Comprehensive evaluation method and device for favorable exploration area

Abstract

The invention belongs to the technical field of oil and gas geophysical prospecting, and discloses a comprehensive evaluation method and device for a favorable exploration area, and the method comprises the steps: obtaining plane geological data; carrying out normalization and value domain overturning processing on the plane geological data to obtain an attribute value set; defining each attribute value in the attribute value set as a sample point, inputting the sample point into the cross plot, and determining an initial center point in the cross plot; determining a final center point, and performing sample point classification according to the final center point to obtain a plurality of classified sample point clusters; and dividing the plurality of sample points into a plurality of categories, and assigning each category to the favorable region stratum plane graph in a numerical mode to obtain a favorable region distribution graph. The exploration favorable area is comprehensively evaluated directly according to each attribute parameter of the geological parameters, manual participation is not needed, and the method is suitable for comprehensive evaluation of multiple factors and is not limited by the number of the factors, so that the application range is wider, the influence of human factors is avoided, and the evaluation reliability of the favorable area is higher.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas geophysical exploration, and particularly relates to a comprehensive evaluation method and device for a favorable exploration area. Background Art

[0002] Comprehensive zone selection plays an extremely important role in exploration and development. Currently, there are methods such as comprehensive information superposition method, comprehensive evaluation index method and weight factor method for zone evaluation.

[0003] Among them, the comprehensive information superposition method considers the differences in geological conditions in different regions, selects various geological parameters, and overlaps the favorable areas of multiple geological parameters to delineate the final exploration area. This method can select the optimal exploration prospect in terms of scope, but due to the large number of evaluation indicators, the parameters affect each other, and the favorable strata cannot be evaluated vertically. It is only a qualitative evaluation of the sweet spot, which is not accurate and difficult to select the best. The comprehensive evaluation index method considers the weight of each parameter and adds it up quantitatively, but the weight coefficients are all given artificially, resulting in a large difference in the final evaluation results. The comprehensive weight factor evaluation method selects the main geological parameters (permeability, Young's modulus and other parameters) to construct a function, and quantitatively evaluates the reservoir based on the comprehensive weight factor.

[0004] In summary, the existing zone evaluation methods consider a limited number of factors and require pre-specified weight coefficients, which are strongly influenced by human factors and have many subjective influences on the evaluation results, resulting in low accuracy of automatic classification evaluation of favorable areas. Summary of the invention

[0005] In view of the above problems, the present invention provides a comprehensive evaluation method and device for exploration favorable areas, which adopts the following technical solutions:

[0006] A comprehensive evaluation method for an exploration favorable area comprises the following steps: arranging the geological parameters of the acquired favorable area according to the corresponding horizontal and vertical coordinate values ​​to obtain plane geological data; normalizing and value range flipping the plane geological data to obtain an attribute value set; defining each attribute value in the attribute value set as a sample point input into an intersection diagram, and determining an initial center point in the intersection diagram; determining a final center point according to the shortest distance from the sample point to the initial center point, and classifying the sample points according to the final center point to obtain a plurality of classified sample point clusters; dividing a plurality of sample points into a plurality of categories according to the shortest distance from each sample point to the center point of each classified sample point cluster, and assigning each category to a favorable area stratum plane map in a numerical manner to obtain a favorable area distribution map.

[0007] Furthermore, the plane geological data is normalized and the value range is flipped to obtain a set of attribute values, including the following steps:

[0008] Normalize each type of attribute in the plane geological data, calculate the minimum and maximum values ​​of each type of attribute, and determine the normalized attribute value based on the minimum and maximum values ​​of each type of attribute;

[0009] The normalized attribute values ​​are subjected to value range flipping processing to obtain an attribute value set.

[0010] Further, determining the initial center point in the intersection graph includes the following steps:

[0011] K sample points are randomly selected on the intersection graph as initial center points, where the number of initial center points is the same as the number of categories divided into favorable areas.

[0012] Further, according to the shortest distance from the sample point to the initial center point, the final center point is determined, and the sample points are classified according to the final center point to obtain multiple classified sample point clusters, including the following steps:

[0013] S41, calculating and comparing the distance between the sample point and each initial center point, marking each sample point as a category closest to the center point of the k initial sample point clusters, and obtaining k sample point clusters;

[0014] S42, recalculating the average value of the sample points in each sample point cluster, and taking the average value of the sample points in each sample point cluster as the center point of the new k sample point clusters;

[0015] S43, repeating steps S41 and S42 until the change of the center point of the sample point cluster tends to be stable, obtaining the final center point, marking each sample point as the category closest to the k final center points, and forming k classified sample point clusters.

[0016] Further, each category is assigned numerically to the favorable area stratum plane map to obtain a favorable area distribution map, including the following steps:

[0017] Define the value of each category, assign the value of each category to the point on the favorable area stratigraphic plane according to the corresponding horizontal and vertical coordinate values, use color coding to represent the value of each category, display the color corresponding to the color coding, and obtain the favorable area distribution map.

[0018] Furthermore, the method further comprises the following steps:

[0019] Mark the center point of each classified sample point cluster to obtain the category center map.

[0020] Furthermore, the geological parameters include seismic attribute parameters, inversion result parameters and geological structure parameters.

[0021] Furthermore, the horizontal and vertical coordinate values ​​are determined according to the stratigraphic plan of the favorable area.

[0022] Furthermore, the normalized attribute value is determined according to the minimum and maximum values ​​of each type of attribute, including:

[0023]

[0024] In the formula, represents the minimum value of each attribute, represents the maximum value of each attribute, z n is the value corresponding to each attribute, z′ n is the normalized attribute value, and n represents the total number of geological parameters.

[0025] The present invention also provides a comprehensive evaluation device for an exploration favorable area, comprising:

[0026] A data acquisition module is used to arrange the acquired geological parameters of the favorable area according to the corresponding horizontal and vertical coordinate values ​​to obtain plane geological data;

[0027] The first data processing module is used to perform normalization and value range flipping processing on the plane geological data to obtain an attribute value set;

[0028] A second data processing module is used to define each attribute value in the attribute value set as a sample point and input it into the intersection map, and determine an initial center point in the intersection map;

[0029] A third data processing module is used to determine the final center point according to the shortest distance from the sample point to the initial center point, and classify the sample points according to the final center point to obtain multiple classified sample point clusters;

[0030] The graphics generation module is used to divide multiple sample points into multiple categories according to the shortest distance from each sample point to the center point of each classified sample point cluster, and assign each category to the favorable area stratum plan map in a numerical manner to obtain a favorable area distribution map.

[0031] Furthermore, the second data processing module is specifically used for:

[0032] Normalize each type of attribute in the plane geological data, calculate the minimum and maximum values ​​of each type of attribute, and determine the normalized attribute value based on the minimum and maximum values ​​of each type of attribute;

[0033] The normalized attribute values ​​are subjected to value range flipping processing to obtain an attribute value set.

[0034] Furthermore, the third data processing module is specifically used for:

[0035] S41, calculating and comparing the distance between the sample point and each initial center point, marking each sample point as a category closest to the center point of the k initial sample point clusters, and obtaining k sample point clusters;

[0036] S42, recalculating the average value of the sample points in each sample point cluster, and taking the average value of the sample points in each sample point cluster as the center point of the new k sample point clusters;

[0037] S43, repeating steps S41 and S42 until the change of the center point of the sample point cluster tends to be stable, obtaining the final center point, marking each sample point as the category closest to the k final center points, and forming k classified sample point clusters.

[0038] The beneficial effects of the present invention are as follows: the present invention directly conducts a comprehensive evaluation of the favorable exploration area according to various attribute parameters of the geological parameters, without the need for human intervention, and is suitable for the comprehensive evaluation of multiple factors without being limited by the number of factors. Therefore, it has a wider scope of application, avoids the influence of human factors, and has a higher reliability in the evaluation of favorable areas.

[0039] Other features and advantages of the present invention will be described in the following description, and partly become obvious from the description, or be understood by implementing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 A schematic flow chart of a comprehensive evaluation method for a favorable exploration area according to an embodiment of the present invention is shown;

[0042] Figure 2 A schematic diagram showing marking the center point of a sample point cluster according to an embodiment of the present invention is shown;

[0043] Figure 3 A planar clustering feature analysis diagram of a sample point cluster after classification according to an embodiment of the present invention is shown;

[0044] Figure 4 A diagram showing automatic classification of favorable areas by a certain work area according to an embodiment of the present invention is shown;

[0045] Figure 5 A schematic structural diagram of a comprehensive evaluation device for exploring favorable areas according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] It should be noted that the terms "first", "second", etc. in this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so as to describe the embodiments of the present application described here.

[0048] The present invention provides a comprehensive evaluation method and device for exploration favorable areas, which starts from seismic attributes, various geological parameters predicted by inversion, and directly realizes automatic classification evaluation of favorable areas through data characteristics. It can be applied to the evaluation of multiple geological elements, and the evaluation results are not affected by human factors and are reliable.

[0049] like Figure 1 As shown, a comprehensive evaluation method for exploration favorable areas includes the following steps:

[0050] S1. Arrange the acquired geological parameters of the favorable area according to the corresponding horizontal and vertical coordinate values ​​to obtain plane geological data, wherein the horizontal and vertical coordinate values ​​are determined according to the stratigraphic plane map of the favorable area.

[0051] For example, geological parameters include seismic attribute parameters, inversion result parameters, and geological structure parameters. According to the obtained seismic attribute parameters, inversion result parameters, geological structure parameters and other geological elements, they are defined in the xyz format, where x and y correspond to the horizontal and vertical coordinate values ​​on the favorable area stratigraphic plane map, respectively, and z is the attribute of each type of geological parameter. The attributes of each type of geological parameter are represented by z1, z2, ... z n Indicates that plane geological data is obtained, where n represents the total number of geological parameters.

[0052] In this step, on the basis of sorting the various evaluation attributes of geological parameters according to the plane coordinates x and y, each point is automatically classified according to the distance from the point to the center, so as to achieve the goal of automatic classification and evaluation of favorable areas.

[0053] S2. Normalizing and reversing the plane geological data to obtain a set of attribute values ​​includes the following steps:

[0054] S21. Normalize each attribute in the plane geological data and calculate the minimum value of each attribute and maximum value The normalized attribute value is determined according to the minimum and maximum values ​​of each attribute type, where the normalization calculation formula is as follows:

[0055]

[0056] In the formula, z n is the value corresponding to each attribute, z′ n is the normalized attribute value.

[0057] S22, performing value range flipping processing on the normalized attribute value to obtain an attribute value set, as follows:

[0058] z″ n =1-z′ n

[0059] In the formula, z″ n The attribute value after the value range is flipped.

[0060] In this step, in order to facilitate better subsequent classification, a unified high-value index is used to evaluate the attributes, that is, the higher the attribute value, the more favorable the contribution to the favorable area. For example, if the structural attribute is favorable with a low value, after normalizing the structure, the structural information needs to be flipped in the value range, so that all evaluation indicators are high values ​​representing favorable.

[0061] S3, defining each attribute value in the attribute value set as a sample point and inputting it into the intersection graph, and determining an initial center point in the intersection graph, including the following steps:

[0062] K sample points are randomly selected on the intersection map as initial center points, where the number of initial center points is the same as the number of categories divided into favorable areas. For example, if it is divided into 3 categories, 3 sample points are randomly selected on the intersection map as initial center points.

[0063] S4, determining the final center point according to the shortest distance from the sample point to the initial center point, and classifying the sample points according to the final center point to obtain multiple classified sample point clusters, including the following steps:

[0064] S41, calculating and comparing the distance between the sample point and each initial center point, marking each sample point as a category closest to the center point of the k initial sample point clusters, and obtaining k sample point clusters.

[0065] S42, recalculating the average value of the sample points in each sample point cluster, and taking the average value of the sample points in each sample point cluster as the center point of the new k sample point clusters.

[0066] S43, repeating steps S41 and S42 until the change of the center point of the sample point cluster tends to be stable, obtaining the final center point, marking each sample point as the category closest to the k final center points, and forming k classified sample point clusters.

[0067] S5. Mark the center point of each classified sample point cluster to obtain a category center map.

[0068] For example, Figure 2 As shown in FIG. 1 , the favorable area classification is divided into three categories, and three sample point clusters are obtained through steps S3 and S4. In the intersection diagram, five-pointed stars are used to mark the center points corresponding to categories 1, 2, and 3, respectively, as shown in FIG. Figure 3 As shown in the cluster feature analysis diagram, the eigenvalues ​​corresponding to each category can be clearly seen.

[0069] S6. According to the shortest distance from each sample point to the center point of each classified sample point cluster, the multiple sample points are divided into multiple categories, and each category is numerically assigned to the favorable area stratigraphic plan map to obtain a favorable area distribution map.

[0070] In this step, the shortest distance from each sample point to the center of each classified sample point cluster is calculated, and the shortest distance is selected to determine the specified category. The category is assigned to each point on the stratigraphic plan in a numerical way (corresponding to the horizontal and vertical table values ​​x and the vertical coordinate value y), and each category is color-coded for plan display.

[0071] Among them, assigning each category to the favorable area stratigraphic plan map to obtain the favorable area distribution map includes the following steps: defining the numerical value of each category, assigning the numerical value of each category to the point on the favorable area stratigraphic plan map according to the corresponding horizontal and vertical coordinate values, using color coding to represent the numerical value of each category, displaying the color corresponding to the color coding, and obtaining the favorable area distribution map.

[0072] The actual geological parameters show that the present invention has good operability and predictability. Figure 4 As shown, Figure 4 In order to apply the present invention in a certain work area, the colors in the figure represent the classification results, and the color values ​​are represented by color codes, so that the boundary characteristics of each classification area can be clearly defined.

[0073] Based on the above comprehensive evaluation method of favorable exploration areas, such as Figure 5 As shown, the present invention also provides a comprehensive evaluation device for exploration favorable areas, including a data acquisition module, a first data processing module, a second data processing module, a third data processing module and a graphics generation module.

[0074] Among them, the data acquisition module is used to arrange the geological parameters of the acquired favorable area according to the corresponding horizontal and vertical coordinate values ​​to obtain plane geological data; the first data processing module is used to normalize and flip the value range of the plane geological data to obtain an attribute value set; the second data processing module is used to define each attribute value in the attribute value set as a sample point input into the intersection map, and determine the initial center point in the intersection map; the third data processing module is used to determine the final center point according to the shortest distance from the sample point to the initial center point, and classify the sample points according to the final center point to obtain a plurality of classified sample point clusters; the graphics generation module is used to divide the plurality of sample points into a plurality of categories according to the shortest distance from each sample point to the center point of each classified sample point cluster, and assign each category to the favorable area stratigraphic plane map in a numerical manner to obtain a favorable area distribution map.

[0075] The present invention normalizes the attributes of geological parameters and performs inverse processing of the value range, takes this as input, and calculates the center point by iterative updating on the basis of randomly arranging n center points (n is the same as the number of classifications) until the position of the center point does not change, and classifies each point according to the shortest distance to the center, thereby realizing automatic classification of each attribute point.

[0076] The present invention directly starts from the attributes of multiple geological parameters, does not require human intervention, and is suitable for comprehensive evaluation of multiple factors without being limited by the number of factors. Therefore, it has a wider scope of application, avoids the influence of human factors, and has higher reliability in favorable area evaluation.

[0077] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A comprehensive evaluation method for exploration favorable areas, characterized in that: The following steps are involved: Arrange the acquired geological parameters of the favorable area according to the corresponding horizontal and vertical coordinate values ​​to obtain plane geological data; Normalize and flip the range of the plane geological data to obtain a set of attribute values; Each attribute value in the attribute value set is defined as a sample point and input into the intersection graph, and an initial center point is determined in the intersection graph; According to the shortest distance from the sample point to the initial center point, the final center point is determined, and the sample points are classified according to the final center point to obtain multiple classified sample point clusters; According to the shortest distance from each sample point to the center point of each classified sample point cluster, multiple sample points are divided into multiple categories, and each category is numerically assigned to the favorable area stratigraphic plane map to obtain the favorable area distribution map.

2. The comprehensive evaluation method for exploration favorable areas according to claim 1, characterized in that: Normalization and range flipping of plane geological data are performed to obtain a set of attribute values, including the following steps: Normalize each type of attribute in the plane geological data, calculate the minimum and maximum values ​​of each type of attribute, and determine the normalized attribute value based on the minimum and maximum values ​​of each type of attribute; The normalized attribute values ​​are subjected to value range flipping processing to obtain an attribute value set.

3. The comprehensive evaluation method for exploration favorable areas according to claim 1, characterized in that: Determining the initial center point in the intersection diagram includes the following steps: K sample points are randomly selected on the intersection graph as initial center points, where the number of initial center points is the same as the number of categories divided into favorable areas.

4. The comprehensive evaluation method for exploration favorable areas according to claim 3 is characterized in that: According to the shortest distance from the sample point to the initial center point, the final center point is determined, and the sample points are classified according to the final center point to obtain multiple classified sample point clusters, including the following steps: S41, calculating and comparing the distance between the sample point and each initial center point, marking each sample point as a category closest to the center point of the k initial sample point clusters, and obtaining k sample point clusters; S42, recalculating the average value of the sample points in each sample point cluster, and taking the average value of the sample points in each sample point cluster as the center point of the new k sample point clusters; S43, repeating steps S41 and S42 until the change of the center point of the sample point cluster tends to be stable, obtaining the final center point, marking each sample point as the category closest to the k final center points, and forming k classified sample point clusters.

5. The comprehensive evaluation method for exploration favorable areas according to claim 1 is characterized in that: Assigning each category to the favorable area stratigraphic plane map in a numerical manner to obtain a favorable area distribution map includes the following steps: Define the value of each category, assign the value of each category to the point on the favorable area stratigraphic plane according to the corresponding horizontal and vertical coordinate values, use color coding to represent the value of each category, display the color corresponding to the color coding, and obtain the favorable area distribution map.

6. The comprehensive evaluation method for exploration favorable areas according to any one of claims 1 to 5, characterized in that: The following steps are also included: Mark the center point of each classified sample point cluster to obtain the category center map.

7. The comprehensive evaluation method for favorable exploration areas according to any one of claims 1 to 5, characterized in that: Geological parameters include seismic attribute parameters, inversion result parameters and geological structure parameters.

8. The comprehensive evaluation method for exploration favorable areas according to claim 1 is characterized in that: The horizontal and vertical coordinate values ​​are determined based on the stratigraphic plan of the favorable area.

9. The comprehensive evaluation method for exploration favorable areas according to claim 2, characterized in that: The normalized attribute values ​​are determined based on the minimum and maximum values ​​of each attribute, including: In the formula, represents the minimum value of each attribute, represents the maximum value of each attribute, z n is the value corresponding to each attribute, z′ n is the normalized attribute value, and n represents the total number of geological parameters.

10. A comprehensive evaluation device for exploration favorable areas, characterized in that: include: A data acquisition module is used to arrange the acquired geological parameters of the favorable area according to the corresponding horizontal and vertical coordinate values ​​to obtain plane geological data; The first data processing module is used to perform normalization and value range flipping processing on the plane geological data to obtain an attribute value set; A second data processing module is used to define each attribute value in the attribute value set as a sample point and input it into the intersection map, and determine an initial center point in the intersection map; A third data processing module is used to determine the final center point according to the shortest distance from the sample point to the initial center point, and classify the sample points according to the final center point to obtain multiple classified sample point clusters; The graphics generation module is used to divide multiple sample points into multiple categories according to the shortest distance from each sample point to the center point of each classified sample point cluster, and assign each category to the favorable area stratum plan map in a numerical manner to obtain a favorable area distribution map.

11. The comprehensive evaluation device for exploration favorable areas according to claim 10, characterized in that: The second data processing module is specifically used for: Normalize each type of attribute in the plane geological data, calculate the minimum and maximum values ​​of each type of attribute, and determine the normalized attribute value based on the minimum and maximum values ​​of each type of attribute; The normalized attribute values ​​are subjected to value range flipping processing to obtain an attribute value set.

12. The comprehensive evaluation device for exploration favorable areas according to claim 10, characterized in that: The third data processing module is specifically used for: S41, calculating and comparing the distance between the sample point and each initial center point, marking each sample point as a category closest to the center point of the k initial sample point clusters, and obtaining k sample point clusters; S42, recalculating the average value of the sample points in each sample point cluster, and taking the average value of the sample points in each sample point cluster as the center point of the new k sample point clusters; S43, repeating steps S41 and S42 until the change of the center point of the sample point cluster tends to be stable, obtaining the final center point, marking each sample point as the category closest to the k final center points, and forming k classified sample point clusters.

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