Automatic recommendation method and device for entrapment pre-probing item, and electronic equipment

Abstract

The invention provides an automatic recommendation method and device for entrapment pre-probing items and electronic equipment. The method comprises the steps of obtaining entrapment index data of the entrapment pre-probing items; performing normalization processing on the trap index data by using a preset index normalization formula to obtain an index score corresponding to each trap index data; weight corresponding to each index score is calculated through a preset judgment matrix, and the preset judgment matrix is used for describing the importance degree of each trap index relative to any trap index; calculating an evaluation value of the entrapment pre-exploration item according to each index score of the entrapment pre-exploration item and the weight corresponding to each index score; and recommending the entrapment pre-probing item with the evaluation value exceeding a preset requirement to the user. According to the method, the data analysis technology and algorithm are combined, the evaluation accuracy and efficiency of the entrapment pre-exploration project are improved, and then the accuracy of automatically recommending the entrapment pre-exploration project is improved; and multiple data and at least two corresponding evaluation indexes are introduced to realize comprehensive evaluation of the entrapment pre-exploration project.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration, and in particular to an automatic recommendation method, device, and electronic equipment for trap pre-exploration projects. Background Art

[0002] Trap exploration projects are oil and gas exploration projects conducted primarily through drilling in secondary or local structures (or trap zones) with oil and gas potential, based on regional exploration. Their objectives are to locate and discover oil and gas fields, further clarify the morphology and fractures of underground structures, obtain preliminary data on reservoir production, pressure, and oil and gas layer properties, and infer reservoir types.

[0003] Trap prospecting plays a crucial role in oil and gas exploration. It's not only a key step in finding and discovering oil and gas fields, but also a crucial basis for assessing resource potential and formulating exploration strategies. With the continuous advancement of oil and gas exploration technology, trap prospecting, as a crucial stage in oil exploration, is crucial for discovering new oil and gas fields and improving resource utilization. However, traditional trap prospecting methods often rely on manual experience and subjective judgment, resulting in low exploration efficiency and significant resource waste. Summary of the Invention

[0004] Based on this, it is necessary to provide an automatic recommendation method, device, and electronic equipment for trap exploration projects in response to the above technical problems.

[0005] An automatic recommendation method for trap exploration projects, comprising:

[0006] Obtain trap indicator data for trap pre-exploration projects;

[0007] Using a preset indicator normalization formula to normalize the trap indicator data, and obtain the indicator score corresponding to each trap indicator data;

[0008] Calculating the weight corresponding to each indicator score using a preset judgment matrix, wherein the preset judgment matrix is ​​used to describe the importance of each closure indicator compared to any other closure indicator;

[0009] Calculating an evaluation value of the trap preliminary exploration project based on each indicator score of the trap preliminary exploration project and the weight corresponding to each indicator score;

[0010] Recommending to the user the trap exploration projects whose evaluation values ​​exceed the preset requirements.

[0011] In one embodiment, the method further comprises:

[0012] Analyze, organize and hierarchize the trap indicator data to obtain the importance of the trap indicators at the same level compared with the preset criteria at the previous level;

[0013] The importance of each trap indicator at the same level is compared with the preset criteria at the previous level to obtain a comparison result, and a preset judgment matrix is ​​constructed based on the comparison result.

[0014] In one embodiment, after comparing the importance of each trap indicator at the same level with the preset criteria at the previous level to obtain a comparison result and constructing a preset judgment matrix based on the comparison result, the method further includes:

[0015] Obtaining the maximum eigenvalue of the preset judgment matrix and the order of the preset judgment matrix;

[0016] Calculating a consistency index of the preset judgment matrix according to the maximum eigenvalue and the order;

[0017] Calculating the consistency ratio of the preset judgment matrix based on the randomly generated random consistency index and the consistency index;

[0018] Detecting whether the consistency ratio meets a preset accuracy;

[0019] When the consistency ratio does not meet the preset accuracy, the preset judgment matrix is ​​modified until the consistency ratio of the modified preset judgment matrix meets the preset accuracy.

[0020] In one embodiment, the categories of the trap indicators include at least one of reserve indicators, financial indicators, or investment effectiveness indicators.

[0021] In one embodiment, the preset indicator normalization formula is:

[0022]

[0023] Where x i is the i-th trap indicator data, (x i ) min is the minimum closure index data among all objects, (x i ) max It is the maximum closure index data among all objects.

[0024] In one embodiment, the estimated value of the trapped pilot project is calculated according to the following formula:

[0025]

[0026] Where U is the estimated value of the trap pre-exploration project, W iis the weight of the i-th trap indicator; F i is the index score of the i-th closure index; n is the number of closure indicators.

[0027] An automatic recommendation device for trap exploration projects, comprising:

[0028] A first acquisition module is used to acquire trap indicator data of a trap pre-exploration project;

[0029] A normalization module is used to perform normalization processing on the trap index data using a preset index normalization formula to obtain an index score corresponding to each trap index data;

[0030] a weight calculation module, configured to calculate the weight corresponding to each indicator score using a preset judgment matrix, wherein the preset judgment matrix is ​​used to describe the importance of each closure indicator compared to any other closure indicator;

[0031] A construction module is used to calculate an evaluation value of the trap preliminary exploration project based on each indicator score of the trap preliminary exploration project and the weight corresponding to each indicator score;

[0032] The first calculation module is configured to recommend to the user a trap exploration project whose evaluation value exceeds a preset requirement.

[0033] In one embodiment, the automatic recommendation device for trap exploration projects may further include:

[0034] An analysis module is used to analyze, organize, and hierarchically process the trap indicator data to determine the importance of the trap indicators at the same level compared to the preset criteria at the previous level;

[0035] The filling module is used to compare the importance of each trap index at the same level with the preset criteria at the previous level, obtain a comparison result, and construct a preset judgment matrix based on the comparison result.

[0036] In one embodiment, the automatic recommendation device for trap exploration projects may further include:

[0037] A second acquisition module is used to obtain the maximum eigenvalue of the preset judgment matrix and the order of the preset judgment matrix;

[0038] A second calculation module, configured to calculate a consistency index of the preset judgment matrix according to the maximum eigenvalue and the order;

[0039] A third calculation module is used to calculate the consistency ratio of the preset judgment matrix according to the randomly generated random consistency index and the consistency index;

[0040] A detection module, configured to detect whether the consistency ratio meets a preset accuracy;

[0041] The correction module is configured to correct the preset judgment matrix when the consistency ratio does not meet the preset accuracy, until the consistency ratio of the corrected preset judgment matrix meets the preset accuracy.

[0042] In one embodiment, the categories of the trap indicators include at least one of reserve indicators, financial indicators, or investment effectiveness indicators.

[0043] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and wherein the processor performs the following steps when executing the computer program:

[0044] Obtain trap indicator data for trap pre-exploration projects;

[0045] Using a preset indicator normalization formula to normalize the trap indicator data, and obtain the indicator score corresponding to each trap indicator data;

[0046] Calculating the weight corresponding to each indicator score using a preset judgment matrix, wherein the preset judgment matrix is ​​used to describe the importance of each closure indicator compared to any other closure indicator;

[0047] Calculating an evaluation value of the trap preliminary exploration project based on each indicator score of the trap preliminary exploration project and the weight corresponding to each indicator score;

[0048] Recommending to the user the trap exploration projects whose evaluation values ​​exceed the preset requirements.

[0049] The processor is further configured to implement the steps of the method for automatically recommending trap exploration projects described in any one of the above embodiments when executing the computer program.

[0050] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0051] Obtain trap indicator data for trap pre-exploration projects;

[0052] Using a preset indicator normalization formula to normalize the trap indicator data, and obtain the indicator score corresponding to each trap indicator data;

[0053] Calculating the weight corresponding to each indicator score using a preset judgment matrix, wherein the preset judgment matrix is ​​used to describe the importance of each closure indicator compared to any other closure indicator;

[0054] Calculating an evaluation value of the trap preliminary exploration project based on each indicator score of the trap preliminary exploration project and the weight corresponding to each indicator score;

[0055] Recommending to the user the trap exploration projects whose evaluation values ​​exceed the preset requirements.

[0056] When the computer program is executed by a processor, it is further used to implement the steps of the method for automatically recommending trap exploration projects described in any of the above embodiments.

[0057] The above-mentioned automatic recommendation method, device, and electronic device for trap pre-exploration projects combine data analysis technology and algorithms to perform hierarchical analysis on the data of trap pre-exploration projects, and then construct a preset judgment matrix to calculate the index data of the trap pre-exploration projects and the weights corresponding to each index data. Then, based on the index data and the weights corresponding to each index data, the evaluation value of the trap pre-exploration projects can be accurately calculated, thereby improving the accuracy and efficiency of the evaluation of trap pre-exploration projects and thus improving the accuracy of automatic recommendation of trap pre-exploration projects. Secondly, the introduction of multiple data and corresponding at least two evaluation indicators realizes a comprehensive and objective evaluation of the trap pre-exploration projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 1. A schematic flow chart of an automatic recommendation method for trapping preliminary exploration projects in one embodiment;

[0059] Figure 2 1. A schematic flow chart of an automatic recommendation method for trapping preliminary exploration projects in one embodiment;

[0060] Figure 3 It is a structural block diagram of an automatic recommendation device for trap exploration projects in one embodiment;

[0061] Figure 4 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0063] Example 1

[0064] In this embodiment, Figure 1 As shown, a method for automatically recommending trap exploration projects is provided, which includes:

[0065] Step 110: Acquire trap index data of the trap preliminary exploration project.

[0066] In this embodiment, the closure data of the closure pre-exploration project can be obtained from a database; the closure data of the closure pre-exploration project can also be obtained from the cloud; or the basic data of the closure pre-exploration project can be obtained from an input device, and then the basic data can be analyzed and calculated to obtain the closure data of the closure pre-exploration project. This is not specifically limited here.

[0067] In one embodiment, after basic data for a trap pre-exploration project is acquired from an input device, the basic data is analyzed and calculated to obtain trap data for the trap pre-exploration project. The basic data includes reserve index data, address background data, exploration target data, resource assessment and forecast data, and cost budget and investment data. The basic data is analyzed and calculated to obtain the trap data for the trap pre-exploration project. Specifically, the basic data is separated to extract reserve index data, resource assessment and forecast data, and cost budget and investment data. The resource assessment and forecast data is then analyzed and calculated to obtain a first analysis result. This first analysis result is then substituted into a preset financial indicator data calculation formula to obtain financial indicator data corresponding to the trap pre-exploration project. The cost budget and investment data are then analyzed and calculated to obtain a second analysis result. This second analysis result is then substituted into a preset investment effectiveness indicator data calculation formula to obtain investment effectiveness indicator data corresponding to the trap pre-exploration project.

[0068] In one embodiment, the categories of the trap indicators include at least one of reserve indicators, financial indicators, or investment effectiveness indicators.

[0069] For example, trap data can include reserve indicators, financial indicators, and investment performance indicators. Reserve indicators can include reserve size, reserve abundance, controlled oil and gas reserves per unit footage, controlled oil and gas reserves per well, and reserve upgrade factor. Financial indicators can include net present value, internal rate of return, after-risk value, total return on investment (ROI), and net profit rate of return on project capital (ROE). Investment performance indicators can include controlled reserves per unit investment and net present value index.

[0070] Step 120 , normalizing the trap index data using a preset index normalization formula to obtain an index score corresponding to each trap index data.

[0071] Because different trap indicators have different meanings and magnitudes, it's difficult to combine them into a unified platform for comprehensive evaluation. Therefore, in this embodiment, the different indicators in the trap data are normalized, with the corresponding values ​​bounded between [0, 1]. Specifically, the different indicators in the trap data are substituted into a pre-set normalization formula to calculate the corresponding values ​​for each indicator. These values, including at least two, are used as the scores for each indicator.

[0072] For example, when the trap data for a pre-exploration project includes reserve index data, financial index data, and investment effectiveness index data, the data corresponding to the reserve index data is substituted into the preset index normalization formula to calculate a first value corresponding to the reserve index data. Similarly, the data corresponding to the financial index data is substituted into the preset index normalization formula to calculate a second value corresponding to the financial index data. The data corresponding to the investment effectiveness index data is also substituted into the preset index normalization formula to calculate a third value corresponding to the investment effectiveness index data. The first value is used as the first index score, the second value is used as the investment effectiveness index score, and the third value is used as the third index score.

[0073] In one embodiment, the preset indicator normalization formula is:

[0074]

[0075] Where x i is the i-th trap indicator data, (x i ) min is the minimum closure index data among all objects, (x i ) max It is the maximum closure index data among all objects.

[0076] In this embodiment, by performing standardization or normalization based on the maximum trap index value and the minimum trap index value, scale differences between the trap index data can be eliminated, making comparison and calculation more accurate.

[0077] Step 130 : Calculate the weight corresponding to each indicator score using a preset judgment matrix, wherein the preset judgment matrix is ​​used to describe the importance of each trap indicator compared to any other trap indicator.

[0078] Calculate the weights corresponding to the scores of each indicator of the trap pre-exploration project, specifically:

[0079] After calculating the scores of each indicator, the weights of each indicator score are assigned; it is necessary to calculate the weights corresponding to each indicator score, and then more accurately identify the key factors, as well as more accurately weigh and determine the importance of the indicators corresponding to each indicator score.

[0080] In this embodiment, the weights corresponding to the scores of the various indicators are calculated, specifically:

[0081] By analyzing, organizing and hierarchically processing the indicator data corresponding to each indicator score, the correlation between the indicators at the same level and a certain criterion at the previous level is obtained, and then the correlation between the elements at the same level and a certain criterion at the previous level is compared pairwise to obtain the comparison results, and a preset judgment matrix is ​​constructed based on the comparison results.

[0082] (2) Calculate the eigenvalue and eigenvector of the preset judgment matrix. The eigenvalue is a numerical value of the judgment matrix, which is used to represent the overall properties of the preset judgment matrix; the eigenvector is the vector corresponding to the eigenvalue.

[0083] (3) To ensure that the sum of the weights is 1 and satisfies the definition of weight, in this embodiment, the obtained feature vector is normalized and converted into a weight vector, which is used as the weight corresponding to each indicator score.

[0084] Step 140 : Calculate an evaluation value of the trap preliminary exploration project based on the index scores of the trap preliminary exploration project and the weights corresponding to the index scores.

[0085] In this embodiment, after calculating the scores for each indicator of the trap exploration project and the weights corresponding to each indicator score, the evaluation value of the trap exploration project is further calculated. Specifically, since each column in the preset judgment matrix approximately reflects the distribution of weights, the arithmetic mean of all column vectors can be used as the evaluation value corresponding to the indicator, i.e., the evaluation value of the trap exploration project.

[0086] In one embodiment, the estimated value of the trapped pilot project is calculated according to the following formula:

[0087]

[0088] Where U is the estimated value of the trap pre-exploration project, W i is the weight of the i-th trap indicator; F i is the index score of the i-th closure index; n is the number of closure indicators.

[0089] Step 150: recommending to the user the trap exploration projects whose evaluation values ​​exceed the preset requirements.

[0090] In this embodiment, it is detected whether the evaluation value of the enclosed pre-exploration project meets a preset requirement, which is a preset evaluation value. When the evaluation value of the enclosed pre-exploration project is greater than the preset requirement (the preset evaluation value), the enclosed pre-exploration project is recommended to the user.

[0091] In this embodiment, trap index data for a pre-exploration project is obtained; the trap index data is normalized using a preset index normalization formula to obtain an index score corresponding to each trap index data; the weight corresponding to each index score is calculated using a preset judgment matrix, wherein the preset judgment matrix is ​​used to describe the importance of each trap index relative to any other trap index; an evaluation value of the pre-exploration project is calculated based on each index score and the weight corresponding to each index score; and pre-exploration projects with evaluation values ​​exceeding preset requirements are recommended to the user. This embodiment combines data analysis techniques and algorithms to perform a hierarchical analysis of the pre-exploration project data, and then constructs a preset judgment matrix to calculate the trap index data and the weight corresponding to each index data. Based on the index data and the weight corresponding to each index data, the evaluation value of the pre-exploration project can be accurately calculated, thereby improving the accuracy and efficiency of the pre-exploration project evaluation and the accuracy of the automatic recommendation of pre-exploration projects. Furthermore, by introducing multiple data and corresponding at least two evaluation indicators, a comprehensive and objective evaluation of pre-exploration projects is achieved.

[0092] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0093] In one embodiment, the method further comprises:

[0094] 1-1) Analyze, organize, and hierarchize the trap indicator data to determine the importance of the trap indicators at the same level relative to the pre-set criteria at the previous level.

[0095] In this embodiment, the trap data is analyzed, organized, and hierarchically processed to obtain the importance of the trap indicators at the same level relative to a certain criterion at the previous level. For example:

[0096] If the importance of the closure index i relative to the closure index j is a ij , then the importance of the closure index j relative to the closure index i is As shown in Table 1:

[0097] Table 1 Preset judgment matrix scale and its definition

[0098] Scale (or importance) The importance of closure index i relative to closure index j 1 Equal importance 3 Slightly more important 5 Obviously important 7 Strongly important 9 Extremely important 2,4,6,8 The median value of the adjacent trap indicators mentioned above

[0099] Assuming there are only three closure indicators, if the importance of closure indicator 2 relative to closure indicator 1 is 3, then closure indicator 2 is labeled 3; if the importance of closure indicator 3 relative to indicator 1 is 5, then closure indicator 3 is labeled 5 (see Table 2).

[0100] Table 2 Trap indicators and their importance

[0101]

[0102]

[0103] If the closure index 1 has a share of 1 in the total importance, the closure index 2 has a share of 3 in the total importance, and the closure index 3 has a share of 5 in the total importance, then the weight values ​​are as follows:

[0104] Table 3 Importance of trap indicators and their corresponding weights

[0105] index Importance marking Trap indicator 1 1 / 9＝11％ Trap Indicator 2 3 / 9＝33％ Trap indicator 3 5 / 9＝56％ Total weight of the closure indicator 100％

[0106] In this embodiment, the closure index 1 is a reserve index; the closure index 2 is a financial index; and the closure index 3 is an investment effectiveness index.

[0107] 1-2) Comparing the importance of each trap indicator at the same level with the preset criteria at the previous level to obtain a comparison result, and constructing a preset judgment matrix based on the comparison result.

[0108] Specifically, all the results of the pairwise comparisons are constructed into a preset judgment matrix. Each row and each column of the preset judgment matrix represents a closure index. In this embodiment, the preset criteria are: the value in each cell represents the importance of the row closure index relative to the column closure index. For example, when a ij for a 12 When a 12 It is expressed as the importance of the first closure indicator compared to the second closure indicator; when a ij for a 11 When a 11 Expressed as the importance of the first trap indicator compared to the second trap indicator.

[0109] In this embodiment, the preset judgment matrix is ​​symmetrical, and the elements on the diagonal are all 1 (because each indicator is equally important relative to itself).

[0110] In this embodiment, the preset judgment matrix more comprehensively displays the relationship between various indicators. Furthermore, using the preset judgment matrix in the process of calculating the evaluation value of the closed exploration project can reduce the user's bias and subjectivity, thereby ensuring the accuracy of the calculated evaluation value of the closed exploration project.

[0111] In one embodiment, after comparing the importance of each trap indicator at the same level with the preset criteria at the previous level to obtain a comparison result and constructing a preset judgment matrix based on the comparison result, the method further includes:

[0112] 2-1) Obtaining the maximum eigenvalue of the preset judgment matrix and the order of the preset judgment matrix.

[0113] In this embodiment, the maximum eigenvalue of the preset judgment matrix can be obtained by solving the characteristic polynomial method; the maximum eigenvalue of the preset judgment matrix can also be obtained by using the power method; no specific limitation is made here.

[0114] For example, using the method of solving characteristic polynomials: First, we need to find the characteristic polynomial of the matrix, that is, the determinant |λE-A|, where E is the identity matrix, A is the preset judgment matrix, and λ is the eigenvalue. Then, we solve the roots of this characteristic polynomial, that is, solve the equation |λE-A|=0, and the solution we get is the eigenvalue of matrix A. Compare the sizes of these eigenvalues, and the largest one is the maximum eigenvalue of the judgment matrix, denoted by λ max .

[0115] In this embodiment, the order of the preset judgment matrix is ​​determined by both the number of rows and the number of columns. Therefore, obtaining the order of the preset judgment matrix is ​​equivalent to obtaining the number of rows and columns of the preset judgment matrix. For example, when the number of rows and the number of columns of the preset judgment matrix are 2 and 3, the corresponding order of the preset judgment matrix is ​​2*3.

[0116] 2-2) Calculating a consistency index of the preset judgment matrix based on the maximum eigenvalue and the order.

[0117] In this embodiment, before calculating the consistency index of the preset judgment matrix, a first calculation formula for calculating the consistency index is set, wherein the first calculation formula is:

[0118]

[0119] Among them, λ max is the maximum eigenvalue of the preset judgment matrix, n is the order of the preset judgment matrix, and CI is the consistency index.

[0120] The maximum eigenvalue λ max Substitute the order n of the preset judgment matrix into the above first calculation formula to calculate the result CI, and use CI as the consistency index of the preset judgment matrix.

[0121] 2-3) Calculating the consistency ratio of the preset judgment matrix based on the randomly generated random consistency index and the consistency index.

[0122] In order to further verify whether the consistency index of the preset judgment matrix meets the preset accuracy, it is necessary to first calculate the consistency ratio of the preset judgment matrix. Specifically:

[0123] (1) Construct a second calculation formula for calculating the consistency ratio. In this embodiment, the second calculation formula is:

[0124]

[0125] Among them, CI is the consistency index, RI is the random consistency index, and CR is the consistency ratio.

[0126] (2) A random consistency index is generated based on the relationship between the preset random consistency index and the order of the preset judgment matrix, and the consistency index and the random consistency index of the above-mentioned preset judgment matrix are simultaneously substituted into the above-mentioned second calculation formula to obtain a calculation result, which is recorded as the consistency ratio of the preset judgment matrix.

[0127] In this embodiment, the relationship between the preset random consistency index and the preset judgment matrix order is shown in Table 4 below.

[0128] Table 4 Relationship between the preset random consistency index and the preset judgment matrix order

[0129]

[0130]

[0131] 2-4) Checking whether the consistency ratio meets the preset accuracy.

[0132] Specifically, the consistency ratio of the preset judgment matrix is ​​compared with the preset accuracy. When the consistency ratio is less than the preset accuracy, it means that the consistency of the preset judgment matrix meets the preset accuracy; when the consistency ratio is greater than or equal to the preset accuracy, it means that the consistency of the preset judgment matrix does not meet the preset accuracy.

[0133] In this embodiment, the preset accuracy can be set to 0.1, or 0.05, or any specific value, which is not specifically limited here.

[0134] 2-5) When the consistency ratio does not meet the preset accuracy, the preset judgment matrix is ​​modified until the consistency ratio of the modified preset judgment matrix meets the preset accuracy.

[0135] When the consistency ratio of the preset judgment matrix does not meet the preset accuracy, it means that the data in the preset judgment matrix is ​​inconsistent or incomplete. In this case, the preset judgment matrix needs to be corrected.

[0136] In this embodiment, the preset judgment matrix can be corrected by adjusting the element values ​​in the preset judgment matrix; the preset judgment matrix can also be corrected by using the average value or median of the element values ​​in the preset judgment matrix; the preset judgment matrix can also be corrected by reducing the order of the preset judgment matrix and adjusting the elements in the preset judgment matrix; no specific limitations are made here.

[0137] When the consistency ratio of the preset judgment matrix meets the preset accuracy, it means that the data in the preset judgment matrix is ​​consistent and the data is complete, and the preset judgment matrix is ​​determined.

[0138] In this embodiment, the consistency of the preset judgment matrix is ​​checked to ensure the accuracy of the preset judgment matrix. The data deviations in the preset judgment matrix can also be identified and corrected, and the preset judgment matrix can be modified to make the final assessment value corresponding to the trap exploration project calculated based on the preset judgment matrix more accurate.

[0139] In one embodiment, the calculation of the evaluation value of the trap preliminary exploration project based on the index scores of the trap preliminary exploration project and the weights corresponding to the index scores includes:

[0140] 3-1) Substitute the scores of each indicator and the weights corresponding to each indicator score into the evaluation value formula for calculating the trap pre-exploration project to obtain the evaluation value of the trap pre-exploration project.

[0141] In this embodiment, if there are at least two trap exploration projects, the corresponding calculation process can also obtain the corresponding indicator scores and weights for each trap exploration project. Therefore, each trap exploration project can be substituted into the trap exploration project evaluation formula to calculate the evaluation value of at least two trap exploration projects.

[0142] For example, when there are a first, second, and third closed-trap exploration project, the first project is firstly extracted from each indicator score, and the corresponding weight of each indicator score is calculated according to a preset judgment matrix. Subsequently, the indicator scores and corresponding weights of each indicator score in the first project are substituted into the basic calculation formula to calculate the assessment value of the first project, i.e., the first assessment value. Similarly, the assessment value of the second project can be calculated, i.e., the second assessment value; and the assessment value of the third project can also be calculated, i.e., the third assessment value.

[0143] In this embodiment, a bubble sort algorithm may be used to compare the evaluation values; an insertion sort algorithm may be used to sort the evaluation values; or a merge sort algorithm may be used to compare the evaluation values; and no specific limitation is given here.

[0144] 3-2) Compare the assessed values ​​and output the comparison results.

[0145] In this embodiment, to facilitate users in directly observing the assessment values ​​corresponding to various pre-exploration trap projects and to more quickly identify pre-exploration trap projects with higher or lower assessment values, after calculating the assessment values ​​of at least two pre-exploration trap projects, the assessment values ​​are further compared to obtain a comparison result. Based on the comparison result, pre-exploration trap projects whose assessment values ​​exceed a preset requirement are recommended to the user.

[0146] In this embodiment, by comparing or ranking the evaluation values ​​of the trap pre-exploration projects, it is helpful for users to optimize resource allocation of the trap projects, thereby avoiding waste of resources.

[0147] Example 2

[0148] In this embodiment, Figure 2 As shown, a method for automatically recommending trap exploration projects is provided, which includes:

[0149] Step 210: Build a comprehensive indicator system.

[0150] The indicators are used to reflect various data of the trap exploration project. To meet this requirement, reserve indicators, financial indicators and investment performance indicators are introduced.

[0151] Reserve indicators mainly include: reserve scale, reserve abundance, oil and gas controlled reserves per unit footage, oil and gas controlled reserves per well, reserve upgrade coefficient, etc.

[0152] Financial indicators mainly include: net present value, internal rate of return, post-risk value, total return on investment (ROI), net profit rate of project capital (ROE), etc.

[0153] Investment effectiveness indicators mainly include: unit investment control reserves, net present value index, etc.

[0154] Of the aforementioned indicators, reserves indicators are generally obtained from the project's basic information table; financial indicators and investment performance indicators are calculated using the basic parameters of the trap exploration project based on corresponding preset formulas. The indicator system can also add or remove different categories of indicators for specific trap exploration projects.

[0155] Step 220: normalize the trap index.

[0156] Since different trap indicators have different meanings and magnitudes, it is impossible to put them on a unified platform for comprehensive evaluation. It is necessary to normalize the indicators and define the values ​​of different trap indicators between [0,1]. The normalization formula is:

[0157]

[0158] Where x i is the i-th trap indicator data, (x i ) min is the minimum closure index data among all objects, (x i ) max It is the maximum closure index data among all objects.

[0159] Step 230: Assign weights to the trap indicators.

[0160] The decision problem is organized and hierarchical, and a preset judgment matrix is ​​constructed for each level. The preset judgment matrix can also be used to judge the importance of various indicators. If the importance of closure indicator i relative to closure indicator j is a ij , then the importance of the closure index j relative to the closure index i is 1 / a ij , as shown in Table 1.

[0161] Table 5 Judgment matrix scale and its definition

[0162] Scale (or importance) The importance of indicator i relative to indicator j 1 Equal importance 3 Slightly more important 5 Obviously important 7 Strongly important 9 Extremely important 2,4,6,8 The middle value of the adjacent judgment above

[0163] Assuming there are only three indicators, if the importance of closure indicator 2 relative to closure indicator 1 is 3, then closure indicator 2 is labeled 3; if the importance of closure indicator 3 relative to indicator 1 is 5, then closure indicator 3 is labeled 5 (see Table 6).

[0164] Table 6 Trap indicators and their importance

[0165] index Importance marking Trap indicator 1 1 Trap Indicator 2 3 Trap indicator 3 5 Total importance of the trap indicator 1+3+5＝9

[0166] If the closure index 1 has a share of 1 in the total importance, the closure index 2 has a share of 3 in the total importance, and the closure index 3 has a share of 5 in the total importance, then the corresponding weight values ​​are shown in Table 7.

[0167] Table 7 Indicators and their importance

[0168] index Importance marking Trap indicator 1 1 / 9＝11％ Trap Indicator 2 3 / 9＝33％ Trap indicator 3 5 / 9＝56％ Total weight of the closure indicator 100％

[0169] Step 240: Preset judgment matrix consistency check.

[0170] The rationality of the weight assignment corresponding to the evaluation of the trap index is calculated as follows:

[0171]

[0172] Where CI is the consistency index and RI is the random consistency index. The calculation method of CI is as follows:

[0173]

[0174] Among them, λ max is the maximum eigenvalue of the preset judgment matrix, and n is the order of the preset judgment matrix.

[0175] RI is related to the order of the preset judgment matrix, and the corresponding relationship is as follows8.

[0176] Table 8 Average random consistency index RI value

[0177] N 1 2 3 4 5 6 7 RI 0 0 0.52 0.89 1.12 1.24 1.36 8 9 10 11 12 13 14 1.41 1.46 1.49 1.52 1.54 1.56 1.58

[0178] When CR < 0.1, the consistency of the preset judgment matrix is ​​considered acceptable; otherwise, the preset judgment matrix of the trap index needs to be revised.

[0179] Step 250: Calculate the comprehensive score of the trap index, that is, calculate the evaluation value of the trap preliminary exploration project.

[0180] Since each column in the preset judgment matrix approximately reflects the distribution of weights, all columns can be used. The index score of the i-th trap index in the preliminary exploration project; n is the number of trap indicators in the trap preliminary exploration project.

[0181] For example, if the trap pre-exploration projects include Project 1, Project 2, ..., Project 7, and the calculated assessed value of Project 1 is 0.703, Project 2 is 0.733, Project 3 is 0.699, Project 4 is 0.142, Project 5 is 0.323, Project 6 is 0.418, and Project 7 is 0.433, the sorted relationship is shown in Table 9.

[0182] Table 9 Assessment Value

[0183] project Comprehensive score Comprehensive queuing Project 1 0.703 2 Project 2 0.733 1 Project 3 0.699 3 Project 4 0.142 7 Project 5 0.323 6 Project 6 0.418 5 Project 7 0.433 4

[0184] Example 3

[0185] In this embodiment, Figure 3 As shown, an automatic recommendation device for trap exploration projects is provided, comprising: a first acquisition module 310 , a normalization module 320 , a weight calculation module 330 , a construction module 340 and a first calculation module 350 .

[0186] The first acquisition module 310 is used to acquire trap index data of a trap pre-exploration project.

[0187] The normalization module 320 is used to perform normalization processing on the trap index data using a preset index normalization formula to obtain an index score corresponding to each trap index data.

[0188] The weight calculation module 330 is used to calculate the weight corresponding to each indicator score using a preset judgment matrix, wherein the preset judgment matrix is ​​used to describe the importance of each closure indicator compared to any other closure indicator.

[0189] The construction module 340 is configured to calculate an evaluation value of the trap preliminary exploration project according to each indicator score of the trap preliminary exploration project and the weight corresponding to each indicator score.

[0190] The first calculation module 350 is configured to recommend to the user the trap exploration projects whose evaluation values ​​exceed the preset requirements.

[0191] In this embodiment, the first acquisition module 310 acquires trap indicator data for a pre-exploration project and sends the data to the normalization module 320. The normalization module 320 normalizes the trap indicator data using a preset normalization formula to obtain an indicator score corresponding to each trap indicator data. The normalization module 320 then sends each indicator score to the weight calculation module 330. The weight calculation module 330 calculates the weight corresponding to each indicator score using a preset judgment matrix, which describes the importance of each trap indicator relative to any other trap indicator. The weight calculation module 330 then sends the weight corresponding to each indicator score to the construction module 340. The construction module 340 calculates an evaluation value for the pre-exploration project based on each indicator score and the weight corresponding to each indicator score, and then sends the evaluation value to the first calculation module 350. The first calculation module 350 recommends to the user pre-exploration projects whose evaluation values ​​exceed preset requirements. This embodiment combines data analysis technology and algorithms to perform hierarchical analysis on the data of trap pre-exploration projects, and then constructs a preset judgment matrix to calculate the index data of the trap pre-exploration projects and the weights corresponding to each index data. Then, based on the index data and the weights corresponding to each index data, the evaluation value of the trap pre-exploration projects can be accurately calculated, thereby improving the accuracy and efficiency of the evaluation of trap pre-exploration projects and further improving the accuracy of automatic recommendation of trap pre-exploration projects. Secondly, the introduction of multiple data and corresponding at least two evaluation indicators realizes a comprehensive and objective evaluation of the trap pre-exploration projects.

[0192] In one embodiment, the automatic recommendation device for trap exploration projects may further include:

[0193] The analysis module is used to analyze, organize and hierarchically process the trap indicator data to obtain the importance of the trap indicators at the same level compared with the preset criteria at the previous level.

[0194] The filling module is used to compare the importance of each trap index at the same level with the preset criteria at the previous level, obtain a comparison result, and construct a preset judgment matrix based on the comparison result.

[0195] In one embodiment, the automatic recommendation device for trap exploration projects may further include:

[0196] The second acquisition module is used to obtain the maximum eigenvalue of the preset judgment matrix and the order of the preset judgment matrix.

[0197] The second calculation module is used to calculate the consistency index of the preset judgment matrix according to the maximum eigenvalue and the order.

[0198] The third calculation module is used to calculate the consistency ratio of the preset judgment matrix according to the randomly generated random consistency index and the consistency index.

[0199] The detection module is used to detect whether the consistency ratio meets the preset accuracy.

[0200] The correction module is configured to correct the preset judgment matrix when the consistency ratio does not meet the preset accuracy, until the consistency ratio of the corrected preset judgment matrix meets the preset accuracy.

[0201] In one embodiment, the categories of the trap indicators include at least one of reserve indicators, financial indicators, or investment effectiveness indicators.

[0202] In one embodiment, the preset indicator normalization formula is:

[0203]

[0204] Where x i is the i-th trap indicator data, (x i ) min is the minimum closure index data among all objects, (x i ) max It is the maximum closure index data among all objects.

[0205] In one embodiment, the estimated value of the trapped pilot project is calculated according to the following formula:

[0206]

[0207] Where U is the estimated value of the trap pre-exploration project, W i is the weight of the i-th trap indicator; F i is the index score of the i-th closure index; n is the number of indicators.

[0208] The specific definitions of the automatic recommendation device for closed-loop exploration projects can be found in the definitions of the automatic recommendation method for closed-loop exploration projects described above and will not be repeated here. Each unit in the automatic recommendation device for closed-loop exploration projects can be implemented in whole or in part via software, hardware, or a combination thereof. Each of these units can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, allowing the processor to call and execute the corresponding operations of each unit.

[0209] Example 4

[0210] In this embodiment, a computer device is provided. Its internal structure diagram can be shown as follows: Figure 4As shown. The computer device includes a processor, memory, network interface, display screen, and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program, and the non-volatile storage medium is deployed with a database for storing the relevant data involved in the automatic recommendation method for closed exploration projects. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with other computer devices that have application software deployed. When the computer program is executed by the processor, it implements a method for automatically recommending closed exploration projects. The display screen of the computer device can be a liquid crystal display or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or it can be a key, trackball, or touchpad provided on the computer device housing, or it can be an external keyboard, touchpad, or mouse.

[0211] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0212] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0213] Obtain trap indicator data for trap pre-exploration projects;

[0214] Using a preset indicator normalization formula to normalize the trap indicator data, and obtain the indicator score corresponding to each trap indicator data;

[0215] Calculating the weight corresponding to each indicator score using a preset judgment matrix, wherein the preset judgment matrix is ​​used to describe the importance of each closure indicator compared to any other closure indicator;

[0216] Calculating an evaluation value of the trap preliminary exploration project based on each indicator score of the trap preliminary exploration project and the weight corresponding to each indicator score;

[0217] Recommending to the user the trap exploration projects whose evaluation values ​​exceed the preset requirements.

[0218] The memory stores a computer program, and when the processor executes the computer program, it is further configured to implement the following steps:

[0219] Analyze, organize and hierarchize the trap indicator data to obtain the importance of the trap indicators at the same level compared with the preset criteria at the previous level;

[0220] The importance of each trap indicator at the same level is compared with the preset criteria at the previous level to obtain a comparison result, and a preset judgment matrix is ​​constructed based on the comparison result.

[0221] Obtaining the maximum eigenvalue of the preset judgment matrix and the order of the preset judgment matrix;

[0222] Calculating a consistency index of the preset judgment matrix according to the maximum eigenvalue and the order;

[0223] Calculating the consistency ratio of the preset judgment matrix based on the randomly generated random consistency index and the consistency index;

[0224] Detecting whether the consistency ratio meets a preset accuracy;

[0225] When the consistency ratio does not meet the preset accuracy, the preset judgment matrix is ​​modified until the consistency ratio of the modified preset judgment matrix meets the preset accuracy.

[0226] The preset index normalization formula is:

[0227]

[0228] Where x i is the i-th trap indicator data, (x i ) min is the minimum closure index data among all objects, (x i ) max It is the maximum closure index data among all objects.

[0229] The estimated value of the trap pre-exploration project is calculated according to the following formula:

[0230]

[0231] Where U is the estimated value of the trap pre-exploration project, W i is the weight of the i-th trap indicator; F i is the index score of the i-th closure index; n is the number of closure indicators.

[0232] Example 5

[0233] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0234] Obtain trap indicator data for trap pre-exploration projects;

[0235] Using a preset indicator normalization formula to normalize the trap indicator data, and obtain the indicator score corresponding to each trap indicator data;

[0236] Calculating the weight corresponding to each indicator score using a preset judgment matrix, wherein the preset judgment matrix is ​​used to describe the importance of each closure indicator compared to any other closure indicator;

[0237] Calculating an evaluation value of the trap preliminary exploration project based on each indicator score of the trap preliminary exploration project and the weight corresponding to each indicator score;

[0238] Recommending to the user the trap exploration projects whose evaluation values ​​exceed the preset requirements.

[0239] When the computer program is executed by the processor, it is also used to implement the following steps:

[0240] Analyze, organize and hierarchize the trap indicator data to obtain the importance of the trap indicators at the same level compared with the preset criteria at the previous level;

[0241] The importance of each trap indicator at the same level is compared with the preset criteria at the previous level to obtain a comparison result, and a preset judgment matrix is ​​constructed based on the comparison result.

[0242] Obtain the maximum eigenvalue of the preset judgment matrix and the order of the preset judgment matrix.

[0243] A consistency index of the preset judgment matrix is ​​calculated according to the maximum eigenvalue and the order.

[0244] The consistency ratio of the preset judgment matrix is ​​calculated based on the randomly generated random consistency index and the consistency index.

[0245] Check whether the consistency ratio meets the preset accuracy.

[0246] When the consistency ratio does not meet the preset accuracy, the preset judgment matrix is ​​modified until the consistency ratio of the modified preset judgment matrix meets the preset accuracy.

[0247] The preset index normalization formula is:

[0248]

[0249] Where x i is the i-th trap indicator data, (x i ) min is the minimum closure index data among all objects, (xi ) max It is the maximum closure index data among all objects.

[0250] The estimated value of the trap pre-exploration project is calculated according to the following formula:

[0251]

[0252] Where U is the estimated value of the trap pre-exploration project, W i is the weight of the i-th trap indicator; F i is the index score of the i-th closure index; n is the number of closure indicators.

[0253] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0254] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0255] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for automatically recommending trap exploration projects, characterized in that: include: Obtain trap indicator data for trap pre-exploration projects; Using a preset indicator normalization formula to normalize the trap indicator data, the indicator score corresponding to each trap indicator data is obtained; Calculating the weight corresponding to each indicator score using a preset judgment matrix, wherein the preset judgment matrix is ​​used to describe the importance of each closure indicator compared to any other closure indicator; Calculating an evaluation value of the trap preliminary exploration project based on each indicator score of the trap preliminary exploration project and the weight corresponding to each indicator score; Recommending to the user the trap exploration projects whose evaluation values ​​exceed the preset requirements.

2. The method according to claim 1, characterized in that The method further comprises: Analyze, organize and hierarchize the trap indicator data to obtain the importance of the trap indicators at the same level compared with the preset criteria at the previous level; The importance of each trap indicator at the same level is compared with the preset criteria at the previous level to obtain a comparison result, and a preset judgment matrix is ​​constructed based on the comparison result.

3. The method according to claim 2, characterized in that After comparing the importance of each trap indicator at the same level with the preset criteria at the previous level to obtain a comparison result and constructing a preset judgment matrix based on the comparison result, the method further includes: Obtaining the maximum eigenvalue of the preset judgment matrix and the order of the preset judgment matrix; Calculating a consistency index of the preset judgment matrix according to the maximum eigenvalue and the order; Calculating the consistency ratio of the preset judgment matrix based on the randomly generated random consistency index and the consistency index; Detecting whether the consistency ratio meets a preset accuracy; When the consistency ratio does not meet the preset accuracy, the preset judgment matrix is ​​modified until the consistency ratio of the modified preset judgment matrix meets the preset accuracy.

4. The method according to claim 1, wherein The categories of the trap indicators include at least one of the three categories of reserve indicators, financial indicators or investment performance indicators.

5. The method according to any one of claims 1 to 4, characterized in that The preset index normalization formula is: Where x i is the i-th trap indicator data, (x i ) min is the minimum closure index data among all objects, (x i ) max It is the maximum closure index data among all objects.

6. The method according to any one of claims 1 to 4, characterized in that The estimated value of the trap pre-exploration project is calculated according to the following formula: Where U is the estimated value of the trap pre-exploration project, W i is the weight of the i-th trap indicator; F i is the index score of the i-th closure index; n is the number of closure indicators.

7. An automatic recommendation device for trap exploration projects, characterized in that: include: A first acquisition module is used to acquire trap indicator data of a trap pre-exploration project; A normalization module is used to perform normalization processing on the trap index data using a preset index normalization formula to obtain an index score corresponding to each trap index data; a weight calculation module, configured to calculate the weight corresponding to each indicator score using a preset judgment matrix, wherein the preset judgment matrix is ​​used to describe the importance of each closure indicator compared to any other closure indicator; A construction module is used to calculate an evaluation value of the trap preliminary exploration project based on each indicator score of the trap preliminary exploration project and the weight corresponding to each indicator score; The first calculation module is configured to recommend to the user a trap exploration project whose evaluation value exceeds a preset requirement.

8. The automatic recommendation device according to claim 7, characterized in that: An analysis module is used to analyze, organize, and hierarchically process the trap indicator data to determine the importance of the trap indicators at the same level compared to the preset criteria at the previous level; The filling module is used to compare the importance of each trap index at the same level with the preset criteria at the previous level, obtain a comparison result, and construct a preset judgment matrix based on the comparison result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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