A reservoir quality evaluation method, device, equipment and medium based on sandstone reservoir genesis

Abstract

The present invention relates to the technical field of oil and gas exploration, and discloses a reservoir quality evaluation method, device, equipment and medium based on the genesis of sandstone reservoirs, which obtain multi-level genesis parameters of the sandstone reservoir and relative importance data of the multi-level genesis parameters. Based on the relative importance data of the multi-level genesis parameters, the relative weight distribution of the multi-level genesis parameters is determined. According to the relative weight distribution of the multi-level genesis parameters, the quality of multiple single-well reservoirs in the sandstone reservoir is evaluated respectively to obtain a quality score for each single-well reservoir. The quality of the sandstone reservoir is evaluated based on the quality score of each single-well reservoir to obtain a quality score for the sandstone reservoir. The present invention can realize effective evaluation of reservoir quality based on the genesis of the sandstone reservoir, enrich the evaluation methods of reservoir quality, and diversify the evaluation means of reservoir quality.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration, and in particular to a reservoir quality evaluation method, device, equipment and medium based on the genesis of sandstone reservoirs. Background Art

[0002] Currently, oil and gas exploration and development have entered deep areas. As oil and gas exploration continues to deepen, the exploration target has entered the field of complex stratigraphic and lithologic oil and gas reservoirs, facing the dual challenges of high costs and limited drilling numbers.

[0003] In order to effectively improve exploration efficiency and reduce exploration costs, relevant technologies need to be developed from multiple dimensions to construct an effective sandstone reservoir evaluation system and use it to evaluate sandstone reservoirs.

[0004] However, there are few sandstone reservoir evaluation methods in related technologies, and the technical means are relatively simple. Summary of the Invention

[0005] The present invention provides a reservoir quality evaluation method, device, equipment and medium based on the genesis of sandstone reservoirs, which is used to solve the defects of the related technologies of few sandstone reservoir evaluation methods and relatively single technical means, enrich the sandstone reservoir evaluation methods and diversify the evaluation means.

[0006] In a first aspect, the present invention provides a reservoir quality evaluation method based on sandstone reservoir genesis, comprising:

[0007] Obtaining multi-level genetic parameters of a sandstone reservoir and relative importance data of the multi-level genetic parameters;

[0008] Determining relative weight distribution of the multi-level genetic parameters based on the relative importance data of the multi-level genetic parameters;

[0009] According to the relative weight distribution of the multi-level genetic parameters, quality evaluation is performed on multiple single-well reservoirs in the sandstone reservoir to obtain a quality score for each single-well reservoir;

[0010] The quality of the sandstone reservoir is evaluated based on the quality score of each single well reservoir to obtain the quality score of the sandstone reservoir.

[0011] Preferably, the multi-level genetic parameters include a plurality of primary genetic parameters and a plurality of secondary genetic parameters under each of the primary genetic parameters;

[0012] The relative importance data of the multi-level genetic parameters include the importance interval of each of the first-level genetic parameters relative to each of the first-level genetic parameters, and also include the target interval corresponding to each of the first-level genetic parameters;

[0013] The target interval corresponding to any one of the first-level genetic parameters includes the importance interval of each of the second-level genetic parameters under the first-level genetic parameter relative to each of the second-level genetic parameters under the first-level genetic parameter;

[0014] The relative weight distribution of the multi-level genetic parameters includes the relative weight of each of the first-level genetic parameters and the relative weight of each of the second-level genetic parameters.

[0015] Preferably, determining the relative weight distribution of the multi-level genetic parameters based on the relative importance data of the multi-level genetic parameters includes:

[0016] Sorting a plurality of genetic parameters to obtain a corresponding genetic parameter sequence; wherein the plurality of genetic parameters are the plurality of first-level genetic parameters, or the plurality of second-level genetic parameters under any of the first-level genetic parameters;

[0017] Traversing each of the genesis parameters in the genesis parameter sequence based on the order of elements arranged from front to back in the genesis parameter sequence;

[0018] For any target causal parameter traversed in the causal parameter sequence, a value is taken in the importance interval of the target causal parameter relative to the first causal parameter to obtain a first importance, a value is taken in the importance interval of the target causal parameter relative to the second causal parameter to obtain a second importance, and so on until a value is taken in the importance interval of the target causal parameter relative to the Nth causal parameter to obtain N importances, and the N importances are arranged in the order in which they are obtained to obtain an importance sequence corresponding to the target causal parameter; wherein the first causal parameter, the second causal parameter, and the Nth causal parameter are the causal parameters arranged in the first, second, and Nth positions in the causal parameter sequence, respectively;

[0019] Each of the importance sequences is used as a matrix row data, and arranged in the obtained order to construct a judgment matrix;

[0020] The relative weight of each of the causal parameters in the causal parameter sequence is determined based on the judgment matrix.

[0021] Preferably, determining the relative weight of each of the causal parameters in the causal parameter sequence based on the judgment matrix includes:

[0022] Solving the judgment matrix to obtain the maximum eigenvalue and corresponding eigenvector of the judgment matrix;

[0023] Performing a consistency check on the judgment matrix based on the maximum eigenvalue of the judgment matrix;

[0024] If the judgment matrix passes the consistency test, determining the relative weight of each of the causal parameters in the causal parameter sequence according to each component in the eigenvector;

[0025] If the judgment matrix fails the consistency check, the step of traversing each of the causal parameters in the causal parameter sequence in the order of arrangement from front to back is returned to execution until the latest judgment matrix passes the consistency check and the relative weight of each of the causal parameters in the causal parameter sequence is determined.

[0026] Preferably, determining the relative weight of each of the causal parameters in the causal parameter sequence according to each component in the eigenvector comprises:

[0027] Traversing each component in the feature vector according to the order of components arranged from front to back in the feature vector;

[0028] For any of the components traversed in the feature vector, determining the arrangement order of the components, determining the causal parameters in the causal parameter sequence whose parameter arrangement order is equal to the arrangement order of the components, and determining the components as the initial relative weights of the causal parameters;

[0029] The initial relative weight of each of the causal parameters is normalized to obtain the normalized weight of each of the causal parameters, and the normalized weight is used as the relative weight of each of the causal parameters.

[0030] Preferably, the quality evaluation of multiple single-well reservoirs in the sandstone reservoir is performed separately according to the relative weight distribution of the multi-level genetic parameters to obtain the quality score of each single-well reservoir, including:

[0031] For any of the first-level genetic parameters, multiply the relative weight of each of the second-level genetic parameters under the first-level genetic parameter by the relative weight of the first-level genetic parameter to obtain the scoring weight of each of the second-level genetic parameters under the first-level genetic parameter;

[0032] For any of the single-well reservoirs, the membership function of each of the secondary genetic parameters of the single-well reservoir is obtained, and the parameter score of each of the secondary genetic parameters of the single-well reservoir is determined according to the membership function of each of the secondary genetic parameters of the single-well reservoir. A weighted summation is performed based on the parameter score and the score weight of each of the secondary genetic parameters of the single-well reservoir to evaluate the quality of the single-well reservoir and obtain the quality score of the single-well reservoir.

[0033] Preferably, the quality evaluation of the sandstone reservoir is performed based on the quality score of each single well reservoir to obtain the quality score of the sandstone reservoir, including:

[0034] Obtaining the position coordinates of each single well reservoir in the reservoir coordinate system of the sandstone reservoir;

[0035] Based on the location coordinates and quality score of each single well reservoir, score interpolation is performed on the entire plane of the sandstone reservoir to obtain a quality score distribution of the sandstone reservoir; wherein the quality score distribution includes a correspondence between the location coordinates and the quality score;

[0036] The quality score distribution of the sandstone reservoir is used as the quality score of the sandstone reservoir.

[0037] In a second aspect, the present invention provides a reservoir quality evaluation device based on the genesis of a sandstone reservoir, comprising:

[0038] A data acquisition unit, configured to acquire multi-level genetic parameters of the sandstone reservoir and relative importance data of the multi-level genetic parameters;

[0039] a weight determination unit, configured to determine a relative weight distribution of the multi-level genesis parameters based on the relative importance data of the multi-level genesis parameters;

[0040] A single well evaluation unit is used to evaluate the quality of multiple single well reservoirs in the sandstone reservoir according to the relative weight distribution of the multi-level genetic parameters, and obtain a quality score for each single well reservoir;

[0041] A reservoir evaluation unit is used to evaluate the quality of the sandstone reservoir based on the quality score of each single well reservoir to obtain the quality score of the sandstone reservoir.

[0042] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to execute the reservoir quality evaluation method based on the genesis of sandstone reservoirs according to the first aspect or any corresponding embodiment thereof.

[0043] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the reservoir quality evaluation method based on the genesis of sandstone reservoirs according to the first aspect or any corresponding embodiment thereof.

[0044] The reservoir quality evaluation method, device, equipment and medium based on the genesis of sandstone reservoirs provided by the present invention obtain multi-level genesis parameters of the sandstone reservoir and relative importance data of the multi-level genesis parameters. Based on the relative importance data of the multi-level genesis parameters, the relative weight distribution of the multi-level genesis parameters is determined. According to the relative weight distribution of the multi-level genesis parameters, the quality of multiple single-well reservoirs in the sandstone reservoir is evaluated respectively to obtain a quality score for each single-well reservoir. The quality of the sandstone reservoir is evaluated based on the quality score of each single-well reservoir to obtain a quality score for the sandstone reservoir. The present invention can realize effective evaluation of reservoir quality based on the genesis of sandstone reservoirs, enrich the evaluation methods of reservoir quality, and diversify the evaluation means of reservoir quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to 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 any creative work.

[0046] Figure 1 A flow chart of a reservoir quality evaluation method based on sandstone reservoir genesis provided in an embodiment of the present invention;

[0047] Figure 2 A schematic diagram of the composition of a multi-level genetic parameter provided by an embodiment of the present invention;

[0048] Figure 3 A schematic diagram of relevant data of a judgment matrix provided by an embodiment of the present invention;

[0049] Figure 4 A schematic diagram of relative weight distribution of multi-level genetic parameters provided by an embodiment of the present invention;

[0050] Figure 5 A schematic diagram of relevant data of a membership function provided by an embodiment of the present invention;

[0051] Figure 6 A schematic diagram of a quality evaluation result of a single well reservoir provided by an embodiment of the present invention;

[0052] Figure 7 A schematic diagram of a quality evaluation result of a sandstone reservoir provided by an embodiment of the present invention;

[0053] Figure 8 A schematic structural diagram of a reservoir quality evaluation device based on sandstone reservoir genesis provided by an embodiment of the present invention;

[0054] Figure 9A schematic structural diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0056] The following combination Figure 1-Figure 7 The reservoir quality evaluation method based on the sandstone reservoir genesis of the present invention is described.

[0057] like Figure 1 As shown, this embodiment proposes a first reservoir quality evaluation method based on the genesis of sandstone reservoirs, which may include the following steps:

[0058] S101. Obtain multi-level genetic parameters of a sandstone reservoir and relative importance data of the multi-level genetic parameters.

[0059] The sandstone reservoir may be a sandstone reservoir in a certain geographical area that requires reservoir quality evaluation.

[0060] Specifically, the multi-level genetic parameters may include multiple levels of genetic parameters. For example, the multi-level genetic parameters may include multiple first-level genetic parameters, multiple second-level genetic parameters under each first-level genetic parameter, and multiple third-level genetic parameters under each second-level genetic parameter.

[0061] The relative importance data of the multi-level genetic parameters may include the relative importance data between any two genetic parameters within the same level of the multi-level genetic parameters. For example, when the multi-level genetic parameters include primary genetic parameters A and B, secondary genetic parameters A1 and A2 under primary genetic parameter A, and secondary genetic parameters B1 and B2 under primary genetic parameter B, the relative importance data of the multi-level genetic parameters may include the importance of primary genetic parameter A relative to A and B, respectively, the importance of B relative to A and B, respectively, the importance of secondary genetic parameter A1 relative to A1 and A2, respectively, the importance of A2 relative to A1 and A2, respectively, the importance of secondary genetic parameter B1 relative to B1 and B2, respectively, and the importance of B2 relative to B1 and B2, respectively. The product of the relative importance between two genetic parameters is 1. For example, for secondary genetic parameters B1 and B2, the product of the importance of B1 relative to B2 and the importance of B2 relative to B1 is 1.

[0062] It should be noted that, on the one hand, relevant technologies have yet to develop a theoretical system and practical methods for quantitatively evaluating sandstone reservoirs, making it difficult to provide guidance for actual oil and gas exploration. On the other hand, although relevant technologies can directly measure key physical parameters such as porosity and permeability through geophysical methods such as core analysis and well logging interpretation, which are crucial for reservoir evaluation, sandstone reservoirs themselves exhibit significant heterogeneity. This heterogeneity is reflected not only in the complexity of the reservoir's internal structure but also in the variations in physical parameters. Fundamentally, the physical properties of a reservoir are closely constrained by its genetic mechanism, resulting from the combined effects of sedimentary environment, diagenetic evolution, and geological structure. Therefore, considering reservoir genesis can make quantitative reservoir prediction more comprehensive and accurate. This embodiment urgently needs to establish a system for quantitatively evaluating sandstone reservoirs based on reservoir genesis. Quantitative evaluation of sandstone reservoirs based on sandstone genesis can better understand reservoir distribution patterns, predict favorable reservoir zones, and provide a more accurate geological basis for oil and gas exploration and development.

[0063] Specifically, this embodiment can clarify the multi-level genetic parameters that affect reservoir quality based on the understanding of the genesis of the sandstone reservoir in the study area. The multi-level genetic parameters and their relative importance data can be determined by technical personnel based on research.

[0064] S102: Determine the relative weight distribution of the multi-level genetic parameters based on the relative importance data of the multi-level genetic parameters.

[0065] The relative weight distribution of the multi-level genetic parameters may include the relative weight of each genetic parameter in the multi-level genetic parameters.

[0066] Specifically, after acquiring the relative importance data of the multi-level causal parameters, this embodiment may determine the relative weight distribution of the multi-level causal parameters according to the relative importance data.

[0067] Optionally, in other reservoir quality evaluation methods based on sandstone reservoir genesis proposed in this embodiment, the multi-level genesis parameters include multiple first-level genesis parameters and multiple second-level genesis parameters under each first-level genesis parameter.

[0068] The relative importance data of the multi-level genetic parameters include the importance interval of each first-level genetic parameter relative to each first-level genetic parameter, and also include the target interval corresponding to each first-level genetic parameter.

[0069] The target interval corresponding to any first-level genetic parameter includes the importance interval of each second-level genetic parameter under the first-level genetic parameter relative to each second-level genetic parameter under the first-level genetic parameter.

[0070] The relative weight distribution of multi-level genetic parameters includes the relative weight of each first-level genetic parameter and the relative weight of each second-level genetic parameter.

[0071] like Figure 2 As shown, the multiple primary genetic parameters mentioned above may include sedimentary conditions B1 and diagenetic conditions B2. Under sedimentary conditions B1, there are three secondary genetic parameters, namely, structural maturity C 11 , ingredient maturity C 12 and sedimentary microfacies C 13 There are three secondary genetic parameters under the diagenetic condition B2, namely, compaction degree C 21 , dissolution conditions X 22 and degree of cementation G 23 .

[0072] At this time, the relative importance data of the multi-level genetic parameters include the importance intervals of the sedimentary condition B1 relative to B1 and the diagenetic condition B2, the importance intervals of B2 relative to B1 and B2, and the structural maturity C 11 Relative to the structural maturity C 11 , ingredient maturity C 12 and sedimentary microfacies C 13 The importance interval also includes the component maturity C 12 Relative structural maturity C 11 , ingredient maturity C 12 and sedimentary microfacies C 13 The importance interval also includes sedimentary microfacies C 13 Relative to the structural maturity C 11 , ingredient maturity C 12 and sedimentary microfacies C 13 The importance interval also includes the compaction degree C 21 Respectively relative to the compaction degree C 21 , dissolution conditions C 22 and degree of cementation C 23 The importance interval also includes the dissolution condition C 22 Respectively relative to the compaction degree C 21 , dissolution conditions C 22 and degree of cementation C 23 The importance interval also includes the degree of cementation C 23 Respectively relative to the compaction degree C 21 , dissolution conditions C 22 and degree of cementation C 23 The importance interval of .

[0073] At this time, the relative weight distribution of the multi-level genetic parameters includes the relative weights of the first-level genetic parameters, sedimentary conditions B1 and diagenetic conditions B2, as well as the structural maturity C under sedimentary conditions B1.11 , ingredient maturity C 12 and sedimentary microfacies C 13 The relative weight of the compaction degree C under the diagenetic condition B2 is also included. 21 , dissolution conditions C 22 and degree of cementation C 23 relative weight of .

[0074] S103. According to the relative weight distribution of the multi-level genetic parameters, quality evaluation is performed on multiple single-well reservoirs in the sandstone reservoir to obtain a quality score for each single-well reservoir.

[0075] Among them, the single-well reservoir is the reservoir under a single well in the sandstone reservoir.

[0076] The quality score of a single well reservoir is the quality score obtained by evaluating the quality of a single well reservoir.

[0077] Specifically, this embodiment can obtain the parameter score of each genetic parameter in the multi-level genetic parameters of any single well reservoir after determining the relative weight distribution of the multi-level genetic parameters. Based on the relative weight distribution and the parameter score of the genetic parameters, the quality of multiple single well reservoirs in the sandstone reservoir is evaluated to obtain the quality score of the single well reservoir.

[0078] S104. Evaluate the quality of the sandstone reservoir based on the quality score of each single well reservoir to obtain a quality score of the sandstone reservoir.

[0079] Specifically, in this embodiment, after determining the quality scores of multiple single-well reservoirs in the sandstone reservoir, the quality of the sandstone reservoir can be evaluated based on the quality score of each single-well reservoir.

[0080] Optionally, step S104 may include:

[0081] Obtain the position coordinates of each single well reservoir in the reservoir coordinate system of the sandstone reservoir;

[0082] Based on the location coordinates and quality score of each single well reservoir, score interpolation is performed on the entire plane of the sandstone reservoir to obtain the quality score distribution of the sandstone reservoir; wherein the quality score distribution includes the corresponding relationship between the location coordinates and the quality score;

[0083] The quality score distribution of the sandstone reservoir is used as the quality score of the sandstone reservoir.

[0084] Specifically, this embodiment can use the Kriging interpolation method based on multiple existing single-well reservoir quality scores to obtain the reservoir quality score trend on the plane within the work area and realize reservoir quality prediction.

[0085] It should be noted that the Kriging interpolation method is based on the assumption that spatial attributes are consistent, that is, it believes that the closer the points are in space, the more likely their attributes are to be similar. Based on this basic assumption, the method calculates the degree of spatial correlation between known points and unknown points, and then uses the data of known points to infer the attribute values ​​of unknown points. The specific method is to assume that the research variable Z(x) in the research area a is at point x i The attribute value at (i=1, 2, ..., n) is Z(x i ), the attribute value Z at the insertion point x0 * (x0) is the attribute value of the sampling point Z(x i )(i=1, 2, ..., n), that is:

[0086]

[0087] Among them, λ i is the undetermined weight coefficient, and its equation is:

[0088]

[0089] Among them, x i and x j For any two points, γ(x0,x j )=γ(h), where h is x i and x j The distance between them, γ(h) is the variation function model, and μ is the Lagrange multiplier. The weight coefficient needs to satisfy Z * The mathematical expectation of the difference between (x0) and the actual value Z(x0) is 0 and the variance of the difference between the two is the minimum. Therefore, the determination of the weight coefficient involves information such as the variation function and the spatial distribution of the sampling points.

[0090] It should be noted that this example introduces multi-level fuzzy mathematical analysis from the unconventional field into sandstone reservoir evaluation. This method avoids the subjective and qualitative limitations of sandstone reservoir evaluation and prediction, achieving quantitative reservoir evaluation that integrates multiple sub-level reservoir influencing factors. This provides important guidance for reservoir prediction in specific regions and target intervals. It also enables planar prediction of reservoir quality in the study area, which is crucial for selecting exploration areas.

[0091] The reservoir quality evaluation method based on the genesis of sandstone reservoirs proposed in this embodiment obtains multi-level genesis parameters of the sandstone reservoir and relative importance data of the multi-level genesis parameters. Based on the relative importance data of the multi-level genesis parameters, the relative weight distribution of the multi-level genesis parameters is determined. According to the relative weight distribution of the multi-level genesis parameters, the quality of multiple single-well reservoirs in the sandstone reservoir is evaluated respectively to obtain a quality score for each single-well reservoir. The quality of the sandstone reservoir is evaluated based on the quality score of each single-well reservoir to obtain a quality score for the sandstone reservoir. This embodiment can achieve effective evaluation of reservoir quality based on the genesis of sandstone reservoirs, enrich the evaluation methods of reservoir quality, and diversify the means of evaluating reservoir quality.

[0092] based on Figure 1 This embodiment proposes a second reservoir quality evaluation method based on sandstone reservoir genesis. In this method, the multi-level genetic parameters include multiple primary genetic parameters and multiple secondary genetic parameters under each primary genetic parameter.

[0093] The relative importance data of the multi-level genetic parameters include the importance interval of each first-level genetic parameter relative to each first-level genetic parameter, and also include the target interval corresponding to each first-level genetic parameter;

[0094] The target interval corresponding to any first-level genetic parameter includes the importance interval of each second-level genetic parameter under the first-level genetic parameter relative to each second-level genetic parameter under the first-level genetic parameter;

[0095] The relative weight distribution of multi-level genetic parameters includes the relative weight of each first-level genetic parameter and the relative weight of each second-level genetic parameter.

[0096] At this time, the above step S102 may include:

[0097] Sorting the multiple genetic parameters to obtain a corresponding genetic parameter sequence; wherein the multiple genetic parameters are multiple first-level genetic parameters, or multiple second-level genetic parameters under any first-level genetic parameter;

[0098] Based on the order of elements in the causal parameter sequence from front to back, traverse each causal parameter in the causal parameter sequence;

[0099] For any target causal parameter traversed in the causal parameter sequence, a value is taken in the importance interval of the target causal parameter relative to the first causal parameter to obtain a first importance, a value is taken in the importance interval of the target causal parameter relative to the second causal parameter to obtain a second importance, and so on until a value is taken in the importance interval of the target causal parameter relative to the Nth causal parameter to obtain N importances, and the N importances are arranged in the order obtained to obtain an importance sequence corresponding to the target causal parameter; wherein the first causal parameter, the second causal parameter and the Nth causal parameter are the causal parameters arranged in the first, second and Nth positions in the causal parameter sequence respectively;

[0100] Each importance sequence is treated as a matrix row data and arranged in the order obtained to construct a judgment matrix;

[0101] The relative weight of each causal parameter in the causal parameter sequence is determined based on the judgment matrix.

[0102] It is understandable that this embodiment can construct a judgment matrix for the same level of causal parameters. For example, when the multi-level causal parameters include Figure 2 When the various genetic parameters are shown, this embodiment can construct a judgment matrix Q1 for the sedimentary condition B1 and the diagenetic condition B2, and can also construct a judgment matrix Q1 based on the structural maturity C under the sedimentary condition B1. 11 , ingredient maturity C 12 and sedimentary microfacies C 13 Construct a judgment matrix Q2, and also calculate the compaction degree C under the diagenetic condition B2. 21 , dissolution conditions C 22 and degree of cementation C 23 Construct a judgment matrix Q3. This embodiment can determine the relative weights of each genetic parameter of the corresponding level based on each constructed judgment matrix. Specifically, this embodiment can determine the relative weights of the first-level genetic parameters, sedimentary conditions B1 and diagenetic conditions B2, based on the judgment matrix Q1, and determine the structural maturity C under sedimentary conditions B1 based on the judgment matrix Q2. 11 , ingredient maturity C 12 and sedimentary microfacies C 13 The relative weight of the compaction degree C under the diagenetic condition B2 is determined according to the judgment matrix Q3. 21 , dissolution conditions C 22 and degree of cementation C 23 relative weight of .

[0103] The sum of the relative weights of the various genetic parameters at the same level is 1. For example, the sum of the relative weights of the sedimentary conditions B1 and the diagenetic conditions B2 is 1, and the structural maturity C under the sedimentary condition B1 is 11 , ingredient maturity C12 and sedimentary microfacies C 13 The sum of the relative weights is 1.

[0104] Specifically, the importance interval of the causal parameter relative to the causal parameter can be set by a technician based on actual conditions, for example, it can be set to an interval greater than 0 and less than 2, and this embodiment is not limited thereto. The relative importance between the same causal parameters can be fixed to 1. In this case, the importance interval of a causal parameter relative to itself is 1.

[0105] Specifically, this embodiment can sort multiple genetic parameters of the same level to obtain a corresponding genetic parameter sequence. Afterwards, this embodiment can select values ​​in different importance intervals according to the order of arrangement of the genetic parameters in the genetic parameter sequence and construct a judgment matrix. For example, this embodiment can arrange the sedimentary condition B1 and the diagenetic condition B2 to obtain the genetic parameter sequence {B1, B2}, and then perform a judgment on the structural maturity C under the sedimentary condition B1. 11 , ingredient maturity C 12 and sedimentary microfacies C 13 Arrange and get the causal parameter sequence {C 11 , C 12 , C 13}, for the compaction degree C under diagenetic condition B2 21 , dissolution conditions C 22 and cementation degree C 23 Arrange and get the causal parameter sequence {C 21 , C 22 , C 23}. Afterwards, this embodiment can be introduced by taking the construction of a judgment matrix corresponding to the first-level causal parameters as an example. Specifically, this embodiment can sequentially traverse the causal parameter sequence {B1, B2}. For B1 that is traversed first, the value of B1 is taken in the importance interval of B1 relative to B1 to obtain the first importance, and the value of B1 is taken in the importance interval of B2 relative to B2 to obtain the second importance. The first importance and the second importance are arranged to obtain the importance sequence corresponding to B1. For B2 that is traversed, the value of B2 is taken in the importance interval of B2 relative to B1 to obtain the first importance, and the value of B2 is taken in the importance interval of B2 relative to B2 to obtain the second importance. The first importance and the second importance are arranged to obtain the importance sequence corresponding to B2. The importance sequence corresponding to B1 is arranged as a matrix row data in the first row of the matrix to be constructed, and the importance sequence corresponding to B2 is arranged as a matrix row data in the second row of the matrix to be constructed to obtain the corresponding matrix, namely the judgment matrix.

[0106] Specifically, in this embodiment, when selecting values ​​in the importance interval, the values ​​can be selected randomly. The larger the value, the greater the relative importance. Figure 3 As shown in the left half of the figure, when constructing the judgment matrix for sedimentary condition B1 and diagenetic condition B2, the importance interval of B1 relative to B1 takes a value of 1, the importance interval of B1 relative to B2 takes a value of 2, the importance interval of B2 relative to B1 takes a value of 0.5, and the importance interval of B2 relative to B2 takes a value of 1. 11 , ingredient maturity C 12 and sedimentary microfacies C 13 When constructing the judgment matrix, in C 11 Relative to C 11 The importance interval is 1, and the 11 Relative to C 12 The importance interval is 1.5, in C 11 Relative to C 13 The importance interval of C is 0.67. 12 Relative to C 11 The importance interval is 0.67, in C 12 Relative to C 12 The importance interval is 1, and the 12 Relative to C 13 The importance interval of C is 0.5. 13 Relative to C 11 The importance interval is 1.5, in C 13 Relative to C 12 The importance interval is 2, in C 13 Relative to C 13 The importance interval takes the value 1.

[0107] It can be understood that the judgment matrix constructed in this embodiment is:

[0108]

[0109] Among them, a xy is the importance of causal parameter x relative to causal parameter y in the same level of causal parameters. n is the order of the judgment matrix.

[0110] Optionally, the determination of the relative weight of each causal parameter in the causal parameter sequence based on the judgment matrix may include:

[0111] Solve the judgment matrix to obtain the maximum eigenvalue and corresponding eigenvector of the judgment matrix;

[0112] Perform consistency check on the judgment matrix based on the maximum eigenvalue of the judgment matrix;

[0113] If the judgment matrix passes the consistency test, the relative weight of each causal parameter in the causal parameter sequence is determined according to each component in the eigenvector;

[0114] If the judgment matrix fails the consistency check, the process returns to the step of traversing each causal parameter in the causal parameter sequence in the order of arrangement from front to back until the latest judgment matrix passes the consistency check and the relative weight of each causal parameter in the causal parameter sequence is determined.

[0115] It should be noted that, in order to avoid an unreasonable judgment matrix and ensure the reliability and accuracy of the calculation results, this embodiment can perform a consistency check on the judgment matrix. If the consistency check fails, the judgment matrix needs to be reconstructed until the consistency passes.

[0116] Specifically, in this embodiment, the consistency ratio CR of the judgment matrix can be determined first, where:

[0117] CR=CI / RI.

[0118] Among them, RI is the average random consistency index, which is a constant related to the order n of the judgment matrix and can be obtained by looking up the table. CI is the consistency index, and its calculation formula is:

[0119] CI=(λ max -n) / n-1.

[0120] Among them, λ max is the maximum eigenvalue of the judgment matrix, and n is the order of the judgment matrix.

[0121] Specifically, after determining the consistency ratio CR of the judgment matrix, this embodiment can determine whether the CR is less than a preset threshold. If so, it can be determined that the judgment matrix passes the consistency check; otherwise, it can be determined that the judgment matrix fails the consistency check. The preset threshold can be set by a technician based on actual conditions, such as 0.1, and this embodiment is not limited thereto.

[0122] It is understood that when the judgment matrix corresponding to the causal parameters of a certain level fails the consistency check, it is necessary to re-select values ​​within the importance interval of the causal parameters to construct a new judgment matrix until the latest judgment matrix passes the consistency check. This embodiment can determine the relative weights of the causal parameters based on the judgment matrix that passes the consistency check.

[0123] Optionally, the above-mentioned determination of the relative weight of each causal parameter in the causal parameter sequence according to each component in the characteristic vector includes:

[0124] Traverse each component in the feature vector according to the order of components from front to back in the feature vector;

[0125] For any component traversed in the characteristic vector, the arrangement order of the component is determined, and the causal parameter whose parameter arrangement order is equal to the arrangement order of the component is determined in the causal parameter sequence, and the component is determined as the initial relative weight of the causal parameter;

[0126] The initial relative weight of each genetic parameter is normalized to obtain the normalized weight of each genetic parameter, which is used as the relative weight of each genetic parameter.

[0127] It is understood that the judgment matrix is ​​a square matrix whose dimension is determined by the number of rows or columns. The dimension of the judgment matrix is ​​equal to the dimension of the aforementioned eigenvector. For example, if the judgment matrix is ​​a 3×3 square matrix, then its dimension is 3, and the corresponding eigenvector will also be a 3-dimensional vector. Each row of data in the judgment matrix corresponds to a causal parameter. In this embodiment, each component in the eigenvector can be determined as the initial relative weight of the corresponding causal parameter.

[0128] Specifically, in this embodiment, the initial relative weights of the causal parameters may be normalized to obtain the normalized weights of the causal parameters, and the normalized weights may be used as the relative weights of the causal parameters.

[0129] It should be noted that, in this embodiment, after normalizing each component in the feature vector, corresponding normalized components can be obtained, and the sum of the normalized components is 1.

[0130] Optionally, this embodiment may also first normalize each component in the feature vector to obtain a normalized component, which is then determined as the relative weight of the causal parameter. Figure 3 As shown, in this embodiment, after constructing the judgment matrix Q1 for the sedimentary condition B1 and the diagenetic condition B2, the judgment matrix Q1 is solved to obtain the characteristic vector λ max and the eigenvector W b , the CR of the judgment matrix Q1 is 0. Through the consistency test, this embodiment can convert the eigenvector W b The normalized components 0.67 and 0.33 in the equation are respectively determined as the relative weights of the sedimentary condition B1 and the diagenetic condition B2. 11 , ingredient maturity C 12 and sedimentary microfacies C 13 After constructing the judgment matrix Q2, solve the judgment matrix Q2 to obtain the eigenvector λ max and the eigenvector W b , the CR of the judgment matrix Q1 is 0.0046. Through the consistency test, this embodiment can convert the eigenvector Wb The normalized components of 0.32, 0.22 and 0.46 in the 11 , ingredient maturity C 12 and sedimentary microfacies C 13 relative weight of .

[0131] Optionally, in the reservoir quality evaluation method based on sandstone reservoir genesis proposed in this embodiment, step S103 may include:

[0132] For any first-level genetic parameter, the relative weight of each second-level genetic parameter under the first-level genetic parameter is multiplied by the relative weight of the first-level genetic parameter to obtain the scoring weight of each second-level genetic parameter under the first-level genetic parameter;

[0133] For any of the single-well reservoirs, the membership function of each of the secondary genetic parameters of the single-well reservoir is obtained, and the parameter score of each of the secondary genetic parameters of the single-well reservoir is determined according to the membership function of each of the secondary genetic parameters of the single-well reservoir. A weighted summation is performed based on the parameter score and the score weight of each of the secondary genetic parameters of the single-well reservoir to evaluate the quality of the single-well reservoir and obtain the quality score of the single-well reservoir.

[0134] Specifically, this embodiment may determine the scoring weight of the secondary genesis parameter based on the relative weights of the primary genesis parameter and the secondary genesis parameter.

[0135] like Figure 4 As shown in the figure, the relative weights of the first-level genetic parameters sedimentary condition B1 and diagenetic condition B2 are 0.67 and 0.33 respectively. The structural maturity C under sedimentary condition B1 is 11 , ingredient maturity C 12 and sedimentary microfacies C 13 The relative weights of the two groups are 0.32, 0.22 and 0.46 respectively. The compaction degree C under the diagenetic condition B2 is 21 , dissolution conditions C 22 and cementation degree C 23 The relative weights of the sedimentation condition B1 are 0.46, 0.32 and 0.22 respectively. In this embodiment, the relative weight of the sedimentation condition B1, 0.67, can be multiplied by the structural maturity C 11 The relative weight is 0.32, and the structural maturity is C 11 The weight coefficient is 0.21, which is the score weight, and the relative weight of sedimentary condition B1 (0.67) is multiplied by the composition maturity C 12 The relative weight is 0.22, and the component maturity C 12 The scoring weight of 0.15 can be determined by analogy, and the scoring weight of each secondary causal parameter can be determined.

[0136] Specifically, to further evaluate sandstone reservoirs, this embodiment can establish membership functions for various secondary genetic parameters. Membership functions can be divided into qualitative and quantitative functions. Structural maturity and sedimentary microfacies are qualitative functions, while compositional maturity, degree of compaction, dissolution conditions, and degree of cementation are quantitative functions. In the quantitative function, compositional maturity = Q (quartz content) / F (feldspar + granite debris content) + R (mica + other debris content); dissolution porosity = porosity and foraminifera content = the inverse of the linear fit slope of the scatter plot on the horizontal and vertical axes.

[0137] Among them, the qualitative function and the quantitative function can be obtained by researchers. The membership function of each secondary causal parameter in this embodiment is as follows: Figure 5 shown.

[0138] Specifically, this embodiment can determine the parameter score of each secondary genetic parameter of a single well reservoir based on the membership function of the secondary genetic parameter. This embodiment can perform weighted summation based on the parameter score and score weight of each secondary genetic parameter of a single well reservoir to obtain the quality score of the single well reservoir, such as Figure 6 The parameter scores of each secondary genetic parameter of the three single-well reservoirs B-1, B-2 and S-1 in the sandstone reservoir are shown, as well as the comprehensive score of each single-well reservoir, that is, the quality score.

[0139] Afterwards, if Figure 7 As shown, in this embodiment, Kriging interpolation can be performed based on the quality scores of the three single-well reservoirs B-1, B-2, and S-1 to obtain the quality score distribution of the sandstone reservoir.

[0140] The reservoir quality evaluation method based on the sandstone reservoir genesis proposed in this embodiment can avoid the subjective and qualitative shortcomings in sandstone reservoir evaluation and prediction, realize quantitative evaluation of reservoirs by integrating multi-level reservoir influencing factors, and ensure the accuracy of reservoir quality evaluation.

[0141] like Figure 8 As shown, this embodiment proposes a reservoir quality evaluation device based on the genesis of sandstone reservoirs, which may include:

[0142] The data acquisition unit 801 is used to acquire the multi-level genetic parameters of the sandstone reservoir and the relative importance data of the multi-level genetic parameters;

[0143] The weight determination unit 802 is used to determine the relative weight distribution of the multi-level causal parameters based on the relative importance data of the multi-level causal parameters;

[0144] The single well evaluation unit 803 is used to evaluate the quality of multiple single well reservoirs in the sandstone reservoir according to the relative weight distribution of the multi-level genetic parameters, and obtain the quality score of each single well reservoir;

[0145] The reservoir evaluation unit 804 is configured to evaluate the quality of the sandstone reservoir based on the quality score of each single well reservoir to obtain the quality score of the sandstone reservoir.

[0146] It should be noted that the processing of the data acquisition unit 801, the weight determination unit 802, the single well evaluation unit 803 and the reservoir evaluation unit 804 and their beneficial effects can be referred to in the respective Figure 1 Steps S101 to S104 in the above are not described in detail.

[0147] Optionally, the multi-level genetic parameters include multiple first-level genetic parameters and multiple second-level genetic parameters under each first-level genetic parameter;

[0148] The relative importance data of the multi-level genetic parameters include the importance interval of each first-level genetic parameter relative to each first-level genetic parameter, and also include the target interval corresponding to each first-level genetic parameter;

[0149] The target interval corresponding to any first-level genetic parameter includes the importance interval of each second-level genetic parameter under the first-level genetic parameter relative to each second-level genetic parameter under the first-level genetic parameter;

[0150] The relative weight distribution of multi-level genetic parameters includes the relative weight of each first-level genetic parameter and the relative weight of each second-level genetic parameter.

[0151] Optionally, the weight determination unit 802 is further configured to:

[0152] Sorting the multiple genetic parameters to obtain a corresponding genetic parameter sequence; wherein the multiple genetic parameters are multiple first-level genetic parameters, or multiple second-level genetic parameters under any first-level genetic parameter;

[0153] Based on the order of elements in the causal parameter sequence from front to back, traverse each causal parameter in the causal parameter sequence;

[0154] For any target causal parameter traversed in the causal parameter sequence, a value is taken in the importance interval of the target causal parameter relative to the first causal parameter to obtain a first importance, a value is taken in the importance interval of the target causal parameter relative to the second causal parameter to obtain a second importance, and so on until a value is taken in the importance interval of the target causal parameter relative to the Nth causal parameter to obtain N importances, and the N importances are arranged in the order obtained to obtain an importance sequence corresponding to the target causal parameter; wherein the first causal parameter, the second causal parameter and the Nth causal parameter are the causal parameters arranged in the first, second and Nth positions in the causal parameter sequence respectively;

[0155] Each importance sequence is treated as a matrix row data and arranged in the order obtained to construct a judgment matrix;

[0156] The relative weight of each causal parameter in the causal parameter sequence is determined based on the judgment matrix.

[0157] Optionally, the weight determination unit 802 is further configured to:

[0158] Solve the judgment matrix to obtain the maximum eigenvalue and corresponding eigenvector of the judgment matrix;

[0159] Perform consistency check on the judgment matrix based on the maximum eigenvalue of the judgment matrix;

[0160] If the judgment matrix passes the consistency test, the relative weight of each causal parameter in the causal parameter sequence is determined according to each component in the eigenvector;

[0161] If the judgment matrix fails the consistency check, the process returns to the step of traversing each causal parameter in the causal parameter sequence in the order of arrangement from front to back until the latest judgment matrix passes the consistency check and the relative weight of each causal parameter in the causal parameter sequence is determined.

[0162] Optionally, the weight determination unit 802 is further configured to:

[0163] Traverse each component in the feature vector according to the order of components from front to back in the feature vector;

[0164] For any component traversed in the characteristic vector, the arrangement order of the component is determined, and the causal parameter whose parameter arrangement order is equal to the arrangement order of the component is determined in the causal parameter sequence, and the component is determined as the initial relative weight of the causal parameter;

[0165] The initial relative weight of each genetic parameter is normalized to obtain the normalized weight of each genetic parameter, which is used as the relative weight of each genetic parameter.

[0166] Optionally, the single well evaluation unit 803 is further configured to:

[0167] For any first-level genetic parameter, the relative weight of each second-level genetic parameter under the first-level genetic parameter is multiplied by the relative weight of the first-level genetic parameter to obtain the scoring weight of each second-level genetic parameter under the first-level genetic parameter;

[0168] For any of the single-well reservoirs, the membership function of each of the secondary genetic parameters of the single-well reservoir is obtained, and the parameter score of each of the secondary genetic parameters of the single-well reservoir is determined according to the membership function of each of the secondary genetic parameters of the single-well reservoir. A weighted summation is performed based on the parameter score and the score weight of each of the secondary genetic parameters of the single-well reservoir to evaluate the quality of the single-well reservoir and obtain the quality score of the single-well reservoir.

[0169] Optionally, the reservoir evaluation unit 804 is further configured to:

[0170] Obtain the position coordinates of each single well reservoir in the reservoir coordinate system of the sandstone reservoir;

[0171] Based on the location coordinates and quality score of each single well reservoir, score interpolation is performed on the entire plane of the sandstone reservoir to obtain the quality score distribution of the sandstone reservoir; wherein the quality score distribution includes the corresponding relationship between the location coordinates and the quality score;

[0172] The quality score distribution of the sandstone reservoir is used as the quality score of the sandstone reservoir.

[0173] The reservoir quality evaluation device based on the genesis of sandstone reservoirs proposed in this embodiment obtains multi-level genesis parameters of the sandstone reservoir and relative importance data of the multi-level genesis parameters. Based on the relative importance data of the multi-level genesis parameters, the relative weight distribution of the multi-level genesis parameters is determined. According to the relative weight distribution of the multi-level genesis parameters, the quality of multiple single-well reservoirs in the sandstone reservoir is evaluated separately to obtain a quality score for each single-well reservoir. The quality of the sandstone reservoir is evaluated based on the quality score of each single-well reservoir to obtain a quality score for the sandstone reservoir. This embodiment can achieve effective evaluation of reservoir quality based on the genesis of the sandstone reservoir, enrich the evaluation methods of reservoir quality, and diversify the means of evaluating reservoir quality.

[0174] The reservoir quality evaluation device based on the genesis of sandstone reservoirs in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0175] The embodiment of the present invention also provides a computer device having the above Figure 8 The reservoir quality evaluation device based on the genesis of sandstone reservoirs is shown.

[0176] See also Figure 9, a structural diagram of a computer device provided by an optional embodiment of the present invention, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses for communication, and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 9 A processor 10 is taken as an example.

[0177] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0178] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0179] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0180] The memory 20 may include volatile memory, such as random access memory. The memory may also include non-volatile memory, such as flash memory, a hard disk, or a solid-state drive. The memory 20 may also include a combination of the above types of memory.

[0181] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0182] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0183] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. 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 replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A reservoir quality evaluation method based on sandstone reservoir genesis, characterized in that: include: Acquire multi-level genetic parameters of sandstone reservoirs and relative importance data of the multi-level genetic parameters; wherein the multi-level genetic parameters include multiple primary genetic parameters and multiple secondary genetic parameters under each of the primary genetic parameters; the multiple primary genetic parameters include sedimentary conditions and diagenetic conditions , deposition conditions Structural maturity , ingredient maturity and sedimentary microfacies , diagenetic conditions Including the degree of compaction , dissolution conditions and degree of cementation ; Determining relative weight distribution of the multi-level genetic parameters based on the relative importance data of the multi-level genetic parameters; According to the relative weight distribution of the multi-level genetic parameters, quality evaluation is performed on multiple single-well reservoirs in the sandstone reservoir to obtain a quality score for each single-well reservoir; The quality of the sandstone reservoir is evaluated based on the quality score of each single well reservoir to obtain the quality score of the sandstone reservoir.

2. The method according to claim 1, characterized in that The relative importance data of the multi-level genetic parameters include the importance interval of each of the first-level genetic parameters relative to each of the first-level genetic parameters, and also include the target interval corresponding to each of the first-level genetic parameters; The target interval corresponding to any one of the first-level genetic parameters includes the importance interval of each of the second-level genetic parameters under the first-level genetic parameter relative to each of the second-level genetic parameters under the first-level genetic parameter; The relative weight distribution of the multi-level genetic parameters includes the relative weight of each of the first-level genetic parameters and the relative weight of each of the second-level genetic parameters.

3. The method according to claim 2, characterized in that The determining of the relative weight distribution of the multi-level genesis parameters based on the relative importance data of the multi-level genesis parameters includes: Sorting a plurality of genetic parameters to obtain a corresponding genetic parameter sequence; wherein the plurality of genetic parameters are the plurality of first-level genetic parameters, or the plurality of second-level genetic parameters under any of the first-level genetic parameters; Traversing each of the genesis parameters in the genesis parameter sequence based on the order of elements arranged from front to back in the genesis parameter sequence; For any target causal parameter traversed in the causal parameter sequence, a value is taken in the importance interval of the target causal parameter relative to the first causal parameter to obtain a first importance, a value is taken in the importance interval of the target causal parameter relative to the second causal parameter to obtain a second importance, and so on until a value is taken in the importance interval of the target causal parameter relative to the Nth causal parameter to obtain N importances, and the N importances are arranged in the order in which they are obtained to obtain an importance sequence corresponding to the target causal parameter; wherein the first causal parameter, the second causal parameter, and the Nth causal parameter are the causal parameters arranged in the first, second, and Nth positions in the causal parameter sequence, respectively; Each of the importance sequences is used as a matrix row data, and arranged in the obtained order to construct a judgment matrix; The relative weight of each of the causal parameters in the causal parameter sequence is determined based on the judgment matrix.

4. The method according to claim 3, characterized in that Determining the relative weight of each of the causal parameters in the causal parameter sequence based on the judgment matrix includes: Solving the judgment matrix to obtain the maximum eigenvalue and corresponding eigenvector of the judgment matrix; Performing a consistency check on the judgment matrix based on the maximum eigenvalue of the judgment matrix; If the judgment matrix passes the consistency test, determining the relative weight of each of the causal parameters in the causal parameter sequence according to each component in the eigenvector; If the judgment matrix fails the consistency check, the step of traversing each of the causal parameters in the causal parameter sequence in the order of arrangement from front to back in the causal parameter sequence is returned to, until the latest judgment matrix passes the consistency check and the relative weight of each of the causal parameters in the causal parameter sequence is determined.

5. The method according to claim 4, characterized in that Determining the relative weight of each of the causal parameters in the causal parameter sequence according to each component in the characteristic vector includes: Traversing each component in the feature vector according to the order of components arranged from front to back in the feature vector; For any of the components traversed in the feature vector, determining the arrangement order of the components, determining the causal parameters in the causal parameter sequence whose parameter arrangement order is equal to the arrangement order of the components, and determining the components as the initial relative weights of the causal parameters; The initial relative weight of each of the causal parameters is normalized to obtain the normalized weight of each of the causal parameters, and the normalized weight is used as the relative weight of each of the causal parameters.

6. The method according to claim 2, characterized in that The method of performing quality evaluation on multiple single-well reservoirs in the sandstone reservoir according to the relative weight distribution of the multi-level genetic parameters to obtain a quality score for each single-well reservoir includes: For any of the first-level genetic parameters, multiply the relative weight of each of the second-level genetic parameters under the first-level genetic parameter by the relative weight of the first-level genetic parameter to obtain the scoring weight of each of the second-level genetic parameters under the first-level genetic parameter; For any of the single-well reservoirs, the membership function of each of the secondary genetic parameters of the single-well reservoir is obtained, and the parameter score of each of the secondary genetic parameters of the single-well reservoir is determined according to the membership function of each of the secondary genetic parameters of the single-well reservoir. A weighted summation is performed based on the parameter score and the score weight of each of the secondary genetic parameters of the single-well reservoir to evaluate the quality of the single-well reservoir and obtain the quality score of the single-well reservoir.

7. The method according to claim 1, characterized in that The step of evaluating the quality of the sandstone reservoir based on the quality score of each single well reservoir to obtain the quality score of the sandstone reservoir includes: Obtaining the position coordinates of each single well reservoir in the reservoir coordinate system of the sandstone reservoir; Based on the location coordinates and quality score of each single well reservoir, score interpolation is performed on the entire plane of the sandstone reservoir to obtain a quality score distribution of the sandstone reservoir; wherein the quality score distribution includes a correspondence between the location coordinates and the quality score; The quality score distribution of the sandstone reservoir is used as the quality score of the sandstone reservoir.

8. A reservoir quality evaluation device based on sandstone reservoir genesis, characterized in that: include: A data acquisition unit is used to acquire multi-level genetic parameters of sandstone reservoirs and relative importance data of the multi-level genetic parameters; wherein the multi-level genetic parameters include multiple primary genetic parameters and multiple secondary genetic parameters under each of the primary genetic parameters; the multiple primary genetic parameters include sedimentary conditions and diagenetic conditions , deposition conditions Structural maturity , ingredient maturity and sedimentary microfacies , diagenetic conditions Including the degree of compaction , dissolution conditions and degree of cementation ; a weight determination unit, configured to determine a relative weight distribution of the multi-level genesis parameters based on the relative importance data of the multi-level genesis parameters; a single well evaluation unit, configured to perform quality evaluation on a plurality of single well reservoirs in the sandstone reservoir according to the relative weight distribution of the multi-level genetic parameters, and obtain a quality score for each single well reservoir; A reservoir evaluation unit is used to evaluate the quality of the sandstone reservoir based on the quality score of each single well reservoir to obtain the quality score of the sandstone reservoir.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the reservoir quality evaluation method based on the genesis of sandstone reservoirs as described in any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the reservoir quality evaluation method based on sandstone reservoir genesis according to any one of claims 1 to 7.

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