Oil production determination method and device

Abstract

The embodiment of the invention discloses an oil production determination method and device, and belongs to the field of oil-gas exploration. The method comprises the following steps: processing geological data of a first shale oil well through a plurality of principal component expressions to obtain values of a plurality of principal components of the first shale oil well; determining first membership information based on an association relationship between the plurality of principal components and membership functions; second membership information is determined based on the weight of each principal component expression and the first membership information, the second membership information comprises the oil well membership degree of the first shale oil well to each evaluation grade, and the predicted oil production is in positive correlation with the evaluation grades; and based on the second membership information, the oil production of the first shale oil well after hydraulic fracturing is determined. According to the scheme, the oiliness index and the fracturing index of the shale oil well can be comprehensively considered in the process of predicting the oil production, and the oil production of the shale oil well after hydraulic fracturing can be accurately determined.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of oil and gas exploration, and in particular to a method and device for determining oil production. Background Art

[0002] In the field of oil and gas exploration, the development of shale oil has gradually become the main support for increasing oil production. Shale oil refers to a type of oil that is enriched in shale reservoirs. The oil production of shale reservoirs can directly affect the output of shale oil development. Therefore, how to determine the oil production of shale reservoirs is a technical problem that needs to be solved.

[0003] Among the related technologies, hydraulic fracturing technology is commonly used to extract shale oil. Hydraulic fracturing technology refers to a reservoir transformation technology that uses hydraulic force to form artificial fractures in shale reservoirs to improve the flow capacity of fluids in shale reservoirs. The better the fracturing property of a shale reservoir, the more artificial fractures can be formed in the shale reservoir through hydraulic fracturing technology, thereby making the oil production of the shale reservoir higher. Therefore, it is usually possible to determine the number and distribution of artificial fractures in the shale reservoir after hydraulic fracturing by analyzing the fracturing property of the shale reservoir, thereby determining the oil production of the shale reservoir.

[0004] However, the above method only considers the ability of shale reservoirs to form artificial fractures, and considers fewer influencing factors, and cannot accurately determine the oil production of shale reservoirs. Summary of the invention

[0005] The embodiment of the present application provides a method and device for determining oil production, which can comprehensively consider the oil content index and fracturing index of the shale oil well in the process of predicting oil production, and more accurately determine the oil production of the shale oil well after hydraulic fracturing. The technical solution is as follows:

[0006] In one aspect, a method for determining oil production is provided, the method comprising:

[0007] The geological data of the first shale oil well are processed respectively by using a plurality of principal component expressions to obtain the values ​​of a plurality of principal components of the first shale oil well, wherein the first shale oil well is a shale oil well that has not been hydraulically fractured, the geological data is used to represent the oil content index and the fracturing index of the shale reservoir where the first shale oil well is located, the oil content index is used to represent the oil content of the shale reservoir, the fracturing index is used to represent the anti-fracturing strength of the shale reservoir, and the principal component expressions are used to determine the values ​​of the principal components in the geological data;

[0008] Based on the association relationship between the plurality of principal components and the membership function, first membership information is determined, wherein the first membership information includes the principal component membership of each principal component to each evaluation level, and the principal component membership is used to indicate the probability that the value of the principal component belongs to the principal component value range associated with the corresponding evaluation level;

[0009] Based on the weight of each principal component expression and the first membership information, second membership information is determined, wherein the second membership information includes the oil well membership of the first shale oil well to each of the evaluation levels, the oil well membership is used to indicate the probability that the predicted oil production of the first shale oil well belongs to the oil production range indicated by the corresponding evaluation level, and the predicted oil production is positively correlated with the evaluation level;

[0010] Based on the second affiliation information, the oil production of the first shale oil well after hydraulic fracturing is determined.

[0011] In another aspect, an oil production determination device is provided, the device comprising:

[0012] a data processing module, used to process the geological data of the first shale oil well respectively through a plurality of principal component expressions to obtain the values ​​of the plurality of principal components of the first shale oil well, wherein the first shale oil well is a shale oil well that has not been hydraulically fractured, the geological data is used to represent the oil content index and the fracturing index of the shale reservoir where the first shale oil well is located, the oil content index is used to represent the oil content of the shale reservoir, the fracturing index is used to represent the anti-fracturing strength of the shale reservoir, and the principal component expressions are used to determine the values ​​of the principal components in the geological data;

[0013] A first determination module is used to determine first membership information based on the association relationship between the plurality of principal components and the membership function, wherein the first membership information includes the principal component membership of each principal component to each evaluation level, and the principal component membership is used to indicate the probability that the value of the principal component belongs to the principal component value range associated with the corresponding evaluation level;

[0014] A second determination module is used to determine second membership information based on the weight of each principal component expression and the first membership information, wherein the second membership information includes the oil well membership of the first shale oil well to each evaluation level, the oil well membership is used to indicate the probability that the predicted oil production of the first shale oil well belongs to the oil production range indicated by the corresponding evaluation level, and the predicted oil production is positively correlated with the evaluation level;

[0015] The third determination module is used to determine the oil production of the first shale oil well after hydraulic fracturing based on the second affiliation information.

[0016] On the other hand, a terminal is provided, comprising a processor and a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor to implement the oil production determination method as described in the above aspects.

[0017] 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 the oil production determination method as described in the above aspects.

[0018] On the other hand, a computer program product is provided, comprising a computer program, wherein the computer program is loaded and executed by a processor to implement the oil production determination method as described in the above aspects.

[0019] The embodiment of the present application provides a scheme for determining oil production. The oil content index of the shale reservoir can reflect the oil content of the shale reservoir, and the fracturing index can reflect the fracturing resistance strength of the shale reservoir. The above indexes can all be used as influencing factors of the oil production of the shale reservoir. By performing principal component analysis on the influencing factors, several principal components with a greater degree of influence on the oil production of the shale reservoir can be determined. Then, when predicting the oil production of the shale reservoir where the shale oil well is located, it is possible to comprehensively consider multiple indicators, determine the values ​​of multiple principal components of the shale oil well, and determine the degree of membership of the shale oil well to each evaluation level based on the values ​​of the multiple principal components. Since the predicted oil production of the shale oil well is positively correlated with the evaluation level, the shale reservoir with higher oil production has a higher degree of membership to the higher evaluation level. Therefore, according to the degree of membership of the shale oil well to each evaluation level, the oil production of the shale oil well after hydraulic fracturing can be more accurately determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] 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 embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 It is a schematic diagram of an implementation environment provided by an embodiment of the present application;

[0022] Figure 2 is a flow chart of a method for determining oil production provided in an embodiment of the present application;

[0023] Figure 3 is a flow chart of another method for determining oil production provided in an embodiment of the present application;

[0024] Figure 4is a schematic diagram of the weight of a principal component expression provided in an embodiment of the present application;

[0025] Figure 5 It is a schematic diagram of a principal component-production data provided in an embodiment of the present application;

[0026] Figure 6 is a schematic diagram of oil production information provided by an embodiment of the present application;

[0027] Figure 7 This is a flow chart for predicting oil production provided in an embodiment of the present application;

[0028] Figure 8 It is a structural schematic diagram of an oil production determination device provided in an embodiment of the present application;

[0029] Fig. 9 is a structural schematic diagram of another oil production determination device provided in an embodiment of the present application;

[0030] Fig.10 It is a structural diagram of a terminal provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0032] It is understood that the terms "first", "second", etc. used in this application can be used in this article to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of this application, a first shale oil well can be referred to as a second shale oil well, and similarly, a second shale oil well can be referred to as a first shale oil well.

[0033] 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. For example, the geological data and production data of shale oil wells involved in this application are all obtained with full authorization.

[0034] The implementation environment of the embodiments of the present application is introduced below.

[0035] Figure 1 is a schematic diagram of an implementation environment provided by an embodiment of the present application, see Figure 1, the implementation environment includes: a terminal 101 and a server 102. The terminal 101 can be connected to the server 102 via a wireless network or a wired network.

[0036] Optionally, the terminal 101 may be at least one of a smart phone, a desktop computer, a laptop computer, and a portable computer. An application may be installed and run on the terminal 101, and the application is used to analyze the oil content index and the fracturing index of the shale oil well to determine the oil production of the shale oil well. The application is associated with the server 102, and the server 102 provides background services.

[0037] Optionally, the 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, CDK (Coktekt Delivery Ketwork, content distribution network), and big data and artificial intelligence platforms. In some embodiments, 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.

[0038] Terminal 101 may generally refer to one of multiple terminals. This embodiment is illustrated by taking terminal 101 as an example. Those skilled in the art may know that the number of the above terminals may be more or less. For example, the above terminals may be several, or the above terminals may be dozens or hundreds, or more. The embodiment of the present application does not limit the number and device type of the terminals.

[0039] Figure 2 is a flow chart of a method for determining oil production provided in an embodiment of the present application. The embodiment of the present application is executed by a terminal. Figure 2 , the method comprising:

[0040] 201. The terminal processes the geological data of the first shale oil well through multiple principal component expressions to obtain multiple principal component values ​​of the first shale oil well. The first shale oil well is a shale oil well that has not been hydraulically fractured. The geological data is used to represent the oil content index and fracturing index of the shale reservoir where the first shale oil well is located. The oil content index is used to represent the oil content of the shale reservoir, and the fracturing index is used to represent the anti-fracturing strength of the shale reservoir. The principal component expressions are used to determine the values ​​of the principal components in the geological data.

[0041] In the embodiment of the present application, a shale oil well refers to an oil well mined in a shale reservoir, and a shale oil well is used to mine shale oil enriched in a shale reservoir. Since the shale oil enriched in the shale reservoir has a large flow resistance and difficulty in flowing, in the process of mining shale oil, artificial fractures can be formed in the shale reservoir by hydraulic fracturing technology, thereby improving the flow capacity of shale oil in the shale reservoir, so that more shale oil can flow into the mining pipeline, thereby increasing the oil production of the shale oil well. It can be seen that the oil production of a shale oil well is related to the oil content of the shale reservoir and the degree of fracturing resistance of the shale reservoir. Among them, the higher the degree of fracturing resistance of the shale reservoir, the less likely the shale reservoir is to be fractured, and the fewer artificial fractures are formed in the shale reservoir by hydraulic fracturing technology; the lower the degree of fracturing resistance of the shale reservoir, the easier the shale reservoir is to be fractured, and the more artificial fractures are formed in the shale reservoir by hydraulic fracturing technology. Therefore, the higher the oil content of the shale reservoir, the lower the resistance to fracturing, and the higher the oil production of the shale reservoir; the lower the oil content of the shale reservoir, the higher the resistance to fracturing, and the lower the oil production of the shale reservoir.

[0042] Since there are many oil content indicators that can reflect the oil content of shale reservoirs and fracturing indicators that can reflect the fracturing resistance of shale reservoirs. Therefore, in order to clarify the influence of various indicators in geological data on the oil production of shale reservoirs, the terminal can pre-perform principal component analysis on the geological data based on the geological data and oil production of shale oil wells that have been hydraulically fractured, and obtain multiple principal components and each principal component expression that have a greater impact on oil production. Among them, the principal component includes the above-mentioned oil content indicators and fracturing indicators, and the value of the principal component is the weighted sum of the above-mentioned oil content indicators and fracturing indicators. The principal component expression specifies the weight of each indicator. The weight of the indicator is positively correlated with the degree of influence of the indicator on oil production. Through the principal component expression, the weighted sum of the oil content indicators and fracturing indicators in the geological data can be performed to obtain the value of the principal component.

[0043] Therefore, in the process of determining the oil production of a shale oil well that has not been hydraulically fractured (hereinafter referred to as the first shale oil well for ease of description), the terminal can obtain geological data of the first shale oil well from the well logging data and the mud logging data, such as multiple oil content indicators and multiple fracturing indicators. Then, the terminal performs weighted summation of multiple indicators in the geological data of the first shale oil well through multiple principal component expressions to obtain the value of each principal component.

[0044] 202. The terminal determines first membership information based on the association between multiple principal components and membership functions. The first membership information includes the principal component membership of each principal component to each evaluation level. The principal component membership is used to indicate the probability that the value of the principal component belongs to the principal component value range associated with the corresponding evaluation level.

[0045] In an embodiment of the present application, the correlation relationship between the principal component and the membership function can indicate the membership function associated with the principal component. The membership function is used to determine the principal component membership of the principal component to each evaluation level. Among them, the evaluation level refers to the level for evaluating the oil production of the shale oil well. The higher the principal component membership of each principal component of the shale oil well to the principal component of the higher evaluation level, the higher the oil production of the shale oil well. Therefore, the terminal can determine the membership function associated with the principal component in advance based on the correlation between the principal component and the oil production of the shale oil well that has been hydraulically fractured. The correlation between the principal component and the oil production refers to the trend of the oil production with the value of the principal component. For example, when the oil production increases with the increase of the value of the principal component, the terminal determines that the principal component is associated with the membership function of the ascending function type. Therefore, the larger the value of the principal component, the higher the principal component membership of the principal component determined by the membership function to the higher evaluation level, and the higher the oil production of the determined shale oil well.

[0046] After the terminal determines the values ​​of multiple principal components of the first shale oil well, the terminal determines the membership function associated with each principal component based on the correlation between the multiple principal components and the membership function, and determines the principal component membership of each principal component to each evaluation level based on the membership function.

[0047] 203. The terminal determines the second membership information based on the weight of each principal component expression and the first membership information. The second membership information includes the oil well membership of the first shale oil well to each evaluation level. The oil well membership is used to indicate the probability that the predicted oil production of the first shale oil well belongs to the oil production range indicated by the corresponding evaluation level. The predicted oil production is positively correlated with the evaluation level.

[0048] In an embodiment of the present application, in the process of performing principal component analysis on the geological data of a shale oil well that has been hydraulically fractured, the terminal can not only obtain multiple principal component expressions, but also determine the weight of each principal component expression according to the degree of influence of the principal component on the oil production. Among them, the weight of the principal component expression is positively correlated with the degree of influence of the corresponding principal component on the oil production of the shale oil well. According to the weight of each principal component expression, the terminal performs a weighted summation of the principal component membership of each principal component in the first shale oil well to each evaluation level, and obtains the oil well membership of the first shale oil well to each evaluation level.

[0049] 204. The terminal determines the oil production of the first shale oil well after hydraulic fracturing based on the second affiliation information.

[0050] In the embodiment of the present application, the higher the membership of the first shale oil well to the oil wells with higher evaluation levels, the lower the membership to the oil wells with lower evaluation levels, and the higher the predicted oil production of the first shale oil well; the lower the membership of the first shale oil well to the oil wells with higher evaluation levels, the higher the membership to the oil wells with lower evaluation levels, and the lower the predicted oil production of the first shale oil well. The terminal determines the predicted oil production of the first shale oil well based on the membership of the first shale oil well to the oil wells of each evaluation level, that is, the oil production of the first shale oil well after the first shale oil well is hydraulically fractured.

[0051] The embodiment of the present application provides a method for determining oil production. The oil content index of the shale reservoir can reflect the oil content of the shale reservoir, and the fracturing index can reflect the fracturing resistance strength of the shale reservoir. The above indexes can all be used as influencing factors of the oil production of the shale reservoir. By performing principal component analysis on the influencing factors, several principal components with a greater degree of influence on the oil production of the shale reservoir can be determined. Then, when predicting the oil production of the shale reservoir where the shale oil well is located, it is possible to comprehensively consider multiple indicators, determine the values ​​of multiple principal components of the shale oil well, and determine the degree of membership of the shale oil well to each evaluation level based on the values ​​of the multiple principal components. Since the predicted oil production of the shale oil well is positively correlated with the evaluation level, the shale reservoir with higher oil production has a higher degree of membership to the higher evaluation level. Therefore, according to the degree of membership of the shale oil well to each evaluation level, the oil production of the shale oil well after hydraulic fracturing can be more accurately determined.

[0052] Above Figure 2 The main process of the oil production determination method provided in the embodiment of the present application is exemplified, and the oil production determination method is described in detail below. Figure 3 is a flowchart of another oil production determination method provided in an embodiment of the present application, which is executed by a terminal, see Figure 3 , the method comprising:

[0053] 301. The terminal performs principal component analysis on the geological data of the second shale oil well to obtain multiple principal component expressions, the weight of each principal component expression, and the values ​​of multiple principal components of the second shale oil well. The second shale oil well is a shale oil well that has been hydraulically fractured. The geological data is used to represent the oil content index and the fracturing index of the shale reservoir where the second shale oil well is located. The oil content index is used to represent the oil content of the shale reservoir, and the fracturing index is used to represent the anti-fracturing strength of the shale reservoir. The principal component expressions are used to determine the values ​​of the principal components in the geological data.

[0054] In the embodiment of the present application, a shale oil well refers to an oil well mined in a shale reservoir, and a shale oil well is used to mine shale oil enriched in a shale reservoir. Since the shale oil enriched in the shale reservoir has a large flow resistance and difficulty in flowing, in the process of mining shale oil, artificial fractures can be formed in the shale reservoir by hydraulic fracturing technology, thereby improving the flow capacity of shale oil in the shale reservoir, so that more shale oil can flow into the mining pipeline, thereby increasing the oil production of the shale oil well. It can be seen that the oil production of a shale oil well is related to the oil content of the shale reservoir and the degree of fracturing resistance of the shale reservoir. Among them, the higher the degree of fracturing resistance of the shale reservoir, the less likely the shale reservoir is to be fractured, and the fewer artificial fractures are formed in the shale reservoir by hydraulic fracturing technology; the lower the degree of fracturing resistance of the shale reservoir, the easier the shale reservoir is to be fractured, and the more artificial fractures are formed in the shale reservoir by hydraulic fracturing technology. Therefore, the higher the oil content of the shale reservoir, the lower the resistance to fracturing, and the higher the oil production of the shale reservoir; the lower the oil content of the shale reservoir, the higher the resistance to fracturing, and the lower the oil production of the shale reservoir.

[0055] Among them, the geological data include many oil content indicators and fracturing indicators. For example, oil content indicators include but are not limited to natural gamma (GR), total organic carbon content (TOC), organic matter thermal evolution maturity (Ro), pyrolysis parameters (S1) and oil saturation index (OSI). Fracturing indicators include but are not limited to mineral brittleness index (MBI), Young's modulus (E), Poisson's ratio (ν), minimum horizontal principal stress (σ h ) and horizontal principal stress difference coefficient (σ dif ).

[0056] Therefore, in order to clarify the influence of the above-mentioned multiple indicators on the oil production of the shale reservoir, the terminal can determine the correlation between the various indicators in the geological data and the correlation between the various indicators and the production data based on the geological data and production data of the shale oil well that has been hydraulically fractured (hereinafter referred to as the second shale oil well for ease of description). Since the mining cycles and mining systems of shale oil wells of different specifications are different, the oil production in a short period of time cannot accurately reflect the oil production of the shale reservoir where the shale oil well is located. At the same time, in order to eliminate the influence of the length of the horizontal pipeline of the shale oil well on the oil production, the embodiment of the present application adopts the oil production of the shale oil well per unit length (1 km) within a unit time (12 months) as the production data (Q) of the shale oil well. 12 ).

[0057] The following is an explanation of the process by which the terminal determines the correlation between various indicators in geological data and the correlation between various indicators and production data. First, the terminal determines at least one second shale oil well, and determines the geological data and production data of each second shale oil well from the well logging data and the mud logging data. Then, the terminal processes abnormal data or missing data. For abnormal data, the terminal uses the box plot method to set the upper and lower bounds of the box plot as the value range of normal data, and determines the data in the box plot that exceeds the upper bound and is less than the lower bound as abnormal data, and discards the abnormal data. For missing data, the terminal can use the mean replacement formula shown in the following formula (1) to interpolate the missing data.

[0058]

[0059] in, Indicates the i-th missing data. N is the total number of samples, x k The total sample N may be the geological data of the oil wells on the same platform as the shale oil well with missing data, or the geological data of the oil wells on the adjacent platform, which is not limited in the present embodiment.

[0060] Then, the terminal uses the Pearson correlation coefficient calculation formula shown in the following formula (2) to process any two indicators in the geological data, and any one indicator and production data, respectively, to obtain the correlation coefficients between the indicators and the correlation coefficients between the indicators and the production data. Among them, the correlation coefficient can reflect the Pearson correlation between the indicators or the Pearson correlation between any indicator and the production data.

[0061]

[0062] Among them, ρ X,Y is the Pearson correlation coefficient between factor X and factor Y. X,Y is a real number in [-1,1]. X,Y >0, factor X and factor Y are positively correlated; ρ X,Y <0, factor X and factor Y are negatively correlated; ρ X,Y =0, factor X and factor Y are unrelated or nonlinearly related. X,Y The larger the absolute value of is, the higher the correlation between factor X and factor Y is. Factor X and factor Y can be any two indicators or any one indicator and production data. Cov(X,Y) is the covariance between factor X and factor Y. σ X is the standard deviation of factor X, σ Y is the standard deviation of factor Y. m is the number of second shale oil wells. i represents the value of factor X in the i-th second shale oil well, yi represents the value of factor Y in the i-th second shale oil well. For example, factor X is the pyrolysis parameter and factor Y is the mineral brittleness index, then x i represents the value of the pyrolysis parameter of the i-th second shale oil well, y i Represents the value of the mineral brittleness index of the i-th second shale oil well.

[0063] Table 1 below shows the geological data and production data of the 13 second shale oil wells in Block W.

[0064] Table 1

[0065]

[0066] Among them, API is the unit of measurement for natural gamma. Mpa (megapascal) is the unit of measurement for Young's modulus (E) and minimum horizontal principal stress (σ h ) is the unit of measurement for production data. t / km (tons per kilometer) is the unit of measurement for production data.

[0067] It can be seen from Table 1 that there are many geological data in Block W, and the ranges of various indicators in the geological data are different. Some indicators have a large range of variation, such as the range of natural gamma values ​​of 84.94 to 108.99 API, and the range of pyrolysis parameters of 2.97 to 6.14. Some indicators have a small range of variation, such as the range of Poisson's ratio values ​​of 0.227 to 0.229, and the thermal evolution maturity of organic matter is even constant at 2. Therefore, in order to accurately determine the correlation between various indicators and production data, the terminal can determine the Pearson correlation coefficient between various indicators and production data through the above formula (2). The Pearson correlation coefficient shown in Table 2 below represents the Pearson correlation coefficient between various indicators and the Pearson correlation coefficient between various indicators and production data.

[0068] Table 2

[0069]

[0070]

[0071] From Table 2, we can see that among the above indicators, the most relevant to production data (Q 12 ) are MBI, σ dif ,E,OSI,σ h , S1, ν, TOC and GR. Among them, MBI, σ dif , OSI, S1 and GR and Q 12 Positively correlated; E, σ h , ν and TOC and Q 12 Negatively correlated, Ro and Q 12 Not relevant.

[0072] Therefore, the terminal selects the above 9 indicators that are correlated with the production data, performs principal component analysis on the above 9 indicators, and obtains multiple principal components and each principal component expression. Among them, the principal component includes the above 9 indicators, and the value of the principal component is the weighted sum of the above 9 indicators. The principal component expression specifies the weight of each indicator. Through the principal component expression, the above 9 indicators can be weighted summed to obtain the value of the principal component.

[0073] In some embodiments, the terminal can determine the principal component expression through the eigenvector of the covariance matrix. The terminal constructs a covariance matrix based on the covariance between the various indicators selected from the geological data of the second shale oil well, and determines multiple eigenvalues ​​and multiple eigenvectors of the covariance matrix. Among them, the multiple eigenvalues ​​correspond to the multiple eigenvectors one by one, and the dimension of the eigenvector is the same as the number of indicators in the geological data. For any eigenvector, the terminal performs weighted summation on the various indicators in the geological data based on the elements in the eigenvector to obtain the principal component expression corresponding to the eigenvector. In response to obtaining the principal component expression corresponding to each eigenvector, the terminal processes the geological data of the second shale oil well through multiple principal component expressions respectively to obtain the values ​​of multiple principal components of the second shale oil well. The terminal determines the information entropy of each principal component based on the values ​​of the multiple principal components of the second shale oil well after normalization, and the information entropy is negatively correlated with the amount of information contained in the principal component. Based on the information entropy of each principal component, the terminal determines the weight of each principal component expression by the entropy weight method, and the weight is negatively correlated with the information entropy.

[0074] In combination with the data shown in Table 1 and Table 2 above, the process of constructing a covariance matrix, determining multiple principal component expressions, and determining the weight of each principal component expression in the following steps 1 to 6 is described below.

[0075] 1. The terminal constructs a 9×9 covariance matrix as shown in the following formula (3) based on the covariance between the selected 9 indicators.

[0076]

[0077] Where R is the covariance matrix. ij represents the covariance between index i and index j.

[0078] 2. The terminal calculates the eigenvalues ​​of the covariance matrix: λ1≥λ2≥…≥λ9≥0; and determines the eigenvectors of the covariance matrix: v1,v2,…,v9, where v z =(v 1z ,v 2z ,…,v 92 ) T .v nzRepresents the nth element in the zth eigenvector, where z is not greater than 9.

[0079] 3. The terminal performs weighted summation on the above 9 indicators according to the 9 elements in the above eigenvector, and obtains the 9 principal component expressions shown in the following formula (4).

[0080]

[0081] Among them, z1-z9 are the 9 principal component expressions determined by the terminal. x1-x9 represent GR, TOC, S1, OSI, MBI, E, ν, σ respectively. h , and σ dif .

[0082] Fourth, the terminal processes the relevant indicators of the second shale oil well in Table 1 respectively through the 9 principal component expressions shown in the above formula (4) to obtain the values ​​of the 9 principal components of the second shale oil well.

[0083] It should be noted that, since the above indicators have different orders of magnitude, it is difficult to compare or weight them. Therefore, before processing the above indicators through the principal component expression, the terminal can normalize each indicator through the normalization formula shown in the following formula (5).

[0084]

[0085] Among them, x * Used to represent the normalized value of indicator x, x * The value range of x is [0,1]. min Used to indicate the minimum value of the indicator x in the second shale oil well; x max Used to indicate the maximum value of the indicator x in the second shale oil well.

[0086] 5. After the terminal obtains the values ​​of the 9 principal components of each second shale oil well, the terminal normalizes each principal component by the above formula (5). Then, the terminal constructs a matrix as shown in the following formula (6) based on the values ​​of the normalized principal components.

[0087]

[0088] Where X is a matrix representing the values ​​of the normalized principal components. a (b) is the value of the ath principal component in the bth second shale oil well after normalization.

[0089] 6. The terminal determines the information entropy of each principal component according to the following formula (7) and formula (8), and determines the weight of each principal component expression by the following formula (9).

[0090]

[0091]

[0092]

[0093] Among them, p ba is the ratio of the value of the ath principal component in the bth second shale oil well to the sum of the values ​​of all ath principal components in the 13 second shale oil wells. a is the information entropy of the ath principal component, e a The value range of c is [0,1]. a is the weight of the principal component expression corresponding to the ath principal component, c a The value range is [0,1].

[0094] In some embodiments, the terminal is able to filter the above-mentioned multiple principal component expressions. For any principal component expression, the terminal determines that the eigenvalue corresponding to the eigenvector is the eigenvalue corresponding to the principal component expression based on the eigenvector that generates the principal component expression. The terminal uses the ratio of the eigenvalue to the sum of multiple eigenvalues ​​as the variance contribution rate of the principal component expression. In response to obtaining the eigenvalue corresponding to each principal component expression, the terminal sorts the multiple principal component expressions in order of the eigenvalues ​​from large to small. When the sum of the variance contributions of the first k principal component expressions is greater than a preset threshold, the terminal retains the first k principal component expressions. Among them, the preset threshold can be a preset percentage value, such as 80%, 85% or 90%, and the embodiments of the present application are not limited to this. The embodiments of the present application are illustrated by taking the preset threshold of 85% as an example.

[0095] For example, after the terminal obtains the 9 eigenvalues ​​of the covariance matrix: λ1≥λ2≥…≥λ9≥0, 9 eigenvectors: v1, v2,…, v9 and the above 9 principal component expressions, the terminal determines the variance contribution rate and cumulative variance contribution rate of each principal component expression through the following formulas (10) and (11).

[0096]

[0097]

[0098] Among them, δ a is the variance contribution rate of the ath principal component expression. a is the eigenvalue corresponding to the ath principal component expression. kis the sum of the variance contribution rates of the first k principal component expressions, that is, the cumulative variance contribution rate of the kth principal component expression. When the sum of the variance contribution rates of the first k principal component expressions is greater than 85%, the terminal retains the first k principal component expressions among the 9 principal component expressions and discards the remaining principal component expressions. Among them, k is not greater than 9.

[0099] Table 3 below shows the eigenvalues, variance contribution rates and cumulative variance contribution rates corresponding to the above 9 principal component expressions.

[0100] Table 3

[0101]

[0102]

[0103] It can be seen from Table 3 that the sum of the variance contribution rates of the first four principal components is 86.66%, which is greater than the preset threshold of 85%. Therefore, the terminal retains the first four principal component expressions. According to the eigenvectors corresponding to the principal component expressions, the terminal can determine that the first four principal component expressions are:

[0104] z1=-0.0337x1+0.4653x2-0.5753x3+0.5894x4+0.0535x5+0.0404x6+0.1189x7-0.2504x8-0.1503x9 (12)

[0106] z2=0.1895x1+0.1432x2-0.309x3-0.3848x4-0.1121x5-0.3631x6-0.4101x7-0.4744x8+0.4031x9 (13)

[0108] z3=-0.3282x1+0.4399x2+0.4827x3+0.0995x4+0.5376x5-0.281x6+0.0317x7-0.096x8+0.2786x9 (14)

[0110] z4=0.0164x1-0.6841x2-0.2323x3+0.347x4+0.5056x5-0.1171x6-0.0905x7-0.0443x8+0.2792x9 (15)

[0112] From the above four principal component expressions, we can see that the three indicators that have the greatest impact on the first principal component are OSI, S1 and TOC, which mainly reflect the oil content of shale reservoirs; the three indicators that have the greatest impact on the second principal component are ν, σ h , and σ dif These three indicators mainly reflect the fracturing ability of shale reservoirs; MBI and TOC have the greatest impact on the third and fourth principal components respectively. These two indicators mainly reflect the impact of the mineral composition characteristics of shale reservoirs on the fracturing ability of shale reservoirs.

[0113] In some embodiments, the terminal can determine the weights of the four selected principal component expressions according to the above formulas (7)-(9). Figure 4 is a weight diagram of the above four principal component expressions. Figure 4 As shown in the figure, the second principal component expression, that is, principal component expression 2, has the highest weight of 0.4913, indicating that the fracturing property of shale reservoir has a greater impact on the oil production of shale reservoir. The weight of principal component 3 is second, which is 0.3992, indicating that the mineral composition characteristics of shale reservoir also have a greater impact on the oil production of shale reservoir.

[0114] 302. The terminal determines the correlation between multiple principal components and membership functions based on the correlation between the value of each principal component of the second shale oil well and the production data of the second shale oil well, where the production data is the oil production of the second shale oil well per unit length per unit time.

[0115] In an embodiment of the present application, after the terminal obtains the value of each principal component of the second shale oil well, for any principal component, the terminal determines the membership function associated with the principal component based on the correlation between the value of the principal component and the production data, that is, determines the correlation between the principal component and the membership function. Among them, the correlation between the value of the principal component and the production data refers to the trend of oil production changing with the value of the principal component. For example, when the oil production increases with the increase of the value of the principal component, the terminal can determine that the principal component is associated with a membership function of the ascending function type; when the oil production decreases with the increase of the value of the principal component, the terminal can determine that the principal component is associated with a membership function of the descending function type.

[0116] In some embodiments, the terminal is capable of determining the type of membership function associated with each principal component. For any principal component, when the value of the principal component is positively correlated with the production data, the terminal determines that the principal component is associated with a slightly large membership function. When the value of the principal component is negatively correlated with the production data, the terminal determines that the principal component is associated with a slightly small membership function. When the value of the principal component is normally distributed with the production data, the terminal determines that the principal component is associated with an intermediate membership function. Among them, the input and output of the slightly large membership function are positively correlated, the input and output of the slightly small membership function are negatively correlated, and the input and output of the intermediate membership function are normally distributed.

[0117] The following describes the process of determining the membership function of the principal component association by the terminal, combining the production data of the above-mentioned 13 second shale oil wells and the four principal component expressions screened by the terminal.

[0118] First, the terminal processes the geological data of each second shale oil well according to the above four principal component expressions to obtain the values ​​of the four principal components of each second shale oil well. Then, the terminal normalizes the values ​​of the principal components and the production data respectively. Then, for each principal component, the terminal draws the following graphs based on the values ​​of the principal component in the 13 shale oil wells and the production data of the 13 second shale oil wells: Figure 5 Schematic diagram of the principal component-production data shown in Figure 2. Figure 5 As shown, the horizontal axis is the value of the normalized principal component, and the vertical axis is the normalized oil production obtained by normalizing the production data. The data points drawn in the schematic diagram of the principal component-production data represent any second shale oil well. Figure 5 It can be seen that for principal component 1, within the range of 0-0.25, the value of principal component 1 is positively correlated with the production data; within the range of 0.25-0.8, the value of principal component 1 is normally distributed with the production data; within the range of 0.8-1, principal component 1 is positively correlated with the production data. Therefore, the terminal determines that the membership functions associated with principal component 1 in the above corresponding ranges are respectively large-scale membership functions, intermediate membership functions, and large-scale membership functions. Similarly, the terminal determines that the membership functions associated with principal component 2 in the corresponding ranges are respectively intermediate membership functions, large-scale membership functions, and intermediate membership functions; the membership functions associated with principal component 2 in the corresponding ranges are respectively intermediate membership functions, small-scale membership functions, and large-scale membership functions; the membership functions associated with principal component 4 in the corresponding ranges are respectively small-scale membership functions, intermediate membership functions, and large-scale membership functions.

[0119] At this point, the terminal has completed some preparations for predicting the oil production of shale oil wells. Then, the terminal can predict the oil production of shale oil wells that have not been hydraulically fractured (hereinafter referred to as the first shale oil well for ease of description) based on the determined multiple principal component expressions, the weight of each principal component expression, and the correlation between the principal component and the membership function. It should be noted that the oil production predicted by the embodiment of the present application is the oil production of the first shale oil well per unit length per unit time, that is, the embodiment of the present application predicts the production data of the first shale oil well. In addition, the embodiment of the present application is illustrated by taking the terminal executing the above steps 301-302 as an example. In some embodiments, the above steps 301-302 can also be executed by the server, that is, the server completes the above part of the preparation work, and the embodiment of the present application does not limit this.

[0120] The following is an explanation of the process of terminal prediction of the production data of the first shale oil well.

[0121] 303. The terminal processes the geological data of the first shale oil well through multiple principal component expressions to obtain multiple principal component values ​​of the first shale oil well, where the first shale oil well is a shale oil well that has not been hydraulically fractured.

[0122] In an embodiment of the present application, the terminal performs weighted summation on various indicators selected from the geological data of the first shale oil well according to the determined multiple principal component expressions, so as to obtain the values ​​of multiple principal components of the first shale oil well.

[0123] Table 4 below shows various indicators selected from the geological data of the two first shale oil wells.

[0124] Table 4

[0125]

[0126] Then, the terminal processes the corresponding indicators of the two first shale oil wells T1 and T2 shown in Table 4 respectively through the four principal component expressions shown in the above formulas (12)-(15), and obtains the values ​​of multiple principal components shown in Table 5 below.

[0127] Table 5

[0128]

[0129] It can be seen from Table 5 that the values ​​of the four principal components of the first shale oil well corresponding to T1 are -0.1548, 0.0577, 0.7733 and 0.4309 respectively; the values ​​of the four principal components of the first shale oil well corresponding to T2 are 0.1355, -0.0505, -0.6766 and -0.3770 respectively.

[0130] 304. The terminal determines first membership information based on the association between multiple principal components and membership functions. The first membership information includes the principal component membership of each principal component to each evaluation level. The principal component membership is used to indicate the probability that the value of the principal component belongs to the principal component value range associated with the corresponding evaluation level.

[0131] In the embodiment of the present application, after the terminal determines the values ​​of the multiple principal components of the first shale oil well, the terminal normalizes the values ​​of the multiple principal components. Then, the terminal determines the membership function associated with each principal component based on the values ​​of the normalized principal components and the association relationship between the multiple principal components and the membership function, and determines the principal component membership of each principal component to each evaluation level based on the membership function.

[0132] In some embodiments, the terminal can determine the membership of the principal component to each evaluation level based on the membership function associated with the principal component. For any principal component, the terminal determines the membership function associated with the principal component based on the association between multiple principal components and the membership function. Among them, the terminal can determine the association between the principal component and the membership function through the principal component-production data relationship diagram drawn in the above preparation work. The terminal processes the value of the principal component based on the membership function associated with the principal component to obtain the principal component membership of the principal component to each evaluation level.

[0133] For example, when the value of the principal component 1 of the first shale oil well after normalization is 0.1, the terminal Figure 4 The principal component-production data relationship diagram shown in the figure determines that the principal component 1 of the first shale oil well is associated with the partial large membership function. Then, the terminal determines the principal component membership of the principal component 1 to the four evaluation levels according to the four partial large membership functions shown in the following formulas (16)-(19).

[0134]

[0135]

[0136]

[0137]

[0138] Among them, x is the normalized value of principal component 1. It is used to determine the principal component membership of the principal component to the first evaluation level; It is used to determine the principal component membership of the principal component to the second evaluation level; It is used to determine the principal component membership of the principal component to the third evaluation level; It is used to determine the principal component membership of the principal component to the fourth evaluation level. Among them, the first evaluation level is higher than the second evaluation level, the second evaluation level is higher than the third evaluation level, and the fourth evaluation level is the lowest.

[0139] Similarly, when the terminal determines that the principal component is associated with a small-sized membership function or an intermediate-sized membership function, the terminal can determine the principal component membership of the principal component to the above four evaluation levels respectively through the four small-sized membership functions shown in the following formulas (20)-(23) or the four intermediate-sized membership functions shown in the following formulas (24)-(27).

[0140]

[0141]

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148] Among them, μ is the value of the principal component corresponding to the peak value of the intermediate membership function curve. Similar to the large membership function, and Both are used to determine the principal component membership of the principal component to the first evaluation level; and Both are used to determine the principal component membership of the principal component to the second evaluation level; and Both are used to determine the principal component membership of the principal component to the third evaluation level; and Both are used to determine the principal component membership of the principal component to the fourth evaluation level.

[0149] The terminal processes the value of each principal component of the first shale oil well through the membership function shown in the above formulas (16)-(27) to obtain the principal component membership of each principal component to the above four evaluation levels. It should be noted that the embodiment of the present application is described by taking four evaluation levels as an example. In some embodiments, the number of evaluation levels can be three, five or any number, and the embodiment of the present application is not limited to this.

[0150] 305. The terminal determines second membership information based on the weight of each principal component expression and the first membership information, where the second membership information includes the membership of the first shale oil well to each evaluation level.

[0151] In an embodiment of the present application, the terminal performs weighted summation of the principal component memberships of the principal component in the first membership information to the four evaluation levels according to the weights of the principal component expressions determined in the above step 301, and obtains the membership of the first shale oil well to the four evaluation levels.

[0152] For example, the terminal determines that the weights of the four principal component expressions are 0.0391, 0.4913, 0.3992, and 0.0705, respectively. The terminal determines that the principal component memberships of the first principal component to the first evaluation level to the fourth evaluation level are 1, 0, 0, 0, respectively; the principal component memberships of the second principal component to the first evaluation level to the fourth evaluation level are 0, 0.1029, 0.8971, 0, respectively; the principal component memberships of the third principal component to the first evaluation level to the fourth evaluation level are 0.0597, 0.9403, 0, 0, respectively; the principal component memberships of the fourth principal component to the first evaluation level to the fourth evaluation level are 0, 0, 0.0857, 0.9143, respectively. Then, the terminal can determine that the first shale oil well has a membership degree of the first evaluation level: 0.0391*1+0.4913*0+0.3992*0.0597+0.0705*0=0.0629; and a membership degree of the second evaluation level: 0.0391*0+0.4913*0.1029+0.3992*0.9043+0.0705*0=0.41 16; The terminal determines that the first shale oil well's membership to the third evaluation level is: 0.0391*0+0.4913*0.8971+0.3992*0+0.0705*0.0857=0.4468; the membership to the fourth evaluation level is: 0.0391*0+0.4913*0+0.3992*0+0.0705*0.9143=0.0645.

[0153] In some embodiments, the terminal can determine the oil well membership of the first shale oil well to each evaluation level by means of matrix multiplication. When the number of principal component expressions is k and the number of evaluation levels is m (k and m are positive integers). The terminal constructs a principal component weight matrix of 1 row and k columns based on the weight of each principal component expression. Among them, the elements in the principal component weight matrix are used to represent the weight of each principal component expression respectively. The terminal constructs a principal component membership matrix of k rows and m columns based on the principal component membership of each principal component of the first shale oil well to each evaluation level. Among them, the elements in the principal component membership matrix are used to represent the principal component membership of each principal component to each evaluation level in the m evaluation levels respectively. The terminal multiplies the principal component weight matrix and the principal component membership matrix to obtain an oil well membership matrix of 1 row and m columns. Among them, the elements in the oil well membership matrix are used to represent the oil well membership of the first shale oil well to each evaluation level in the m evaluation levels respectively.

[0154] For example, when the terminal determines the four principal component expressions shown in the above formulas (12)-(15) and the number of evaluation levels is 4, the terminal constructs the principal component weight matrix C shown in the following formula (28) according to the weight of each principal component expression determined in the above step 301. The terminal constructs the principal component membership matrix A shown in the following formula (29) according to the above first membership information.

[0155] C=[c1 c2 c3 c4] (28)

[0156]

[0157] Among them, c1 to c4 represent the weights of the four principal component expressions respectively. The elements of the first row in the principal component membership matrix A represent the principal component membership of the first principal component of the first shale oil well to the first evaluation level to the fourth evaluation level; the elements of the second row represent the principal component membership of the second principal component of the first shale oil well to the first evaluation level to the fourth evaluation level; the elements of the third row represent the principal component membership of the third principal component of the first shale oil well to the first evaluation level to the fourth evaluation level; the elements of the fourth row represent the principal component membership of the fourth principal component of the first shale oil well to the first evaluation level to the fourth evaluation level. Then, the terminal multiplies the principal component weight matrix C and the principal component membership matrix A by the following formula (30) to obtain the oil well membership matrix S.

[0158]

[0159] Among them, s1 to s4 respectively represent the membership of the first shale oil well to the first evaluation level to the fourth evaluation level.

[0160] Therefore, the terminal can determine the oil well membership matrix according to the weights of the above four principal component expressions, the first membership information and the above formulas (28)-(30):

[0161]

[0162] 306. The terminal determines the oil production of the first shale oil well after hydraulic fracturing based on the second affiliation information.

[0163] In the embodiment of the present application, the higher the membership of the first shale oil well to the oil wells with higher evaluation levels, the lower the membership to the oil wells with lower evaluation levels, and the higher the predicted oil production of the first shale oil well; the lower the membership of the first shale oil well to the oil wells with higher evaluation levels, the higher the membership to the oil wells with lower evaluation levels, and the lower the predicted oil production of the first shale oil well. The terminal determines the predicted oil production of the first shale oil well based on the membership of the first shale oil well to the oil wells of each evaluation level, that is, the oil production of the first shale oil well after the first shale oil well is hydraulically fractured.

[0164] In some embodiments, the terminal can determine the oil production of the first shale oil well after hydraulic fracturing based on the evaluation score of the first shale oil well. The terminal performs weighted summation of the evaluation scores associated with each evaluation level based on the oil well membership of the first shale oil well to each evaluation level, and obtains the absolute evaluation score of the first shale oil well. Among them, each evaluation level is associated with an evaluation score, and the higher the evaluation level, the higher the associated evaluation score. The terminal determines the relative evaluation score of the first shale oil well based on the absolute evaluation score of the first shale oil well and the sum of the evaluation scores associated with each evaluation level. The terminal determines the oil production associated with the relative evaluation score of the first shale oil well based on the oil production information, and determines the oil production as the oil production of the first shale oil well after hydraulic fracturing. Among them, the oil production information is used to represent the correlation between the relative evaluation score of the shale oil well and the oil production of the shale oil well. The oil production information can be generated based on the geological data and production data of the shale oil well that has been hydraulically fractured.

[0165] For example, the evaluation scores associated with the first evaluation level to the fourth evaluation level are 100, 75, 50 and 25 respectively. The terminal determines the relative evaluation score of the first shale oil well according to the oil well membership of the first shale oil well to each evaluation level through the following formula (31).

[0166]

[0167] Among them, u i Represents the evaluation score associated with the i-th evaluation level. i It represents the membership of the first shale oil well to the i-th evaluation level. represents the absolute evaluation score of the first shale oil well. r represents the relative evaluation score of the first shale oil well.

[0168] Therefore, based on the oil well membership of the first shale oil well with the well number T1 determined in the above step 305 to each evaluation level, the terminal can determine that the relative evaluation score of the first shale oil well is 62.2 through formula (31). Similarly, the terminal can determine that the relative evaluation score of the first shale oil well with the well number T2 is 45.7. The terminal predicts the oil production of the two shale oil wells T1 and T2 based on the oil production information. Then, after hydraulic fracturing the above two shale oil wells, the actual oil production and the predicted oil production are compared to determine the relative error of the predicted oil production. Table 6 below shows the predicted oil production, actual oil production and relative error of the two shale oil wells T1 and T2.

[0169] Table 6

[0170]

[0171] It can be seen from the above Table 6 that the oil production determination method provided in the embodiment of the present application can integrate various geological data to more accurately predict the oil production of shale oil wells that have not been hydraulically fracturing.

[0172] It should be noted that the terminal can determine the oil production information before executing step 303, that is, the terminal determines the oil production information according to the geological data and production data of the shale oil well that has been hydraulically fractured during the preparation process. The process of determining the oil production information by the terminal is described below.

[0173] In some embodiments, after the terminal determines the multiple principal components of the second shale oil well, the terminal determines the third membership information based on the association relationship between the multiple principal components determined above and the membership function. The third membership information includes the principal component membership of each principal component of the second shale oil well to each evaluation level. The terminal determines the fourth membership information based on the weight of each principal component expression and the third membership information. The fourth membership information includes the oil well membership of the second shale oil well to each evaluation level. Based on the fourth membership information, the terminal performs weighted summation of the evaluation scores associated with each evaluation level to obtain the absolute evaluation score of the second shale oil well. The terminal determines the relative evaluation score of the second shale oil well based on the absolute evaluation score of the second shale oil well and the sum of the evaluation scores associated with each evaluation level. The terminal determines the oil production information based on the relative evaluation score of the second shale oil well shown in Table 7 below and the oil production indicated by the production data of the second shale oil well.

[0174] Table 7

[0175]

[0176]

[0177] It can be seen from Table 7 that for shale oil wells with higher production data, such as the second shale oil well with well number 12, the membership degree is higher for oil wells with higher evaluation levels, and lower for oil wells with lower evaluation levels. For shale oil wells with lower production data, such as the second shale oil well with well number 8, the membership degree is lower for oil wells with higher evaluation levels, and higher for oil wells with lower evaluation levels.

[0178] The oil production information may be an expression fitted by the terminal based on the relative evaluation score of the second shale oil well and the production data of the second shale oil well, or may be a fitted curve graph. The embodiment of the present application does not limit the form of the oil production information. For example, Figure 6 A schematic diagram of the fitted oil production information is shown in Figure 2. Figure 6 As shown in the figure, the horizontal axis is the oil production, in tons per kilometer; the vertical axis is the relative evaluation score. Among them, the correlation coefficient between the relative evaluation score and the oil production is as high as 0.9499. Therefore, after the terminal determines the relative evaluation score of the first shale oil well, it can Figure 6 In the curve graph shown, the predicted oil production of the first shale oil well is determined more accurately.

[0179] In order to more clearly illustrate the process of terminal prediction of the oil production of the first shale oil well, the following is combined with Figure 7 The flowchart shown in the figure illustrates the above process. Figure 7 As shown, first, the terminal analyzes the correlation between various indicators in the geological data of the second shale oil well and the production data, and excludes indicators that are not related to the production data. Then, the terminal normalizes the remaining geological data and production data, and performs principal component analysis on various indicators in the geological data to obtain multiple principal components to achieve data dimension reduction. Then, the terminal determines the principal component membership of the first shale oil well to each evaluation level, and determines the oil well membership of the first shale oil well to each evaluation level according to the weight of the principal component and the principal component membership of the principal component to each evaluation level. Finally, the terminal determines the relative evaluation score of the first shale oil well according to the oil well membership of the first shale oil well to each evaluation level and the evaluation score associated with each evaluation level. The terminal then determines the oil production of the first shale oil well according to the relationship between the relative evaluation score and the oil production indicated by the oil production information, thereby predicting the oil production of the first shale oil well.

[0180] The embodiment of the present application provides a method for determining oil production. The oil content index of the shale reservoir can reflect the oil content of the shale reservoir, and the fracturing index can reflect the fracturing resistance strength of the shale reservoir. The above indexes can all be used as influencing factors of the oil production of the shale reservoir. By performing principal component analysis on the influencing factors, several principal components with a greater degree of influence on the oil production of the shale reservoir can be determined. Then, when predicting the oil production of the shale reservoir where the shale oil well is located, it is possible to comprehensively consider multiple indicators, determine the values ​​of multiple principal components of the shale oil well, and determine the degree of membership of the shale oil well to each evaluation level based on the values ​​of the multiple principal components. Since the predicted oil production of the shale oil well is positively correlated with the evaluation level, the shale reservoir with higher oil production has a higher degree of membership to the higher evaluation level. Therefore, according to the degree of membership of the shale oil well to each evaluation level, the oil production of the shale oil well after hydraulic fracturing can be more accurately determined.

[0181] Figure 8 Schematic diagram of a device for determining oil production provided in an embodiment of the present application. Figure 8 The device includes: a data processing module 801, a first determination module 802, a second determination module 803 and a third determination module 804.

[0182] The data processing module 801 is used to process the geological data of the first shale oil well through multiple principal component expressions to obtain the values ​​of multiple principal components of the first shale oil well. The first shale oil well is a shale oil well that has not been hydraulically fractured. The geological data is used to represent the oil content index and the fracturing index of the shale reservoir where the first shale oil well is located. The oil content index is used to represent the oil content of the shale reservoir, and the fracturing index is used to represent the anti-fracturing strength of the shale reservoir. The principal component expressions are used to determine the values ​​of the principal components in the geological data.

[0183] A first determination module 802 is used to determine first membership information based on the association relationship between the plurality of principal components and the membership function, wherein the first membership information includes the principal component membership of each principal component to each evaluation level, and the principal component membership is used to indicate the probability that the value of the principal component belongs to the principal component value range associated with the corresponding evaluation level;

[0184] A second determination module 803 is used to determine second membership information based on the weight of each principal component expression and the first membership information, wherein the second membership information includes the oil well membership of the first shale oil well to each evaluation level, and the oil well membership is used to indicate the probability that the predicted oil production of the first shale oil well belongs to the oil production range indicated by the corresponding evaluation level, and the predicted oil production is positively correlated with the evaluation level;

[0185] The third determination module 804 is used to determine the oil production of the first shale oil well after hydraulic fracturing based on the second affiliation information.

[0186] In some embodiments, the first determination module 802 is used to determine the membership function associated with any principal component based on the association relationship between multiple principal components and the membership function; based on the membership function associated with the principal component, the value of the principal component is processed to obtain the principal component membership of the principal component to each evaluation level.

[0187] In some embodiments, the number of principal component expressions is k, the number of evaluation levels is m, k and m are positive integers; the second determination module 803 is used to construct a principal component weight matrix with 1 row and k columns based on the weight of each principal component expression, and the elements in the principal component weight matrix are used to respectively represent the weight of each principal component expression; based on the first membership information, a principal component membership matrix with k rows and m columns is constructed, and the elements in the principal component membership matrix are used to respectively represent the principal component membership of each principal component to each of the m evaluation levels; the principal component weight matrix and the principal component membership matrix are multiplied to obtain an oil well membership matrix with 1 row and m columns, and the elements in the oil well membership matrix are used to respectively represent the oil well membership of the first shale oil well to each of the m evaluation levels.

[0188] In some embodiments, Fig. 9 is a schematic diagram of another oil production determination device provided in an embodiment of the present application, see Fig. 9 , the device further comprises:

[0189] A principal component analysis module 805 is used to perform principal component analysis on the geological data of the second shale oil well to obtain multiple principal component expressions, the weight of each principal component expression, and multiple principal component values ​​of the second shale oil well, wherein the second shale oil well is a shale oil well that has been hydraulically fractured;

[0190] The fourth determination module 806 is used to determine the correlation between multiple principal components and membership functions based on the correlation between the value of each principal component of the second shale oil well and the production data of the second shale oil well, where the production data is the oil production of the second shale oil well per unit length per unit time.

[0191] In some embodiments, the principal component analysis module 805 is used to construct a covariance matrix based on the covariance between each indicator in the geological data of the second shale oil well, and determine multiple eigenvalues ​​and multiple eigenvectors of the covariance matrix, the multiple eigenvalues ​​and the multiple eigenvectors correspond one to one, and the dimension of the eigenvector is the same as the number of indicators in the geological data; for any eigenvector, based on the elements in the eigenvector, the various indicators in the geological data are weighted and summed to obtain the principal component expression corresponding to the eigenvector; in response to obtaining the principal component expression corresponding to each eigenvector, the geological data of the second shale oil well are processed respectively by multiple principal component expressions to obtain the values ​​of multiple principal components of the second shale oil well; based on the normalized values ​​of the multiple principal components of the second shale oil well, the information entropy of each principal component is determined, and the information entropy is negatively correlated with the amount of information contained in the principal component; based on the information entropy of each principal component, the weight of each principal component expression is determined by the entropy weight method, and the weight is negatively correlated with the information entropy.

[0192] In some embodiments, the principal component analysis module 805 is also used to determine, for any principal component expression, based on the eigenvector that generates the principal component expression, that the eigenvalue corresponding to the eigenvector is the eigenvalue corresponding to the principal component expression; use the ratio of the eigenvalue to the sum of multiple eigenvalues ​​as the variance contribution rate of the principal component expression; in response to obtaining the eigenvalue corresponding to each principal component expression, sort the multiple principal component expressions in descending order of the eigenvalues; and retain the first k principal component expressions when the sum of the variance contributions of the first k principal component expressions is greater than a preset threshold.

[0193] In some embodiments, the fourth determination module 806 is used to, for any principal component, determine that the principal component is associated with a large-sized membership function, and the input and output of the large-sized membership function are positively correlated when the value of the principal component is positively correlated with the production data; determine that the principal component is associated with a small-sized membership function, and the input and output of the small-sized membership function are negatively correlated when the value of the principal component is negatively correlated with the production data; and determine that the principal component is associated with an intermediate-type membership function, and the input and output of the intermediate-type membership function are normally distributed when the value of the principal component is normally distributed.

[0194] In some embodiments, the third determination module 804 is used to perform weighted summation of the evaluation scores associated with each evaluation level based on the oil well membership of the first shale oil well to each evaluation level, so as to obtain the absolute evaluation score of the first shale oil well; determine the relative evaluation score of the first shale oil well based on the absolute evaluation score of the first shale oil well and the sum of the evaluation scores associated with each evaluation level; determine the oil production associated with the relative evaluation score of the first shale oil well based on the oil production information, and determine the oil production as the oil production of the first shale oil well after hydraulic fracturing, and the oil production information is used to represent the correlation between the relative evaluation score of the shale oil well and the oil production of the shale oil well.

[0195] In some embodiments, the first determination module 802 is further used to determine third membership information based on the association relationship between the plurality of principal components and the membership function, the third membership information including the principal component membership of each principal component of the second shale oil well to each evaluation level;

[0196] The second determination module 803 is further used to determine fourth membership information based on the weight of each principal component expression and the third membership information, where the fourth membership information includes the membership of the second shale oil well to each evaluation level;

[0197] The third determination module 804 is further used to perform weighted summation of the evaluation scores associated with each evaluation level based on the fourth affiliation information to obtain an absolute evaluation score of the second shale oil well;

[0198] The third determination module 804 is further used to determine the relative evaluation score of the second shale oil well based on the absolute evaluation score of the second shale oil well and the sum of the evaluation scores associated with each evaluation level;

[0199] The third determination module 804 is further used to determine the oil production information based on the relative evaluation score of the second shale oil well and the oil production indicated by the production data of the second shale oil well.

[0200] The embodiment of the present application provides an oil production determination device, the oil content index of the shale reservoir can reflect the oil content of the shale reservoir, the fracturing index can reflect the fracturing resistance strength of the shale reservoir, and the above indexes can be used as influencing factors of the oil production of the shale reservoir. By performing principal component analysis on the influencing factors, several principal components with a greater degree of influence on the oil production of the shale reservoir can be determined, and then when predicting the oil production of the shale reservoir where the shale oil well is located, it is possible to comprehensively consider multiple indicators, determine the values ​​of multiple principal components of the shale oil well, and determine the degree of membership of the shale oil well to each evaluation level based on the values ​​of the multiple principal components. Since the predicted oil production of the shale oil well is positively correlated with the evaluation level, the shale reservoir with higher oil production has a higher degree of membership to the higher evaluation level. Therefore, according to the degree of membership of the shale oil well to each evaluation level, the oil production of the shale oil well after hydraulic fracturing can be more accurately determined.

[0201] It should be noted that: the oil production determination device provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the terminal is divided into different functional modules to complete all or part of the functions described above. In addition, the oil production determination device provided in the above embodiment and the oil production determination method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0202] An embodiment of the present application further provides a terminal, which includes a processor and a memory, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to implement the offset speed determination method of the above embodiment.

[0203] Fig.10 It is a structural diagram of a terminal provided in an embodiment of the present application.

[0204] The terminal 1000 includes: a processor 1001 and a memory 1002 .

[0205] The processor 1001 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 1001 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 1001 may also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 1001 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 1001 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0206] The memory 1002 may include one or more computer-readable storage media, which may be non-transitory. The memory 1002 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 1002 is used to store at least one computer program, which is used to be possessed by the processor 1001 to implement the oil production determination method provided in the method embodiment of the present application.

[0207] In some embodiments, the terminal 1000 may further optionally include: a peripheral device interface 1003 and at least one peripheral device. The processor 1001, the memory 1002 and the peripheral device interface 1003 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 1003 via a bus, a signal line or a circuit board. Optionally, the peripheral device includes: at least one of a radio frequency circuit 1004, a display screen 1005, a camera assembly 1006, an audio circuit 1007 and a power supply 1008.

[0208] The peripheral device interface 1003 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 1001 and the memory 1002. In some embodiments, the processor 1001, the memory 1002, and the peripheral device interface 1003 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 1001, the memory 1002, and the peripheral device interface 1003 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0209] The radio frequency circuit 1004 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 1004 communicates with communication networks and other communication devices through electromagnetic signals. The radio frequency circuit 1004 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 1004 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 1004 can communicate with other devices through at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: a metropolitan area network, 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 1004 may also include circuits related to NFC (Near Field Communication), which is not limited in this application.

[0210] The display screen 1005 is used to display a UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 1005 is a touch display screen, the display screen 1005 also has the ability to collect touch signals on the surface or above the surface of the display screen 1005. The touch signal can be input to the processor 1001 as a control signal for processing. At this time, the display screen 1005 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 1005 can be one, arranged on the front panel of the terminal 1000; in other embodiments, the display screen 1005 can be at least two, respectively arranged on different surfaces of the terminal 1000 or in a folding design; in other embodiments, the display screen 1005 can be a flexible display screen, arranged on a curved surface or a folding surface of the terminal 1000. Even, the display screen 1005 can also be arranged as a non-rectangular irregular figure, that is, a special-shaped screen. The display screen 1005 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0211] The camera assembly 1006 is used to capture images or videos. Optionally, the camera assembly 1006 includes a front camera and a rear camera. The front camera is arranged on the front panel of the terminal 1000, and the rear camera is arranged on the back of the terminal 1000. 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 the panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 1006 may also include a flash. The flash can be a monochrome temperature flash or a dual-color temperature flash. The 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.

[0212] The audio circuit 1007 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 1001 for processing, or input them into the radio frequency circuit 1004 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the terminal 1000. The microphone may also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 1001 or the radio frequency circuit 1004 into sound waves. The speaker may 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 1007 may also include a headphone jack.

[0213] The power supply 1008 is used to power various components in the terminal 1000. The power supply 1008 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 1008 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.

[0214] In some embodiments, the terminal 1000 further includes one or more sensors 1009 . The one or more sensors 1009 include, but are not limited to: an acceleration sensor 1000 , a gyroscope sensor 1011 , a pressure sensor 1012 , an optical sensor 1013 , and a proximity sensor 1014 .

[0215] The acceleration sensor 1100 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established by the terminal 1000. For example, the acceleration sensor 1100 can be used to detect the components of gravity acceleration on the three coordinate axes. The processor 1001 can control the display screen 1005 to display the user interface in a horizontal view or a vertical view according to the gravity acceleration signal collected by the acceleration sensor 1100. The acceleration sensor 1100 can also be used to collect game or user motion data.

[0216] The gyro sensor 1011 can detect the body direction and rotation angle of the terminal 1000, and the gyro sensor 1011 can cooperate with the acceleration sensor 1100 to collect the user's 3D actions on the terminal 1000. The processor 1001 can implement the following functions based on the data collected by the gyro sensor 1011: motion sensing (such as changing the UI according to the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0217] The pressure sensor 1012 can be set in the side frame of the terminal 1000 and / or the lower layer of the display screen 1005. When the pressure sensor 1012 is set in the side frame of the terminal 1000, it can detect the user's holding signal of the terminal 1000, and the processor 1001 performs left and right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 1012. When the pressure sensor 1012 is set in the lower layer of the display screen 1005, the processor 1001 controls the operability controls on the UI interface according to the user's pressure operation on the display screen 1005. The operability controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0218] The optical sensor 1013 is used to collect the ambient light intensity. In one embodiment, the processor 1001 can control the display brightness of the display screen 1005 according to the ambient light intensity collected by the optical sensor 1013. Optionally, when the ambient light intensity is high, the display brightness of the display screen 1005 is increased; when the ambient light intensity is low, the display brightness of the display screen 1005 is decreased. In another embodiment, the processor 1001 can also dynamically adjust the shooting parameters of the camera assembly 1006 according to the ambient light intensity collected by the optical sensor 1013.

[0219] The proximity sensor 1014, also called a distance sensor, is disposed on the front panel of the terminal 1000. The proximity sensor 1014 is used to collect the distance between the user and the front of the terminal 1000. In one embodiment, when the proximity sensor 1014 detects that the distance between the user and the front of the terminal 1000 is gradually decreasing, the processor 1001 controls the display screen 1005 to switch from the screen-on state to the screen-off state; when the proximity sensor 1014 detects that the distance between the user and the front of the terminal 1000 is gradually increasing, the processor 1001 controls the display screen 1005 to switch from the screen-off state to the screen-on state.

[0220] Those skilled in the art will understand that Fig.10 The structure shown in the figure does not constitute a limitation on the terminal 1000, and the terminal 1000 may include more or less components than those shown in the figure, or combine some components, or adopt a different component arrangement.

[0221] An embodiment of the present application further provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is loaded and executed by a processor to implement the oil production determination method of the above embodiment.

[0222] The embodiment of the present application also provides a computer program product, including a computer program, wherein the computer program is loaded and executed by a processor to implement the oil production determination method as described in the above embodiment.

[0223] 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. The above program may be stored in a computer-readable storage medium, and the above storage medium may be a read-only memory, a disk or an optical disk, etc.

[0224] The above are only optional embodiments of the embodiments of the present application and are not intended to limit the embodiments of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the protection scope of the present application.

Claims

1. A method for determining oil production, characterized in that: The method comprises: The geological data of the first shale oil well are processed respectively by using a plurality of principal component expressions to obtain the values ​​of a plurality of principal components of the first shale oil well, wherein the first shale oil well is a shale oil well that has not been hydraulically fractured, the geological data is used to represent the oil content index and the fracturing index of the shale reservoir where the first shale oil well is located, the oil content index is used to represent the oil content of the shale reservoir, the fracturing index is used to represent the anti-fracturing strength of the shale reservoir, and the principal component expressions are used to determine the values ​​of the principal components in the geological data; Based on the association relationship between the plurality of principal components and the membership function, first membership information is determined, wherein the first membership information includes the principal component membership of each principal component to each evaluation level, and the principal component membership is used to indicate the probability that the value of the principal component belongs to the principal component value range associated with the corresponding evaluation level; Based on the weight of each principal component expression and the first membership information, second membership information is determined, wherein the second membership information includes the oil well membership of the first shale oil well to each of the evaluation levels, the oil well membership is used to indicate the probability that the predicted oil production of the first shale oil well belongs to the oil production range indicated by the corresponding evaluation level, and the predicted oil production is positively correlated with the evaluation level; Based on the second affiliation information, the oil production of the first shale oil well after hydraulic fracturing is determined.

2. The method according to claim 1, characterized in that The determining of the first membership information based on the association relationship between the plurality of principal components and the membership functions comprises: For any principal component, based on the association relationship between the plurality of principal components and the membership function, determining the membership function associated with the principal component; Based on the membership function associated with the principal component, the value of the principal component is processed to obtain the principal component membership of the principal component to each evaluation level.

3. The method according to claim 1, characterized in that The number of the principal component expressions is k, the number of the evaluation levels is m, and k and m are positive integers; The determining of the second membership information based on the weight of each principal component expression and the first membership information comprises: Based on the weight of each principal component expression, construct a principal component weight matrix of 1 row and k columns, wherein the elements in the principal component weight matrix are used to respectively represent the weight of each principal component expression; Based on the first membership information, construct a principal component membership matrix with k rows and m columns, wherein the elements in the principal component membership matrix are used to respectively represent the principal component membership of each principal component to each evaluation level in the m evaluation levels; The principal component weight matrix and the principal component membership matrix are multiplied to obtain an oil well membership matrix of 1 row and m columns, and the elements in the oil well membership matrix are used to respectively represent the oil well membership of the first shale oil well to each of the m evaluation levels.

4. The method according to claim 1, characterized in that: The method further comprises: Performing principal component analysis on the geological data of the second shale oil well to obtain the multiple principal component expressions, the weight of each principal component expression, and the values ​​of the multiple principal components of the second shale oil well, wherein the second shale oil well is a shale oil well that has been hydraulically fractured; Based on the correlation between the value of each principal component of the second shale oil well and the production data of the second shale oil well, the correlation relationship between the multiple principal components and the membership function is determined, and the production data is the oil production of the second shale oil well per unit length per unit time.

5. The method according to claim 4, characterized in that The performing principal component analysis on the geological data of the second shale oil well to obtain the plurality of principal component expressions, the weight of each principal component expression, and the values ​​of the plurality of principal components of the second shale oil well includes: Based on the covariance between the various indicators in the geological data of the second shale oil well, a covariance matrix is ​​constructed, and a plurality of eigenvalues ​​and a plurality of eigenvectors of the covariance matrix are determined, wherein the plurality of eigenvalues ​​and the plurality of eigenvectors correspond one to one, and the dimension of the eigenvector is the same as the number of indicators in the geological data; For any eigenvector, based on the elements in the eigenvector, weighted summation is performed on each index in the geological data to obtain a principal component expression corresponding to the eigenvector; In response to obtaining the principal component expression corresponding to each eigenvector, the geological data of the second shale oil well is processed respectively by using the plurality of principal component expressions to obtain the values ​​of the plurality of principal components of the second shale oil well; Based on the normalized values ​​of the plurality of principal components of the second shale oil well, determining the information entropy of each principal component, wherein the information entropy is negatively correlated with the amount of information contained in the principal component; Based on the information entropy of each principal component, the weight of each principal component expression is determined by an entropy weight method, and the weight is negatively correlated with the information entropy.

6. The method according to claim 5, characterized in that Before respectively processing the geological data of the second shale oil well by using the multiple principal component expressions to obtain the values ​​of the multiple principal components of the second shale oil well, the method further includes: For any principal component expression, based on a feature vector generating the principal component expression, determining the feature value corresponding to the feature vector as the feature value corresponding to the principal component expression; Using the ratio of the eigenvalue to the sum of the multiple eigenvalues ​​as the variance contribution rate of the principal component expression; In response to obtaining the eigenvalue corresponding to each principal component expression, sorting the plurality of principal component expressions in descending order of the eigenvalues; When the sum of the variance contribution rates of the first k principal component expressions is greater than a preset threshold, the first k principal component expressions are retained.

7. The method according to claim 4, characterized in that The determining, based on the correlation between the value of each principal component of the second shale oil well and the production data of the second shale oil well, the association relationship between the plurality of principal components and the membership function comprises: For any principal component, when the value of the principal component is positively correlated with the production data, determining that the principal component is associated with a biased large membership function, the input and output of the biased large membership function are positively correlated; In the case where the value of the principal component is negatively correlated with the production data, determining that the principal component is associated with a partial small-size membership function, the input and output of the partial small-size membership function are negatively correlated; In the case where the value of the principal component and the production data are normally distributed, it is determined that the principal component is associated with an intermediate membership function whose input and output are normally distributed.

8. The method according to claim 4, characterized in that The step of determining the oil production of the first shale oil well after hydraulic fracturing based on the second affiliation information includes: Based on the oil well membership of the first shale oil well to each evaluation level, weighted summation is performed on the evaluation scores associated with each evaluation level to obtain an absolute evaluation score of the first shale oil well; Determining a relative evaluation score of the first shale oil well based on the absolute evaluation score of the first shale oil well and the sum of the evaluation scores associated with each evaluation level; Based on the oil production information, the oil production associated with the relative evaluation score of the first shale oil well is determined, and the oil production is determined as the oil production of the first shale oil well after hydraulic fracturing. The oil production information is used to represent the correlation between the relative evaluation score of the shale oil well and the oil production of the shale oil well.

9. The method according to claim 8, characterized in that The method further comprises: Based on the association relationship between the plurality of principal components and the membership function, determining third membership information, the third membership information comprising the principal component membership degree of each principal component of the second shale oil well to each evaluation level; Based on the weight of each principal component expression and the third membership information, determining fourth membership information, wherein the fourth membership information includes the oil well membership of the second shale oil well to each evaluation level; Based on the fourth affiliation information, weighted summing the evaluation scores associated with each evaluation level is performed to obtain an absolute evaluation score of the second shale oil well; Determining a relative evaluation score of the second shale oil well based on the absolute evaluation score of the second shale oil well and the sum of the evaluation scores associated with each evaluation level; The oil production information is determined based on the relative evaluation score of the second shale oil well and the oil production indicated by the production data of the second shale oil well.

10. An oil production determination device, characterized in that: The device comprises: a data processing module, used to process the geological data of the first shale oil well respectively through a plurality of principal component expressions to obtain the values ​​of the plurality of principal components of the first shale oil well, wherein the first shale oil well is a shale oil well that has not been hydraulically fractured, the geological data is used to represent the oil content index and the fracturing index of the shale reservoir where the first shale oil well is located, the oil content index is used to represent the oil content of the shale reservoir, the fracturing index is used to represent the anti-fracturing strength of the shale reservoir, and the principal component expressions are used to determine the values ​​of the principal components in the geological data; A first determination module is used to determine first membership information based on the association relationship between the plurality of principal components and the membership function, wherein the first membership information includes the principal component membership of each principal component to each evaluation level, and the principal component membership is used to indicate the probability that the value of the principal component belongs to the principal component value range associated with the corresponding evaluation level; A second determination module is used to determine second membership information based on the weight of each principal component expression and the first membership information, wherein the second membership information includes the oil well membership of the first shale oil well to each evaluation level, the oil well membership is used to indicate the probability that the predicted oil production of the first shale oil well belongs to the oil production range indicated by the corresponding evaluation level, and the predicted oil production is positively correlated with the evaluation level; The third determination module is used to determine the oil production of the first shale oil well after hydraulic fracturing based on the second affiliation information.

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