Data screening method and system for oil field exploration and development and storage medium

Abstract

The invention relates to the technical field of oil exploration, in particular to a data screening method and system for oil field exploration and development and a storage medium, and the method comprises the steps: S1, obtaining the historical exploration data of an exploration region, and calculating a data collection abnormality index and an equipment collection stability index; s2, calculating a value evaluation coefficient; s3, performing primary screening on the historical exploration data according to the value evaluation coefficient to obtain first reserved data; s4, acquiring current geological data, and calculating the similarity between the first reserved data and the current geological data; and S5, performing secondary screening on the first reserved data to obtain second reserved data. According to the method, the historical exploration and development data are screened twice to obtain the second reserved data, the data with reference values not meeting requirements are removed, the load of an analysis system is reduced, the exploration accuracy and efficiency are improved, and meanwhile, other similar exploration contents in the database can be synchronously optimized to facilitate subsequent exploration operation.

Description

Technical Field

[0001] The present invention relates to the technical field of petroleum exploration, and in particular to a data screening method, system and storage medium for oilfield exploration and development. Background Art

[0002] Geological exploration involves surveying and detecting the geology through various means and methods to obtain geological data. This data is then analyzed to identify areas and strata favorable for oil exploration. Geological data is crucial in the early stages of exploration, helping to understand the subsurface structure and the potential distribution of oil and gas resources. However, some oil exploration and development areas may have been de-explored for some reason and then resumed. In these cases, historical data from previous monitoring periods can be highly valuable. However, some of this historical data may be outdated, inaccurate, or contain errors, while others may be reliable and useful. Initial decisions regarding renewed exploration rely on historical geological data, but it can be difficult to distinguish between useful and inaccurate data. Uncertain data quality can compromise the reliability of decision-making and increase exploration risk.

[0003] Prior art CN116468288A discloses a decision-making method for offshore oil and gas exploration planning, including: determining a target value for proven reserves within a future planning period for the study area; calculating exploration planning parameters based on historical exploration data; determining future development goals based on the proven reserves target; determining decision-making objectives for the exploration planning scheme based on the future development goals; determining constraints for the exploration planning scheme; establishing a decision model based on the decision goals and constraints; solving the decision model to obtain newly added proven geological reserves for each planning area; and calculating exploration planning indicators based on the newly added proven geological reserves and exploration planning parameters to form an exploration planning scheme. This method establishes a decision model, comprehensively considers future development goals and other objective factors, quantifies and models various decision-making attribute conditions, and solves for a satisfactory solution to the overall load plan based on the priority of the determined goals. However, it does not involve value assessment of historical exploration and development data.

[0004] Prior art CN113988433A discloses a method for predicting oil well production in tight oil reservoirs based on a polynomial regression algorithm. This method includes obtaining relevant production data sets; preprocessing the data to retain valid data; using gray correlation to screen features; establishing a model based on the polynomial regression algorithm and optimizing the model parameters through a grid search method; and finally, predicting oil well production by combining the model with production status data. By adopting the above technical solutions, this method analyzes the relationship between production status data and oil production from historical data, deriving an algorithmic model, and then predicting oil well production using new production status data. This provides guidance for construction plans. Although this method involves screening historical data, it is limited to eliminating abnormal and erroneous data and does not analyze or screen historical data that is not useful for exploration.

[0005] Therefore, there is an urgent need to provide a data screening method, system and storage medium for oil field exploration and development to evaluate and analyze historical exploration data in geological exploration and screen historical exploration data with reference value. Summary of the Invention

[0006] In order to solve the above-mentioned technical problems existing in the prior art, the present invention provides a data screening method, system and storage medium for oilfield exploration and development, which comprehensively evaluate historical exploration data, screen out historical exploration data with reference value, and adjust the actual exploration direction and cycle based on this data to adjust the accuracy and efficiency of exploration.

[0007] To achieve the above object, the technical solution of the present invention is as follows:

[0008] A data screening method for oilfield exploration and development comprises the following steps:

[0009] S1. Obtain historical exploration data of the exploration area and calculate the data acquisition anomaly index and equipment acquisition stability index based on the historical exploration data;

[0010] S2. Calculate the value assessment coefficient based on the data collection anomaly index and the equipment collection stability index;

[0011] S3. Screen the historical exploration data based on the value assessment coefficient to obtain the first retained data;

[0012] S4, obtaining current geological data, and calculating the similarity between the first retained data and the current geological data;

[0013] S5. Perform a secondary screening on the first retained data according to the similarity to obtain second retained data, and use the second retained data as the screened historical geological data.

[0014] Furthermore, the calculation method of the data collection abnormality index is:

[0015]

[0016] Among them, SJC represents the data collection abnormality index, YC i Represents the abnormal data set in the T time period {YC i} is the i-th abnormal data value; n represents the number of abnormal data in the T time period.

[0017] Furthermore, the abnormal data set {YC i The method for obtaining} is as follows: obtain the error data ratio ACw, missing data ratio AQs, and duplicate data ratio ACf in the historical exploration data per unit time within the time period T, and calculate the abnormal data value YC.

[0018]

[0019] Establish the abnormal data set YC in the time period T = {YC i}={YC1, YC2, YC3..., YC n}, n is an integer, i=1,2,3……n.

[0020] Furthermore, the calculation method of the equipment acquisition stability index is:

[0021] CYL=2×Dk×Sn×Ts

[0022] Among them, CYL represents the equipment acquisition stability index, Dk represents the bandwidth of the data acquisition equipment signal, Sn represents the operating signal-to-noise ratio of the data acquisition equipment, and Ts represents the duration of the acquisition signal of the data acquisition equipment.

[0023] Furthermore, the calculation method of the operating signal-to-noise ratio Sn of the data acquisition device is:

[0024] Sn=10×log 10 (E1 / E2)

[0025] Where E1 represents the operating power of the data acquisition device, and E2 represents the background noise power of the data acquisition device.

[0026] Furthermore, the calculation method of the value assessment coefficient is:

[0027]

[0028] Among them, Z g represents the value assessment coefficient, SJC represents the data collection abnormality index, CYL represents the equipment collection stability index, and α and β are proportional coefficients.

[0029] Furthermore, step S3 specifically includes:

[0030] A value assessment threshold is set. If the value assessment coefficient is greater than or equal to the value assessment threshold, the historical exploration and development data is marked as normal quality exploration data (i.e., the first retained data), indicating that the results of the historical exploration and development data after collection and processing meet the data quality assessment requirements, the historical exploration and development data is of high value, and can be used as data for subsequent analysis;

[0031] If the value assessment coefficient is less than the value assessment threshold, the historical exploration and development data will be marked as abnormal quality exploration data, indicating that the results of the historical exploration and development data after collection and processing do not meet the data quality assessment requirements, the value of the historical exploration and development data is low, and this part of the historical exploration and development data is discarded and cannot be used as data for subsequent analysis.

[0032] Furthermore, step S4 specifically includes:

[0033] S41, expressing the first retained data in the form of a numerical vector to obtain a plurality of historical exploration ground vectors A; obtaining current geological data, expressing the current geological data in the form of a numerical vector to obtain a current exploration ground vector B;

[0034] S42: Calculate the similarity between the historical exploration ground vector A and the current exploration ground vector B.

[0035] Furthermore, the similarity is calculated as follows:

[0036] First, calculate the inner product NJ of the historical exploration vector A and the current exploration vector B:

[0037]

[0038] Among them, A i and B i is the value of the historical exploration site vector A and the previous exploration site vector B in the i-th dimension, and n is the number of dimensions of the vector;

[0039] Then, calculate the modulus of the historical exploration vector A and the current exploration vector B:

[0040]

[0041]

[0042] Where, (A) represents the modulus of the historical exploration vector A, and (B) represents the modulus of the previous exploration vector B;

[0043] Then calculate the similarity:

[0044] F=NJ / ((A)×(B))

[0045] Among them, F represents the similarity.

[0046] Furthermore, step S5 specifically includes setting a similarity threshold, determining the first retained data having a similarity greater than the similarity threshold as the second retained data; and using the second retained data as the screened historical geological data.

[0047] The present invention also provides a data screening system for oilfield exploration and development, which adopts the above-mentioned data screening method for oilfield exploration and development, comprising:

[0048] Data acquisition module, used to collect historical exploration data and current geological data;

[0049] Calculation module, used for data collection anomaly index, equipment collection stability index, value assessment coefficient and similarity;

[0050] An analysis module for primary screening based on value assessment coefficients and secondary screening based on similarity;

[0051] The output module outputs the data after secondary screening.

[0052] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-mentioned data screening method for oilfield exploration and development.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The data screening method and system for oilfield exploration and development provided by the present invention screen out historical exploration and development data with reference value by comprehensively evaluating historical exploration and development data, and use the historical exploration and development data that meets the requirements as first retained data, thereby reducing the impact of erroneous data. The first retained data is traced back to each historical exploration site, and the overall data of the historical exploration site and the current exploration site are analyzed to determine the similarity between the two. The historical exploration sites with a similarity reaching a set threshold are extracted, and the first retained data is traced back to each historical exploration site for optimized secondary screening. The data that still meets the conditions are used as second retained data, and data with reference value that does not meet the requirements is eliminated, thereby reducing the load of the analysis system. The exploration strategy of the current exploration site is adjusted according to the exploration strategy of the historical exploration site that meets the second retained data, thereby improving the accuracy and efficiency of exploration. At the same time, other similar exploration content in the database can be synchronously optimized to facilitate subsequent exploration operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Flow chart of the method of the present invention.

[0056] Figure 2 This is a system framework diagram of the present invention. DETAILED DESCRIPTION

[0057] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0058] It should be noted that, unless otherwise specifically stated, the relative arrangements of components and steps, and numerical expressions set forth in these embodiments should not be construed as limiting the scope of the present invention.

[0059] The following description of exemplary embodiments is merely illustrative and is not intended to limit the present invention, its application, or use in any sense. Technologies, methods, and apparatus known to those skilled in the art may not be discussed in detail herein, but to the extent applicable, such technologies, methods, and apparatuses should be considered part of this specification.

[0060] Example 1

[0061] The present invention provides a data screening method for oil field exploration and development, such as Figure 1 As shown, the following steps are included:

[0062] S1. Obtain historical exploration data of the exploration area and calculate the data acquisition anomaly index and equipment acquisition stability index based on the historical exploration data;

[0063] The acquisition logic of the data collection anomaly index is:

[0064] Obtain the error data ratio ACw, missing data ratio AQs, and duplicate data ratio ACf in the historical exploration data per unit time within the T time period, and calculate the abnormal data value YC.

[0065]

[0066] Establish the abnormal data set YC in the time period T = {YC i}={YC1, YC2, YC3..., YC n}, n is an integer, i=1,2,3……n.

[0067] Calculate the data collection anomaly index:

[0068]

[0069] Among them, SJC represents the data collection abnormality index, YC i Represents the abnormal data set in the T time period {YC i} is the i-th abnormal data value; n represents the number of abnormal data in the T time period.

[0070] The acquisition logic of the device acquisition stability index is:

[0071] Based on the exploration equipment data in the historical exploration data, the bandwidth Dk of the data acquisition equipment signal is obtained, and the operating power E1 and background noise power E2 of the data acquisition equipment are obtained. The calculation method for the operating signal-to-noise ratio Sn of the data acquisition equipment is as follows:

[0072] Sn=10×log 10 (E1 / E2)

[0073] Obtain the duration Ts of the acquisition signal from the data acquisition device and calculate the device acquisition stability index:

[0074] CYL=2×Dk×Sn×Ts

[0075] Among them, CYL represents the equipment acquisition stability index.

[0076] S2. Calculate the value assessment coefficient based on the data collection anomaly index and the equipment collection stability index;

[0077] The calculated data acquisition anomaly index and equipment acquisition stability index are dimensionless, and the value assessment coefficient is calculated after removing the unit. The specific formula is:

[0078]

[0079] Among them, Z g represents the value assessment coefficient, SJC represents the data collection anomaly index, CYL represents the equipment collection stability index, α and β represent proportional coefficients, and both α and β are greater than 0. The value assessment coefficient reflects the value of the corresponding data.

[0080] The proportional coefficients α and β are actually the weights of the data collection anomaly index and the equipment collection stability index, which can be obtained through linear regression model iteration or determined through historical data and experience.

[0081] S3. Screen the historical exploration data based on the value assessment coefficient to obtain the first retained data; specifically, the following steps are involved:

[0082] A value assessment threshold is set. If the value assessment coefficient is greater than or equal to the value assessment threshold, the historical exploration and development data is marked as normal quality exploration data (i.e., the first retained data), indicating that the results of the historical exploration and development data after collection and processing meet the data quality assessment requirements, the historical exploration and development data is of high value, and can be used as data for subsequent analysis;

[0083] If the value assessment coefficient is less than the value assessment threshold, the historical exploration and development data will be marked as abnormal quality exploration data, indicating that the results of the historical exploration and development data after collection and processing do not meet the data quality assessment requirements, the value of the historical exploration and development data is low, and this part of the historical exploration and development data is discarded and cannot be used as data for subsequent analysis.

[0084] S4, obtaining current geological data, and calculating the similarity between the first retained data and the current geological data; specifically including:

[0085] S41, expressing the first retained data in the form of a numerical vector to obtain a plurality of historical exploration ground vectors A; obtaining current geological data, expressing the current geological data in the form of a numerical vector to obtain a current exploration ground vector B;

[0086] Calculate the inner product NJ of the historical exploration site vector A and the current exploration site vector B, that is, multiply the elements of the corresponding dimensions of the two vectors and add them up as the inner product. The calculation formula is:

[0087]

[0088] Among them, A i and B i is the value of the historical exploration site vector A and the previous exploration site vector B in the i-th dimension, and n is the number of dimensions of the vector;

[0089] Then, calculate the modulus of the historical exploration vector A and the current exploration vector B:

[0090]

[0091]

[0092] Where, (A) represents the modulus of the historical exploration vector A, and (B) represents the modulus of the previous exploration vector B;

[0093] S42: Calculate the similarity between the historical exploration ground vector A and the current exploration ground vector B. The similarity is calculated as follows:

[0094] F=NJ / ((A)×(B))

[0095] Among them, F represents the similarity.

[0096] S5. Perform a secondary screening on the first retained data according to the similarity to obtain second retained data, and use the second retained data as the screened historical geological data.

[0097] Specifically, the method includes setting a similarity threshold, determining the first retained data having a similarity greater than the similarity threshold as the second retained data; and using the second retained data as the screened historical geological data.

[0098] Among them, the standard for determining the similarity threshold: the similarity threshold is used to determine the current exploration value. The specific setting should be based on actual conditions and experience. For example, 80% of the similarity level under completely similar conditions is used as the similarity threshold.

[0099] This method comprehensively evaluates historical exploration data, screens for valuable reference data, and adjusts the actual exploration direction and cycle accordingly, improving exploration accuracy and efficiency. It also simultaneously optimizes other similar exploration content in the database, facilitating subsequent exploration operations. Furthermore, the proposed method and process can be widely applied in the field of petroleum exploration and development to improve the accuracy and efficiency of petroleum exploration and development.

[0100] Example 2

[0101] The present invention also provides a data screening system for oilfield exploration and development, which adopts the data screening method for oilfield exploration and development provided in the first embodiment. Figure 2 As shown, including:

[0102] Data acquisition module, used to collect historical exploration data and current geological data;

[0103] Calculation module, used for data collection anomaly index, equipment collection stability index, value assessment coefficient and similarity;

[0104] An analysis module for primary screening based on value assessment coefficients and secondary screening based on similarity;

[0105] The output module outputs the data after secondary screening.

[0106] Example 3

[0107] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the data screening method for oilfield exploration and development provided in the first embodiment is implemented.

[0108] The above specific embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the scope of the technical solutions of the present invention, and all of these should be included in the scope of the claims of the present invention.

Claims

1. A data screening method for oilfield exploration and development, characterized in that: The following steps are involved: S1. Obtain historical exploration data of the exploration area and calculate the data acquisition anomaly index and equipment acquisition stability index based on the historical exploration data; S2. Calculate the value assessment coefficient based on the data collection anomaly index and the equipment collection stability index; S3. Screen the historical exploration data based on the value assessment coefficient to obtain the first retained data; S4, obtaining current geological data, and calculating the similarity between the first retained data and the current geological data; S5. Perform a secondary screening on the first retained data according to the similarity to obtain second retained data, and use the second retained data as the screened historical geological data.

2. The data screening method according to claim 1, characterized in that: The calculation method of the data collection abnormality index is: Among them, SJC represents the data collection abnormality index, YC i Represents the abnormal data set in the T time period {YC i } is the i-th abnormal data value; n represents the number of abnormal data in the T time period.

3. The data screening method according to claim 2, characterized in that: The abnormal data set within the T time period {YC i The method for obtaining} is as follows: obtain the error data ratio ACw, missing data ratio AQs, and duplicate data ratio ACf in the historical exploration data per unit time within the time period T, and calculate the abnormal data value YC. Establish the abnormal data set YC in the time period T = {YC i }={YC1, YC2, YC3..., YC n }, n is an integer, i=1,2,3……n.

4. The data screening method according to claim 1, wherein: The calculation method of the equipment acquisition stability index is: CYL=2×Dk×Sn×Ts Among them, CYL represents the equipment acquisition stability index, Dk represents the bandwidth of the data acquisition equipment signal, Sn represents the operating signal-to-noise ratio of the data acquisition equipment, and Ts represents the duration of the acquisition signal of the data acquisition equipment.

5. The data screening method according to claim 4, characterized in that: The calculation method of the operating signal-to-noise ratio Sn of the data acquisition equipment is: Sn=10×log 10 (E1 / E2) Where E1 represents the operating power of the data acquisition device, and E2 represents the background noise power of the data acquisition device.

6. The data screening method according to claim 1, wherein: The calculation method of the value assessment coefficient is: Among them, Z g represents the value assessment coefficient, SJC represents the data collection abnormality index, CYL represents the equipment collection stability index, and α and β are proportional coefficients.

7. The data screening method according to claim 1, characterized in that: Step S3 specifically includes: A value assessment threshold is set, and if the value assessment coefficient is greater than or equal to the value assessment threshold, the historical exploration and development data is marked as the first retained data; If the value assessment coefficient is less than the value assessment threshold, the historical exploration and development data will be marked as exploration data with abnormal quality.

8. The data screening method according to claim 1, wherein: Step S4 specifically includes: S41, expressing the first retained data in the form of a numerical vector to obtain a plurality of historical exploration ground vectors A; obtaining current geological data, expressing the current geological data in the form of a numerical vector to obtain a current exploration ground vector B; S42: Calculate the similarity between the historical exploration ground vector A and the current exploration ground vector B.

9. The data screening method according to claim 8, characterized in that: The similarity is calculated as follows: First, calculate the inner product NJ of the historical exploration vector A and the current exploration vector B: Among them, A i and B i is the value of the historical exploration site vector A and the previous exploration site vector B in the i-th dimension, and n is the number of dimensions of the vector; Then, calculate the modulus of the historical exploration vector A and the current exploration vector B: Where, (A) represents the modulus of the historical exploration vector A, and (B) represents the modulus of the previous exploration vector B; Then calculate the similarity: F=NJ / ((A)×(B)) Among them, F represents the similarity.

10. The data screening method according to claim 1, wherein: Step S5 specifically includes setting a similarity threshold, determining the first retained data with a similarity greater than the similarity threshold as the second retained data; and using the second retained data as the screened historical geological data.

11. A data screening system for oilfield exploration and development, using the data screening method for oilfield exploration and development according to any one of claims 1 to 10, characterized in that: include: Data acquisition module, used to collect historical exploration data and current geological data; Calculation module, used for data collection anomaly index, equipment collection stability index, value assessment coefficient and similarity; An analysis module for primary screening based on value assessment coefficients and secondary screening based on similarity; The output module outputs the data after secondary screening.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the data screening method for oil field exploration and development according to any one of claims 1 to 10 is implemented.

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