Reservoir quality evaluation method, device and equipment based on sandstone reservoir formation causes and medium

Abstract

The invention relates to the technical field of petroleum and natural gas exploration, and discloses a reservoir quality evaluation method, device, equipment and medium based on sandstone reservoir causes, and the method and device are used for obtaining multi-level cause parameters of a sandstone reservoir and relative importance data of the multi-level cause parameters. And determining relative weight distribution of the multi-level cause parameters based on the relative importance data of the multi-level cause parameters. According to the relative weight distribution of the multi-level cause parameters, quality evaluation is conducted on the multiple single-well reservoirs in the sandstone reservoir, and the quality score of each single-well reservoir is obtained. And performing quality evaluation on the sandstone reservoir based on the quality score of each single-well reservoir to obtain the quality score of the sandstone reservoir. According to the method, the reservoir quality can be effectively evaluated on the basis of the sandstone reservoir formation causes, the evaluation modes of the reservoir quality are enriched, and the evaluation means of the reservoir quality are diversified.

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] At present, oil and gas exploration and development have entered deep areas. With the continuous deepening of oil and gas exploration, the exploration target has entered the field of complex stratigraphic-lithological 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 are used to solve the defects of the related technologies that there are 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] Acquire multi-level genetic parameters of sandstone reservoirs and relative importance data of the multi-level genetic parameters;

[0008] Determining the 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 a plurality of 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 of the first-level genesis parameters includes the importance interval of each of the second-level genesis parameters under the first-level genesis parameter relative to each of the second-level genesis parameters under the first-level genesis 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, the determining the relative weight distribution of the multi-level genetic parameters based on the relative importance data of the multi-level genetic parameters comprises:

[0016] Sorting a plurality of genetic parameters to obtain a corresponding genetic parameter sequence; wherein the plurality of genetic parameters are the plurality of primary genetic parameters, or the plurality of secondary genetic parameters under any of the primary genetic parameters;

[0017] Based on the order of the elements in the causal parameter sequence from front to back, traverse each of the causal parameters in the causal 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 the first importance, and a value is taken in the importance interval of the target causal parameter relative to the second causal parameter to obtain the second importance, 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 of obtaining to obtain the 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, then 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 in the causal parameter sequence 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 genesis parameters in the genesis parameter sequence according to each component in the characteristic vector comprises:

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

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

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

[0030] Preferably, the quality evaluation of multiple single well reservoirs in the sandstone reservoir is performed respectively 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, the relative weight of each of the second-level genetic parameters 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 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 of the single well reservoirs in the reservoir coordinate system of the sandstone reservoir;

[0035] Based on the position coordinates and quality score of each single well reservoir, score interpolation is performed on the overall 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 position 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, used 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 the 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, used to respectively evaluate the quality of 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 of the single well reservoirs;

[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 reservoir quality evaluation method based on the genesis of sandstone reservoirs according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[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 sandstone reservoir genesis of the above-mentioned 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 sandstone reservoirs and relative importance data of multi-level genesis parameters. Based on the relative importance data of multi-level genesis parameters, the relative weight distribution of multi-level genesis parameters is determined. According to the relative weight distribution of multi-level genesis parameters, the quality of multiple single well reservoirs in the sandstone reservoir is evaluated respectively to obtain the quality score of 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. 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, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[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 genesis 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 in 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 in an embodiment of the present invention;

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

[0055] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Combine the following Figure 1-Figure 7 The reservoir quality evaluation method based on 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 sandstone reservoirs 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 genesis parameters may include genesis parameters of multiple levels. For example, the multi-level genesis parameters may include multiple primary genesis parameters, multiple secondary genesis parameters under each primary genesis parameter, and multiple tertiary genesis parameters under each secondary genesis parameter.

[0061] The relative importance data of the multi-level genetic parameters may include the relative importance data between the two genetic parameters in the same level in the multi-level genetic data. For example, when the multi-level genetic parameters include the primary genetic parameters A and B, the secondary genetic parameters A1 and A2 under the primary genetic parameter A, and the secondary genetic parameters B1 and B2 under the primary genetic parameter B, the relative importance data of the multi-level genetic parameters may include the importance of the primary genetic parameter A relative to A and B, the importance of B relative to A and B, the importance of the secondary genetic parameter A1 relative to A1 and A2, the importance of A2 relative to A1 and A2, the importance of the secondary genetic parameter B1 relative to B1 and B2, and the importance of B2 relative to B1 and B2. The product of the relative importance between two genetic parameters is 1. Taking the secondary genetic parameters B1 and B2 as an example, 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, the relevant technology has not yet formed a theoretical system and practical method for quantitative evaluation of sandstone reservoirs, and it is difficult to form guidance for actual oil and gas exploration. On the other hand, although the relevant technology can directly measure key physical parameters such as porosity and permeability through geophysical means such as core analysis and logging interpretation of sandstone reservoirs, these parameters are crucial for evaluating reservoirs, but sandstone reservoirs themselves have significant heterogeneity characteristics, and heterogeneity is not only reflected in the complexity of the internal structure of the reservoir, but also in the changes in physical parameters. Fundamentally speaking, the physical properties of the reservoir are closely subject to its genetic mechanism, which is the result of the combined effects of sedimentary environment, diagenetic evolution and geological structure. Therefore, from the perspective of reservoir genesis, the quantitative prediction of the reservoir can be made more comprehensive and more accurate. In this embodiment, a system for quantitatively evaluating sandstone reservoirs from the perspective of reservoir genesis is urgently needed. Through the quantitative evaluation of sandstone reservoirs based on the genesis of sandstone reservoirs, the distribution law of reservoirs can be better understood, favorable reservoir zones can be predicted, and a more accurate geological basis can be provided for oil and gas exploration and development.

[0063] Specifically, this embodiment can clarify the multi-level genetic parameters that affect the 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 technicians 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 obtaining the relative importance data of the multi-level causal parameters, this embodiment can 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 primary genesis parameters and multiple secondary genesis parameters under each primary 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 above-mentioned multiple primary genetic parameters may include sedimentary conditions B1 and diagenetic conditions B2. There are three secondary genetic parameters under sedimentary conditions B1, 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, after determining the relative weight distribution of the multi-level genetic parameters, this embodiment can obtain, for any single well reservoir, the parameter score of each genetic parameter in the multi-level genetic parameters of the single well reservoir, and based on the relative weight distribution and the parameter scores of the genetic parameters, perform quality evaluation on multiple single well reservoirs in the sandstone reservoir 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 the 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] Obtaining 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 overall 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 taken 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) on 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 variogram and the spatial distribution of the sampling points.

[0090] It should be noted that this embodiment introduces the multi-level fuzzy mathematical analysis method from the unconventional field into the sandstone reservoir evaluation, which can avoid the subjective and qualitative shortcomings in the sandstone reservoir evaluation and prediction, and realize the quantitative evaluation of the reservoir by integrating the multi-level reservoir influencing factors, which has important guiding significance for the reservoir prediction in specific areas and target layers. It can realize the plane prediction of the reservoir quality in the study area, which is of great significance for the selection of 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 sandstone reservoirs and relative importance data of multi-level genesis parameters. Based on the relative importance data of multi-level genesis parameters, the relative weight distribution of multi-level genesis parameters is determined. According to the relative weight distribution of multi-level genesis parameters, the quality of multiple single-well reservoirs in the sandstone reservoir is evaluated respectively to obtain the quality score of 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. This embodiment can realize the 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.

[0092] based on Figure 1 , this embodiment proposes a second reservoir quality evaluation method based on sandstone reservoir genesis, in which the multi-level genesis parameters include multiple primary genesis parameters and multiple secondary genesis parameters under each primary genesis 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 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 the elements in the causal parameter sequence from the front to the 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 the first importance, and a value is taken in the importance interval of the target causal parameter relative to the second causal parameter to obtain the second importance, 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 the 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 taken 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 in this embodiment, a judgment matrix can be constructed 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 according to 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 according to 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, according to the judgment matrix Q1, and determine the structural maturity C under sedimentary conditions B1 according to 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 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 their relative weights is 1.

[0104] Specifically, the importance interval of the causal parameter relative to the causal parameter can be set by the technician according to the actual situation, for example, it can be set to an interval greater than 0 and less than 2, which is not limited in this embodiment. Among them, the relative importance between the same causal parameters can be fixedly set to 1, in which 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 arrangement order 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 the structural maturity C under the sedimentary condition B1 is 11 , ingredient maturity C 12 and sedimentary microfacies C 13 Arrange them to 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 degree of cementation 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, a value is taken in the importance interval of B1 relative to B1 to obtain the first importance, and a value is taken in the importance interval of B1 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, a value is taken in the importance interval of B2 relative to B1 to obtain the first importance, and a value 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 ​​may 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 the sedimentary condition B1 and the 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 of C 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 of C is 0.67. 12 Relative to C 12 The importance interval of C 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 of 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 above-mentioned 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] The consistency test of the judgment matrix is ​​performed 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 step of traversing each causal parameter in the causal parameter sequence in the order of arrangement from front to back is returned to be executed 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 unreasonable judgment matrices 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 rebuilt 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 judgment matrix order n 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 test, otherwise it can be determined that the judgment matrix fails the consistency test. The preset threshold can be set by a technician according to actual conditions, such as 0.1, and this embodiment does not limit it.

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

[0123] Optionally, the above-mentioned determining the relative weight of each genetic parameter in the genetic 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, determine the arrangement order of the component, determine the causal parameter in the causal parameter sequence whose parameter arrangement order is equal to the arrangement order of the component, and determine the component 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 can be understood that the judgment matrix is ​​a square matrix, and its dimension is determined by the number of rows or columns of the square matrix. The dimension of the judgment matrix is ​​equal to the dimension of the above-mentioned 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, and this embodiment can determine each component in the eigenvector 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 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, after normalizing each component in the feature vector in this embodiment, corresponding normalized components can be obtained, and the sum of the normalized components is 1.

[0130] Optionally, in this embodiment, each component in the feature vector may be normalized first 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 check, this embodiment can convert the feature vector W b The normalized components 0.67 and 0.33 in 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 check, this embodiment can convert the feature vector Wb The normalized components 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, the above 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 are 0.32, 0.22 and 0.46 respectively. The compaction degree C under the diagenetic condition B2 21 、Dissolution conditions C 22 and degree of cementation C 23 The relative weights of the deposition condition B1 are 0.46, 0.32 and 0.22 respectively. In this embodiment, the relative weight of the deposition 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 relative weight of sedimentary condition B1 (0.67) 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, in order to further evaluate the sandstone reservoir, this embodiment can establish the membership function of each secondary genetic parameter. The membership function can be divided into qualitative function and quantitative function, among which structural maturity and sedimentary microfacies are qualitative functions. Compositional maturity, compaction degree, dissolution conditions and cementation degree are quantitative functions. In the quantitative function, compositional maturity is = Q (quartz content) / F (feldspar + granite debris content) + R (mica + other debris content); dissolution porosity = the inverse of the linear fitting slope of the scatter plot with the surface porosity and foraminifera content as the horizontal and vertical coordinates.

[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 scores and score weights 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 sandstone reservoir genesis proposed in this embodiment can avoid the subjective and qualitative deficiencies in sandstone reservoir evaluation and prediction, realize quantitative reservoir evaluation based on comprehensive 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 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.

[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 the beneficial effects thereof can be referred to in detail. 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 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 the elements in the causal parameter sequence from the front to the 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 the first importance, and a value is taken in the importance interval of the target causal parameter relative to the second causal parameter to obtain the second importance, 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 the 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 taken 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] The consistency test of the judgment matrix is ​​performed 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 step of traversing each causal parameter in the causal parameter sequence in the order of arrangement from front to back is returned to be executed 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, determine the arrangement order of the component, determine the causal parameter in the causal parameter sequence whose parameter arrangement order is equal to the arrangement order of the component, and determine the component 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 used for:

[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] Obtaining 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 overall 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 taken 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 sandstone reservoirs and relative importance data of multi-level genesis parameters. Based on the relative importance data of multi-level genesis parameters, the relative weight distribution of multi-level genesis parameters is determined. According to the relative weight distribution of multi-level genesis parameters, the quality of multiple single well reservoirs in the sandstone reservoir is evaluated respectively to obtain the quality score of 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. This embodiment can realize the 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.

[0174] The reservoir quality evaluation device based on sandstone reservoir genesis 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 the sandstone reservoir is shown.

[0176] See also Fig. 9, a schematic diagram of the structure 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). Fig. 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 a dedicated 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 executable by at least one processor 10, so that at least one processor 10 executes 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, an application 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 devices. 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 a volatile memory, such as a random access memory. The memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive. The memory 20 may also include a combination of the above-mentioned types of memory.

[0181] The computer device further comprises 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 method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, 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 hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. 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 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; Determining the 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 a plurality of 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 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; 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 of the first-level genesis parameters includes the importance interval of each of the second-level genesis parameters under the first-level genesis parameter relative to each of the second-level genesis parameters under the first-level genesis 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 step of determining the relative weight distribution of the multi-level genesis parameters based on the relative importance data of the multi-level genesis parameters comprises: Sorting a plurality of genetic parameters to obtain a corresponding genetic parameter sequence; wherein the plurality of genetic parameters are the plurality of primary genetic parameters, or the plurality of secondary genetic parameters under any of the primary genetic parameters; Based on the order of the elements in the causal parameter sequence from front to back, traverse each of the causal parameters in the causal 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 the first importance, and a value is taken in the importance interval of the target causal parameter relative to the second causal parameter to obtain the second importance, 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 of obtaining to obtain the 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 The determining the relative weight of each of the causal parameters in the causal parameter sequence based on the judgment matrix comprises: 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, then 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 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.

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 comprises: Traversing each component in the feature vector according to the order of arrangement of the components from front to back in the feature vector; For any of the components traversed in the feature vector, determine the arrangement order of the components, determine the causal parameters whose parameter arrangement order is equal to the arrangement order of the components in the causal parameter sequence, and determine the components as the initial relative weights of the causal parameters; The initial relative weight of each of the genetic parameters is normalized to obtain the normalized weight of each of the genetic parameters, and the normalized weight is used as the relative weight of each of the genetic parameters.

6. The method according to claim 2, characterized in that According to the relative weight distribution of the multi-level genetic parameters, the quality of multiple single well reservoirs in the sandstone reservoir is evaluated respectively to obtain the quality score of each single well reservoir, including: For any of the first-level genetic parameters, the relative weight of each of the second-level genetic parameters 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 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 of the single well reservoirs in the reservoir coordinate system of the sandstone reservoir; Based on the position coordinates and quality score of each single well reservoir, score interpolation is performed on the overall 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 position 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 the genesis of sandstone reservoirs, characterized in that: include: A data acquisition unit, used to acquire multi-level genetic parameters of the sandstone reservoir and relative importance data of the multi-level genetic parameters; A weight determination unit, configured to determine the 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, used to respectively evaluate the quality of 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 of the single well reservoirs; 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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