Construction-lithology oil and gas reservoir favorable area evaluation method and evaluation system

Abstract

The application discloses a method and system for evaluating a favorable area of a structural-lithologic oil and gas reservoir, and the method comprises the following steps: selecting a plurality of favorable reservoir-controlling parameters, wherein the plurality of favorable reservoir-controlling parameters at least include a trap evaluation parameter and a source rock evaluation parameter; dividing a research area into a plurality of small areas, and obtaining original data of each favorable reservoir-controlling parameter of each small area; determining a trap condition of each small area, and performing data non-dimensionalization processing on the trap evaluation parameter based on the trap condition; performing data non-dimensionalization processing on the source rock evaluation parameter of each small area; obtaining a weight coefficient of each favorable reservoir-controlling parameter, and assigning the weight coefficient to the non-dimensional data of each favorable reservoir-controlling parameter to obtain weighted data; superimposing and calculating the weighted data of the plurality of favorable reservoir-controlling parameters to obtain a favorable property value of each small area; and evaluating and judging the favorable area of the oil and gas reservoir of the research area according to the favorable property value.

Description

Technical Field

[0001] This invention relates to the field of oil and gas geological exploration technology, specifically to a method and system for evaluating favorable areas of structural-lithological oil and gas reservoirs. Background Technology

[0002] There are many existing technical methods for selecting favorable blocks both domestically and internationally, which can be broadly categorized into two types: quantitative analysis and qualitative analysis. Qualitative analysis mainly involves superimposing multiple key reservoir-controlling factors (including geological conditions such as reservoir thickness, source rock thickness, porosity, permeability, burial depth, and organic carbon content) to identify favorable blocks. Quantitative analysis involves performing mathematical standardization analysis on reservoir-controlling factors and establishing mathematical analysis models based on these factors to select different favorable areas.

[0003] However, quantitative analysis methods are difficult to apply in the selection of favorable blocks, such as in cases where the structure is relatively high but not a closed area with rich geological resources, or where it is impossible to determine whether a trap is formed due to factors such as the boundaries of the work area. Furthermore, the structural values ​​cannot be directly processed according to the high and low points of the work area using standardized formulas, making it difficult to intuitively select exploration or development areas, and the accuracy of favorable area evaluation is low. Summary of the Invention

[0004] The main objective of this invention is to propose a method and system for evaluating favorable areas of structural-lithologic oil and gas reservoirs, aiming to solve the aforementioned problems.

[0005] To achieve the above objectives, this invention proposes a method for evaluating favorable areas of structural-lithologic oil and gas reservoirs, which includes the following steps:

[0006] Multiple favorable hydrocarbon-controlling parameters are selected, and these multiple favorable hydrocarbon-controlling parameters include at least trap evaluation parameters and source rock evaluation parameters;

[0007] The study area was divided into multiple smaller regions, and the raw data of each favorable storage control parameter in each of the smaller regions were obtained.

[0008] The trapping conditions of each small region are determined, and the trapping evaluation parameters of each small region are processed by data dedimensionalization based on the trapping conditions of each small region.

[0009] The evaluation parameters of source rocks in each of the aforementioned small regions were subjected to dimensionless processing.

[0010] Obtain the weight coefficients of each of the advantageous storage control parameters, and assign corresponding weight coefficients to the dimensionless data of each of the advantageous storage control parameters to obtain the weighted data of each of the advantageous storage control parameters;

[0011] The weighted data of multiple favorable storage control parameters are superimposed to calculate the favorable trait values ​​of each small region.

[0012] Based on the favorable characteristics of each sub-region, the favorable oil and gas reservoir areas of the study area are evaluated and judged.

[0013] Furthermore, the trap evaluation parameters include current structural parameters, paleostructural parameters, and reservoir thickness parameters;

[0014] The source rock evaluation parameters include the source rock thickness parameter.

[0015] Furthermore, the step of determining the closure conditions of each of the small regions and, based on the closure conditions of each of the small regions, performing data dimensionless processing on the closure evaluation parameters of each of the small regions specifically includes:

[0016] The spatial moving average value of each trap evaluation parameter of each of the aforementioned small regions is calculated according to the spatial moving average formula.

[0017] The trapping conditions of each small region are determined based on the difference between the original data and the spatial moving average of each trapping evaluation parameter.

[0018] For small regions with poor closure, the closure evaluation parameters of the small region are processed by dedimensionalization according to the first assignment formula. For small regions with good closure, the closure evaluation parameters of the small region are processed by dedimensionalization according to the second assignment formula.

[0019] Furthermore, if the difference between the original data of the trapping evaluation parameter and the spatial moving average is less than 0, then the trapping of the small area is determined to be poor.

[0020] If the difference between the original data of the trapping evaluation parameter and the spatial moving average is greater than or equal to 0, then the trapping property of the small area is determined to be good.

[0021] Further, after the step of determining the trapping conditions of each small region based on the difference between the original data and the spatial moving average of each trapping evaluation parameter, and before the step of performing data dedimensionalization processing on the trapping evaluation parameters of the small region with poor trapping properties according to the first assignment formula, and on the step of performing data dedimensionalization processing on the trapping evaluation parameters of the small region with good trapping properties according to the second assignment formula, the method further includes:

[0022] The original data of each trap evaluation parameter were subjected to a shift-standard deviation transformation.

[0023] Furthermore, the first assignment formula is:

[0024]

[0025] The second assignment formula is:

[0026]

[0027] In the formula, k is the evaluation parameter for each trap;

[0028] X i ′ k The data are after shift-standard deviation transformation of the evaluation parameters of each trap in each of the aforementioned small regions, in meters;

[0029] max{X i ′ k} represents the maximum value among the series of trap evaluation parameters;

[0030] min{X i ′ k} represents the minimum value among the series of trap evaluation parameters.

[0031] X i ′ k ′ represents the dimensionless data of the evaluation parameters of each trap in each of the aforementioned small regions.

[0032] Furthermore, the spatial moving average formula is as follows:

[0033]

[0034] In the formula, N is the number of points contained in the side length of the small square selected with each of the small regions as the center;

[0035] X and Y are the horizontal and vertical coordinates of the study area;

[0036] x and y are the horizontal and vertical coordinates of each of the small regions;

[0037] Z (X,Y) The constructed value of the study area with x-coordinate and y-coordinate, in meters;

[0038] The value is the average spatial movement of each of the aforementioned small regions, with x as the horizontal coordinate and y as the vertical coordinate, in meters.

[0039] Furthermore, the step of performing dimensionless data processing on the source rock evaluation parameters of each of the aforementioned small regions specifically includes:

[0040] The raw data of the source rock thickness parameters of each of the aforementioned small regions were standardized to obtain standardized data.

[0041] According to the translation-standard deviation transformation formula, the standardized data are transformed by data translation-standard deviation to obtain the transformed data of the source rock thickness parameter;

[0042] Based on the translation-range transformation formula, the transformation data are subjected to translation-range transformation to obtain dimensionless data of the source rock thickness parameters.

[0043] This invention also provides a system for evaluating favorable areas of structural-lithologic oil and gas reservoirs, the system comprising:

[0044] A favorable reservoir control parameter selection module is used to select multiple favorable reservoir control parameters, wherein the multiple favorable reservoir control parameters include at least trap evaluation parameters and source rock evaluation parameters;

[0045] The region division module is used to divide the research area into multiple smaller regions and obtain the original data of each advantageous storage control parameter in each of the smaller regions;

[0046] The trapping evaluation parameter data processing module includes a trapping condition determination unit and a data dimensionless processing unit. The trapping condition determination unit is used to determine the trapping conditions of each of the small regions, and the data dimensionless processing unit is used to perform data dimensionless processing on the trapping evaluation parameters of each of the small regions based on the results output by the trapping condition determination unit.

[0047] The hydrocarbon source rock evaluation parameter data processing module is used to perform dimensionless processing on the hydrocarbon source rock evaluation parameters.

[0048] The weighting module is used to obtain the weight coefficients of each of the favorable reservoir control parameters, and to assign corresponding weight coefficients to the dimensionless data output by the trap evaluation parameter data processing module and the dimensionless data output by the source rock evaluation parameter data processing module, so as to obtain the weighted data of multiple favorable reservoir control parameters.

[0049] A favorable trait value determination module is used to superimpose weighted data from multiple favorable storage control parameters output by the weighting module to obtain favorable trait values ​​for each of the small regions; and,

[0050] The evaluation module is used to determine the favorable trait values ​​of each small region output by the module based on the favorable trait values, and to evaluate and judge the favorable oil and gas reservoir areas of the study area.

[0051] The present invention provides a method for evaluating favorable areas of structural-lithologic oil and gas reservoirs. This method integrates multiple geological parameters with trap conditions to form an evaluation parameter, namely a favorable characteristic value. Based on the evaluation parameter, favorable areas can be quickly and accurately distinguished in the target stratigraphic interval of structural-lithologic oil and gas reservoirs. This solves the problem that the previous division of favorable areas was greatly affected by subjective human factors, and further improves the accuracy and convenience of favorable area selection, so as to more intuitively select exploration or development areas in the evaluation of favorable areas. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0053] Figure 1 A flowchart of an embodiment of the method for evaluating favorable areas of structural-lithological oil and gas reservoirs provided by the present invention;

[0054] Figure 2 for Figure 1 Flowchart of step S300;

[0055] Figure 3 for Figure 1 Flowchart of step S400;

[0056] Figure 4 This is a current tectonic plan view of the Chang 81 member of the Mesozoic Yanchang Formation in a certain area in the south-central part of the Tianhuan Depression in the Ordos Basin.

[0057] Figure 5 A paleotectonic plan view of the Chang 81 segment of the Mesozoic Yanchang Formation in a region in the south-central part of the Tianhuan Depression in the Ordos Basin;

[0058] Figure 6 A planar distribution map of the thickness of source rocks in the Chang 7 Member of the Mesozoic Yanchang Formation in a certain area in the central and southern part of the Tianhuan Depression in the Ordos Basin;

[0059] Figure 7 Plan view of the distribution of the Chang 81 member sand body of the Mesozoic Yanchang Formation in a certain area in the south-central part of the Tianhuan Depression in the Ordos Basin;

[0060] Figure 8 A stereoscopic diagram showing the present structure and the mean present structure of the Chang 81 member of the Mesozoic Yanchang Formation in a region in the south-central part of the Tianhuan Depression in the Ordos Basin;

[0061] Figure 9A stereoscopic image of the paleotectonic structure and paleotectonic mean of the Chang 81 segment of the Mesozoic Yanchang Formation in a region in the south-central part of the Tianhuan Depression in the Ordos Basin.

[0062] Figure 10 A stereoscopic image showing the reservoir thickness and mean reservoir thickness of the Chang 81 member of the Mesozoic Yanchang Formation in a region in the south-central part of the Tianhuan Depression in the Ordos Basin.

[0063] Figure 11 Map showing the distribution of favorable areas for the Chang 81 member of the Mesozoic Yanchang Formation in a region in the south-central part of the Tianhuan Depression in the Ordos Basin;

[0064] Figure 12 This is a schematic diagram of the structure of the structural-lithologic oil and gas reservoir favorable area evaluation system provided by the present invention.

[0065] Explanation of icon numbers:

[0066]

[0067]

[0068] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0070] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0071] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0072] There are many existing technical methods for selecting favorable blocks both domestically and internationally, which can be broadly categorized into two types: quantitative analysis and qualitative analysis. Qualitative analysis mainly involves superimposing multiple key reservoir-controlling factors (including geological conditions such as reservoir thickness, source rock thickness, porosity, permeability, burial depth, and organic carbon content) to identify favorable blocks. Quantitative analysis involves performing mathematical standardization analysis on reservoir-controlling factors and establishing mathematical analysis models based on these factors to select different favorable areas.

[0073] However, quantitative analysis methods are difficult to apply in the selection of favorable blocks, such as in cases where the structure is relatively high but not a closed area with rich geological resources, or where it is impossible to determine whether a trap is formed due to factors such as the boundaries of the work area. Furthermore, the structural values ​​cannot be directly processed according to the high and low points of the work area using standardized formulas, making it difficult to intuitively select exploration or development areas, and the accuracy of favorable area evaluation is low.

[0074] In view of this, the present invention provides a method and system for evaluating favorable areas of structural-lithologic oil and gas reservoirs. Figures 1 to 3 This invention provides an example of the method for evaluating favorable areas of tectonic-lithologic oil and gas reservoirs. The following example uses the Chang 81 member of the Mesozoic Yanchang Formation in the southern part of the Tianhuan Depression in the Ordos Basin as a specific example to illustrate the application of the method for evaluating favorable areas of tectonic-lithologic oil and gas reservoirs.

[0075] Please see Figure 1 The method for evaluating favorable areas of structural-lithological oil and gas reservoirs includes the following steps:

[0076] Step S100: Select multiple favorable reservoir control parameters, wherein the multiple favorable reservoir control parameters include at least trap evaluation parameters and source rock evaluation parameters.

[0077] In a specific embodiment, based on previous research findings, the conditions of the surrounding work area, and exploration observations of the Chang 81 member of the Mesozoic Yanchang Formation in a certain area in the southern part of the Tianhuan Depression in the Ordos Basin, the Chang 81 member was confirmed as a typical tight reservoir. Furthermore, project interpretation determined that the Chang 81 member in this area is a typical tectonic-lithologic reservoir. Therefore, the trap evaluation parameters include current tectonic parameters, paleotectonic parameters, and reservoir thickness parameters; the source rock evaluation parameters include source rock thickness parameters. More specifically, the current tectonic structure, paleotectonic structure during the key hydrocarbon generation period, the distribution of source rocks in the Chang 7 member, and sand body thickness are considered as favorable factors for the reservoir.

[0078] Step S200: Divide the study area into multiple smaller regions and obtain the original data of each advantageous storage control parameter for each of the smaller regions.

[0079] In a specific embodiment, a present-day tectonic plan of the study area (i.e., the Chang 81 segment of the Mesozoic Yanchang Formation in a certain area in the southern part of the Tianhuan Depression in the Ordos Basin) is drawn (e.g., Figure 4 (as shown), paleotectonic plan view of the key hydrocarbon generation period (as shown) Figure 5 As shown), a planar diagram of sand body thickness (as shown). Figure 7 (as shown) and a plan view of the distribution of source rocks in section 7 (as shown) Figure 6 As shown, the coordinate information and corresponding data of the study area are exported in a scatter plot. Then, based on these coordinates, the key paleotectonic structures of the corresponding hydrocarbon generation period and the thickness data of the Chang 7 hydrocarbon source rock are exported and matched one by one to each small area, thereby obtaining the original data of the favorable hydrocarbon-controlling parameters (current tectonic parameters, paleotectonic parameters, reservoir thickness parameters, and hydrocarbon source rock thickness parameters) of each small area.

[0080] Step S300: Determine the closure conditions of each small region, and perform data dedimensionalization processing on the closure evaluation parameters of each small region based on the closure conditions of each small region.

[0081] Further, please refer to Figure 2 Step S300 specifically includes:

[0082] Step S310: Calculate the spatial moving average of each trap evaluation parameter of each small region according to the spatial moving average formula.

[0083] In this step, considering the trap conditions, lithological traps, modern structural traps, and ancient structural traps are all conducive to oil and gas storage. At the same time, non-trap areas also have oil and gas storage. Therefore, the spatial moving average values ​​of modern structures, ancient structures, and reservoir structures are calculated respectively, so that the resulting structural morphologies are approximately planar. The geological and physical significance of this approach is to obtain the average value of the structures in the specific small area.

[0084] Specifically, the spatial moving average formula is as follows:

[0085]

[0086] In equation (1), N is the number of points contained in the side length of the small square selected with each of the small regions as the center;

[0087] X and Y are the horizontal and vertical coordinates of the study area;

[0088] x and y are the horizontal and vertical coordinates of each of the small regions;

[0089] Z (X,Y) The constructed value of the study area with x-coordinate and y-coordinate, in meters;

[0090] The value is the average spatial movement of each of the aforementioned small regions, with x as the horizontal coordinate and y as the vertical coordinate, in meters.

[0091] The specific algorithm for the spatial moving average can be understood as follows: The planar map of the trap evaluation parameters of the study area (including the current tectonic planar map, the paleotectonic planar map of the key hydrocarbon generation period, and the sand body thickness planar map) is scattered. Taking each small region as the center, N / 2 points are selected in the east, south, west, and north directions to form a small square. The average value of these points (that is, the average value of all points in the square area formed with that small region as the center) is calculated, which is the spatial moving average. Figures 8 to 10 The figure shown is a 3D diagram of the evaluation parameters of each trap and their mean values.

[0092] Step S320: Determine the trapping conditions of each small region based on the difference between the original data and the spatial moving average of each trapping evaluation parameter.

[0093] Specifically, if the difference between the original data and the spatial moving average of the trap evaluation parameter is less than 0, i.e. ΔX ik <0 (i is a small region within the study area, k is one of multiple trap evaluation parameters, and ik represents a trap evaluation parameter for a small region), then the trapping of the small region is determined to be poor, that is, the small region is a low-amplitude structure; if the difference between the original data of the trap evaluation parameter and the spatial moving average is greater than or equal to 0, that is, ΔX ik If the value is ≥0, then the small region is considered to have good closure.

[0094] In other words, areas with a difference greater than 0 are structural traps. Although they do not only contain reservoirs, they are definitely more favorable than non-trap areas (i.e., areas with a difference less than 0). The same logic applies to lithological traps.

[0095] Step S340: For small regions with poor closure, the closure evaluation parameters of the small region are processed by dedimensionalization according to the first assignment formula. For small regions with good closure, the closure evaluation parameters of the small region are processed by dedimensionalization according to the second assignment formula.

[0096] Furthermore, in order to reduce data variability and improve data clustering patterns, the steps following step S320 and before step S340 include:

[0097] Step S330: Perform a shift-standard deviation transformation on the original data of each of the closed-loop evaluation parameters.

[0098] Specifically, the translation-standard deviation transformation formula is as follows:

[0099]

[0100] In equation (2), n is the number of the small regions;

[0101] k represents the closed-loop evaluation parameter for each of the aforementioned parameters;

[0102] X ik The index for the closed-loop evaluation parameter k at location i in the small region;

[0103] X i ′ k The values ​​of each closed-loop evaluation parameter after translation-standard deviation transformation are expressed in meters.

[0104] Specifically, the first assignment formula is:

[0105]

[0106] The second assignment formula is:

[0107]

[0108] In equations (3) and (4), k is the evaluation parameter for each trap;

[0109] X i ′ k The data are after shift-standard deviation transformation of the evaluation parameters of each trap in each of the aforementioned small regions, in meters;

[0110] max{X i ′ k} represents the maximum value among the series of trap evaluation parameters;

[0111] min{X i ′ k} represents the minimum value among the series of trap evaluation parameters.

[0112] X i ′ k ′ represents the dimensionless data of the evaluation parameters of each trap in each of the aforementioned small regions.

[0113] Thus, for small regions with poor trapping properties, the values ​​of each trapping evaluation parameter are defined in the interval [0, 0.5); for small regions with good trapping properties, the values ​​of each trapping evaluation parameter are defined in the interval [0.5, 1].

[0114] It should be noted that the defined intervals [0,0.5) and [0.5,1] are empirically derived, and can also be set to the defined intervals [0,0.4) and [0.4,1].

[0115] Step S400: Perform dimensionless processing on the hydrocarbon source rock evaluation parameters of each of the aforementioned small regions.

[0116] Further, please refer to Figure 3 Step S400 specifically includes:

[0117] Step S410: Perform data standardization processing on the original data of the source rock thickness parameters of each small region to obtain standardized data.

[0118] In this step, the data standardization process is calculated according to the following formula:

[0119]

[0120] In equation (5), x i The raw data for the thickness parameters of the source rock;

[0121] x min It is the minimum value in the series of source rock thickness parameters;

[0122] x max This is the maximum value in the series of source rock thickness parameters;

[0123] x i ′ represents standardized data for the thickness parameters of the source rock.

[0124] Step S420: According to the translation-standard deviation transformation formula, perform data translation-standard deviation transformation on each of the standardized data to obtain the transformed data of the source rock thickness parameter.

[0125] In this step, the translation-standard deviation transformation formula is:

[0126]

[0127] In equation (6), n is the number of the small regions;

[0128] k is the thickness parameter of each of the aforementioned source rocks;

[0129] X ik The parameter k is an index of the source rock thickness at location i in a small region;

[0130] X i ′ k The transformed data for the thickness parameters of each source rock are given in meters.

[0131] Step S430: According to the translation-range transformation formula, perform translation-range transformation on each of the transformed data to obtain dimensionless data of the source rock thickness parameters.

[0132] In this step, the translation-range transformation formula is:

[0133]

[0134] In equation (7),

[0135] X i ′ k The transformed data for the thickness parameters of each source rock are given, in meters.

[0136] max{X i ′ k} represents the maximum value in the series of source rock thickness parameters;

[0137] min{X i ′ k} represents the minimum value in the series of source rock thickness parameters;

[0138] X i ′ k ′ represents the dimensionless data for the thickness parameters of each of the aforementioned source rocks.

[0139] Thus, the values ​​of the thickness parameters of each source rock are defined in the range [0,1].

[0140] It should be noted that, in this invention, the order of operations of steps S300 and S400 is not important.

[0141] Step S500: Obtain the weight coefficients of each of the advantageous storage control parameters, and assign corresponding weight coefficients to the dimensionless data of each of the advantageous storage control parameters to obtain the weighted data of each of the advantageous storage control parameters.

[0142] In this step, four scatter plots are established using the well oil production and the current structure at the well location, the paleostructure during the key hydrocarbon generation period, the sandstone thickness, and the source rock thickness, respectively. The importance scale of the hydrocarbon-controlling factors is determined based on the scatter plot distribution characteristics, and the weight coefficients of each favorable hydrocarbon-controlling parameter are obtained based on the AHP hierarchical analysis method.

[0143] In a specific embodiment of the present invention, the index coefficients are shown in Table 1 below:

[0144] Table 1

[0145] index Current Structure ancient structures reservoir thickness Source rock thickness Current Structure 1 1.515 4 1.176 ancient structures 0.66 1 2.5 0.8 reservoir thickness 0.25 0.4 1 0.66 Source rock thickness 0.85 1.25 1.5 1

[0146] The results of the calculation and analysis are shown in Table 2 below:

[0147] Table 2

[0148]

[0149] In other words, in a specific embodiment, based on the analysis using the AHP (Analytic Hierarchy Process), the weighting coefficients of the present structural parameters are 37.672%, the weighting coefficients of the paleostructural parameters are 24.711%, the weighting coefficients of the reservoir thickness parameters are 11.714%, and the weighting coefficients of the source rock thickness parameters are 25.903%.

[0150] Specifically, the weighting formulas for each of the advantageous storage control parameters are as follows:

[0151] Y ik =ω k gX i ′ k ′, (8)

[0152] In equation (8),

[0153] X i ′ k ′ is the dimensionless data after dedimensionalizing each advantageous storage control parameter of each small region;

[0154] ω k These are the weighting coefficients for each favorable resource control parameter;

[0155] Y ik Weighted data for each of the advantageous control parameters of each of the aforementioned small regions.

[0156] Step S600: Weighted data of multiple favorable storage control parameters are superimposed and calculated to obtain the favorable trait values ​​of each small region.

[0157] In a specific embodiment, the formula for calculating the favorable trait values ​​of each small region is as follows:

[0158]

[0159] In equation (9),

[0160] Y ik Weighted data for each advantageous control parameter of each of the aforementioned small regions;

[0161] Fi , where represents the favorable trait value for each of the aforementioned small regions.

[0162] Step S700: Evaluate and determine the favorable oil and gas reservoir areas of the study area based on the favorable characteristic values ​​of each small region.

[0163] Further, step S700 specifically includes:

[0164] Import the favorable trait values ​​corresponding to each of the aforementioned small regions into the Petrol geological software, convert the scatter plot into a plane contour map, and distinguish the favorable oil and gas reservoir areas in the study area through numerical comparison.

[0165] Specifically, the advantageous trait value F i The larger the value, the more advantageous it is, and it can be used to evaluate and determine the favorable oil and gas reservoir areas in the study area.

[0166] In a specific embodiment, based on the favorable trait values, the scatter plot is transformed into a planar contour map, as shown below. Figure 11 As shown, the favorable areas of oil and gas reservoirs in the study area can be identified and distinguished intuitively and quickly.

[0167] In the technical solution of this invention, a method for evaluating favorable areas of structural-lithologic oil and gas reservoirs is provided. This method combines multiple geological parameters with trap conditions to form an evaluation parameter, namely, a favorable characteristic value. Based on the evaluation parameter, favorable areas can be quickly and accurately distinguished in the target stratigraphic interval of structural-lithologic oil and gas reservoirs. This solves the problem that the previous division of favorable areas was greatly affected by human subjective factors, and further improves the accuracy and convenience of favorable area selection, so as to more intuitively select exploration or development areas in the evaluation of favorable areas.

[0168] This invention also provides a structural-lithologic reservoir favorable area evaluation system 100, please refer to [link / reference]. Figure 12The structural-lithologic hydrocarbon reservoir favorable area evaluation system 100 includes a favorable reservoir control parameter selection module 1, a regional division module 2, a trap evaluation parameter data processing module 3, a source rock evaluation parameter data processing module 4, a weighting module 5, a favorable trait value determination module 6, and an evaluation module 7. The favorable reservoir control parameter selection module 1 is used to select multiple favorable reservoir control parameters, which include at least trap evaluation parameters and source rock evaluation parameters. The regional division module 2 is used to divide the study area into multiple smaller areas. The trap evaluation parameter data processing module 3 includes a trap condition determination unit and a data dimensionless processing unit. The trap condition determination unit is used to determine the trap conditions of each smaller area, and the data dimensionless processing unit is used to evaluate the trap conditions of each smaller area based on the results output by the trap condition determination unit. The trap evaluation parameters of the small area are processed to remove dimensions; the source rock evaluation parameter data processing module 4 is used to process the source rock evaluation parameters to remove dimensions; the weighting module 5 is used to obtain the weight coefficients of each of the favorable reservoir control parameters, and assign corresponding weight coefficients to the dimensionless data output by the trap evaluation parameter data processing module 3 and the dimensionless data output by the source rock evaluation parameter data processing module 4 to obtain weighted data of multiple favorable reservoir control parameters; the favorable trait value determination module 6 is used to superimpose and calculate the weighted data of multiple favorable reservoir control parameters output by the weighting module 5 to obtain the favorable trait value of each small area; the evaluation module 7 is used to evaluate and judge the favorable oil and gas reservoir areas of the study area based on the favorable trait values ​​of each small area output by the favorable trait value determination module 6.

[0169] The structural-lithological oil and gas reservoir favorable area evaluation system 100 provided by the present invention can quickly and accurately evaluate and distinguish favorable areas in the study area, which is conducive to the intuitive selection of exploration or development areas by staff.

[0170] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for evaluating favorable areas of structural-lithologic oil and gas reservoirs, characterized in that, The method for evaluating favorable areas of structural-lithological oil and gas reservoirs includes the following steps: Multiple favorable hydrocarbon-controlling parameters are selected, and these multiple favorable hydrocarbon-controlling parameters include at least trap evaluation parameters and source rock evaluation parameters; The study area was divided into multiple smaller regions, and the raw data of each favorable storage control parameter in each of the smaller regions were obtained. The trapping conditions of each small region are determined, and the trapping evaluation parameters of each small region are processed by data dedimensionalization based on the trapping conditions of each small region. The evaluation parameters of source rocks in each of the aforementioned small regions were subjected to dimensionless processing. Obtain the weight coefficients of each of the advantageous storage control parameters, and assign corresponding weight coefficients to the dimensionless data of each of the advantageous storage control parameters to obtain the weighted data of each of the advantageous storage control parameters; The weighted data of multiple favorable storage control parameters are superimposed to calculate the favorable trait values ​​of each small region. Based on the favorable characteristics of each sub-region, the favorable oil and gas reservoir areas of the study area are evaluated and judged. The steps of determining the closure conditions of each small region and, based on the closure conditions of each small region, performing data dimensionless processing on the closure evaluation parameters of each small region specifically include: The spatial moving average value of each trap evaluation parameter of each of the aforementioned small regions is calculated according to the spatial moving average formula. The trapping conditions of each small region are determined based on the difference between the original data and the spatial moving average of each trapping evaluation parameter. For small regions with poor closure, the closure evaluation parameters of the small region are processed by dedimensionalization according to the first assignment formula. For small regions with good closure, the closure evaluation parameters of the small region are processed by dedimensionalization according to the second assignment formula. The first assignment formula is: ； The second assignment formula is: ； In the formula, k is the evaluation parameter for each trap; The data are after shift-standard deviation transformation of the evaluation parameters of each trap in each of the aforementioned small regions, in meters; The maximum value among the series of trap evaluation parameters; The minimum value among the series of trap evaluation parameters; The dimensionless data for each trap evaluation parameter of each of the aforementioned small regions; The formula for the spatial moving average is: ； In the formula, N is the number of points contained in the side length of the small square selected with each of the small regions as the center; X and Y are the horizontal and vertical coordinates of the study area; x and y are the horizontal and vertical coordinates of each of the small regions; The constructed value of the study area with x-coordinate and y-coordinate, in meters; The value is the average spatial movement of each of the aforementioned small regions, with x as the horizontal coordinate and y as the vertical coordinate, in meters.

2. The method for evaluating favorable areas of structural-lithological oil and gas reservoirs as described in claim 1, characterized in that, The trap evaluation parameters include current structural parameters, paleostructural parameters, and reservoir thickness parameters; The source rock evaluation parameters include the source rock thickness parameter.

3. The method for evaluating favorable areas of structural-lithological oil and gas reservoirs as described in claim 2, characterized in that, If the difference between the original data of the trapping evaluation parameter and the spatial moving average is less than 0, then the trapping of the small area is determined to be poor. If the difference between the original data of the trapping evaluation parameter and the spatial moving average is greater than or equal to 0, then the trapping property of the small area is determined to be good.

4. The method for evaluating favorable areas of structural-lithologic oil and gas reservoirs as described in claim 3, characterized in that, After determining the trapping conditions of each small region based on the difference between the original data and the spatial moving average of each trapping evaluation parameter, and before performing data dedimensionalization processing on the trapping evaluation parameters of the small region with poor trapping properties according to the first assignment formula, and before performing data dedimensionalization processing on the trapping evaluation parameters of the small region with good trapping properties according to the second assignment formula, the method further includes: The original data of each trap evaluation parameter were subjected to a shift-standard deviation transformation.

5. The method for evaluating favorable areas of structural-lithologic oil and gas reservoirs as described in claim 2, characterized in that, The step of performing dimensionless data processing on the source rock evaluation parameters of each of the aforementioned small regions specifically includes: The raw data of the source rock thickness parameters of each of the aforementioned small regions were standardized to obtain standardized data. According to the translation-standard deviation transformation formula, the standardized data are transformed by data translation-standard deviation to obtain the transformed data of the source rock thickness parameter; Based on the translation-range transformation formula, the transformation data are subjected to translation-range transformation to obtain dimensionless data of the source rock thickness parameters.

6. A system for evaluating favorable areas of structural-lithologic oil and gas reservoirs, characterized in that, The structural-lithological hydrocarbon reservoir favorable area evaluation system includes: A favorable reservoir control parameter selection module is used to select multiple favorable reservoir control parameters, wherein the multiple favorable reservoir control parameters include at least trap evaluation parameters and source rock evaluation parameters; The region division module is used to divide the study area into multiple smaller regions and obtain the original data of each favorable storage control parameter in each of the smaller regions; The trapping evaluation parameter data processing module includes a trapping condition determination unit and a data dimensionless processing unit. The trapping condition determination unit calculates the spatial moving average of each trapping evaluation parameter for each of the sub-regions according to a spatial moving average formula, and determines the trapping condition of each sub-region based on the difference between the original data of each trapping evaluation parameter and the spatial moving average. The data dimensionless processing unit, based on the output of the trapping condition determination unit, performs dimensionless processing on the trapping evaluation parameters of sub-regions with poor trapping properties according to a first assignment formula, and performs dimensionless processing on the trapping evaluation parameters of sub-regions with good trapping properties according to a second assignment formula. The first assignment formula is... The second assignment formula is In the formula, k represents each of the trap evaluation parameters. The data are the translation-standard deviation transformed data for the evaluation parameters of each trap in each of the aforementioned small regions, in meters. The maximum value among the series of trap evaluation parameters. The minimum value among the series of trap evaluation parameters. The spatial moving average formula is as follows: (This refers to the dimensionless data obtained by evaluating the trapping parameters of each of the aforementioned small regions.) In the formula, N is the number of points contained in the side length of the small square selected with each of the small regions as the center, X and Y are the horizontal and vertical coordinates of the study area, and x and y are the horizontal and vertical coordinates of each of the small regions. The constructed value of the study area with x-coordinate and y-coordinate, in meters; The average spatial movement of each of the aforementioned small regions, with x as the horizontal coordinate and y as the vertical coordinate, is expressed in meters. The hydrocarbon source rock evaluation parameter data processing module is used to perform dimensionless processing on the hydrocarbon source rock evaluation parameters. The weighting module is used to obtain the weight coefficients of each of the favorable reservoir control parameters, and to assign corresponding weight coefficients to the dimensionless data output by the trap evaluation parameter data processing module and the dimensionless data output by the source rock evaluation parameter data processing module, so as to obtain the weighted data of multiple favorable reservoir control parameters. A favorable trait value determination module is used to superimpose weighted data from multiple favorable storage control parameters output by the weighting module to obtain favorable trait values ​​for each of the small regions; and, The evaluation module is used to determine the favorable trait values ​​of each small region output by the module based on the favorable trait values, and to evaluate and judge the favorable oil and gas reservoir areas of the study area.

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