Method for evaluating lateral sealing of shallow faults

Abstract

The present application relates to oil and gas exploration technical field, and disclose a kind of shallow fault lateral sealing evaluation method, comprising the following steps: S1: data collection and pretreatment: the lithology data of collecting research area hanging wall and footwall, oil and gas layer depth data D, fault throw data T, and fluid property, pressure condition, temperature;S2: calculate the smearing of mudstone on the hanging wall of fault to research point: calculate the mudstone smearing ASGRHih of all calculation points of single mudstone Hih to research point, the mudstone smearing of all mudstones in the hanging wall of fault to research point is superimposed, and ASGRh is obtained;S3: calculate the smearing of mudstone on the footwall of fault to research point: calculate the mudstone smearing ASGRHif of all calculation points of single mudstone Hif to research point, the mudstone smearing of all mudstones in the footwall of fault to research point is superimposed, and ASGRf is obtained;S4: multi-source data fusion and comprehensive evaluation: using K-means determines threshold Q, and consider the geological background of research area, fault type, oil and gas reservoir characteristic factor carries out customization adjustment.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to a method for evaluating the lateral sealing of shallow faults. Background Technology

[0002] In shallow geological environments (typically at depths above 1600 meters), mudstone smearing plays a significant role in controlling the lateral sealing of faults due to the generally strong plasticity of mudstone. Therefore, evaluating the lateral sealing of shallow faults is of paramount importance for oil and gas exploration and development. Mudstone smearing is a core mechanism in the formation of fault lateral sealing. Specifically, mudstone smearing refers to the process where, when strata fracture, mudstone, due to its plasticity, fills the fault fractures, forming a smearing layer. This process has a significant impact on the sealing of the fault.

[0003] In existing technologies, using the minimum ASGR value (Assessment of the Contribution of Mudstone at Depth D to the Lateral Sealing of Faults) as the threshold Q may be somewhat subjective, as the selection of the minimum value can be influenced by data distribution and outliers, leading to inaccurate determination of the threshold Q. This inaccuracy may further affect the evaluation results of fault lateral sealing. Furthermore, the applicability of the Q value may be limited by geological conditions and fault characteristics. Especially under different geological backgrounds, blindly using the same Q value for evaluation may affect the accuracy of the results. Therefore, a method for evaluating the lateral sealing of shallow faults is proposed. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies. Using the minimum ASGR (Assessment and Ranging) value of mudstone at depth D as the threshold Q can be subjective, as the selection of the minimum value may be influenced by data distribution and outliers, leading to inaccurate determination of the threshold Q. This inaccuracy may further affect the evaluation results of fault lateral sealing. Furthermore, the applicability of the Q value may be limited by geological conditions and fault characteristics. Especially under different geological backgrounds, blindly using the same Q value for evaluation may compromise the accuracy of the results. Therefore, this invention proposes a method for evaluating the lateral sealing of shallow faults.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for evaluating the lateral sealing of shallow faults includes the following steps:

[0007] S1: Data collection and preprocessing: Collect lithological data of the hanging wall and footwall of the fault in the study area, oil and gas reservoir depth data D, fault displacement data T, as well as fluid properties, pressure conditions, and temperature;

[0008] S2: Calculate the mudstone smear ASGRHih of the hanging wall mudstone on the study point: Take the point at depth D as the study point, and the depth range [D, D+T] as the study range, where T represents a depth range. Count the total number n of mudstones in the hanging wall, as well as the thickness, location, fluid properties, pressure conditions, and temperature of each mudstone. Treat points at different depths within mudstone Hih as independent calculation points, then each calculation point has a different distance r from the study point; denote the distance from the study point to the top of mudstone Hih as rih, then the value range of r is [rih, rih+ΔHih]. Calculate the mudstone smear ASGRHih of all calculation points of a single mudstone Hih on the study point. Superimpose the mudstone smears of all mudstones in the hanging wall on the study point to obtain ASGRh.

[0009] S3: Calculate the mudstone smear ASGRHif of the footwall mudstone on the study point: Take the point at depth D as the study point, and the depth range [DT,D] as the study range. Count the total number m of mudstones in the footwall, as well as the thickness, location, fluid properties, pressure conditions, and temperature of each mudstone. Treat points at different depths within the mudstone Hif as independent calculation points. Each calculation point has a different distance r from the study point. Denote the distance from the study point to the bottom of the mudstone Hif as rif. The value range of r is [rif, rif+ΔHif]. Calculate the mudstone smear ASGRHif of all calculation points on the study point for a single mudstone Hif. Superimpose the mudstone smears of all mudstones in the footwall on the study point to obtain ASGRf.

[0010] S4: Multi-source data fusion and comprehensive evaluation: K-means was used to determine the threshold Q, and adjustments were made considering the geological background, fault type, and hydrocarbon reservoir characteristics of the study area;

[0011] S5: Calculate the fault lateral sealing evaluation results at the study point: Combine ASGRh and ASGRf to obtain the fault lateral sealing evaluation result ASGR at the study point, which is used to quantify the contribution or effect of mudstone on the fault lateral sealing.

[0012] S6: Evaluate the lateral closure of the fault at non-depth D study points: Based on the threshold Q obtained in S4, calculate the ASGR values ​​corresponding to other points in the fault at non-depth D, and use intelligent prediction technology to predict and evaluate the lateral closure of the fault at non-depth D study points. If the ASGR value of a non-depth D study point is greater than or equal to Q, then the study point has lateral closure of the fault; otherwise, the study point is considered not to have lateral closure of the fault.

[0013] S7: Multi-scale analysis and independent verification: Combining macroscopic geological structures and microscopic geological features, multi-scale analysis is conducted to verify the evaluation results, ensuring the accuracy and reliability of the evaluation.

[0014] The above further includes:

[0015] Furthermore, in S1, the lithological data includes core samples and cuttings. The core samples are obtained through drilling operations and are described and analyzed in detail in the laboratory. The cuttings are initially classified and recorded at the drilling site. The oil and gas reservoir depth data is obtained through logging techniques to obtain information on the physical and chemical properties of the underground rocks, thereby determining the depth and thickness of the oil and gas reservoir. The logging techniques include sonic logging, resistivity logging, natural gamma logging, etc. The fluid properties, pressure conditions, and temperature are also obtained through logging techniques. For example, by measuring parameters such as rock resistivity, sonic velocity, and density, the type and properties of underground fluids can be inferred.

[0016] Furthermore, in S1, after collecting the raw data, data cleaning is performed to remove erroneous, duplicate, or inconsistent data. The data is normalized or standardized to eliminate the influence of different units. Logarithmic transformation and square root transformation are performed on the data to improve the distribution characteristics of the data.

[0017] Furthermore, in S2, considering fluid properties, pressure conditions, and temperature, a smearing efficiency function f(r, fluid properties, pressure conditions, temperature) = g(r) * h(fluid properties) * i(pressure conditions) * j(temperature) is established. This smearing efficiency function describes the contribution of different depth points r in mudstone to the smearing point of the study point. Here, g(r) is a function describing the influence of distance, and h(fluid properties), i(pressure conditions), and j(temperature) are functions describing the influence of fluid properties, pressure conditions, and temperature, respectively.

[0018] The formula for calculating ASGRHih for mudstone smears is:

[0019]

[0020] Where rih is the distance from the research point to the top of the mudstone Hih, and ΔHih is the thickness of the mudstone;

[0021] ASGRh = ∑(ASGRHih).

[0022] Furthermore, in S3, considering fluid properties, pressure conditions, and temperature, a smearing efficiency function f(r, fluid properties, pressure conditions, temperature) = g(r) * h(fluid properties) * i(pressure conditions) * j(temperature) is established. This smearing efficiency function describes the contribution of different depth points r in mudstone to the smearing point of the study point. Here, g(r) is a function describing the influence of distance, and h(fluid properties), i(pressure conditions), and j(temperature) are functions describing the influence of fluid properties, pressure conditions, and temperature, respectively.

[0023] The formula for calculating ASGRHif for mudstone smears is:

[0024]

[0025] Where rif is the distance from the research point to the top of mudstone Hif, and ΔHif is the thickness of mudstone;

[0026] ASGRf = ∑(ASGRHif).

[0027] Further, in S4, the specific steps for determining the threshold Q using K-means are as follows:

[0028] Data collection:

[0029] Collect geological, geophysical, and geochemical data within the study area, including fault distribution, lithological information, and oil and gas reservoir characteristics;

[0030] Data preprocessing:

[0031] The data is standardized to eliminate the influence of different units, and a relationship matrix is ​​constructed to describe the similarity or affinity between different data points.

[0032] Determine the number of clusters:

[0033] The silhouette coefficient and dispersion score are used to evaluate the effect of different numbers of clusters, and the number of clusters with a silhouette coefficient close to 1 or a small dispersion score is selected as the optimal solution.

[0034] Perform cluster analysis:

[0035] K-means was applied to perform cluster analysis on the data, and the clustering results were described, including the cluster centers, cluster size, and cluster density characteristics.

[0036] Observation cluster diagram:

[0037] Clustering graphs (such as clustering trees, scatter plots, etc.) are used to observe the separation between different clusters and determine a suitable threshold T so that the distance between classes does not exceed the value of T.

[0038] Calculate the threshold Q:

[0039] Based on the clustering results and geological background information, a reasonable threshold Q is determined;

[0040] Considering the geological background:

[0041] Based on the geological background information of the study area (such as strata, structure, lithology, etc.), the threshold Q should be adjusted as necessary. For example, in fault-developed areas, the threshold Q may need to be reduced to more accurately identify the impact of faults on oil and gas reservoirs.

[0042] Consider fault type:

[0043] The threshold Q is customized according to the fault type (such as normal fault, reverse fault, strike-slip fault, etc.). Different types of faults have different effects on oil and gas reservoirs, so different thresholds Q are needed to reflect this difference.

[0044] Considering the characteristics of oil and gas reservoirs:

[0045] The threshold Q can be customized based on the characteristics of the oil and gas reservoir (such as reservoir type, fluid properties, pressure conditions, etc.). For example, in high-pressure oil and gas reservoirs, the threshold Q may need to be increased to more accurately identify the boundaries of the oil and gas reservoir.

[0046] Furthermore, the specific steps for applying K-means to cluster the data during cluster analysis are as follows:

[0047] Number of clusters selected (K):

[0048] Specify how many clusters the data should be divided into; this is usually a parameter provided by the user before the algorithm is applied.

[0049] Initialize cluster centers:

[0050] K data points are randomly selected as initial cluster centers, which represent the center of each cluster;

[0051] Assign data points to the nearest cluster center:

[0052] For each data point, calculate its distance to each cluster center and assign it to the cluster containing the nearest cluster center;

[0053] Update cluster centers:

[0054] For each cluster, calculate the average of all data points in that cluster, and use this average as the new cluster center;

[0055] Iteration:

[0056] Repeatedly assign data points to the nearest cluster center and update the cluster centers until the cluster centers no longer change significantly, or the predetermined number of iterations is reached.

[0057] Furthermore, in S5, the fault lateral closure evaluation result of the study point is calculated: ASGRh and ASGRf are combined to obtain the fault lateral closure evaluation result ASGR of the study point, ASGR=ASGRh+ASGRf=∑(ASGRHih)+∑(ASGRHif).

[0058] Furthermore, in S7, the multi-scale analysis combining macroscopic geological structures and microscopic geological features involves the following specific steps:

[0059] Macroscopic geological structure analysis: Collect geological structure data of the study area, including information on stratigraphic distribution, fault strike, dip and dip angle, etc. Use geological modeling software or draw geological structure maps by hand to show the spatial relationship between the fault and the surrounding strata. Kriging interpolation can be used to estimate the physical properties of the strata on both sides of the fault.

[0060] Microscopic geological feature analysis: Rock samples are collected from both sides of the fault, and experiments such as thin section identification, scanning electron microscopy, and X-ray diffraction analysis are conducted to reveal information such as the microstructure, mineral composition, and cementation characteristics of the rocks. Based on the information, the mechanical properties and sealing capacity of the rocks on both sides of the fault are assessed.

[0061] Furthermore, in S2-S6, if the distance between a single mudstone and the study point is r=0, it is assumed that the mudstone must have a lateral sealing effect on oil and gas, and the ASGR value of the study point is not included in the determination of the Q value.

[0062] The present invention has the following beneficial effects:

[0063] 1. In this invention, K-means is used to determine the threshold Q, and it is adjusted in combination with factors such as the geological background, fault type, and oil and gas reservoir characteristics of the study area, so that the determination of the threshold is more scientific and reasonable. This helps to more accurately judge the lateral sealing state of the fault and improve the accuracy and practicality of the evaluation.

[0064] 2. In this invention, by introducing more factors that affect mudstone smearing, such as fluid properties, pressure conditions, and temperature, and by combining numerical simulation and laboratory experiments for verification, the accuracy and reliability of the calculation results are significantly improved. This helps to more accurately assess the lateral sealing of faults and provides a more scientific basis for oil and gas exploration and development. Attached Figure Description

[0065] Figure 1This is a flowchart illustrating the method steps of a shallow fault lateral sealing evaluation method proposed in this invention. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] Please see Figure 1 As shown, this invention provides a method for evaluating the lateral sealing of shallow faults, comprising the following steps:

[0068] S1: Data collection and preprocessing: Collect lithological data of the hanging wall and footwall of the fault in the study area, oil and gas reservoir depth data D, fault displacement data T, as well as fluid properties, pressure conditions, and temperature;

[0069] S2: Calculate the mudstone smear ASGRHih of the hanging wall mudstone on the study point: Take the point at depth D as the study point, and the depth range [D, D+T] as the study range, where T represents a depth range. Count the total number n of mudstones in the hanging wall, as well as the thickness, location, fluid properties, pressure conditions, and temperature of each mudstone. Treat points at different depths within mudstone Hih as independent calculation points, then each calculation point has a different distance r from the study point; denote the distance from the study point to the top of mudstone Hih as rih, then the value range of r is [rih, rih+ΔHih]. Calculate the mudstone smear ASGRHih of all calculation points of a single mudstone Hih on the study point. Superimpose the mudstone smears of all mudstones in the hanging wall on the study point to obtain ASGRh.

[0070] S3: Calculate the mudstone smear ASGRHif of the footwall mudstone on the study point: Take the point at depth D as the study point, and the depth range [DT,D] as the study range. Count the total number m of mudstones in the footwall, as well as the thickness, location, fluid properties, pressure conditions, and temperature of each mudstone. Treat points at different depths within the mudstone Hif as independent calculation points. Each calculation point has a different distance r from the study point. Denote the distance from the study point to the bottom of the mudstone Hif as rif. The value range of r is [rif, rif+ΔHif]. Calculate the mudstone smear ASGRHif of all calculation points on the study point for a single mudstone Hif. Superimpose the mudstone smears of all mudstones in the footwall on the study point to obtain ASGRf.

[0071] S4: Multi-source data fusion and comprehensive evaluation: K-means was used to determine the threshold Q, and adjustments were made considering the geological background, fault type, and hydrocarbon reservoir characteristics of the study area;

[0072] S5: Calculate the fault lateral closure evaluation results at the study point: Combine ASGRh and ASGRf to obtain the fault lateral closure evaluation results ASGR at the study point;

[0073] S6: Evaluate the lateral closure of the fault at non-depth D study points: Based on the threshold Q obtained in S4, calculate the ASGR values ​​corresponding to other points in the fault at non-depth D, and use intelligent prediction technology to predict and evaluate the lateral closure of the fault at non-depth D study points. If the ASGR value of a non-depth D study point is greater than or equal to Q, then the study point has lateral closure of the fault; otherwise, the study point is considered not to have lateral closure of the fault.

[0074] S7: Multi-scale analysis and verification: Combining macroscopic geological structures and microscopic geological features, multi-scale analysis is conducted to verify the evaluation results, ensuring the accuracy and reliability of the evaluation.

[0075] In one embodiment, for the above-mentioned S1, the lithological data includes core samples and cuttings. The core samples are obtained through drilling operations and are described and analyzed in detail in the laboratory. The cuttings are preliminarily classified and recorded at the drilling site. The oil and gas reservoir depth data is obtained through logging techniques to obtain information on the physical and chemical properties of the underground rocks, thereby determining the depth and thickness of the oil and gas reservoir. The logging techniques include sonic logging, resistivity logging, natural gamma logging, etc. The fluid properties, pressure conditions, and temperature are also obtained through logging techniques. For example, by measuring parameters such as the resistivity, acoustic velocity, and density of the rock, the type and properties of the underground fluid can be inferred.

[0076] In one embodiment, for the above S1, after collecting the raw data, data cleaning is performed to remove erroneous, duplicate, or inconsistent data, and the data is normalized or standardized to eliminate the influence of different units. Logarithmic transformation and square root transformation are performed on the data to improve the distribution characteristics of the data.

[0077] In one embodiment, for the above S2, considering fluid properties, pressure conditions, and temperature, a smearing efficiency function f(r, fluid properties, pressure conditions, temperature) = g(r) * h(fluid properties) * i(pressure conditions) * j(temperature) is established. The smearing efficiency function describes the contribution of different depth points r in mudstone to the smearing point of the study point, where g(r) is a function describing the influence of distance, and h(fluid properties), i(pressure conditions), and j(temperature) are functions describing the influence of fluid properties, pressure conditions, and temperature, respectively.

[0078] The formula for calculating ASGRHih for mudstone smears is:

[0079]

[0080] Where rih is the distance from the research point to the top of the mudstone Hih, and ΔHih is the thickness of the mudstone;

[0081] ASGRh = ∑(ASGRHih).

[0082] In one embodiment, for the above S3, considering fluid properties, pressure conditions, and temperature, a smearing efficiency function f(r, fluid properties, pressure conditions, temperature) = g(r) * h(fluid properties) * i(pressure conditions) * j(temperature) is established. The smearing efficiency function describes the contribution of different depth points r in mudstone to the smearing point of the study point, where g(r) is a function describing the influence of distance, and h(fluid properties), i(pressure conditions), and j(temperature) are functions describing the influence of fluid properties, pressure conditions, and temperature, respectively.

[0083] The formula for calculating ASGRHif for mudstone smears is:

[0084]

[0085] Where rif is the distance from the research point to the top of mudstone Hif, and ΔHif is the thickness of mudstone;

[0086] ASGRf = ∑(ASGRHif).

[0087] In one embodiment, for S4 above, the specific steps for determining the threshold Q using cluster analysis are as follows:

[0088] Data collection:

[0089] Collect geological, geophysical, and geochemical data within the study area, including fault distribution, lithological information, and oil and gas reservoir characteristics;

[0090] Data preprocessing:

[0091] The data is standardized to eliminate the influence of different units, and a relationship matrix is ​​constructed to describe the similarity or affinity between different data points.

[0092] Determine the number of clusters:

[0093] The silhouette coefficient and dispersion score are used to evaluate the effect of different numbers of clusters, and the number of clusters with a silhouette coefficient close to 1 or a small dispersion score is selected as the optimal solution.

[0094] Perform cluster analysis:

[0095] K-means was applied to perform cluster analysis on the data, and the clustering results were described, including the cluster centers, cluster size, and cluster density characteristics.

[0096] Observation cluster diagram:

[0097] Clustering graphs (such as clustering trees, scatter plots, etc.) are used to observe the separation between different clusters and determine a threshold T so that the distance between classes does not exceed the value of T.

[0098] Calculate the threshold Q:

[0099] The threshold Q is determined based on the clustering results and geological background information;

[0100] Considering the geological background:

[0101] The threshold Q is adjusted based on the geological background information of the study area (such as strata, structure, lithology, etc.). For example, in fault-developed areas, the threshold Q may need to be reduced to more accurately identify the impact of faults on oil and gas reservoirs.

[0102] Consider fault type:

[0103] The threshold Q is adjusted according to the fault type (such as normal fault, reverse fault, strike-slip fault, etc.). Different types of faults have different effects on oil and gas reservoirs, so different thresholds Q are needed to reflect this difference.

[0104] Considering the characteristics of oil and gas reservoirs:

[0105] The threshold Q is adjusted according to the characteristics of the oil and gas reservoir (such as reservoir type, fluid properties, pressure conditions, etc.). For example, in high-pressure oil and gas reservoirs, it may be necessary to increase the threshold Q to more accurately identify the boundaries of the oil and gas reservoir.

[0106] Suppose we have a study area containing multiple faults and several oil and gas reservoirs. We collected geological, geophysical, and geochemical data for this area and standardized them. Then, we selected the K-means clustering algorithm to perform cluster analysis on the data and determined the optimal number of clusters to be 3. Next, we observed the cluster plots and determined a reasonable threshold Q = 0.5 based on the distance between cluster centers and the compactness of samples within clusters. Finally, we customized the threshold Q according to the geological background, fault types, and oil and gas reservoir characteristics of the study area, obtaining a final threshold Q' = 0.45. This threshold Q' will be used in subsequent oil and gas exploration and development work to more accurately identify the boundaries of oil and gas reservoirs and the influence of faults.

[0107] In one embodiment, for the above-described cluster analysis, the specific steps of applying K-means to cluster the data are as follows:

[0108] Number of clusters selected (K):

[0109] Specify how many clusters the data should be divided into; this is usually a parameter provided by the user before the algorithm is applied.

[0110] Initialize cluster centers:

[0111] K data points are randomly selected as initial cluster centers, which represent the center of each cluster;

[0112] Assign data points to the nearest cluster center:

[0113] For each data point, calculate its distance to each cluster center and assign it to the cluster containing the nearest cluster center;

[0114] Update cluster centers:

[0115] For each cluster, calculate the average of all data points in that cluster, and use this average as the new cluster center;

[0116] Iteration:

[0117] Repeatedly assign data points to the nearest cluster center and update the cluster centers until the cluster centers no longer change significantly, or the predetermined number of iterations is reached.

[0118] In one embodiment, for the above S5, in S5, the fault lateral closure evaluation result of the study point is calculated: ASGRh and ASGRf are combined to obtain the fault lateral closure evaluation result ASGR of the study point, ASGR=ASGRh+ASGRf=∑(ASGRHih)+∑(ASGRHif).

[0119] In one embodiment, for S5 above, in S7, the step of combining macroscopic geological structures and microscopic geological features to perform multi-scale analysis includes the following specific steps:

[0120] Macroscopic geological structure analysis: Collect geological structure data of the study area, including information on stratigraphic distribution, fault strike, dip and dip angle, etc. Use geological modeling software or draw geological structure maps by hand to show the spatial relationship between the fault and the surrounding strata. Kriging interpolation can be used to estimate the physical properties of the strata on both sides of the fault.

[0121] Assuming there is a northwest-dipping fault with an angle of about 45° in the study area, analysis of the geological structure map reveals significant differences in thickness and lithological variations in the strata on both sides of the fault, which may affect the lateral sealing of the fault.

[0122] Microscopic geological feature analysis: Rock samples are collected from both sides of the fault, and experiments such as thin section identification, scanning electron microscopy, and X-ray diffraction analysis are conducted to reveal information such as the microstructure, mineral composition, and cement characteristics of the rocks. Based on the information, the mechanical properties and sealing capacity of the rocks on both sides of the fault are evaluated.

[0123] Thin section analysis of the rocks on both sides of the fault revealed that the rocks on one side were rich in clay minerals and had low porosity, indicating that they had good sealing capacity. The rocks on the other side, however, had higher porosity and contained more brittle minerals, which may have resulted in poorer sealing of the fault on that side.

[0124] In one embodiment, for the above S2-S6, if the distance between a single mudstone and the study point is r=0, then the mudstone is considered to have a lateral sealing effect on oil and gas, and the ASGR value of the study point is not included in the determination of the Q value.

[0125] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the lateral sealing of shallow faults, characterized in that, Includes the following steps: S1: Data Collection and Preprocessing: Collect lithological data and hydrocarbon reservoir depth data for the hanging wall and footwall of the fault in the study area. Displacement data And fluid properties, pressure conditions, and temperature; S2: Calculation of mudstone smearing at the study point by mudstone on the hanging wall of the fault. : based on depth Taking the points as research points, with depth segments Within the scope of the study, Represents a depth range, counting the total number of mudstone formations in the hanging wall of a fault. In addition, the thickness, location, fluid properties, pressure conditions, and temperature of each mudstone are used to calculate the individual mudstone. All calculation points relative to the mudstone smear at the study points By superimposing mudstone smears from all mudstones in the hanging wall of the fault onto the mudstone smears at the study point, we obtain... ; Considering fluid properties, pressure conditions, and temperature, establish a coating efficiency function. The smearing efficiency function describes the different depth points in the mudstone. The contribution of smearing to the study points, among which, It is a function that describes the effect of distance. i (pressure condition) and j (temperature) are functions describing the fluid properties, pressure conditions, and the effects of temperature, respectively. (Mudstone smearing) The calculation formula is: ,in, To study mudstone Distance at the top, The thickness of the mudstone. h); S3: Calculate the mudstone smearing effect of the footwall mudstone on the study point. : based on depth Taking the points as research points, with depth segments To define the scope of the study, the total number of mudstone formations in the footwall of the fault was counted. In addition to the thickness, location, fluid properties, pressure conditions, and temperature of each mudstone, calculate the individual mudstone. All calculation points relative to the mudstone smear at the study points By superimposing mudstone smears from all mudstones in the footwall of the fault onto the mudstone smears at the study point, the following results were obtained. ; Considering fluid properties, pressure conditions, and temperature, establish a coating efficiency function. The smearing efficiency function describes the different depth points in the mudstone. The contribution of smearing to the study points, among which, It is a function that describes the effect of distance. i (pressure condition) and j (temperature) are functions describing the fluid properties, pressure conditions, and the effects of temperature, respectively. (Mudstone smearing) The calculation formula is: ,in, To study mudstone Distance at the top, The thickness of the mudstone. f); S4: Multi-source data fusion and comprehensive evaluation: Determining the threshold using K-means Furthermore, customized adjustments were made taking into account the geological background, fault type, and oil and gas reservoir characteristics of the study area. S5: Calculate the fault lateral sealing evaluation results at the study point: and Combined, the results of the fault lateral sealing evaluation at the study point were obtained. , ; S6: Evaluation of Non-Depth Lateral fault closure at the study point: based on the threshold obtained in S4. Calculate non-depth faults Other points corresponding Values, utilizing intelligent prediction technology for non-depth The lateral closure of the fault at the study point is predicted and evaluated. S7: Multi-scale analysis and verification: Combining macroscopic geological structures and microscopic geological characteristics, multi-scale analysis is conducted to verify the evaluation results.

2. The method for evaluating the lateral sealing of shallow faults according to claim 1, characterized in that, In S1, the lithological data includes core samples and cuttings. The core samples are obtained through drilling operations and are described and analyzed in detail in the laboratory. The cuttings are initially classified and recorded at the drilling site. The oil and gas reservoir depth data are obtained through logging technology to obtain information on the physical and chemical properties of the underground rocks, thereby determining the depth and thickness of the oil and gas reservoir. The fluid properties, pressure conditions, and additional temperature parameters are also obtained through logging technology.

3. The method for evaluating the lateral sealing of shallow faults according to claim 1, characterized in that, In S1, after the raw data is collected, data cleaning is performed to remove erroneous, duplicate, or inconsistent data. The data is normalized or standardized to eliminate the influence of different units. Logarithmic transformation and square root transformation are performed on the data to improve the distribution characteristics of the data.

4. The method for evaluating the lateral sealing of shallow faults according to claim 1, characterized in that, In S4, the threshold is determined using K-means. Specific steps: Data collection: Collect geological, geophysical, and geochemical data within the study area; Data preprocessing: The data is standardized to eliminate the influence of different units, and a relationship matrix is ​​constructed to describe the similarity or affinity between different data points. Determine the number of clusters: The silhouette coefficient and dispersion are used to evaluate the effect of different numbers of clusters, and the number of clusters with a silhouette coefficient close to 1 or a small dispersion is selected as the optimal solution. Perform cluster analysis: K-means was applied to perform cluster analysis on the data, and the clustering results were described, including the cluster centers, cluster size, and cluster density characteristics. Observation cluster diagram: Clustering graphs are used to observe the separation between different clusters, and a threshold T is determined so that the distance between classes does not exceed the value of T. Calculate the threshold Q: Based on the clustering results and geological background information, a threshold Q is determined; Considering the geological background: The threshold Q is adjusted based on the geological background information of the study area; Consider fault type: The threshold Q is adjusted according to the fault type; Considering the characteristics of oil and gas reservoirs: The threshold Q is adjusted based on the characteristics of the oil and gas reservoir.

5. The method for evaluating the lateral sealing of shallow faults according to claim 4, characterized in that, The specific steps for applying K-means to cluster the data in performing cluster analysis are as follows: Number of clusters selected (K): Specify how many clusters to divide the data into; Initialize cluster centers: Random selection 10 data points are used as initial cluster centers, which represent the center of each cluster; Assign data points to the nearest cluster center: For each data point, calculate its distance to each cluster center and assign it to the cluster containing the nearest cluster center; Update cluster centers: For each cluster, calculate the average of all data points in that cluster, and use this average as the new cluster center; Iteration: Repeatedly assign data points to the nearest cluster center and update the cluster centers until the cluster centers no longer change significantly, or the predetermined number of iterations is reached.

6. The method for evaluating the lateral sealing of shallow faults according to claim 1, characterized in that, In S7, the multi-scale analysis is carried out by combining macroscopic geological structures and microscopic geological features. The specific steps are as follows: Macroscopic geological structure analysis: collect geological structure data of the study area, use geological modeling software or draw geological structure maps by hand to show the spatial relationship between the fault and the surrounding strata; Microscopic geological feature analysis: collect rock samples from both sides of the fault, conduct experiments to reveal the information of the rocks, and evaluate the mechanical properties and sealing capacity of the rocks on both sides of the fault based on the information.

7. The method for evaluating the lateral sealing of shallow faults according to claim 1, characterized in that, In S2-S6, if the distance between a single mudstone and the study point exists... In such cases, it is believed that the mudstone must have a lateral sealing effect on oil and gas, and this research site... Value does not participate Value setting.

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