Oil reservoir judgment method and device based on mineral logging cosine similarity method

Abstract

The invention relates to the technical field of petroleum and natural gas exploration, in particular to an oil layer judgment method and device based on a mineral logging cosine similarity method.The method comprises the steps that content data of mineral components of a target stratum and a known stratum in a target area are obtained, and the content data of the mineral components are subjected to standardization processing; feature vectors representing mineral components and contents of the target stratum and the known stratum are obtained; calculating the cosine similarity between the target stratum and the known stratum in the target area according to the feature vector; and judging the stratum type of the target stratum according to the cosine similarity. According to the method, the accurate position of the current drilling layer can be rapidly and accurately judged in real time by using the cosine similarity, the accuracy and efficiency of horizontal well guiding are improved, more powerful support is provided for engineering adjustment, a guarantee is provided for the subsequent oil and gas recovery ratio, and the method has wide application prospects and important practical significance and is worthy of popularization and application. The method plays an important role in promoting oil field exploration and development.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and is a method, apparatus, equipment and storage medium for determining oil layers based on the cosine similarity method of mineral logging. Background Technology

[0002] Traditional horizontal well steering methods rely primarily on physical drilling parameters (such as drilling fluid properties, downhole pressure, and temperature) and geological data (such as formation rock type and rock physical properties). However, these methods often fail to achieve the desired steering effect under complex geological conditions because the geological data is highly uncertain and the physical drilling parameters are not sensitive enough to geological changes.

[0003] In oil and gas exploration, horizontal well geological steering is a crucial technology that helps drilling teams adjust their drilling direction based on geological data to maximize reservoir recovery. Geological steering typically relies on detailed analysis of thin sections of rock sampled from the wellbore to determine formation type and mineral composition. However, this method has limitations, such as limited sampling points, long analysis times, and high costs. Therefore, a new method is needed to more effectively utilize mineral data to more accurately guide horizontal well drilling. Furthermore, traditional horizontal well steering methods primarily rely on physical drilling parameters (such as drilling fluid properties, downhole pressure, and temperature) and geological data (such as formation rock type and rock physical properties). However, these methods often fail to achieve the desired steering effect under complex geological conditions due to the significant uncertainty in geological data and the insufficient sensitivity of physical drilling parameters to geological changes. In recent years, with advancements in logging technology, the acquisition and analysis of mineral logging data have become increasingly convenient and rapid. Mineral logging can continuously and in real-time provide information on the mineral composition of formations, offering new data support for horizontal well steering. However, how to effectively utilize this massive amount of mineral data to quickly and accurately guide the drilling of horizontal wells remains a problem that urgently needs to be solved. Therefore, developing a new guidance method that fully utilizes mineral logging data to improve the accuracy and reliability of horizontal well guidance has become an urgent need for the industry. Summary of the Invention

[0004] This invention provides a method for determining oil layers based on the cosine similarity method of mineral logging, which overcomes the shortcomings of the prior art. It can effectively solve the problems of limited formation type determination, limited sampling points, long analysis time and high cost in the prior art.

[0005] One of the technical solutions of this invention is achieved through the following measures: a method for determining oil reservoirs based on the cosine similarity method of mineral logging, comprising the following steps:

[0006] Obtain mineral composition data of target strata and known strata within the target area, wherein the known strata include known oil-bearing mudstone strata and oil-bearing strata within the target area;

[0007] The mineral composition content data are standardized to obtain feature vectors representing the mineral composition and content of the target strata and known strata.

[0008] Calculate the cosine similarity between the target strata and known strata within the target area based on the feature vectors;

[0009] Determine the stratigraphic type of the target stratum based on cosine similarity.

[0010] The following are further optimizations and / or improvements to one of the above-mentioned technical solutions:

[0011] The above-mentioned mineral components include quartz, plagioclase, calcite, montmorillonite, illite, kaolinite, analcime, zeolite, anhydrite, and amorphous materials.

[0012] The above-mentioned standardization processing of mineral composition data yields feature vectors characterizing the mineral composition and content of the target strata and known strata, including:

[0013] S1, Calculate the oil cap mineral composition vector X a Oil reservoir mineral composition vector X b and the target strata mineral composition vector X c ,

[0014] Oil cap mineral composition vector X a This represents the mineral composition vector of the known oil-top mudstone formations within the target area.

[0015] Oil reservoir mineral composition vector X b This is the mineral composition vector of the known oil-bearing strata within the target area.

[0016] Target stratigraphic mineral composition vector X c The mineral composition vector of the target stratum is represented as follows:

[0017] X a ={X a1 X a2 , ...X am}

[0018] X b ={X b1 X b2 , ...X bm}

[0019] X c ={X c1 X c2, ...X cm}

[0020] Among them, the oil cap mineral composition vector X a Oil reservoir mineral composition vector X b and the target strata mineral composition vector X c Each dimension within the vector represents the content data of each mineral component, where m is the dimension of the vector.

[0021] S2, calculate the average content X of each mineral component. 平均值 ,

[0022]

[0023] S3, perform data standardization to obtain the normalized vector sum, and the standardized oil cap mineral composition vector X. i Standardized oil reservoir mineral composition vector X h Standardized target stratigraphic mineral composition vector X j They are respectively:

[0024] X i ={X a1 -X 1平均值 X a2 -X 2平均值 , ...X am -X m平均值}

[0025] X h ={X b1 -X 1平均值 X b2 -X 2平均值 , ...X bm -X m平均值}

[0026] X j ={X c1 -X 1平均值 X c2 -X 2平均值 , ...X cm -X m平均值}

[0027] The above calculation of the cosine similarity between the target strata and known strata within the target area based on eigenvectors includes calculating the cosine similarity cosθ between the target strata and the oil-top mudstone strata. ij And calculate the cosine similarity cosθ between the target formation and the oil-bearing formation. hj ,

[0028]

[0029] Among them, m is the dimension of the vector.

[0030] The above formation types include oil-top mudstone formation, oil formation and oil-bottom mudstone formation.

[0031] The above method for judging the reservoir type of the target formation according to the cosine similarity includes:

[0032] If the cosine similarity cc between the target formation and the oil-top mudstone formation is cc≥n, then the target formation and the oil-top mudstone formation have the same type;

[0033] If the cosine similarity co between the target formation and the oil formation is co≥n, then the target formation and the oil formation have the same type;

[0034] If the cosine similarity cc between the target formation and the oil-top mudstone formation is cc < n, and the cosine similarity co between the target formation and the oil formation is co < n, the target formation is an oil-bottom mudstone formation, where n is the judgment threshold.

[0035] The above n = 0.6.

[0036] The second technical solution of the present invention is achieved by the following measures: An oil layer determination device based on the cosine similarity method of mineral logging, including:

[0037] A data acquisition module that obtains the content data of the mineral components of the target formation and the known formations in the target area, and the known formations include the known oil-top mudstone formation and oil formation in the target area;

[0038] A vector calculation module that standardizes the mineral component data to obtain the characteristic vectors representing the mineral components and contents of the target formation and the known formations;

[0039] A cosine similarity module that calculates the cosine similarity between the target formation and the known formations in the target area according to the characteristic vectors;

[0040] A type judgment module that judges the formation type of the target formation according to the cosine similarity.

[0041] The third technical solution of the present invention is achieved by the following measures: A storage medium, on which a computer program readable by a computer is stored, and the computer program is set to execute the oil layer determination method based on the cosine similarity method of mineral logging when running.

[0042] The fourth technical solution of the present invention is achieved by the following measures: An electronic device, including a processor and a memory, and a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the oil layer determination method based on the cosine similarity method of mineral logging.

[0043] This invention provides a method for guiding horizontal wells using mineral logging data and cosine similarity. This method can process mineral data collected during drilling in real time, calculate the cosine similarity between the target formation and known formation mineral data, and guide the drill bit along the optimal path in real time, thereby optimizing drilling efficiency and reducing drilling into non-target formations, improving the accuracy and efficiency of horizontal well guidance, and increasing the recovery rate of oil and gas reservoirs. Attached Figure Description

[0044] Appendix Figure 1 This is a schematic diagram of the oil layer determination device based on the cosine similarity method of mineral logging according to the present invention.

[0045] Appendix Figure 2 This is a comprehensive mineral logging diagram from Embodiment 11 of the present invention.

[0046] Appendix Figure 3 This is a comprehensive mineral logging diagram after adjusting the well inclination in Embodiment 11 of the present invention. Detailed Implementation

[0047] The present invention is not limited to the following embodiments, and the specific implementation can be determined according to the technical solution of the present invention and the actual situation.

[0048] The present invention will be further described below with reference to embodiments:

[0049] Example 1: The oil reservoir determination method based on the cosine similarity method of mineral logging includes the following steps:

[0050] Obtain mineral composition data of target strata and known strata within the target area, wherein the known strata include known oil-bearing mudstone strata and oil-bearing strata within the target area;

[0051] The mineral composition content data are standardized to obtain feature vectors representing the mineral composition and content of the target strata and known strata.

[0052] Calculate the cosine similarity between the target strata and known strata within the target area based on the feature vectors;

[0053] Determine the stratigraphic type of the target stratum based on cosine similarity.

[0054] Cosine similarity It is an index that measures the similarity between two vectors. Where X... I *X J Represents vector X I and X J The dot product, |X I ||X J | represent vectors X and X respectively I and X JThe invention treats the mineral composition of each formation as a multidimensional vector, with the content of each mineral as a component of the vector. The cosine similarity value ranges from -1 to 1; the closer the value is to 1, the more similar the directions of the two vectors are, meaning the more similar the mineral compositions of the two formations are. By calculating the cosine similarity between the target formation and known formations, it quickly determines which known formations the target formation most closely resemble in mineral composition, thereby inferring the geological characteristics of the target formation. This avoids the tedious process of analyzing a large number of formation samples one by one, improving the efficiency and accuracy of horizontal well guidance.

[0055] Example 2: As an optimization of the above examples, the mineral composition includes quartz, plagioclase, calcite, montmorillonite, illite, kaolinite, analcime, zeolite, anhydrite, and amorphous materials.

[0056] Example 3: As an optimization of the above examples, the mineral composition content data is standardized to obtain feature vectors representing the mineral composition and content of the target stratum and known strata, including:

[0057] S1, Calculate the oil cap mineral composition vector X a Oil reservoir mineral composition vector X b and the target strata mineral composition vector X c ,

[0058] Oil cap mineral composition vector X a This represents the mineral composition vector of the known oil-top mudstone formations within the target area.

[0059] Oil reservoir mineral composition vector X b This is the mineral composition vector of the known oil-bearing strata within the target area.

[0060] Target stratigraphic mineral composition vector X c The mineral composition vector of the target stratum is represented as follows:

[0061] X a ={X a1 X a2 , ...X am}

[0062] X b ={X b1 X b2 , ...X bm}

[0063] X c ={X c1 X c2 , ...X cm}

[0064] Among them, the oil cap mineral composition vector Xa Oil reservoir mineral composition vector X b and the target strata mineral composition vector X c Each dimension within the vector represents the content data of each mineral component, where m is the dimension of the vector.

[0065] S2, calculate the average content X of each mineral component. 平均值 ,

[0066]

[0067] S3, perform data standardization to obtain the normalized vector sum, and the standardized oil cap mineral composition vector X. i Standardized oil reservoir mineral composition vector X h Standardized target stratigraphic mineral composition vector X j They are respectively:

[0068] X i ={X a1 -X 1平均值 X a2 -X 2平均值 , ...X am -X m平均值}

[0069] X h ={X b1 -X 1平均值 X b2 -X 2平均值 , ...X bm -X m平均值}

[0070] X j ={X c1 -X 1平均值 X c2 -X 2平均值 , ...X cm -X m平均值}

[0071] Example 4: As an optimization of the above example, the cosine similarity between the target strata and known strata within the target area is calculated based on the feature vectors, including calculating the cosine similarity cosθ between the target strata and the oil-top mudstone strata. ij And calculate the cosine similarity cosθ between the target formation and the oil-bearing formation. hj ,

[0072]

[0073] Where m is the dimension of the vector.

[0074] Example 5: As an optimization of the above embodiments, the formation types include an oil-top mudstone formation, an oil formation, and an oil-bottom mudstone formation.

[0075] Example 6: As an optimization of the above embodiments, determining the reservoir type of the target formation according to the cosine similarity includes:

[0076] If the cosine similarity cc between the target formation and the oil-top mudstone formation is cc≥n, then the target formation and the oil-top mudstone formation have the same type;

[0077] If the cosine similarity co between the target formation and the oil formation is co≥n, then the target formation and the oil formation have the same type;

[0078] If the cosine similarity cc between the target formation and the oil-top mudstone formation is cc < n, and the cosine similarity co between the target formation and the oil formation is co < n, the target formation is an oil-bottom mudstone formation, where n is a judgment threshold.

[0079] Example 7: As an optimization of the above embodiments, n = 0.6.

[0080] Example 8: The oil formation determination device based on the mineral logging cosine similarity method includes:

[0081] A data acquisition module that obtains the content data of the mineral components of the target formation and the known formations in the target area, and the known formations include the known oil-top mudstone formation and oil formation in the target area;

[0082] A vector calculation module that performs standardization processing on the mineral component data to obtain the feature vectors representing the mineral components and contents of the target formation and the known formations;

[0083] A cosine similarity module that calculates the cosine similarity between the target formation and the known formations in the target area according to the feature vectors;

[0084] A type judgment module that determines the formation type of the target formation according to the cosine similarity.

[0085] Example 9: A storage medium stores a computer program readable by a computer, and the computer program is set to execute the oil formation determination method based on the mineral logging cosine similarity method when running.

[0086] Example 10: An electronic device includes a processor and a memory. The memory stores a computer program, and the computer program is loaded and executed by the processor to implement the oil formation determination method based on the mineral logging cosine similarity method.

[0087] The processor described above can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an ASIC, an FPGA, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. It can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The memory can include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, portable hard drives, magnetic disks, or optical disks.

[0088] Example 11: The specific implementation process of this oil reservoir determination method based on the cosine similarity method of mineral logging is as follows:

[0089] (1) Obtain the mineral composition data of the target strata and the known oil top mudstone strata and oil layer strata within the target area.

[0090] Based on lithology and GR curves, comparison well sections were delineated and determined. (See attached...) Figure 2 The oil-bearing mudstone ranges from 2480m to 2554m, the oil layer from 2572m to 3320m, and the target strata from 3340m to 3382m.

[0091] Mineral composition data of the target strata and known oil-bearing mudstone and oil-bearing strata were collected using X-ray diffraction analysis and other methods. The average XRD data of each segment is shown in Table 1.

[0092] (2) Standardize the mineral composition data to obtain feature vectors representing the mineral composition and content of the target strata and known strata;

[0093] Based on Table 1, the oil cap mineral composition vector X is obtained. a Oil reservoir mineral composition vector X b and the target strata mineral composition vector X c ,

[0094] X a ={X a1 X a2 , ...X am}

[0095] X b ={X b1 X b2 , ...X bm}

[0096] X c ={X c1 X c2 , ...X cm}, m = 10;

[0097] The average value for each mineral was calculated, and the results are shown in Table 2.

[0098]

[0099] Data standardization is performed to obtain a normalized vector sum, and the standardized oil cap mineral composition vector X is obtained. i Standardized oil reservoir mineral composition vector X h Standardized target stratigraphic mineral composition vector X j They are respectively:

[0100] X i ={X a1 -X 1平均值 X a2 -X 2平均值 , ...X am -X m平均值}

[0101] X h ={X b1 -X 1平均值 X b2 -X 2平均值 , ...X bm -X m平均值}

[0102] X j ={X c1 -X 1平均值 X c2 -X 2平均值 , ...X cm -X m平均值}

[0103] The normalized data are shown in Table 3.

[0104] (3) Calculate the cosine similarity between the target strata and known strata within the target area based on the feature vectors;

[0105] Cosine similarity between the target formation and the oil-top mudstone formation (cosθ) ij Cosine similarity (cosθ) between the target formation and the oil-bearing formation hj They are respectively

[0106]

[0107] Where m is the dimension of the vector, and in this embodiment m = 10, that is:

[0108]

[0109] Based on Table 3, the cosine similarity cosθ between the target formation and the oil-top mudstone formation was calculated. ij The cosine similarity cosθ between the target formation and the oil-bearing formation is 0.41. hj It is -0.86.

[0110] (4) The stratigraphic type of the target formation is determined based on the cosine similarity. In this embodiment, the threshold n is set to 0.6. The cosine similarity between the target formation and the oil-bearing formation is cosθ. hj A value of -0.86 indicates that the target formation and the oil-bearing formation are completely dissimilar, with a high degree of distinguishability. The cosine similarity (cosθ) between the target formation and the oil-bearing mudstone formation is... ij A similarity of 0.41 (less than 0.6) indicates a low degree of similarity between the target formation and the top mudstone formation, with significant differences, suggesting that the two mudstone sections are not the same layer. The cosine similarity calculation provides decision support for horizontal well guidance. Specifically, by comparing the similarity between predicted formation samples and known formation samples during drilling in different directions, the drill bit's direction is guided to achieve precise drilling of specific formations. The geological characteristics of the target formation are determined based on the cosine similarity results and compared with the mineral data of the top mudstone formation. A consistent similarity indicates drilling to the top of the oil layer; otherwise, it indicates drilling to the bottom. This comparison guides the horizontal well's drilling direction, adjusts the drilling trajectory, and allows for rapid and accurate re-drilling into the oil layer to optimize oil and gas reservoir recovery. The cosine similarity (cosθ) between the target formation and the top mudstone formation is used to determine the drilling direction. ij 0.41 and the cosine similarity cosθ between the target formation and the oil-bearing formation. hj -0.86. Based on the cosine similarity calculation results between the target formation and the known upper formation, it is determined that the target formation and the known upper formation are not the same geological body. This indicates that the drill bit trajectory has penetrated the oil layer and is traveling at the bottom of the oil layer. The well inclination is adjusted, the drilling trajectory is adjusted upward, and the drill bit returns to the oil layer. The mineral logging composite chart after adjusting the well inclination is shown below. Figure 3 .

[0111] Through the practical application of this invention in the Jimsar shale oil block of Xinjiang Oilfield, the method of using mineral data and cosine similarity to assist in the guidance of horizontal wells provides new ideas and methods for the development of horizontal well drilling technology.

[0112] The present invention has the following beneficial effects:

[0113] (1) High accuracy: Through accurate mineral data and cosine similarity calculation, this invention can more accurately predict and guide the direction of the drill bit, thereby improving the target hit rate of drilling.

[0114] (2) High adaptability: This invention is not limited by the complexity of geological conditions and can be effectively guided even in areas with large variations in geological conditions.

[0115] (3) Easy to operate: By processing mineral data and calculating similarity, it can quickly provide support for guiding decision-making and reduce the interference of human factors.

[0116] (4) Use mineral data and cosine similarity to more accurately determine the geological characteristics of the target strata.

[0117] (5) It enables real-time guidance of the drilling direction of horizontal wells, thereby improving the recovery rate of oil and gas reservoirs.

[0118] In summary, this invention provides an oil layer determination method based on the cosine similarity method of mineral logging. It can quickly, accurately and in real time determine the precise location of the currently encountered layer by utilizing cosine similarity, thereby improving the accuracy and efficiency of horizontal well steering, providing stronger support for engineering adjustments, ensuring subsequent oil and gas recovery, and having broad application prospects and important practical significance. It plays an important role in promoting oilfield exploration and development.

[0119] The above technical features constitute the preferred embodiment of the present invention, which has strong adaptability and optimal implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the requirements of different situations.

[0120] Table 1

[0121]

[0122] Table 2

[0123]

[0124] Table 3

[0125]

Claims

1. A method for determining oil reservoirs based on the cosine similarity method of mineral logging, characterized in that... Includes the following steps: Obtain mineral composition data of target strata and known strata within the target area, wherein the known strata include known oil-bearing mudstone strata and oil-bearing strata within the target area; The mineral composition data are standardized to obtain feature vectors representing the mineral composition and content of the target strata and known strata. Calculate the cosine similarity between the target strata and known strata within the target area based on the feature vectors; Determine the stratigraphic type of the target stratum based on cosine similarity.

2. The method for determining oil reservoirs based on the cosine similarity method of mineral logging according to claim 1, characterized in that... The mineral composition includes quartz, plagioclase, calcite, montmorillonite, illite, kaolinite, analcime, zeolite, anhydrite, and amorphous materials.

3. The method for determining oil reservoirs based on the cosine similarity method of mineral logging according to claim 1 or 2, characterized in that... The mineral composition data are standardized to obtain feature vectors representing the mineral composition and content of the target strata and known strata, including: S1, Calculate the oil cap mineral composition vector X a Oil reservoir mineral composition vector X b and the target strata mineral composition vector X c Oil cap mineral composition vector X a Let X be the mineral composition vector of the known oil-bearing mudstone formation within the target area, and X be the mineral composition vector of the oil layer. b The target formation mineral composition vector is the known oil-bearing strata mineral composition vector X within the target area. c The mineral composition vector of the target stratum is represented as follows: X a ={X a1 ,X a2 ,......X am } X b ={X b1 ,X b2 ,.....X bm } X c ={X c1 ,X c2 ,......X cm } Among them, the oil cap mineral composition vector X a Oil reservoir mineral composition vector X b and the target strata mineral composition vector X c Each dimension within the vector represents the content data for each mineral component, where m is the dimension of the vector. S2, calculate the average content X of each mineral component. 平均值 , S3, perform data standardization to obtain the normalized vector sum, and the standardized oil cap mineral composition vector X. i Standardized oil reservoir mineral composition vector X h Standardized target stratigraphic mineral composition vector X j They are respectively: X i ={X a1 -X 1平均值 ,X a2 -X 2平均值 ,......X am -X m平均值 } X h ={X b1 -X 1平均值 ,X b2 -X 2平均值 ,......X bm -X m平均值 } X j ={X c1 -X 1平均值 ,X c2 -X 平均值 ,......X cm -X m平均值 }。 4. The method for determining oil reservoirs based on the cosine similarity method of mineral logging according to any one of claims 1 to 3, characterized in that... Calculate the cosine similarity between the target strata and known strata within the target area based on the feature vectors, including calculating the cosine similarity cosθ between the target strata and the oil-top mudstone strata. ij And calculate the cosine similarity cosθ between the target formation and the oil-bearing formation. hj , Where m is the dimension of the vector.

5. The method for determining oil reservoirs based on the cosine similarity method of mineral logging according to any one of claims 1 to 4, characterized in that... The stratigraphic types include oil-top mudstone formations, oil-bearing formations, and oil-bottom mudstone formations.

6. The method for determining oil reservoirs based on the cosine similarity method of mineral logging according to any one of claims 1 to 5, characterized in that... Determining the stratigraphic type of a target stratum based on cosine similarity includes: If the cosine similarity co ≥ n between the target formation and the oil-top mudstone formation, then the target formation and the oil-top mudstone formation are of the same type. If the cosine similarity co ≥ n between the target formation and the oil-bearing formation, then the target formation and the oil-bearing formation are of the same type. Cosine similarity between the target formation and the oil-top mudstone formation (cosθ) ij <n, and the cosine similarity between the target formation and the oil-bearing formation is cosθ hj <n, the target formation is an oil-bearing mudstone formation, where n is the judgment threshold.

7. The method for determining oil reservoirs based on the cosine similarity method of mineral logging according to claim 6, characterized in that... n＝0.6。 8. An oil layer determination device based on the cosine similarity method of mineral logging, wherein the oil layer determination device based on the cosine similarity method of mineral logging uses the oil layer determination method based on the cosine similarity method of mineral logging as described in any one of claims 1 to 7, characterized in that... include: The data acquisition module obtains the mineral composition content data of the target strata and known strata within the target area, wherein the known strata include the known oil-top mudstone strata and oil-bearing strata within the target area; The vector calculation module standardizes the mineral composition data to obtain feature vectors representing the mineral composition and content of the target stratum and known strata. The cosine similarity module calculates the cosine similarity between the target strata and known strata within the target area based on the feature vectors. The type determination module determines the stratigraphic type of the target stratum based on cosine similarity.

9. A storage medium, characterized in that... The storage medium stores a computer program that can be read by a computer, and the computer program is configured to execute the oil layer determination method based on the mineral logging cosine similarity method as described in any one of claims 1 to 7 when it runs.

10. An electronic device, characterized in that... It includes a processor and a memory, wherein the memory stores a computer program, which is loaded and executed by the processor to implement the oil layer determination method based on the mineral logging cosine similarity method as described in any one of claims 1 to 7.

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