Deep underground reservoir fluid identification method, system and device and medium

Abstract

The invention discloses a deep underground reservoir fluid identification method, system and device and a medium. The method comprises the following steps: acquiring longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data and longitudinal wide-area electromagnetic inversion resistivity sampling point data; principal component data and variance contribution rates of the longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data and the longitudinal wide-area electromagnetic inversion resistivity sampling point data are calculated; based on a spider diagram evaluation method, determining a target fluid indication factor of each longitudinal sampling point according to principal component data and variance contribution rates of the longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data and the longitudinal wide-area electromagnetic inversion resistivity sampling point data; and extracting a plurality of well logging fluid indication factors and fluid well logging interpretation results of the well position, and determining fluid indication factor numerical value ranges corresponding to different fluid types so as to determine the overall fluid distribution condition. The fluid identification precision can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field related to oil and gas exploration and development, and particularly relates to a method, a system, a device and a medium for identifying deep underground reservoir fluids. Background Art

[0002] Facing the special deep geological structure environment, the conventional single geophysical exploration technology faces the problem of the limitation of the method principle. Breaking through the conventional exploration concept and framework and developing a high-precision fluid identification method and technology for deep geological bodies are the keys to improving the detection ability of deep target bodies.

[0003] In the prior art, there are methods of fusing multiple data to improve the fluid identification accuracy. The fusion of multiple data often uses the traditional principal component analysis method and the principal component analysis method improved based on the entropy weight method. The principal component analysis method improved based on the entropy weight method mainly calculates the weights of each principal component again by using the entropy weight method according to the variation degree of each principal component of the traditional principal component analysis method, and finally takes the comprehensive value of the entropy weight principal component as the final result. The traditional principal component analysis method mainly assigns weights to each principal component data by using the variance contribution rate, and represents the fused result with a simple mathematical relationship. For deep fluid identification, the accuracy is insufficient; at the same time, each principal component is independent of each other, and the information amount of its calculation result may not increase but decrease, which cannot meet the accuracy requirement of fluid identification and classification. The principal component analysis method improved based on the entropy weight method only calculates the entropy value of the principal component data, and then obtains the weight coefficients of each principal component data, without using the variance contribution rate parameter calculated in the principal component analysis method, losing all the information required for fusion, and the accuracy of the fused data result is low. Summary of the Invention

[0004] The present invention aims to at least solve one of the technical problems existing in the prior art. For this purpose, the present invention provides a method for identifying deep underground reservoir fluids, which can improve the fluid identification accuracy.

[0005] The present invention also provides a system for identifying deep underground reservoir fluids, a control device for executing the above method for identifying deep underground reservoir fluids, and a computer-readable storage medium.

[0006] According to the method for identifying deep underground reservoir fluids according to the first aspect embodiment of the present invention, the method includes: Obtaining longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data corresponding to each longitudinal sampling point of each ground measurement point in the study area, where the study area has a plurality of different ground measurement points, and each ground measurement point corresponds to a plurality of different longitudinal sampling points; Based on the principal component analysis method, the first principal component data, the second principal component data, the third principal component data, the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate corresponding to each longitudinal sampling point are calculated through the longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data, and the longitudinal wide-area electromagnetic inversion resistivity sampling point data corresponding to multiple longitudinal sampling points of each ground measurement point; Based on the cobweb diagram evaluation method, the target fluid indication factor of each longitudinal sampling point corresponding to each ground measurement point is determined according to the first principal component data, the second principal component data, the third principal component data, the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate; Multiple logging fluid indication factors of multiple longitudinal sampling points corresponding to the well positions in the study area are extracted from the multiple target fluid indication factors, the fluid logging interpretation results of the well positions are obtained, and the numerical ranges of the fluid indication factors corresponding to different fluid types are determined according to the fluid logging interpretation results and the multiple logging fluid indication factors; The fluid distribution of the deep underground reservoir is determined according to the numerical range of the fluid indication factor and all the target fluid indication factors in the study area.

[0007] The deep underground reservoir fluid identification method according to the embodiments of the present invention has at least the following beneficial effects: The fluid distribution in the deep underground is mainly affected by the reservoir space and the migration channels. The porosity inversion of the seismic can effectively detect the distribution of the underground reservoir space, the seismic curvature attribute can effectively predict the development characteristics of the fractures, and the fractures provide the migration channels for the fluid. Since the velocity difference between oil and water in the reservoir is not large, the conventional seismic method cannot effectively identify the fluid properties in the reservoir. However, since there are significant differences in the resistivity parameters between oil and gas and water, the wide-area electromagnetic inversion method can be used to identify the fluid properties. The present invention identifies the fluid types in the deep underground with high precision by fusing the longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data, and the longitudinal wide-area electromagnetic inversion resistivity sampling point data. The fusion method is to improve the traditional principal component analysis method by using the cobweb diagram evaluation method, and a fluid indication factor is constructed through a comprehensive evaluation model, which transforms the multi-response problem related to fluid prediction into a single-response problem and improves the fluid prediction accuracy. Compared with the improvement of the traditional principal component analysis method by the conventional entropy weight method, the entropy weight method only uses the principal component data to calculate the entropy value and then obtains the weight. The present invention not only uses the principal component data for fusion, but also focuses on the influence of the variance contribution rate on the data volume weight, effectively utilizes the multiple information of the data, constructs a comprehensive evaluation model, and improves the accuracy of fluid identification.

[0008] According to some embodiments of the present invention, the spider web diagram evaluation method determines the target fluid indication factor for each longitudinal sampling point corresponding to each ground measurement point according to the first principal component data, the second principal component data, the third principal component data, the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate, including: Normalize the first principal component data, the second principal component data, and the third principal component data for each longitudinal sampling point corresponding to each ground measurement point to obtain a first normalized principal component, a second normalized principal component, and a third normalized principal component; Select a three-dimensional non-orthogonal coordinate system, and determine the weighted central angle between adjacent two coordinate axes according to the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate; wherein, the three coordinate axes in the three-dimensional non-orthogonal coordinate system are respectively used to indicate the first normalized principal component, the second normalized principal component, and the third normalized principal component; Taking the origin of the three-dimensional non-orthogonal coordinate system as the starting point, determine three initial points with a distance of 1 from the origin on the three coordinate axes, and connect the three initial points in sequence to form an initial triangle, forming a spider web diagram of a three-dimensional non-orthogonal coordinate system; Taking the origin of the three-dimensional non-orthogonal coordinate system as the starting point, determine three target points with a distance of the first normalized principal component, the second normalized principal component, and the third normalized principal component of the target longitudinal sampling point corresponding to the target ground measurement point from the origin on the three coordinate axes, and connect the three target points in sequence to form a target triangle, so as to obtain the target triangle for each longitudinal sampling point corresponding to each ground measurement point; Determine the first area of the initial triangle and the second area of each target triangle; Determine the target fluid indication factor for each longitudinal sampling point corresponding to each ground measurement point according to the first area and the second area of each target triangle.

[0009] According to some embodiments of the present invention, the three coordinate axes in the three-dimensional non-orthogonal coordinate system are respectively a first coordinate axis, a second coordinate axis, and a third coordinate axis. The first coordinate axis is used to indicate the first normalized principal component, the second coordinate axis is used to indicate the second normalized principal component, and the third coordinate axis is used to indicate the third normalized principal component; The determining the weighted central angle between adjacent two coordinate axes according to the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate includes: Determine the first weighted central angle between the first coordinate axis and the second coordinate axis according to the first variance contribution rate; Determine a second weighted central angle between the second coordinate axis and the third coordinate axis according to the second variance contribution rate; Determine a third weighted central angle between the third coordinate axis and the first coordinate axis according to the third variance contribution rate.

[0010] According to some embodiments of the present invention, the three coordinate axes in the three-dimensional non-orthogonal coordinate system are respectively a first coordinate axis, a second coordinate axis, and a third coordinate axis. The first coordinate axis is used to indicate the first normalized principal component, the second coordinate axis is used to indicate the second normalized principal component, and the third coordinate axis is used to indicate the third normalized principal component; The determining of the weighted central angles between adjacent coordinate axes according to the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate includes: Determine a first weighted central angle between the first coordinate axis and the second coordinate axis according to the second variance contribution rate; Determine a second weighted central angle between the second coordinate axis and the third coordinate axis according to the third variance contribution rate; Determine a third weighted central angle between the third coordinate axis and the first coordinate axis according to the first variance contribution rate.

[0011] According to some embodiments of the present invention, the determining of the target fluid indication factor for each longitudinal sampling point corresponding to each ground measurement point according to the first area and the second area of each target triangle includes: Take the ratio of the second area of each target triangle to the first area as the target fluid indication factor for each longitudinal sampling point corresponding to each ground measurement point.

[0012] According to some embodiments of the present invention, the obtaining of the longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data, and the longitudinal wide-area electromagnetic inversion resistivity sampling point data for each longitudinal sampling point corresponding to each ground measurement point in the study area includes: Obtain the original seismic curvature attribute sampling point data, the original seismic porosity sampling point data, and the original wide-area electromagnetic inversion resistivity sampling point data for each longitudinal sampling point corresponding to each ground measurement point in the study area; Perform spatial calibration processing, data standardization processing, and normalization processing on all the original seismic curvature attribute sampling point data, the original seismic porosity sampling point data, and the original wide-area electromagnetic inversion resistivity sampling point data to obtain the corresponding longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data, and the longitudinal wide-area electromagnetic inversion resistivity sampling point data.

[0013] According to some embodiments of the present invention, based on the principal component analysis method, for each of the longitudinal sampling points corresponding to multiple longitudinal sampling points of each ground measurement point, the longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data, and the longitudinal wide-area electromagnetic inversion resistivity sampling point data are used to calculate the first principal component data, the second principal component data, the third principal component data, the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate corresponding to each longitudinal sampling point, including: Calculate the covariance matrix using the longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data, and the longitudinal wide-area electromagnetic inversion resistivity sampling point data corresponding to multiple longitudinal sampling points of each ground measurement point; Determine the first eigenvalue and the corresponding first eigenvector, the second eigenvalue and the corresponding second eigenvector, and the third eigenvalue and the corresponding third eigenvector according to the covariance matrix, where the first eigenvalue is greater than the second eigenvalue, and the second eigenvalue is greater than the third eigenvalue; Based on the longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data, and the longitudinal wide-area electromagnetic inversion resistivity sampling point data corresponding to each longitudinal sampling point, combine with the first eigenvector to determine the corresponding first principal component data, and determine the corresponding first variance contribution rate according to the proportion of the first eigenvalue in the sum of the first eigenvalue, the second eigenvalue, and the third eigenvalue; Based on the longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data, and the longitudinal wide-area electromagnetic inversion resistivity sampling point data corresponding to each longitudinal sampling point, combine with the second eigenvector to determine the corresponding second principal component data, and determine the corresponding second variance contribution rate according to the proportion of the second eigenvalue in the sum of the first eigenvalue, the second eigenvalue, and the third eigenvalue; Based on the longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data, and the longitudinal wide-area electromagnetic inversion resistivity sampling point data corresponding to each longitudinal sampling point, combine with the third eigenvector to determine the corresponding third principal component data, and determine the corresponding third variance contribution rate according to the proportion of the third eigenvalue in the sum of the first eigenvalue, the second eigenvalue, and the third eigenvalue.

[0014] According to the deep underground reservoir fluid identification system of the second aspect embodiment of the present invention, the system includes: A data acquisition unit for acquiring longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data of each longitudinal sampling point corresponding to each ground measurement point in the study area. The study area has multiple different ground measurement points, and each ground measurement point corresponds to multiple different longitudinal sampling points; A principal component analysis calculation unit for calculating the first principal component data, second principal component data, third principal component data, first variance contribution rate, second variance contribution rate, and third variance contribution rate corresponding to each longitudinal sampling point based on the principal component analysis method through the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data corresponding to the multiple longitudinal sampling points of each ground measurement point; A target fluid indication factor determination unit for determining the target fluid indication factor of each longitudinal sampling point corresponding to each ground measurement point based on the cobweb diagram evaluation method according to the first principal component data, second principal component data, third principal component data, first variance contribution rate, second variance contribution rate, and third variance contribution rate; A fluid indication factor numerical range determination unit for extracting multiple logging fluid indication factors of the multiple longitudinal sampling points corresponding to the well positions in the study area from the multiple target fluid indication factors, obtaining the fluid logging interpretation results of the well positions, and determining the fluid indication factor numerical ranges corresponding to different fluid types according to the fluid logging interpretation results and the multiple logging fluid indication factors; A fluid distribution determination unit for determining the fluid distribution of the deep underground reservoir according to the fluid indication factor numerical ranges and all the target fluid indication factors in the study area.

[0015] The deep underground reservoir fluid identification system according to an embodiment of the present invention has at least the following beneficial effects: The fluid distribution in deep underground is mainly affected by reservoir spaces and migration channels. Seismic porosity inversion can effectively detect the distribution of underground reservoir spaces, seismic curvature attributes can effectively predict the development characteristics of fractures, and fractures provide migration channels for fluids. Since the velocity difference between oil and water in the reservoir is not significant, conventional seismic methods cannot effectively identify the fluid properties in the reservoir. However, since there are significant differences in resistivity parameters between oil / gas and water, the wide-area electromagnetic inversion method can be used to identify fluid properties. The present invention identifies the fluid types in deep underground with high precision by fusing the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data. The fusion method improves the traditional principal component analysis method using the spider web diagram evaluation method, constructs a fluid indicator factor through a comprehensive evaluation model, and converts the multi-response problem related to fluid prediction into a single-response problem, thereby improving the fluid prediction accuracy. Compared with the improvement of the traditional principal component analysis method by the conventional entropy weight method, which only calculates the entropy value using the principal component data to obtain the weight, the present invention not only uses the principal component data for fusion, but also focuses on the influence of the variance contribution rate on the data volume weight, effectively utilizes the multiple information of the data, constructs a comprehensive evaluation model, and improves the accuracy of fluid identification.

[0016] The control device according to the third aspect embodiment of the present invention includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the deep underground reservoir fluid identification method as described in the first aspect embodiment above. Since the control device adopts all the technical solutions of the deep underground reservoir fluid identification method of the above embodiment, it at least has all the beneficial effects brought by the technical solutions of the above embodiment.

[0017] The computer-readable storage medium according to the fourth aspect embodiment of the present invention stores computer-executable instructions, and the computer-executable instructions are used to execute the deep underground reservoir fluid identification method as described in the first aspect embodiment above. Since the computer-readable storage medium adopts all the technical solutions of the deep underground reservoir fluid identification method of the above embodiment, it at least has all the beneficial effects brought by the technical solutions of the above embodiment.

[0018] Other features and advantages of the present invention will be described in the subsequent specification, and part of them will become obvious from the specification or be understood by implementing the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and / or additional aspects and advantages of the present invention will become obvious and easy to understand from the description of the embodiments in conjunction with the following drawings, where: Figure 1It is a flowchart of a method for identifying deep underground reservoir fluids according to an embodiment of the present invention; Figure 2 It is a schematic diagram of a spider web diagram evaluation method according to an embodiment of the present invention; Figure 3 It is a technical roadmap of a method for identifying deep underground reservoir fluids according to an embodiment of the present invention. Detailed implementation manners

[0020] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0021] In the description of the present invention, if the first, second, etc. are described only for the purpose of distinguishing technical features, they should not be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence of the indicated technical features.

[0022] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as up, down, etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the present invention.

[0023] In the description of the present invention, it should be noted that unless otherwise clearly defined, terms such as setting, installation, connection, etc. should be understood in a broad sense, and those skilled in the relevant technical fields can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific content of the technical solution.

[0024] Next, in combination with Figures 1 to 3 A clear and complete description of the method for identifying deep underground reservoir fluids according to the embodiments of the present invention will be given. Obviously, the following described embodiments are some embodiments of the present invention, not all embodiments.

[0025] Referring to Figures 1 to 3 , Figure 1 It is a flowchart of a method for identifying deep underground reservoir fluids according to an embodiment of the present invention; Figure 2 It is a schematic diagram of a spider web diagram evaluation method according to an embodiment of the present invention; Figure 3 It is a technical roadmap of a method for identifying deep underground reservoir fluids according to an embodiment of the present invention.

[0026] According to the method for identifying deep underground reservoir fluids according to the first aspect embodiment of the present invention, the method includes: Obtain the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide - area electromagnetic inversion resistivity sampling point data of each longitudinal sampling point corresponding to each ground measurement point in the study area. The study area has multiple different ground measurement points, and each ground measurement point corresponds to multiple different longitudinal sampling points; Based on the principal component analysis method, calculate the first principal component data, second principal component data, third principal component data, first variance contribution rate, second variance contribution rate, and third variance contribution rate corresponding to each longitudinal sampling point through the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide - area electromagnetic inversion resistivity sampling point data corresponding to multiple longitudinal sampling points of each ground measurement point; Based on the cobweb diagram evaluation method, determine the target fluid indicator factor of each longitudinal sampling point corresponding to each ground measurement point according to the first principal component data, second principal component data, third principal component data, first variance contribution rate, second variance contribution rate, and third variance contribution rate; Extract multiple well - logging fluid indicator factors of multiple longitudinal sampling points corresponding to the well positions in the study area from multiple target fluid indicator factors, obtain the fluid well - logging interpretation results of the well positions, and determine the numerical range of the fluid indicator factors corresponding to different fluid types according to the fluid well - logging interpretation results and multiple well - logging fluid indicator factors; Determine the fluid distribution of the deep underground reservoir according to the numerical range of the fluid indicator factors and all target fluid indicator factors in the study area.

[0027] The fluid distribution in the deep underground is mainly affected by the reservoir space and migration channels. Seismic porosity inversion can effectively detect the distribution of underground reservoir space, seismic curvature attributes can effectively predict the development characteristics of fractures, and fractures provide migration channels for fluids. Since the velocity differences between oil and water in the reservoir are not significant, conventional seismic methods cannot effectively identify the fluid properties in the reservoir. However, since there are significant differences in resistivity parameters between oil - gas and water, the wide - area electromagnetic inversion method can be used to identify fluid properties. The present invention identifies the fluid types in the deep underground with high precision by fusing the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide - area electromagnetic inversion resistivity sampling point data.

[0028] It should be noted that the calculation processes of the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide - area electromagnetic inversion resistivity sampling point data are prior arts known to those skilled in the art and will not be elaborated herein.

[0029] In some embodiments of the present invention, obtaining the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide - area electromagnetic inversion resistivity sampling point data of each longitudinal sampling point corresponding to each ground measurement point in the study area includes: Obtain the original seismic curvature attribute sampling point data, original seismic porosity sampling point data, and original wide-area electromagnetic inversion resistivity sampling point data corresponding to each longitudinal sampling point for each ground measurement point in the study area; Perform spatial calibration processing, data standardization processing, and normalization processing on all the original seismic curvature attribute sampling point data, original seismic porosity sampling point data, and original wide-area electromagnetic inversion resistivity sampling point data to obtain the corresponding longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data.

[0030] It can be understood that the data bins of the original seismic curvature attribute sampling point data, original seismic porosity sampling point data, and original wide-area electromagnetic inversion resistivity sampling point data are not the same. It is necessary to calibrate the original wide-area electromagnetic inversion resistivity sampling point data to have the same bin size and the same longitudinal sampling rate as the seismic data, and unify the data standards in order to perform data fusion on the three types of data.

[0031] Generally, the planar bin of seismic data is 20 m * 20 m, and the planar bin of three-dimensional wide-area electromagnetic inversion resistivity data is 100 m * 100 m. On the plane, it is necessary to calibrate the wide-area electromagnetic inversion resistivity data to have the same bin size as the seismic data.

[0032] In some embodiments, the points to be supplemented can be filled by the method of linear encrypted sampling. For example, for a certain ground measurement point L11 of the original wide-area electromagnetic inversion resistivity sampling point data, the adjacent ground measurement point is L12, and the distance between the two is 100 m. To calibrate it to the same bin as the seismic data, 4 points need to be encrypted in the middle. The value at point L11-1 is L11-1 = 0.8 * L11 + 0.2 * L12, the value at point L11-2 is L11-2 = 0.6 * L11 + 0.4 * L12, the value at point L11-3 is L11-3 = 0.4 * L11 + 0.6 * L12, and the value at point L11-4 is L11-4 = 0.2 * L11 + 0.8 * L12. Using this method of linear encrypted interpolation, the calibration can be completed.

[0033] Then, data standardization is performed on the spatially calibrated data. Taking the data of multiple longitudinal sampling points of a certain ground measurement point as an example, define the original seismic curvature attribute sampling point data, original seismic porosity sampling point data, and original wide-area electromagnetic inversion resistivity sampling point data after spatial calibration as 、 and , where j represents the longitudinal sampling point, j= (1 , 2 , 3 , ... ,n ),n is the total number of longitudinal sampling points for a certain ground measurement point. In some embodiments, the mean method can be used to normalize the original seismic curvature attribute sampling point data, the original seismic porosity sampling point data, and the original wide - area electromagnetic inversion resistivity sampling point data, to obtain the normalized longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data, and the longitudinal wide - area electromagnetic inversion resistivity sampling point data, which are respectively , and , , and . The constraint formulas for ; Formula (1) ; Formula (2) ; Formula (3) In some embodiments of the present invention, based on the principal component analysis method, through the longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data, and the longitudinal wide - area electromagnetic inversion resistivity sampling point data corresponding to multiple longitudinal sampling points of each ground measurement point, the first principal component data, the second principal component data, the third principal component data, the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate corresponding to each longitudinal sampling point are calculated, including:[[]] Calculate the covariance matrix through the longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data, and the longitudinal wide - area electromagnetic inversion resistivity sampling point data corresponding to multiple longitudinal sampling points of each ground measurement point; Determine the first eigenvalue and the corresponding first eigenvector, the second eigenvalue and the corresponding second eigenvector, the third eigenvalue and the corresponding third eigenvector according to the covariance matrix, where the first eigenvalue is greater than the second eigenvalue, and the second eigenvalue is greater than the third eigenvalue; Determine the corresponding first principal component data by combining the first eigenvector according to the longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data, and the longitudinal wide - area electromagnetic inversion resistivity sampling point data corresponding to each longitudinal sampling point, and determine the corresponding first variance contribution rate according to the proportion of the first eigenvalue in the sum of the first eigenvalue, the second eigenvalue, and the third eigenvalue; Determine the corresponding second principal component data by combining the second eigenvector according to the longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data, and the longitudinal wide - area electromagnetic inversion resistivity sampling point data corresponding to each longitudinal sampling point, and determine the corresponding second variance contribution rate according to the proportion of the second eigenvalue in the sum of the first eigenvalue, the second eigenvalue, and the third eigenvalue; Based on the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide - area electromagnetic inversion resistivity sampling point data corresponding to each longitudinal sampling point, the corresponding third - principal - component data is determined by combining with the third eigenvector. The corresponding third - variance contribution rate is determined according to the proportion of the third eigenvalue to the sum of the first eigenvalue, the second eigenvalue, and the third eigenvalue.

[0034] Taking the , and of a certain ground measurement point as an example, calculate the covariance matrix of , and , as well as the eigenvalues and eigenvectors of the covariance matrix. The first eigenvalue, the second eigenvalue, and the third eigenvalue are , and respectively. The first eigenvector, the second eigenvector, and the third eigenvector are , and respectively. The first - principal - component data, the second - principal - component data, and the third - principal - component data are + + , + + and + + respectively. The variance contribution rate is the proportion of a certain eigenvalue to the total number of all eigenvalues, and the constraint formula is: , ( i = 1 , 2 , 3); Formula (4) The first - variance contribution rate, the second - variance contribution rate, and the third - variance contribution rate are , and respectively.

[0035] The traditional principal - component analysis method takes the variance contribution rate as the weight coefficient of each principal component, assigns weight coefficients to each principal component, and then conducts comprehensive calculation to obtain the final fusion result (each principal component is , , , j = 1 , 2 , 3 , ... , n , and the variance contribution rate is (i = 1 , 2 , 3), the variance contribution rate represents the proportion of the variance explained by a single principal component in the total variance. The principal component with a high variance contribution rate is usually more important; the final result of the traditional principal component analysis method is + + , and the variance contribution rate is equivalent to the weight coefficient of each principal component). However, the principal components are independent of each other, and the amount of information in its calculation results may not increase but decrease instead, unable to meet the accuracy requirements for deep underground reservoir fluid identification.

[0036] The present invention improves the traditional principal component analysis method by using the spider web diagram evaluation method, constructs a fluid indication factor through a comprehensive evaluation model, transforms the multi-response problem related to fluid prediction into a single-response problem, and can improve the fluid prediction accuracy. Compared with the improvement of the traditional principal component analysis method by the conventional entropy weight method, the entropy weight method only uses the principal component data to calculate the entropy value and then obtains the weight. The present invention not only uses the principal component data for fusion, but also focuses on using the influence of the variance contribution rate on the data volume weight, effectively utilizes the multiple information of the data, constructs a comprehensive evaluation model, and improves the accuracy of fluid identification.

[0037] In some embodiments of the present invention, referring to Figure 2 , based on the spider web diagram evaluation method, determine the target fluid indication factor for each longitudinal sampling point corresponding to each ground measurement point according to the first principal component data, the second principal component data, the third principal component data, the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate, including: Perform normalization processing on the first principal component data, the second principal component data, and the third principal component data for each longitudinal sampling point corresponding to each ground measurement point to obtain the first normalized principal component, the second normalized principal component, and the third normalized principal component; Select a three-dimensional non-orthogonal coordinate system, and determine the weighted central angle between adjacent two coordinate axes according to the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate; wherein, the three coordinate axes in the three-dimensional non-orthogonal coordinate system are respectively used to indicate the first normalized principal component, the second normalized principal component, and the third normalized principal component; Starting from the origin of the three-dimensional non-orthogonal coordinate system, determine three initial points on the three coordinate axes with a distance of 1 from the origin, and sequentially connect the three initial points to form an initial triangle, forming a spider web diagram of the three-dimensional non-orthogonal coordinate system; Taking the origin of the three-dimensional non-orthogonal coordinate system as the starting point, determine three target points on the three coordinate axes at a distance from the origin equal to the first normalized principal component, the second normalized principal component, and the third normalized principal component of the target longitudinal sampling points corresponding to the target ground measurement points. Connect the three target points in sequence to form a target triangle, so as to obtain the target triangle of each longitudinal sampling point corresponding to each ground measurement point; Determine the first area of the initial triangle and the second area of each target triangle; Determine the target fluid indication factor of each longitudinal sampling point corresponding to each ground measurement point according to the first area and the second area of each target triangle.

[0038] Figure 2 Among them, the origin is the intersection of the three dotted lines, F1, F2, and F3 respectively represent the first normalized principal component, the second normalized principal component, and the third normalized principal component, C represents the initial triangle, and C1 represents the target triangle.

[0039] The first normalized principal component The constraint formula is as follows: ; Formula (5) The second normalized principal component The constraint formula is as follows: ; Formula (6) The third normalized principal component The constraint formula is as follows: ; Formula (7) In some embodiments of the present invention, referring to Figure 2 , the three coordinate axes in the three-dimensional non-orthogonal coordinate system are respectively the first coordinate axis, the second coordinate axis, and the third coordinate axis. The first coordinate axis is used to indicate the first normalized principal component, the second coordinate axis is used to indicate the second normalized principal component, and the third coordinate axis is used to indicate the third normalized principal component; Determine the weighted central angle between adjacent two coordinate axes according to the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate, including: Determine the first weighted central angle between the first coordinate axis and the second coordinate axis according to the first variance contribution rate; Determine the second weighted central angle between the second coordinate axis and the third coordinate axis according to the second variance contribution rate; Determine the third weighted central angle between the third coordinate axis and the first coordinate axis according to the third variance contribution rate.

[0040] In some embodiments of the present invention, referring to Figure 2, the three coordinate axes in the three-dimensional non-orthogonal coordinate system are the first coordinate axis, the second coordinate axis, and the third coordinate axis respectively. The first coordinate axis is used to indicate the first normalized principal component, the second coordinate axis is used to indicate the second normalized principal component, and the third coordinate axis is used to indicate the third normalized principal component; Determine the weighted central angle between adjacent two coordinate axes according to the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate, including: Determine the first weighted central angle between the first coordinate axis and the second coordinate axis according to the second variance contribution rate; Determine the second weighted central angle between the second coordinate axis and the third coordinate axis according to the third variance contribution rate; Determine the third weighted central angle between the third coordinate axis and the first coordinate axis according to the first variance contribution rate.

[0041] The first weighted central angle is Figure 2 the included angle between F1 and F2 in, the second weighted central angle is Figure 2 the included angle between F2 and F3 in, and the third weighted central angle is Figure 2 the included angle between F3 and F1 in. The constraint formula for the weighted central angle is: ; Formula (8) The distances from the three points of the initial triangle to the origin are 1 because the maximum value of the normalized principal component is 1.

[0042] The first area S of the initial triangle has a constraint formula of: ; Formula (9) The second area of the target triangle has a constraint formula of: ,j =(1 , 2 , 3 , ... ,n ); Formula (10) In some embodiments of the present invention, referring to Figure 2 , determine the target fluid indication factor for each longitudinal sampling point corresponding to each ground measurement point according to the first area and the second area of each target triangle, including: Take the ratio of the second area of each target triangle to the first area as the target fluid indication factor for each longitudinal sampling point corresponding to each ground measurement point.

[0043] The constraint formula for the target fluid indication factor is: ; Formula (11) Thus, the target fluid indication factors for each longitudinal sampling point corresponding to each surface measurement point can be obtained.

[0044] In the traditional spider-web diagram evaluation method, with the origin as the center, the coordinate axes represented by the three principal component data are equally divided, and the angles between any two adjacent coordinate axes are equal, that is, the weighted central angles between two adjacent coordinate axes are equal, that is, the weighted coefficients of each principal component data are equal, without considering the importance degree among various evaluation factors. However, in the spider-web diagram evaluation method of the present invention, the principal component data with a higher variance contribution rate accounts for a larger weighted coefficient to achieve data fusion, and then the fluid distribution at the reservoir location is comprehensively evaluated by the way of area ratio, the fluid indication factor is calculated, and the calculation result is more accurate.

[0045] After obtaining the target fluid indication factors for each longitudinal sampling point corresponding to each surface measurement point, multiple logging fluid indication factors for multiple longitudinal sampling points corresponding to the well positions in the study area are extracted from the multiple target fluid indication factors, and the fluid logging interpretation results of the well positions are obtained. The fluid logging interpretation results are known logging data, including water layers, gas layers and tight layers. Therefore, the numerical ranges of the fluid indication factors corresponding to different fluid types can be determined according to the fluid logging interpretation results and the multiple logging fluid indication factors. The different numerical ranges of the fluid indication factors are the boundary values for fluid identification (such as water layers, gas layers, etc.). For example, the numerical range of the fluid indication factor for identifying a water layer is A1~A2, and the numerical range of the fluid indication factor for identifying a gas layer is B1~B2. Thus, the fluid distribution of the deep underground reservoir can be determined according to the numerical range of the fluid indication factor and all the target fluid indication factors in the study area.

[0046] According to the deep underground reservoir fluid identification method of the embodiments of the present invention, the fluid distribution in the deep underground is mainly affected by the reservoir space and migration channels. The seismic porosity inversion can effectively detect the distribution of the underground reservoir space, and the seismic curvature attribute can effectively predict the development characteristics of fractures, which provide the migration channels for fluids. Since the velocity difference between oil and water in the reservoir is not significant, conventional seismic methods cannot effectively identify the fluid properties in the reservoir. However, since there are significant differences in resistivity parameters between oil and gas and water, the wide-area electromagnetic inversion method can be used to identify the fluid properties. The present invention identifies the fluid types in the deep underground with high precision by fusing the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data. The fusion method improves the traditional principal component analysis method using the spider web diagram evaluation method, constructs a fluid indication factor through a comprehensive evaluation model, and transforms the multi-response problem related to fluid prediction into a single-response problem, thereby improving the fluid prediction accuracy. Compared with the improvement of the traditional principal component analysis method by the conventional entropy weight method, the entropy weight method only uses the principal component data to calculate the entropy value and then obtains the weight. The present invention not only uses the principal component data for fusion, but also focuses on the influence of the variance contribution rate on the data volume weight, effectively utilizes the multiple information of the data, constructs a comprehensive evaluation model, and improves the accuracy of fluid identification.

[0047] According to the deep underground reservoir fluid identification system of the second aspect of the present invention, the system includes a data acquisition unit, a principal component analysis calculation unit, a target fluid indication factor determination unit, a fluid indication factor numerical range determination unit, and a fluid distribution determination unit.

[0048] The data acquisition unit is configured to acquire the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data of each longitudinal sampling point corresponding to each ground measurement point in the study area. The study area has multiple different ground measurement points, and each ground measurement point corresponds to multiple different longitudinal sampling points; The principal component analysis calculation unit is configured to calculate, based on the principal component analysis method, the first principal component data, second principal component data, third principal component data, first variance contribution rate, second variance contribution rate, and third variance contribution rate corresponding to each longitudinal sampling point through the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data corresponding to the multiple longitudinal sampling points of each ground measurement point; The target fluid indication factor determination unit is configured to determine the target fluid indication factor of each longitudinal sampling point corresponding to each ground measurement point based on the spider web diagram evaluation method according to the first principal component data, second principal component data, third principal component data, first variance contribution rate, second variance contribution rate, and third variance contribution rate; A fluid indication factor value range determination unit is configured to extract logging fluid indication factors of multiple vertical sampling points corresponding to well positions in the study area from multiple target fluid indication factors, obtain a fluid logging interpretation result of the well positions, and determine the fluid indication factor value ranges corresponding to different fluid types according to the fluid logging interpretation result and the multiple logging fluid indication factors; A fluid distribution determination unit is configured to determine the fluid distribution of the deep underground reservoir according to the fluid indication factor value ranges and all the target fluid indication factors in the study area.

[0049] The fluid distribution in the deep underground is mainly affected by the reservoir space and migration channels. Seismic porosity inversion can effectively detect the distribution of underground reservoir space, and seismic curvature attributes can effectively predict the development characteristics of fractures, which provide migration channels for fluids. Since the velocity difference between oil and water in the reservoir is not significant, conventional seismic methods cannot effectively identify the fluid properties in the reservoir. However, since there are significant differences in resistivity parameters between oil / gas and water, the wide-area electromagnetic inversion method can be used to identify fluid properties. The present invention identifies the fluid types in the deep underground with high precision by fusing the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data.

[0050] In some embodiments of the present invention, obtaining the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data of each vertical sampling point corresponding to each ground measurement point in the study area includes: Obtaining the original seismic curvature attribute sampling point data, original seismic porosity sampling point data, and original wide-area electromagnetic inversion resistivity sampling point data of each vertical sampling point corresponding to each ground measurement point in the study area; Performing spatial calibration processing, data standardization processing, and normalization processing on all the original seismic curvature attribute sampling point data, original seismic porosity sampling point data, and original wide-area electromagnetic inversion resistivity sampling point data to obtain the corresponding longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data.

[0051] It can be understood that the data bins of the original seismic curvature attribute sampling point data, original seismic porosity sampling point data, and original wide-area electromagnetic inversion resistivity sampling point data are not the same. It is necessary to calibrate the original wide-area electromagnetic inversion resistivity sampling point data to the same bin size and the same longitudinal sampling rate as the seismic data to unify the data standard for data fusion of the three types of data.

[0052] The planar bin of general seismic data is 20 m * 20 m, and the planar bin of 3D wide - area electromagnetic inversion resistivity data is 100 m * 100 m. On the plane, it is necessary to calibrate the wide - area electromagnetic inversion resistivity data to be the same size as the seismic bin.

[0053] In some embodiments, the points to be supplemented can be filled by the method of linear encrypted sampling. For example, for a certain ground measurement point L11 of the original wide - area electromagnetic inversion resistivity sampling point data, the adjacent ground measurement point is L12, and the distance between the two is 100 m. To calibrate it to the same seismic bin, 4 points need to be encrypted in the middle. The value at point L11 - 1 is L11 - 1 = 0.8 * L11+0.2 * L12, the value at point L11 - 2 is L11 - 2 = 0.6 * L11+0.4 * L12, the value at point L11 - 3 is L11 - 3 = 0.4 * L11+0.6 * L12, and the value at point L11 - 4 is L11 - 4 = 0.2 * L11+0.8 * L12. Using this linear encrypted interpolation method, the calibration can be completed.

[0054] Then, data standardization is performed on the spatially calibrated data. Taking the data of multiple vertical sampling points of a certain ground measurement point as an example, define the original seismic curvature attribute sampling point data, original seismic porosity sampling point data, and original wide - area electromagnetic inversion resistivity sampling point data after spatial calibration as 、 and respectively, where j represents the vertical sampling point, j= (1 , 2 , 3 , ... ,n ) n is the total number of vertical sampling points of a certain ground measurement point. In some embodiments, the mean method can be used to normalize the original seismic curvature attribute sampling point data, original seismic porosity sampling point data, and original wide - area electromagnetic inversion resistivity sampling point data to obtain the normalized vertical seismic curvature attribute sampling point data, vertical seismic porosity sampling point data, and vertical wide - area electromagnetic inversion resistivity sampling point data.

[0055] In some embodiments of the present invention, based on the principal component analysis method, through the vertical seismic curvature attribute sampling point data, vertical seismic porosity sampling point data, and vertical wide - area electromagnetic inversion resistivity sampling point data corresponding to multiple vertical sampling points of each ground measurement point, the first principal component data, second principal component data, third principal component data, first variance contribution rate, second variance contribution rate, and third variance contribution rate corresponding to each vertical sampling point are calculated, including: Calculate the covariance matrix based on the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide - area electromagnetic inversion resistivity sampling point data corresponding to multiple longitudinal sampling points at each surface measurement point; Determine the first eigenvalue and the corresponding first eigenvector, the second eigenvalue and the corresponding second eigenvector, and the third eigenvalue and the corresponding third eigenvector according to the covariance matrix, where the first eigenvalue is greater than the second eigenvalue, and the second eigenvalue is greater than the third eigenvalue; Determine the corresponding first principal component data by combining the first eigenvector according to the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide - area electromagnetic inversion resistivity sampling point data corresponding to each longitudinal sampling point, and determine the corresponding first variance contribution rate according to the proportion of the first eigenvalue in the sum of the first eigenvalue, the second eigenvalue, and the third eigenvalue; Determine the corresponding second principal component data by combining the second eigenvector according to the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide - area electromagnetic inversion resistivity sampling point data corresponding to each longitudinal sampling point, and determine the corresponding second variance contribution rate according to the proportion of the second eigenvalue in the sum of the first eigenvalue, the second eigenvalue, and the third eigenvalue; Determine the corresponding third principal component data by combining the third eigenvector according to the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide - area electromagnetic inversion resistivity sampling point data corresponding to each longitudinal sampling point, and determine the corresponding third variance contribution rate according to the proportion of the third eigenvalue in the sum of the first eigenvalue, the second eigenvalue, and the third eigenvalue.

[0056] The traditional principal component analysis method uses the variance contribution rate as the weight coefficient of each principal component. After assigning weight coefficients to each principal component, a comprehensive calculation is performed to obtain the final fused result (each principal component is 、 、 , j = 1 , 2 , 3 , ... , n ,and the variance contribution rate is ( i = 1 , 2 , 3). The variance contribution rate represents the proportion of the variance explained by a single principal component in the total variance. The principal component with a high variance contribution rate is usually more important; the final result of the traditional principal component analysis method is + + , the variance contribution rate is equivalent to the weight coefficient of each principal component). However, the principal components are independent of each other, and the amount of information in the calculation results may not increase but decrease, which cannot meet the accuracy requirements for deep underground reservoir fluid identification.

[0057] The present invention improves the traditional principal component analysis method by using the spider web diagram evaluation method, constructs a fluid indication factor through a comprehensive evaluation model, transforms the multi-response problem related to fluid prediction into a single-response problem, and can improve the fluid prediction accuracy. Compared with the improvement of the traditional principal component analysis method by the conventional entropy weight method, the entropy weight method only uses the principal component data to calculate the entropy value and then obtains the weight. The present invention not only uses the principal component data for fusion, but also focuses on using the influence of the variance contribution rate on the data volume weight, effectively utilizes the multiple information of the data, constructs a comprehensive evaluation model, and improves the accuracy of fluid identification.

[0058] In some embodiments of the present invention, referring to Figure 2 , based on the spider web diagram evaluation method, determine the target fluid indication factor for each longitudinal sampling point corresponding to each ground measurement point according to the first principal component data, the second principal component data, the third principal component data, the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate, including: Normalize the first principal component data, the second principal component data, and the third principal component data for each longitudinal sampling point corresponding to each ground measurement point to obtain the first normalized principal component, the second normalized principal component, and the third normalized principal component; Select a three-dimensional non-orthogonal coordinate system, and determine the weight central angle between adjacent two coordinate axes according to the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate; wherein, the three coordinate axes in the three-dimensional non-orthogonal coordinate system are respectively used to indicate the first normalized principal component, the second normalized principal component, and the third normalized principal component; Starting from the origin of the three-dimensional non-orthogonal coordinate system, determine three initial points with a distance of 1 from the origin on the three coordinate axes, and connect the three initial points in sequence to form an initial triangle, forming a spider web diagram of the three-dimensional non-orthogonal coordinate system; Starting from the origin of the three-dimensional non-orthogonal coordinate system, determine three target points with a distance from the origin equal to the first normalized principal component, the second normalized principal component, and the third normalized principal component of the target longitudinal sampling point corresponding to the target ground measurement point on the three coordinate axes, and connect the three target points in sequence to form a target triangle, so as to obtain the target triangle for each longitudinal sampling point corresponding to each ground measurement point; Determine the first area of the initial triangle and the second area of each target triangle; Determine the target fluid indication factor for each longitudinal sampling point corresponding to each ground measurement point according to the first area and the second area of each target triangle.

[0059] Figure 2 In it, the origin is the intersection point of three dashed lines. F1, F2, and F3 respectively represent the first normalized principal component, the second normalized principal component, and the third normalized principal component. C represents the initial triangle, and C1 represents the target triangle.

[0060] In some embodiments of the present invention, referring to Figure 2 , the three coordinate axes in the three-dimensional non-orthogonal coordinate system are respectively the first coordinate axis, the second coordinate axis, and the third coordinate axis. The first coordinate axis is used to indicate the first normalized principal component, the second coordinate axis is used to indicate the second normalized principal component, and the third coordinate axis is used to indicate the third normalized principal component; Determining the weighted central angle between adjacent two coordinate axes according to the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate includes: Determining the first weighted central angle between the first coordinate axis and the second coordinate axis according to the first variance contribution rate; Determining the second weighted central angle between the second coordinate axis and the third coordinate axis according to the second variance contribution rate; Determining the third weighted central angle between the third coordinate axis and the first coordinate axis according to the third variance contribution rate.

[0061] In some embodiments of the present invention, referring to Figure 2 , the three coordinate axes in the three-dimensional non-orthogonal coordinate system are respectively the first coordinate axis, the second coordinate axis, and the third coordinate axis. The first coordinate axis is used to indicate the first normalized principal component, the second coordinate axis is used to indicate the second normalized principal component, and the third coordinate axis is used to indicate the third normalized principal component; Determining the weighted central angle between adjacent two coordinate axes according to the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate includes: Determining the first weighted central angle between the first coordinate axis and the second coordinate axis according to the second variance contribution rate; Determining the second weighted central angle between the second coordinate axis and the third coordinate axis according to the third variance contribution rate; Determining the third weighted central angle between the third coordinate axis and the first coordinate axis according to the first variance contribution rate.

[0062] The first weighted central angle is Figure 2 the included angle between F1 and F2 in Figure 2 The second weighted central angle is Figure 2 the included angle between F2 and F3 in

[0063] In some embodiments of the present invention, referring to Figure 2 , determining the target fluid indication factor of each longitudinal sampling point corresponding to each ground measurement point according to the first area and the second area of each target triangle includes: Use the ratio of the second area to the first area of each target triangle as the target fluid indicator factor for each longitudinal sampling point corresponding to each surface measurement point.

[0064] Thus, the target fluid indicator factor for each longitudinal sampling point corresponding to each surface measurement point can be obtained.

[0065] In the traditional spider-web diagram evaluation method, with the origin as the center of the circle, the coordinate axes represented by the three principal component data are equally divided, and the angles between any two adjacent coordinate axes are equal, that is, the weighted central angles between two adjacent coordinate axes are equal, that is, the weight coefficients of each principal component data are equal, without considering the importance degree among various evaluation factors. However, in the spider-web diagram evaluation method of the present invention, the principal component data with a higher variance contribution rate accounts for a larger weight coefficient to achieve data fusion, and then the fluid distribution at the reservoir location is comprehensively evaluated by the way of area ratio, and the fluid indicator factor is calculated, and the calculation result is more accurate.

[0066] After obtaining the target fluid indicator factor for each longitudinal sampling point corresponding to each surface measurement point, extract the multiple well logging fluid indicator factors of the multiple longitudinal sampling points corresponding to the well locations in the study area from the multiple target fluid indicator factors, and obtain the fluid well logging interpretation results of the well locations. The fluid well logging interpretation results are known well logging data, including water layers, gas layers and tight layers. Therefore, the numerical range of the fluid indicator factor corresponding to different fluid types can be determined according to the fluid well logging interpretation results and the multiple well logging fluid indicator factors. The different numerical ranges of the fluid indicator factor are the boundary values for fluid identification (such as water layer, gas layer, etc.). For example, the numerical range of the fluid indicator factor for identifying the water layer is A1~A2, and the numerical range of the fluid indicator factor for identifying the gas layer is B1~B2. Thus, the fluid distribution of the deep underground reservoir can be determined according to the numerical range of the fluid indicator factor and all the target fluid indicator factors in the study area.

[0067] For the deep underground reservoir fluid identification system according to an embodiment of the present invention, the fluid distribution in the deep underground is mainly affected by the reservoir space and migration channels. Seismic porosity inversion can effectively detect the distribution of the underground reservoir space, and seismic curvature attributes can effectively predict the development characteristics of fractures, which provide the migration channels for fluids. Since the velocity difference between oil and water in the reservoir is not significant, conventional seismic methods cannot effectively identify the fluid properties in the reservoir. However, since there are significant differences in resistivity parameters between oil and gas and water, a wide-area electromagnetic inversion method can be used to identify the fluid properties. The present invention identifies the fluid types in the deep underground with high precision by fusing the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data. The fusion method improves the traditional principal component analysis method using the cobweb diagram evaluation method, constructs a fluid indicator factor through a comprehensive evaluation model, and transforms the multi-response problem related to fluid prediction into a single-response problem to improve the fluid prediction accuracy. Compared with the improvement of the traditional principal component analysis method by the conventional entropy weight method, which only uses the principal component data to calculate the entropy value and then obtains the weight, the present invention not only uses the principal component data for fusion, but also focuses on the influence of the variance contribution rate on the data body weight, effectively utilizes the multiple information of the data, constructs a comprehensive evaluation model, and improves the accuracy of fluid identification.

[0068] In addition, an embodiment of the present invention also provides a control device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor and the memory can be connected through a bus or other means.

[0069] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include a high-speed random access memory, and can also include non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory can optionally include a memory remotely set relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0070] The non-transitory software programs and instructions required to implement the deep underground reservoir fluid identification method of the above embodiment are stored in the memory, and when executed by the processor, they execute the deep underground reservoir fluid identification method in the above embodiment.

[0071] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0072] In addition, an embodiment of the present invention further provides a computer-readable storage medium storing computer-executable instructions, which are executed by a processor or a controller, for example, executed by the processor in the above embodiment, enabling the processor to execute the deep underground reservoir fluid identification method in the above embodiment.

[0073] Those of ordinary skill in the art can understand that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and their appropriate combinations. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include a computer storage medium (or a non-transitory medium) and a communication medium (or a transitory medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technologies, CD-ROM, digital versatile disc (DVD), or other optical disc storage, magnetic cassette, tape, magnetic disk storage, or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that a communication medium typically includes computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium.

[0074] The embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments. Various changes can be made without departing from the spirit of the present invention within the scope of knowledge possessed by those of ordinary skill in the art.

Claims

1. A method for identifying deep underground reservoir fluids, characterized in that, The method includes: Obtaining longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide - area electromagnetic inversion resistivity sampling point data for each longitudinal sampling point corresponding to each ground measurement point in the study area, where the study area has multiple different ground measurement points, and each ground measurement point corresponds to multiple different longitudinal sampling points; Based on the principal component analysis method, calculating the first principal component data, second principal component data, third principal component data, first variance contribution rate, second variance contribution rate, and third variance contribution rate corresponding to each longitudinal sampling point through the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide - area electromagnetic inversion resistivity sampling point data corresponding to the multiple longitudinal sampling points of each ground measurement point; Based on the spider - web diagram evaluation method, determining the target fluid indication factor for each longitudinal sampling point corresponding to each ground measurement point according to the first principal component data, the second principal component data, the third principal component data, the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate; Extracting multiple logging fluid indication factors for the multiple longitudinal sampling points corresponding to the well locations in the study area from the multiple target fluid indication factors, obtaining the fluid logging interpretation results of the well locations, and determining the numerical range of the fluid indication factors corresponding to different fluid types according to the fluid logging interpretation results and the multiple logging fluid indication factors; Determining the fluid distribution in the deep underground reservoir according to the numerical range of the fluid indication factors and all the target fluid indication factors in the study area.

2. The deep underground reservoir fluid identification method according to claim 1, characterized in that, The step of, based on the spider - web diagram evaluation method, determining the target fluid indication factor for each longitudinal sampling point corresponding to each ground measurement point according to the first principal component data, the second principal component data, the third principal component data, the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate includes: Normalizing the first principal component data, the second principal component data, and the third principal component data for each longitudinal sampling point corresponding to each ground measurement point to obtain the first normalized principal component, the second normalized principal component, and the third normalized principal component; Selecting a three - dimensional non - orthogonal coordinate system, and determining the weighted central angle between adjacent two coordinate axes according to the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate; wherein, the three coordinate axes in the three - dimensional non - orthogonal coordinate system are respectively used to indicate the first normalized principal component, the second normalized principal component, and the third normalized principal component; Taking the origin of the three - dimensional non - orthogonal coordinate system as the starting point, determining three initial points with a distance of 1 from the origin on the three coordinate axes, and successively connecting the three initial points to form an initial triangle, thus forming a spider - web diagram of the three - dimensional non - orthogonal coordinate system; Taking the origin of the three-dimensional non-orthogonal coordinate system as the starting point, determine three target points of the first normalized principal component, the second normalized principal component, and the third normalized principal component of the target longitudinal sampling points on the three coordinate axes at a distance from the origin corresponding to the target ground measurement points, and connect the three target points in sequence to form a target triangle, so as to obtain the target triangle of each longitudinal sampling point corresponding to each ground measurement point; Determine the first area of the initial triangle and the second area of each target triangle; Determine the target fluid indication factor of each longitudinal sampling point corresponding to each ground measurement point according to the first area and the second area of each target triangle.

3. The deep underground reservoir fluid identification method according to claim 2, wherein The three coordinate axes in the three-dimensional non-orthogonal coordinate system are respectively the first coordinate axis, the second coordinate axis, and the third coordinate axis. The first coordinate axis is used to indicate the first normalized principal component, the second coordinate axis is used to indicate the second normalized principal component, and the third coordinate axis is used to indicate the third normalized principal component; The determining of the weighted central angle between adjacent two coordinate axes according to the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate includes: Determine the first weighted central angle between the first coordinate axis and the second coordinate axis according to the first variance contribution rate; Determine the second weighted central angle between the second coordinate axis and the third coordinate axis according to the second variance contribution rate; Determine the third weighted central angle between the third coordinate axis and the first coordinate axis according to the third variance contribution rate.

4. The deep underground reservoir fluid identification method according to claim 2, wherein The three coordinate axes in the three-dimensional non-orthogonal coordinate system are respectively the first coordinate axis, the second coordinate axis, and the third coordinate axis. The first coordinate axis is used to indicate the first normalized principal component, the second coordinate axis is used to indicate the second normalized principal component, and the third coordinate axis is used to indicate the third normalized principal component; The determining of the weighted central angle between adjacent two coordinate axes according to the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate includes: Determine the first weighted central angle between the first coordinate axis and the second coordinate axis according to the second variance contribution rate; Determine the second weighted central angle between the second coordinate axis and the third coordinate axis according to the third variance contribution rate; Determine the third weighted central angle between the third coordinate axis and the first coordinate axis according to the first variance contribution rate.

5. The deep underground reservoir fluid identification method according to claim 2, wherein The determining of the target fluid indication factor of each longitudinal sampling point corresponding to each ground measurement point according to the first area and the second area of each target triangle includes: Taking the ratio of the second area of each target triangle to the first area as the target fluid indication factor of each longitudinal sampling point corresponding to each ground measurement point.

6. The deep underground reservoir fluid identification method according to claim 1, characterized in that The obtaining of the longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data, and the longitudinal wide-area electromagnetic inversion resistivity sampling point data of each longitudinal sampling point corresponding to each ground measurement point in the study area includes: Obtain the original seismic curvature attribute sampling point data, original seismic porosity sampling point data, and original wide-area electromagnetic inversion resistivity sampling point data corresponding to each longitudinal sampling point for each ground measurement point in the study area; Perform spatial calibration processing, data standardization processing, and normalization processing on all the original seismic curvature attribute sampling point data, the original seismic porosity sampling point data, and the original wide-area electromagnetic inversion resistivity sampling point data to obtain the corresponding longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data.

7. The deep underground reservoir fluid identification method according to claim 1, characterized in that Based on the principal component analysis method, calculate the first principal component data, second principal component data, third principal component data, first variance contribution rate, second variance contribution rate, and third variance contribution rate corresponding to each longitudinal sampling point through the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data corresponding to multiple longitudinal sampling points of each ground measurement point, including: Calculate the covariance matrix through the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data corresponding to multiple longitudinal sampling points of each ground measurement point; Determine the first eigenvalue and the corresponding first eigenvector, second eigenvalue and the corresponding second eigenvector, and third eigenvalue and the corresponding third eigenvector according to the covariance matrix, where the first eigenvalue is greater than the second eigenvalue, and the second eigenvalue is greater than the third eigenvalue; Determine the corresponding first principal component data by combining the first eigenvector according to the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data corresponding to each longitudinal sampling point, and determine the corresponding first variance contribution rate according to the proportion of the first eigenvalue in the sum of the first eigenvalue, the second eigenvalue, and the third eigenvalue; Determine the corresponding second principal component data by combining the second eigenvector according to the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data corresponding to each longitudinal sampling point, and determine the corresponding second variance contribution rate according to the proportion of the second eigenvalue in the sum of the first eigenvalue, the second eigenvalue, and the third eigenvalue; Determine the corresponding third principal component data by combining the third eigenvector according to the longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide-area electromagnetic inversion resistivity sampling point data corresponding to each longitudinal sampling point, and determine the corresponding third variance contribution rate according to the proportion of the third eigenvalue in the sum of the first eigenvalue, the second eigenvalue, and the third eigenvalue.

8. A deep underground reservoir fluid identification system, characterized in that, The system includes: A data acquisition unit for acquiring longitudinal seismic curvature attribute sampling point data, longitudinal seismic porosity sampling point data, and longitudinal wide - area electromagnetic inversion resistivity sampling point data of each longitudinal sampling point corresponding to each ground measurement point in the study area. The study area has a plurality of different ground measurement points, and each ground measurement point corresponds to a plurality of different longitudinal sampling points; A principal component analysis calculation unit for calculating, based on the principal component analysis method, the first principal component data, the second principal component data, the third principal component data, the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate corresponding to each longitudinal sampling point through the longitudinal seismic curvature attribute sampling point data, the longitudinal seismic porosity sampling point data, and the longitudinal wide - area electromagnetic inversion resistivity sampling point data corresponding to the plurality of longitudinal sampling points of each ground measurement point; A target fluid indicator factor determination unit for determining the target fluid indicator factor of each longitudinal sampling point corresponding to each ground measurement point based on the cobweb diagram evaluation method according to the first principal component data, the second principal component data, the third principal component data, the first variance contribution rate, the second variance contribution rate, and the third variance contribution rate; A fluid indicator factor numerical range determination unit for extracting a plurality of logging fluid indicator factors of the plurality of longitudinal sampling points corresponding to the well positions in the study area from the plurality of target fluid indicator factors, obtaining the fluid logging interpretation result of the well positions, and determining the fluid indicator factor numerical ranges corresponding to different fluid types according to the fluid logging interpretation result and the plurality of logging fluid indicator factors; A fluid distribution situation determination unit for determining the fluid distribution situation of the deep underground reservoir according to the fluid indicator factor numerical ranges and all the target fluid indicator factors in the study area.

9. A control device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the deep underground reservoir fluid identification method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that, The computer - executable instructions are used to execute the deep underground reservoir fluid identification method according to any one of claims 1 to 7.

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