and the fuzzy weights represent the importance of the n principal components to the evaluation result.

[0067] In some embodiments, the evaluation fuzzy set building unit is used to:

[0068] Determining a membership function based on a fitting relationship between the n principal components and the production rates of the plurality of horizontal wells;

[0069] Based on the membership function and the n principal components, obtaining the membership degrees of the n principal components of the plurality of horizontal wells;

[0070] Based on the fuzzy weights of the n principal components and the membership degrees of the n principal components, an evaluation fuzzy set of the multiple horizontal wells is obtained, and the evaluation fuzzy set of the multiple horizontal wells includes evaluation values ​​of different principal components of the multiple horizontal wells.

[0071] On the other hand, a computer device is provided, which includes a processor and a memory, wherein the memory is used to store at least one computer program, and the at least one computer program is loaded and executed by the processor to implement the operations performed by the shale oil fracturing effect evaluation method in the embodiment of the present application.

[0072] On the other hand, a computer-readable storage medium is provided, in which at least one computer program is stored. The at least one computer program is loaded and executed by a processor to implement operations performed by the shale oil fracturing effect evaluation method in the embodiment of the present application.

[0073] On the other hand, a computer program product or a computer program is provided, which includes a computer program code, which is stored in a computer-readable storage medium, and a processor of a computer device reads the computer program code from the computer-readable storage medium, and the processor executes the computer program code, so that the computer device executes the shale oil fracturing effect evaluation method provided in the various optional implementations of the above-mentioned machine learning aspects.

[0074] The beneficial effects of the technical solution provided by the embodiment of the present application are:

[0075] By using the fuzzy comprehensive evaluation method, the basic parameters such as the target reservoir parameters and the target engineering parameters are fuzzy processed, making the basic parameter values ​​more representative rather than a definite value, so that the evaluation results closer to the actual can be obtained, thereby improving the accuracy of the calculation results and optimizing the shale oil fracturing effect evaluation method. In addition, by fitting the function model of the main controlling factors and production, the function model is used to evaluate the fracturing effect, simplifying the model construction process and making the evaluation more efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0077] Figure 1 It is an implementation environment of a shale oil fracturing effect evaluation method provided in an embodiment of the present application;

[0078] Figure 2 is a flow chart of a method provided according to an embodiment of the present application;

[0079] Figure 3 It is a terminal display result diagram provided according to an embodiment of the present application;

[0080] Figure 4 It is a terminal display result diagram provided according to an embodiment of the present application;

[0081] Figure 5 It is a terminal display result diagram provided according to an embodiment of the present application;

[0082] Figure 6 is a block diagram of a device provided according to an embodiment of the present application;

[0083] Figure 7 is a structural block diagram of a computer device provided according to an embodiment of the present application;

[0084] Figure 8 It is a structural diagram of a server provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0085] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0086] In this application, the terms "first", "second", etc. are used to distinguish identical or similar items with basically the same effects and functions. It should be understood that there is no logical or temporal dependency between "first", "second", and "nth", nor is there any limitation on quantity and execution order.

[0087] In the present application, the term "at least one" means one or more, and the term "plurality" means two or more.

[0088] It should be noted that the 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 are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0089] For ease of understanding, the terms involved in this application are explained below:

[0090] Fracturing technology: Fracturing technology is mainly used to increase oil or gas production. During operation, a ground fracturing truck is used to inject fracturing fluid into the well at a rate that greatly exceeds the absorption capacity of the formation. High pressure is built up near the bottom of the well to form cracks in the formation. As liquid with proppant is injected into the cracks, the cracks gradually extend forward, forming sand-filled cracks of sufficient length, certain width and height in the formation. The cracks formed by fracturing are closely related to the distribution of ground stress, rock mechanical properties, properties of fracturing fluid and injection method.

[0091] Horizontal well: A horizontal well is a special well with a maximum well inclination of 90 degrees or close to 90 degrees, generally not less than 86 degrees, and a certain length of horizontal well section maintained in the target layer. Sometimes, for some special needs, the well inclination can exceed 90 degrees and "warp upward". Generally speaking, horizontal wells are suitable for thin oil and gas layers or fractured oil and gas reservoirs, with the purpose of increasing the exposed area of ​​the oil and gas layers.

[0092] Figure 1 Schematic diagram of the implementation environment of a shale oil fracturing effect evaluation method provided in accordance with an embodiment of the present application. Figure 1 , the implementation environment includes a terminal 101 and a server 102.

[0093] The terminal 101 and the server 102 can be connected directly or indirectly via wired or wireless communication, which is not limited in this application.

[0094] In some embodiments, the terminal 101 is a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal 101 is installed and runs an application that supports the shale oil fracturing effect evaluation function. Schematically, the terminal 101 is a terminal used by a user, and the user can trigger the data analysis function on the terminal 101 to display the corresponding analysis results to achieve the evaluation of the shale oil fracturing effect.

[0095] In some embodiments, server 102 is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), big data and artificial intelligence platforms. Server 102 is used to provide background services for applications that support shale oil fracturing effect evaluation functions. In some embodiments, server 102 undertakes the main computing work and terminal 101 undertakes the secondary computing work; or, server 102 undertakes the secondary computing work and terminal 101 undertakes the main computing work; or, server 102 and terminal 101 use a distributed computing architecture for collaborative computing.

[0096] Those skilled in the art will appreciate that the number of the above terminals may be more or less. For example, the above terminal may be only one, or the above terminals may be dozens or hundreds, or more. The embodiment of the present application does not limit the number of terminals and device types.

[0097] In some embodiments, the wireless network or wired network described above uses standard communication technology and / or protocols. The network is typically the Internet, but can also be any network, including but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a dedicated network or any combination of a virtual private network. In some embodiments, technologies and / or formats including Hyper Text Mark-up Language (HTML), Extensible Markup Language (XML), etc. are used to represent data exchanged over the network. In addition, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet Protocol Security (IPsec), etc. can also be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can also be used to replace or supplement the above data communication technologies.

[0098] Figure 2 is a flow chart of a shale oil fracturing effect evaluation method provided in an embodiment of the present application, such as Figure 2 As shown, in the embodiment of the present application, a server is used as an example for explanation. The method comprises the following steps:

[0099] 201. Acquire reservoir data and engineering data of multiple horizontal wells in a target block, wherein the reservoir data includes data of multiple reservoir parameters, and the engineering data includes data of multiple engineering parameters.

[0100] The reservoir data is the data of reservoir parameters of each fracturing section in the horizontal well, which is used to indicate the geological conditions of the fracturing section in the horizontal well, and the engineering data is the data of engineering parameters for fracturing operations on each fracturing section. The above reservoir parameters and engineering parameters are factors that affect the fracturing effect of the horizontal well.

[0101] In some embodiments, the geology of m horizontal wells in the target block is detected using detection equipment to obtain data on reservoir parameters of the reservoir at each fracturing section. The reservoir parameters may include brittleness index, natural gamma, Poisson's ratio, Ro (kerogen maturity index), OSI (oil saturation), TOC (total organic carbon content), S1 (liquid hydrocarbon content of C8-C29 light and medium hydrocarbon components measured at 300°C), Young's modulus, minimum horizontal principal stress, horizontal principal stress difference coefficient, total length of the fracturing section, angle between the wellbore and the principal stress direction, and horizontal section length, wherein m is an integer greater than 1. The embodiments of the present application do not limit what the reservoir parameters specifically include.

[0102] For any fracturing section of a horizontal well, data of engineering parameters used in the fracturing operation are obtained. The engineering parameters may include average section length, average cluster spacing, liquid volume per meter, slickwater ratio, sand volume per meter, quartz sand ratio, sand-liquid ratio, average displacement, well soaking time, etc. The embodiments of the present application do not limit what the engineering parameters specifically include.

[0103] In one example, the data of 24 horizontal wells in zone w are used as an example for explanation, where:

[0104] There are 22 geological parameters and engineering parameters in total, among which the specific data of reservoir parameters are shown in Table 1 below and the specific data of engineering parameters are shown in Table 2 below.

[0105] Table 1

[0106]

[0107]

[0108] Table 2

[0109]

[0110]

[0111] 202. Based on the random forest algorithm, the reservoir data and the engineering data of multiple horizontal wells are analyzed to obtain e main controlling factors affecting the fracturing effect, wherein the e main controlling factors include target reservoir parameters and target engineering parameters, wherein e is an integer greater than 1.

[0112] In some embodiments, based on the reservoir data and engineering data of multiple horizontal wells, the importance of reservoir parameters and engineering parameters of multiple horizontal wells is obtained based on the random forest algorithm, and the parameters ranked in the top e positions are screened out. The screened parameters are the main controlling factors affecting the fracturing effect. The importance of a parameter is used to indicate the influence of the parameter on the fracturing effect.

[0113] In some embodiments, the importance of each parameter can be obtained by the following formula (1).

[0114]

[0115] Wherein: I is the importance of a certain parameter, dimensionless; N is the number of trees in the random forest, that is, the number of parameters in the embodiment of the present application, N=e, dimensionless; r1 is the out-of-bag error, dimensionless; r2 is the out-of-bag error after the order of a certain parameter is randomly transformed, dimensionless.

[0116] Taking e=16 as an example, 16 main control factors are screened out by calculating the importance of different parameters. The importance corresponding to the 16 main control factors is shown in Table 3 below.

[0117] Table 3

[0118] Serial number property Importance Serial number property Importance 1 Brittleness Index 0.3563 9 OSI 0.0245 2 Sand-liquid ratio / % 0.1355 10 Natural Gamma 0.0191 3 Quartz sand ratio 0.0887 11 Horizontal section length / m 0.0189 4 S1 0.0745 12 Slippery water ratio 0.0183 5 Well soaking time / day 0.0489 13 Average cluster spacing / m 0.0158 6 <![CDATA[Rice liquid volume / m 3 / m]]> 0.0476 14 TOC / % 0.0155 7 Angle between wellbore and principal stress direction / ° 0.0380 15 <![CDATA[Average displacement / m 3 / min]]> 0.0152 8 Total length of fracturing section / m 0.0361 16 Poisson's ratio 0.0120

[0119] 203. Based on the principal component analysis method, the e main controlling factors are reduced in dimension to obtain n principal components affecting the fracturing effect, wherein e is greater than n, and e and n are integers greater than 1.

[0120] Based on the correlation between the main control factors, the original factors are replaced by fewer principal components after dimensionality reduction, and the principal components are made to retain as much information reflected by the original factors as possible.

[0121] In some embodiments, the m horizontal wells and the e main controlling factors are taken as sample matrices, and dimension reduction operations are performed on them to obtain the required principal components. The process shown in this step 203 can be implemented by the process shown in the following steps 203A-203E.

[0122] Step 203A: Perform data collection. If there are m horizontal wells and e main controlling factor indicators, a sample matrix of size m×e can be constructed. This sample matrix can be represented by the following a.

[0123]

[0124] Where: a is the sample matrix; a ij is the main controlling factor; j is the main controlling factor vector.

[0125] Step 203B: When the dimensions of the main controlling factor indicators are inconsistent, the standardized data method is used to standardize the data based on the calculation of the mean and standard deviation, that is, the mean of each column value is 0 and the standard deviation is 1, and then a correlation coefficient matrix is ​​established through the standardized matrix, which indicates the degree of correlation between the e indicators.

[0126] The original sample matrix can be represented by the following matrix A after standardization.

[0127]

[0128] Where: A is the standardized sample matrix; A ij is the main control factor after standardization; A j is the standardized main control factor vector.

[0129] The correlation coefficient matrix corresponding to the sample matrix can be obtained through the standardized matrix A.

[0130]

[0131] Where: R is the correlation coefficient matrix; r ij is the correlation coefficient, where r ij It is obtained by the following formulas (5) and (6).

[0132]

[0133]

[0134] Step 203C: After obtaining the correlation coefficient matrix R, calculate its eigenvalues ​​and eigenvectors.

[0135] Eigenvalue: λ1≥λ2≥…≥λ m ≥0(R is a semi-positive definite matrix, )

[0136] Eigenvectors:

[0137]

[0138] Step 203D: Based on the obtained eigenvalues ​​and eigenvectors, calculate the variance contribution rate b of the corresponding eigenvalues ​​and eigenvectors i And the cumulative contribution rate b(o) is calculated by the following formulas (8) and (9).

[0139]

[0140]

[0141] Where: b i is the variance contribution rate of each eigenvector of the eigenvalue, dimensionless; b(o) is the cumulative contribution rate of each eigenvector of the eigenvalue, dimensionless.

[0142] Step 203E: Obtain the number of principal components according to the obtained variance contribution rate and cumulative contribution rate, and calculate the expression of each principal component.

[0143] In some embodiments, the number of eigenvectors corresponding to eigenvalues ​​with values ​​greater than or equal to 1 and cumulative contribution rates exceeding 85% is taken as the number of principal components, and 6 different principal components are obtained. The score of each principal component is calculated according to the linear expression composed of its corresponding eigenvector and each main control factor index. The i-th principal component F i The calculation formula is obtained from the following formula (10).

[0144] F i =c 1i A1+…+c ei A e ,i=1,2,…,n (10)

[0145] Where: F i is the i-th principal component, dimensionless.

[0146] Among them, the factors of the six principal components correspond to the principal component characteristics, and the values ​​of the principal component characteristics are used to indicate the possibility that the main control factors have corresponding principal components, as shown in Table 4 below.

[0147] Table 4

[0148]

[0149]

[0150] According to the data in Table 4, the main controlling factor data screened out from different horizontal wells are substituted into equations (3) and (10) to obtain the principal component values ​​of different horizontal wells, so as to form the principal component matrix of multiple horizontal wells. The results are shown in Table 5. The principal component matrix is ​​normalized to obtain the principal component normalized matrix, which is shown in Table 6.

[0151] Table 5

[0152]

[0153]

[0154] Table 6

[0155] hashtag Principal component 1 Principal component 2 Principal component 3 Principal component 4 Principal component 5 Principal component 6 w1 0.1567 0.0000 1.0000 0.7987 0.3731 0.0660 w2 0.1829 0.0422 0.8560 1.0000 0.4676 0.6683 w3 0.0000 0.1041 0.5414 0.0066 0.4202 0.6828 w4 0.3539 0.2696 0.5900 0.1541 0.4818 0.3914 w5 0.1134 0.5459 0.3565 0.3247 0.0814 0.1212 w6 0.2889 0.2851 0.6486 0.0000 0.3171 0.3715 w7 0.1297 0.1113 0.5792 0.0563 0.2692 0.3519 w8 0.3957 0.3687 0.6237 0.1619 0.4092 0.0000 w9 0.2268 0.4173 0.2899 0.4595 0.0000 0.3711 w10 0.3610 0.5761 0.1298 0.6018 0.3555 0.1193 w11 0.4180 0.5549 0.4353 0.4803 0.4941 0.1984 w12 0.3655 0.5214 0.0573 0.5468 0.1712 0.3149 w13 0.3477 0.0922 0.0000 0.5370 0.0969 0.4033 w14 0.1634 0.8533 0.5298 0.4620 0.3668 0.4468 w15 0.1094 0.7763 0.4650 0.5066 0.4167 0.2440 w16 0.0698 0.7746 0.4916 0.4330 0.3893 0.2762 w17 0.4128 1.0000 0.9984 0.3760 0.1601 0.8442 w18 0.3563 0.7797 0.8291 0.3705 0.3963 0.2298 w19 0.1053 0.4621 0.0071 0.4043 1.0000 0.5707 w20 0.0900 0.4725 0.2546 0.4428 0.2345 1.0000 w21 0.7957 0.5401 0.5024 0.3414 0.6215 0.3147 w22 0.9148 0.2389 0.4868 0.3086 0.2693 0.4755 w23 1.0000 0.3781 0.4097 0.5031 0.3559 0.6159

[0156] 204. Based on the fuzzy comprehensive evaluation method, the n principal components of each horizontal well are scored to obtain the fuzzy comprehensive score of each horizontal well.

[0157] In some embodiments, based on the normalized matrix of the principal components, an evaluation factor set and an evaluation set are established, and a fuzzy weight is configured for each principal component. Based on the fuzzy weight of each principal component and the degree of membership of different principal components, an evaluation fuzzy set of the multiple horizontal wells is obtained. The evaluation fuzzy set of each horizontal well includes the evaluation values ​​of different principal components of the horizontal well. Based on the evaluation values ​​of different principal components in the evaluation fuzzy set of each horizontal well, the fuzzy comprehensive scores of different horizontal wells are determined.

[0158] Among them, the evaluation set includes multiple fracturing effect levels corresponding to each principal component in the horizontal well, that is, the production level. The membership function is used to calculate the membership degree of different principal components, and the membership function is obtained by fitting the normalized principal component value with the production, and through the fitting relationship, further division is performed according to the production level in the evaluation set.

[0159] In some embodiments, the process shown in step 204 can be implemented by the process shown in steps 204A-204D described below.

[0160] Step 204A: Establish an evaluation set, which is used to classify the horizontal well fracturing effects.

[0161] Establish an evaluation set v = (v1, v2, ..., vn), where v is a fully ordered set, that is, the level difference between any two comments in v. v is the evaluation standard set corresponding to the evaluation factor in F. For example, the evaluation set v is the set of production levels (level I, level II, level III and level IV) corresponding to each evaluation factor. In this paper, v = [100, 75, 50, 25], which is also used to assist in evaluating the fracturing effect of horizontal wells.

[0162] Step 204B: Establish an evaluation factor set based on the n principal components of the horizontal well.

[0163] Among them, the evaluation factor set includes n principal components of the horizontal well. The principal components contained in the evaluation factor set are evaluation indicators that affect the production of the horizontal well. The evaluation factor set can be understood as a fuzzy subset composed of n principal components used to evaluate the horizontal well, recorded as F = (F1, F2, ..., Fn), which is used to evaluate the fracturing effect of the horizontal well.

[0164] Step 204C: assigning a fuzzy weight to each principal component in the evaluation factor set.

[0165] The random forest algorithm is applied to obtain the importance of multiple principal components, and different fuzzy weights are configured for the multiple principal components based on the importance of the multiple principal components. The fuzzy weight represents the importance of each principal component to the evaluation result.

[0166] The fuzzy weight is expressed in the form of a vector. The fuzzy weight is used to indicate the importance of the principal component to the evaluation result. Different fuzzy weights have different importance to the evaluation result. i The fuzzy weight is w i (i=1, 2, 3, ..., n) indicates that the fuzzy weights of the n principal components can be expressed as a weight set W.

[0167] In some embodiments, a random forest algorithm is applied to obtain fuzzy weights of different principal components. The random forest uses impurity as the best indicator to measure the segmentation effect of the classification tree, and the impurity is calculated by the Gini index method. In the Gini index method, assuming that the set T contains k categories of data, the Gini index is shown by the following formula (11):

[0168]

[0169] In the formula, Gini(T) is the Gini index of set T, dimensionless; k is the number of categories, dimensionless; p j Represents the frequency of occurrence of category j, dimensionless.

[0170] The set T represents the data set of different horizontal wells after dimensionality reduction, k is the number of principal components, and in the embodiment of the present application, k=n.

[0171] When Gini(T) is the smallest, that is, all records at this node T belong to the same category, that is, there is only one principal component in the set T, which means that the maximum useful information can be obtained at this time; when Gini(T) is the largest, all records in this node T are evenly distributed for each category, that is, there are different types of principal components in the set T, which means that the minimum useful information is obtained. For the classification and regression tree, if the node T does not satisfy that all samples in T belong to the same category or there is only one sample left in T, where a sample refers to the principal component and its principal component value contained in a horizontal well, then this node is a non-leaf node, and this node needs to be further split. If the set T is split into s parts T i (i=1, 2, ..., s), then the Gini index for this segmentation is shown by the following formula (12):

[0172]

[0173] Where: Gini split (T) is the segmented Gini index, dimensionless. i is in the child node T i N is the number of samples at the parent node T.

[0174] In order to obtain the fuzzy weight of the principal component, it is necessary to calculate the principal component x used for segmentation i The reduction value of the Gini index at each split node. In certain embodiments, for the principal component x i For example, the original segmentation Gini index of the i-th classification regression tree is Gini split (x i ), the Gini index after randomly replacing the variable attribute value (that is, category, that is, principal component) of the separation point is Gini split (x ij ), then the importance of variable attribute value j in the corresponding single classification and regression tree can be expressed as Gini split (x i )-Gini split (x ij ), where a single classification and regression tree is used to indicate a single horizontal well, and the variable attribute value in the tree is used to indicate the principal component value in the well. Importance of variable attribute value j The average reduction value of the Gini index of trees in the forest is calculated, and the average reduction value is obtained by the following formula (13):

[0175]

[0176] Where: is the importance of variable attribute value j, dimensionless; Gini split (x i ) is the original split Gini index of the i-th classification and regression tree, dimensionless; Gini split (x ij ) is the Gini index after randomly replacing the variable attribute value j of the separation point, dimensionless; B is the number of trees in the random forest, dimensionless.

[0177] According to the importance of the principal component, the fuzzy weight of the principal component is obtained as shown in the following formula (14):

[0178]

[0179] Where: w j is the fuzzy weight of the jth principal component, dimensionless; n is the number of principal components, dimensionless.

[0180] Substituting the principal component data of different horizontal wells into equations (11), (12), (13), and (14) we can obtain the fuzzy weights w of different principal components: i .

[0181] W=(w1,w2,…,w n ) (15)

[0182] The fuzzy weight set of the principal components is expressed by formula (15).

[0183] Step 204D: Based on the fuzzy weight of each principal component and the membership degrees of different principal components, the evaluation fuzzy sets of the multiple horizontal wells are obtained, and based on the evaluation values ​​of different principal components in the evaluation fuzzy sets of each horizontal well, the fuzzy comprehensive scores of the different horizontal wells are determined, wherein the evaluation fuzzy set of each horizontal well includes the evaluation values ​​of different principal components of the horizontal well.

[0184] Different principal components have different degrees of influence on the fracturing effect of horizontal wells. Therefore, the impact on the evaluation results of horizontal wells is also different. By determining the evaluation value of each principal component for each evaluation level, the influence of different principal components on the final fracturing effect can be obtained. Based on the evaluation value of the principal component for each evaluation level, corresponding to the evaluation set, the evaluation level to which different principal component factors belong can also be obtained.

[0185] This step can be implemented by the process shown in the following steps 204D-1 to 204D-4.

[0186] Step 204D-1: Fit the principal components and production of multiple horizontal wells to obtain a fitting relationship between the principal components and the production, and determine a membership function based on the fitting relationship.

[0187] The membership function is a generalization of the indicator function in a set. The indicator function can indicate whether an element in a set belongs to a specific subset. For example, the indicator function value of an element may be 0 or 1, while the membership function value of the element may be a value between 0 and 1, indicating the "real degree" of the element belonging to a fuzzy set.

[0188] There are generally four methods for determining the membership function: fuzzy statistics, assignment method, borrowing the existing "objective" scale method, and binary comparison sorting method. According to the corresponding yield level of each principal component (level I, level II, level III, level IV), the membership function is determined based on the comprehensive fuzzy statistics and assignment method, and three forms of membership functions, large-scale, small-scale, and intermediate, are selected. According to the normalized values ​​of different principal component data and the yield fitting, the membership functions of different yield levels in different forms are obtained, among which the membership degree is divided into four levels: level I, level II, level III, and level IV. The following membership function includes the membership function expressions of large-scale, small-scale, and intermediate types under four levels. As shown in Table 7 below.

[0189] surface

[0190]

[0191] Where: G is the membership function; x is the normalized principal component value; s is the peak value of the normalized principal component normal distribution.

[0192] Step 204D-2: Based on the membership function and each principal component, obtain the membership degree of each principal component of the plurality of horizontal wells.

[0193] Among them, the membership degree of each principal component of multiple horizontal wells is expressed in matrix form.

[0194] Substitute the normalized principal component values ​​of multiple horizontal wells into the membership function expressions of the four levels to obtain the membership matrix H of each horizontal well. i , the H i is an n×4 matrix, where n is the number of principal components.

[0195]

[0196] Where: H i is the membership matrix; h ij is the degree of membership of different principal components, dimensionless.

[0197] Step 204D-3: Based on the fuzzy weight of each principal component and the membership degrees of different principal components, the evaluation fuzzy sets of the multiple horizontal wells are obtained, and the evaluation fuzzy set of each horizontal well includes the evaluation values ​​of different principal components of the horizontal well.

[0198] Among them, the evaluation values ​​of different principal components of the horizontal wells are obtained by calculating the membership matrix of each horizontal well and the fuzzy weights of different principal components. The comprehensive evaluation fuzzy set composed of the evaluation values ​​of different principal components of different horizontal wells is expressed in a matrix. The specific calculation process is shown in the following formula (17):

[0199]

[0200] Where: D i is the comprehensive evaluation fuzzy set of the i-th horizontal well; d j is the fuzzy comprehensive evaluation value of different principal components of horizontal wells.

[0201] Based on the above examples and processing flow, the evaluation fuzzy set of the horizontal well can be obtained as shown in Table 8 below.

[0202] Table 8

[0203]

[0204]

[0205] Step 204D-4: Determine the fuzzy comprehensive scores of different horizontal wells based on the evaluation values ​​of different principal components in the evaluation fuzzy set of each horizontal well.

[0206] In the embodiment of the present application, after obtaining the evaluation fuzzy set, the fuzzy comprehensive scores of different horizontal wells are calculated based on the evaluation values ​​therein, which are used as the evaluation index of the fracturing effect of the horizontal well. The fuzzy comprehensive scores of different horizontal wells are determined by the following formula (18).

[0207]

[0208] In the formula, f i is the fuzzy comprehensive score of the i-th fracturing well.

[0209] 205. Fit the relationship between the fuzzy comprehensive scores and the production of the multiple horizontal wells to obtain a function model between the main controlling factors and the production.

[0210] In some embodiments, the comprehensive fuzzy scores of the multiple horizontal wells obtained in step 204D are fitted with the 12-month kilometer cumulative production of each horizontal well to obtain a function model between the main controlling factors and the production. The specific application data is shown in Table 9 below.

[0211] Table 9

[0212]

[0213]

[0214] 206. For any horizontal well, based on the e main controlling factors of the horizontal well and the function model, determine the predicted production of the horizontal well.

[0215] Among them, the predicted production can indicate the fracturing effect. In some embodiments, after the data of the main control factors of the test horizontal well are subjected to principal component dimensionality reduction, the fuzzy comprehensive score is obtained based on the fuzzy comprehensive evaluation method of step 204 above, and then the predicted production is 4549.12t based on the function model fitting, and the actual production of the test horizontal well is 4339.58t, with a relative error of 4.8%. It can be found that the error rate of the prediction result is less than 5%. The evaluation effect results are shown in the following Table 10.

[0216] Table 10

[0217]

[0218] Figure 6 is a block diagram of a shale oil fracturing effect evaluation device provided according to an embodiment of the present application. The device is used to execute the steps of the above-mentioned shale oil fracturing effect evaluation method, see Figure 6 , the device comprises:

[0219] The data acquisition module 601 is used to acquire reservoir data and engineering data of multiple horizontal wells in the target block;

[0220] A data analysis module 602 is used to analyze the reservoir data and engineering data of the plurality of horizontal wells based on a random forest algorithm to obtain e main control factors affecting the fracturing effect, wherein the e main control factors include target reservoir parameters and target engineering parameters, wherein e is an integer greater than 1;

[0221] A data dimension reduction module 603 is used to reduce the dimension of the e main control factors based on the principal component analysis method to obtain n principal components that affect the fracturing effect, wherein e is greater than n, and e and n are integers greater than 1;

[0222] A model generation module 604 is used to score the n principal components of the multiple horizontal wells based on a fuzzy comprehensive evaluation method to obtain fuzzy comprehensive scores of the multiple horizontal wells, fit the relationship between the fuzzy comprehensive scores of the multiple horizontal wells and production, and obtain a function model between the main controlling factors and production;

[0223] The prediction module 605 is used to determine the predicted production of any horizontal well based on the e main controlling factors and the function model of the multiple horizontal wells.

[0224] In some embodiments, the data analysis module is used to:

[0225] Based on the reservoir data and the engineering data of the multiple horizontal wells and the random forest algorithm, the importance of the reservoir parameters and the engineering parameters of the multiple horizontal wells is obtained, the importance is sorted, and the parameters ranked in the top e positions are screened out.

[0226] In some embodiments, the data dimension reduction module 603 includes:

[0227] A correlation coefficient matrix acquisition unit, used to establish a correlation coefficient matrix based on the e main control factors and the plurality of horizontal wells, wherein the correlation coefficient matrix indicates the correlation between different main control factors;

[0228] A principal component matrix acquisition unit is used to obtain a normalized principal component matrix of the plurality of horizontal wells based on the correlation coefficient matrix, wherein the principal component matrix includes principal component values ​​of the n principal components.

[0229] In some embodiments, the correlation coefficient matrix acquisition unit is used to:

[0230] Based on the e main controlling factors and the multiple horizontal wells, a sample matrix is ​​established, wherein the sample matrix includes the e main controlling factors of the multiple horizontal wells;

[0231] According to the sample matrix, applying a standardized data method to obtain a standardized matrix;

[0232] The correlation coefficient matrix is ​​established according to the standardized matrix.

[0233] In some embodiments, the principal component matrix acquisition unit is used to:

[0234] Based on the correlation coefficient matrix, corresponding eigenvalues ​​and eigenvectors are calculated;

[0235] Based on the eigenvector and the standardized matrix, the n principal component values ​​are calculated, where the number n of the n principal components is determined by the variance contribution rate and the cumulative contribution rate, and the variance contribution rate and the cumulative contribution rate are calculated by the eigenvalue and the eigenvector;

[0236] Based on the principal component values, a principal component matrix is ​​established, and the principal component matrix is ​​normalized to obtain a normalized principal component matrix of the plurality of horizontal wells.

[0237] In some embodiments, the model generation module 604 includes:

[0238] An evaluation set establishing unit, used for classifying the fracturing effects of the plurality of horizontal wells;

[0239] An evaluation factor set establishing unit, used for participating in the evaluation of the fracturing effects of the plurality of horizontal wells;

[0240] A fuzzy weight configuration unit, used for configuring fuzzy weights for the n principal components in the evaluation factor set;

[0241] An evaluation fuzzy set establishing unit, configured to obtain the evaluation fuzzy sets of the plurality of horizontal wells based on the fuzzy weights and the membership degrees of the n principal components, wherein the evaluation fuzzy sets of the plurality of horizontal wells are established based on the evaluation values ​​of the n principal components of the plurality of horizontal wells, and the membership degrees are calculated based on membership functions;

[0242] A scoring unit is used to determine the fuzzy comprehensive scores of the multiple horizontal wells based on the evaluation fuzzy sets of the multiple horizontal wells and the evaluation set.

[0243] In some embodiments, the fuzzy weight configuration unit is used to:

[0244] Obtaining importance scores of the n principal components, wherein the importance scores are calculated based on a random forest algorithm;

[0245] Based on the importance scores of the n principal components, different fuzzy weights are configured for the n principal components, and the fuzzy weights represent the importance of the n principal components to the evaluation result.

[0246] In some embodiments, the evaluation fuzzy set building unit is used to:

[0247] Determining a membership function based on a fitting relationship between the n principal components and the production rates of the plurality of horizontal wells;

[0248] Based on the membership function and the n principal components, obtaining the membership degrees of the n principal components of the plurality of horizontal wells;

[0249] Based on the fuzzy weights of the n principal components and the membership degrees of the n principal components, an evaluation fuzzy set of the multiple horizontal wells is obtained, and the evaluation fuzzy set of the multiple horizontal wells includes evaluation values ​​of different principal components of the multiple horizontal wells.

[0250] It should be noted that: the shale oil fracturing effect evaluation device provided in the above embodiment only uses the division of the above functional modules as an example when running the application program. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the shale oil fracturing effect evaluation device provided in the above embodiment and the shale oil fracturing effect evaluation method embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0251] In the embodiments of the present application, the computer device can be configured as a terminal or a server. When the computer device is configured as a terminal, the terminal can be used as the execution subject to implement the technical solution provided in the embodiments of the present application. When the computer device is configured as a server, the server can be used as the execution subject to implement the technical solution provided in the embodiments of the present application. The technical solution provided in the present application can also be implemented through interaction between the terminal and the server. The embodiments of the present application are not limited to this.

[0252] Figure 7 1 is a block diagram of a computer device 700 provided according to an embodiment of the present application. The computer device 700 may be a portable mobile terminal, such as a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer or a desktop computer. The computer device 700 may also be referred to as a user device, a portable terminal, a laptop terminal, a desktop terminal or other names.

[0253] Typically, the computer device 700 includes a processor 701 and a memory 702 .

[0254] The processor 701 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 701 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 701 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 701 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 701 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0255] The memory 702 may include one or more computer-readable storage media, which may be non-transitory. The memory 702 may also include a high-speed random access memory and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 702 is used to store at least one computer program, which is used to be executed by the processor 701 to implement the shale oil fracturing effect evaluation method provided in the method embodiment of the present application.

[0256] In some embodiments, the computer device 700 may further optionally include: a peripheral device interface 703 and at least one peripheral device. The processor 701, the memory 702 and the peripheral device interface 703 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 703 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 704, a display screen 705, a camera assembly 706, an audio circuit 707 and a power supply 708.

[0257] The peripheral device interface 703 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 701 and the memory 702. In some embodiments, the processor 701, the memory 702, and the peripheral device interface 703 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 701, the memory 702, and the peripheral device interface 703 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0258] The radio frequency circuit 704 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 704 communicates with the communication network and other communication devices through electromagnetic signals. The radio frequency circuit 704 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. In some embodiments, the radio frequency circuit 704 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, and the like. The radio frequency circuit 704 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: the World Wide Web, a metropolitan area network, an intranet, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 704 may also include circuits related to NFC (Near Field Communication), which is not limited in this application.

[0259] The display screen 705 is used to display the UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 705 is a touch display screen, the display screen 705 also has the ability to collect touch signals on the surface or above the surface of the display screen 705. The touch signal can be input to the processor 701 as a control signal for processing. At this time, the display screen 705 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 705 can be one, set on the front panel of the computer device 700; in other embodiments, the display screen 705 can be at least two, respectively set on different surfaces of the computer device 700 or in a folding design; in other embodiments, the display screen 705 can be a flexible display screen, set on the curved surface or folding surface of the computer device 700. Even, the display screen 705 can also be set to a non-rectangular irregular shape, that is, a special-shaped screen. The display screen 705 can be made of materials such as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode, organic light-emitting diode).

[0260] The camera assembly 706 is used to capture images or videos. In some embodiments, the camera assembly 706 includes a front camera and a rear camera. Typically, the front camera is disposed on the front panel of the terminal, and the rear camera is disposed on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 706 may also include a flash. The flash may be a monochrome temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0261] The audio circuit 707 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor 701 for processing, or input them into the radio frequency circuit 704 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there can be multiple microphones, which are respectively arranged at different parts of the computer device 700. The microphone can also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 701 or the radio frequency circuit 704 into sound waves. The speaker can be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 707 may also include a headphone jack.

[0262] The power supply 708 is used to power various components in the computer device 700. The power supply 708 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 708 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged through a wired line, and a wireless rechargeable battery is a battery that is charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0263] In some embodiments, the computer device 700 further includes one or more sensors 709 , including but not limited to: an acceleration sensor 710 , a gyroscope sensor 711 , a pressure sensor 712 , an optical sensor 713 , and a proximity sensor 714 .

[0264] The acceleration sensor 710 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established by the computer device 700. For example, the acceleration sensor 710 can be used to detect the components of gravity acceleration on the three coordinate axes. The processor 701 can control the display screen 705 to display the user interface in a horizontal view or a vertical view according to the gravity acceleration signal collected by the acceleration sensor 710. The acceleration sensor 710 can also be used to collect game or user motion data.

[0265] The gyro sensor 711 can detect the body direction and rotation angle of the computer device 700, and the gyro sensor 711 can cooperate with the acceleration sensor 710 to collect the user's 3D actions on the computer device 700. The processor 701 can implement the following functions based on the data collected by the gyro sensor 711: motion sensing (such as changing the UI according to the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0266] The pressure sensor 712 can be set on the side frame of the computer device 700 and / or the lower layer of the display screen 705. When the pressure sensor 712 is set on the side frame of the computer device 700, it can detect the user's grip signal of the computer device 700, and the processor 701 performs left and right hand recognition or shortcut operation according to the grip signal collected by the pressure sensor 712. When the pressure sensor 712 is set on the lower layer of the display screen 705, the processor 701 controls the operability controls on the UI interface according to the user's pressure operation on the display screen 705. The operability controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0267] The optical sensor 713 is used to collect the ambient light intensity. In one embodiment, the processor 701 can control the display brightness of the display screen 705 according to the ambient light intensity collected by the optical sensor 713. Specifically, when the ambient light intensity is high, the display brightness of the display screen 705 is increased; when the ambient light intensity is low, the display brightness of the display screen 705 is reduced. In another embodiment, the processor 701 can also dynamically adjust the shooting parameters of the camera assembly 706 according to the ambient light intensity collected by the optical sensor 713.

[0268] The proximity sensor 714, also called a distance sensor, is usually disposed on the front panel of the computer device 700. The proximity sensor 714 is used to collect the distance between the user and the front of the computer device 700. In one embodiment, when the proximity sensor 714 detects that the distance between the user and the front of the computer device 700 is gradually decreasing, the processor 701 controls the display screen 705 to switch from the screen-on state to the screen-off state; when the proximity sensor 714 detects that the distance between the user and the front of the computer device 700 is gradually increasing, the processor 701 controls the display screen 705 to switch from the screen-off state to the screen-on state.

[0269] Those skilled in the art will understand that Figure 7 The structure shown in the figure does not constitute a limitation on the computer device 700, and the computer device 700 may include more or less components than those shown in the figure, or combine some components, or adopt a different arrangement of components.

[0270] Figure 8It is a structural diagram of a server provided according to an embodiment of the present application. The server 800 may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) 801 and one or more memories 802, wherein the memory 802 stores at least one computer program, and the at least one computer program is loaded and executed by the processor 801 to implement the shale oil fracturing effect evaluation method provided by the above-mentioned various method embodiments. Of course, the server may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output, and the server may also include other components for realizing device functions, which will not be repeated here.

[0271] The embodiment of the present application also provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is loaded and executed by the processor of the terminal to implement the operation performed by the terminal in the shale oil fracturing effect evaluation method of the above embodiment. For example, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device.

[0272] In some embodiments, the computer program involved in the embodiments of the present application may be deployed and executed on a computer device, or on multiple computer devices located at one location, or on multiple computer devices distributed at multiple locations and interconnected by a communication network. Multiple computer devices distributed at multiple locations and interconnected by a communication network may constitute a blockchain system.

[0273] The embodiment of the present application also provides a computer program product or a computer program, which includes a computer program code, and the computer program code is stored in a computer-readable storage medium. The processor of the terminal reads the computer program code from the computer-readable storage medium, and the processor executes the computer program code, so that the terminal executes the shale oil fracturing effect evaluation method provided in the above various optional implementations.

[0274] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0275] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for evaluating the effect of shale oil fracturing, characterized in that: The method comprises: Obtain reservoir data and engineering data for multiple horizontal wells in the target block; Based on the random forest algorithm, the reservoir data and engineering data of the plurality of horizontal wells are analyzed to obtain e main control factors affecting the fracturing effect, wherein the e main control factors include target reservoir parameters and target engineering parameters, wherein e is an integer greater than 1; Based on the principal component analysis method, the e main control factors are reduced in dimension to obtain n principal components that affect the fracturing effect, wherein e is greater than n, and e and n are integers greater than 1; Based on the fuzzy comprehensive evaluation method, scoring the n principal components of the multiple horizontal wells to obtain fuzzy comprehensive scores of the multiple horizontal wells; Fitting the relationship between the fuzzy comprehensive scores and the production of the multiple horizontal wells to obtain a function model between the main controlling factors and the production; For any horizontal well, the predicted production of the multiple horizontal wells is determined based on the e main controlling factors of the multiple horizontal wells and the function model.

2. The method according to claim 1, characterized in that The random forest algorithm is used to analyze the reservoir data and engineering data of the plurality of horizontal wells to obtain e main control factors affecting the fracturing effect, including: Based on the reservoir data and the engineering data of the multiple horizontal wells and the random forest algorithm, the importance of the reservoir parameters and the engineering parameters of the multiple horizontal wells is obtained, the importance is sorted, and the parameters ranked in the top e positions are screened out.

3. The method according to claim 1, characterized in that The principal component analysis method is used to reduce the dimensions of the e main control factors to obtain n principal components that affect the fracturing effect, including: Based on the e main controlling factors and the plurality of horizontal wells, a correlation coefficient matrix is ​​established, wherein the correlation coefficient matrix indicates the correlation between different main controlling factors; Based on the correlation coefficient matrix, a normalized principal component matrix of the plurality of horizontal wells is obtained, wherein the principal component matrix includes principal component values ​​of the n principal components.

4. The method according to claim 3, characterized in that The method of establishing a correlation coefficient matrix based on the e main controlling factors and the plurality of horizontal wells includes: Based on the e main controlling factors and the multiple horizontal wells, a sample matrix is ​​established, wherein the sample matrix includes the e main controlling factors of the multiple horizontal wells; According to the sample matrix, applying a standardized data method to obtain a standardized matrix; The correlation coefficient matrix is ​​established according to the standardized matrix.

5. The method according to claim 3, characterized in that: The step of obtaining a normalized principal component matrix of the plurality of horizontal wells based on the correlation coefficient matrix includes: Calculate based on the correlation coefficient matrix to obtain corresponding eigenvalues ​​and eigenvectors; Calculating based on the eigenvector and the standardized matrix to obtain the n principal component values, wherein the number n of the n principal components is determined by the variance contribution rate and the cumulative contribution rate, and the variance contribution rate and the cumulative contribution rate are calculated from the eigenvalue and the eigenvector; Based on the principal component values, a principal component matrix is ​​established, and the principal component matrix is ​​normalized to obtain a normalized principal component matrix of the plurality of horizontal wells.

6. The method according to claim 1, characterized in that The method of scoring the n principal components of the plurality of horizontal wells based on the fuzzy comprehensive evaluation method to obtain the fuzzy comprehensive scores of the plurality of horizontal wells includes: Establishing an evaluation set for grading the fracturing effects of the plurality of horizontal wells; Based on the n principal components of the horizontal wells, an evaluation factor set is established to participate in the evaluation of the fracturing effects of the plurality of horizontal wells; Based on the evaluation factor set, configuring fuzzy weights for the n principal components in the evaluation factor set; Based on the fuzzy weights and membership degrees of the n principal components, an evaluation fuzzy set of the multiple horizontal wells is obtained, wherein the evaluation fuzzy set of the multiple horizontal wells is established based on the evaluation values ​​of the n principal components of the multiple horizontal wells, and the membership degrees are calculated based on membership functions; Based on the evaluation fuzzy sets of the plurality of horizontal wells and the evaluation set, fuzzy comprehensive scores of the plurality of horizontal wells are determined.

7. The method according to claim 6, characterized in that The configuring fuzzy weights for the n principal components in the evaluation factor set based on the evaluation factor set includes: Obtaining importance scores of the n principal components, wherein the importance scores are calculated based on a random forest algorithm; Based on the importance scores of the n principal components, different fuzzy weights are configured for the n principal components, and the fuzzy weights represent the importance of the n principal components to the evaluation result.

8. The method according to claim 6, characterized in that The evaluation fuzzy sets of the plurality of horizontal wells are obtained based on the fuzzy weights of the n principal components and the membership degrees of the n principal components, including: Determining a membership function based on a fitting relationship between the n principal components and the production rates of the plurality of horizontal wells; Based on the membership function and the n principal components, obtaining the membership degrees of the n principal components of the plurality of horizontal wells; Based on the fuzzy weights of the n principal components and the membership degrees of the n principal components, an evaluation fuzzy set of the multiple horizontal wells is obtained, and the evaluation fuzzy set of the multiple horizontal wells includes evaluation values ​​of different principal components of the multiple horizontal wells.

9. A shale oil fracturing effect evaluation device, characterized in that: The device comprises: A data acquisition module is used to acquire reservoir data and engineering data of multiple horizontal wells in the target block; A data analysis module, for analyzing the reservoir data and engineering data of the plurality of horizontal wells based on a random forest algorithm, to obtain e main control factors affecting the fracturing effect, wherein the e main control factors include target reservoir parameters and target engineering parameters, wherein e is an integer greater than 1; A data dimension reduction module, used for reducing the dimensions of the e main control factors based on principal component analysis to obtain n principal components affecting the fracturing effect, wherein e is greater than n, and e and n are integers greater than 1; A model generation module is used to score the n principal components of the multiple horizontal wells based on a fuzzy comprehensive evaluation method to obtain fuzzy comprehensive scores of the multiple horizontal wells, fit the relationship between the fuzzy comprehensive scores of the multiple horizontal wells and production, and obtain a function model between the main control factors and production; A prediction module is used to determine the predicted production of the multiple horizontal wells for any horizontal well based on the e main controlling factors of the multiple horizontal wells and the function model.

10. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory is used to store at least one computer program, and the at least one computer program is loaded by the processor and executes the shale oil fracturing effect evaluation method described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store at least one computer program, and the at least one computer program is used to execute the shale oil fracturing effect evaluation method described in any one of claims 1 to 8.

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