Method for evaluating lateral sealing performance of shallow fault

Abstract

The invention relates to the technical field of oil-gas exploration, and discloses a shallow fault lateral sealing performance evaluation method, which comprises the following steps: S1, data collection and preprocessing: collecting lithology data, hydrocarbon reservoir depth data D, fault displacement data T, fluid properties, pressure conditions and temperature of a fault hanging wall and a footwall of a research area; s2, calculating the smearing of the fault hanging wall mudstone on the research point: calculating the mudstone smearing ASGRHih of all calculation points on the research point of a single mudstone Hih, and superposing the mudstone smearing of all the mudstones on the research point in the fault hanging wall to obtain ASGRh; s3, calculating the smearing of the fault footwall mudstone on the research point: calculating the mudstone smearing ASGRHif of all calculation points on the research point of a single mudstone Hif, and superposing the mudstone smearing of all the mudstones on the research point in the fault footwall to obtain ASGRf; and S4, multi-source data fusion and comprehensive evaluation: determining a threshold value Q by using K-means, and performing customized adjustment by considering the geological background, fault type and oil and gas reservoir characteristic factors of the research area.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas exploration, and in particular to a method for evaluating the lateral sealing performance of a shallow fault. Background Art

[0002] For shallow geological environments (usually those with a depth of more than 1,600 meters), mudstone smear plays a significant role in controlling the lateral closure of faults because mudstone generally exhibits strong plastic characteristics. Therefore, evaluating the lateral closure of shallow faults is extremely important for oil and gas exploration and development. Mudstone smear is a core mechanism for the formation of lateral closure of faults. Specifically, mudstone smear refers to the process in which mudstone fills into the fault gap due to its plastic properties when a stratum fractures, forming a smear layer. This process has an important impact on the closure of the fault.

[0003] In the prior art, the use of the minimum ASGR result of the evaluation value of the contribution of mudstone at a depth of D to the lateral sealing of the fault as the threshold Q may be subjective to a certain extent, because the selection of the minimum value may be affected by data distribution and outliers, resulting in inaccurate determination of the threshold Q. This inaccuracy may further affect the evaluation results of the lateral sealing of the fault. At the same time, the applicability of the Q value may be limited by geological conditions and fault characteristics. In particular, in different geological backgrounds, if the same Q value is blindly used for evaluation, the accuracy of the evaluation results may be affected. Therefore, a method for evaluating the lateral sealing of shallow faults is proposed. Summary of the invention

[0004] The purpose of the present invention is to solve the problem in the prior art that the minimum value of the ASGR result of the evaluation value of the contribution of mudstone at a depth of D to the lateral sealing of the fault may be subjective, because the selection of the minimum value may be affected by data distribution and outliers, resulting in inaccurate determination of the threshold Q. This inaccuracy may further affect the evaluation results of the lateral sealing of the fault. At the same time, the applicability of the Q value may be limited by geological conditions and fault characteristics, especially under different geological backgrounds. If the same Q value is blindly used for evaluation, the accuracy of the evaluation results may be affected. A method for evaluating the lateral sealing of shallow faults is proposed.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for evaluating the lateral sealing of a shallow fault comprises the following steps:

[0007] S1: Data collection and preprocessing: Collect lithology data of the upper and lower walls of the fault in the study area, oil and gas layer depth data D, fault distance data T, as well as fluid properties, pressure conditions, and temperature;

[0008] S2: Calculate the mudstone smear ASGRHih of the upper wall of the fault to the study point: take the point with depth D as the study point, and the depth segment [D, D+T] as the study range, where T represents a depth range, and count the total number n of mudstones in the upper wall of the fault, as well as the thickness, position, fluid properties, pressure conditions, and temperature of each mudstone. The points at different depths in the mudstone Hih are regarded as independent calculation points, and each calculation point has a different distance r from the study point; the distance from the study point to the top of the mudstone Hih is recorded as rih, and the value range of r is [rih, rih+ΔHih]. Calculate the mudstone smear ASGRHih of all calculation points of a single mudstone Hih to the study point, and superimpose the mudstone smears of all mudstones in the upper wall of the fault to the study point to obtain ASGRh;

[0009] S3: Calculate the mudstone smear ASGRHif of the fault footwall mudstone to the study point: Take the point with depth D as the study point and the depth segment [DT, D] as the study range, count the total number m of mudstones in the fault footwall, as well as the thickness, position, fluid properties, pressure conditions, and temperature of each mudstone, and treat the points at different depths in the mudstone Hif as independent calculation points, so that each calculation point has a different distance r from the study point; record the distance from the study point to the bottom of the mudstone Hif as rif, then the value range of r is [rif, rif+ΔHif], calculate the mudstone smear ASGRHif of all calculation points of a single mudstone Hif to the study point, and superimpose the mudstone smear of all mudstones in the fault footwall to the study point to obtain ASGRf;

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

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

[0012] S6: Evaluate the lateral closure of the fault at the non-depth D research point: According to 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 the non-depth D research point. If the ASGR value of the non-depth D research point is greater than or equal to Q, the research point has the lateral closure of the fault; otherwise, it is considered that the research point does not have the lateral closure of the fault;

[0013] S7: Multi-scale analysis and independent verification: Multi-scale analysis is conducted in combination with macroscopic geological structures and microscopic geological characteristics to verify the evaluation results to ensure the accuracy and reliability of the evaluation.

[0014] The above further includes:

[0015] Furthermore, in S1, the lithological data includes cores and cuttings. The cores 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 layer depth data obtains physical and chemical property information of underground rocks through logging technology, thereby determining the depth and thickness of the oil and gas layer. The logging technology includes sonic logging, resistivity logging, natural gamma logging, etc. The fluid properties, pressure conditions, and temperature are also obtained through logging technology. For example, by measuring parameters such as rock resistivity, sonic wave velocity and density, the type and properties of underground fluids can be inferred.

[0016] Furthermore, in S1, after the raw data are collected, data cleaning is performed to remove erroneous, duplicate or inconsistent data, the data is normalized or standardized to eliminate the influence of different dimensions, and the data is logarithmically transformed and square root transformed to improve the distribution characteristics of the data.

[0017] Furthermore, in S2, considering the 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 smearing contribution of different depth points r in the mudstone to the research point, where g(r) is a function describing the influence of distance, h(fluid properties), i(pressure conditions), and j(temperature) are functions describing the influence of fluid properties, pressure conditions, and temperature, respectively;

[0018] The calculation formula of mudstone smear ASGRHih is:

[0019]

[0020] Among them, rih is the distance from the study point to the top of the mudstone Hih, and ΔHih is the thickness of the mudstone;

[0021] ASGRh=∑(ASGRHih).

[0022] Furthermore, in S3, considering the 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 smearing contribution of different depth points r in the mudstone to the research point, where g(r) is a function describing the influence of distance, h(fluid properties), i(pressure conditions), and j(temperature) are functions describing the influence of fluid properties, pressure conditions, and temperature, respectively;

[0023] The calculation formula of mudstone smear ASGRHif is:

[0024]

[0025] Among them, rif is the distance from the study point to the top of the mudstone Hif, and ΔHif is the thickness of the mudstone;

[0026] ASGRf=∑(ASGRHif).

[0027] Furthermore, in S4, the threshold Q is determined by using K-means, specifically in the following steps:

[0028] Data Collection:

[0029] Collect geological, geophysical and geochemical data in the study area, including fault distribution, lithology information, oil and gas reservoir characteristics, etc.;

[0030] Data preprocessing:

[0031] Standardize the data to eliminate the influence of different dimensions, construct a relationship matrix, and describe the similarity or closeness between different data points;

[0032] Determine the number of clusters:

[0033] Use the Silhouette Coefficient and Dispersion Score to evaluate the effects of different numbers of clusters, and choose the number of clusters with a Silhouette Coefficient close to 1 or a small dispersion as the optimal solution;

[0034] Perform cluster analysis:

[0035] K-means is used to cluster the data. After the clustering results are obtained, the characteristics of cluster center, cluster size and cluster density are described.

[0036] Observation clustering diagram:

[0037] Use cluster diagrams (such as cluster trees, scatter plots, etc.) to observe the separation between different clusters and determine a suitable threshold T so that the distance between classes does not exceed the T value;

[0038] Calculate the threshold Q:

[0039] Determine a reasonable threshold Q based on clustering results and geological background information;

[0040] Consider the geological context:

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

[0042] Consider the fault type:

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

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

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

[0046] Furthermore, in performing cluster analysis, the specific steps of applying K-means to perform cluster analysis on data are:

[0047] Choose the number of clusters (K):

[0048] Specify how many clusters to divide the data into, which is usually a parameter provided by the user before the algorithm is applied;

[0049] Initialize the cluster centers:

[0050] Randomly select K data points as initial cluster centers (centroids), which represent the center of each cluster;

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

[0052] For each data point, calculate its distance from each cluster center and assign it to the cluster with the closest cluster center.

[0053] Update cluster centers:

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

[0055] Iteration:

[0056] Repeat the process of assigning data points to the nearest cluster center and updating the cluster center until the cluster center no longer changes significantly or the predetermined number of iterations is reached.

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

[0058] Further, in S7, the macroscopic geological structure and microscopic geological characteristics are combined to perform multi-scale analysis, the specific steps are:

[0059] Macro-geological structural analysis: Collect geological structural data of the study area, including information on stratum distribution, fault strike, dip and inclination, and use geological modeling software or manually draw geological structural maps 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] Analysis of microscopic geological characteristics: rock samples are collected from both sides of the fault, and experiments such as thin section identification, scanning electron microscopy observation, and X-ray diffraction analysis are conducted to reveal information such as the rock's microstructure, mineral composition, and cement characteristics. Based on this information, the mechanical properties and sealing capacity of the rocks on both sides of the fault are evaluated.

[0061] Furthermore, in S2-S6, if the distance between a single mudstone and the research point is r = 0, it is considered that the mudstone must have the function of laterally sealing oil and gas, and the ASGR value of the research point is not involved in the formulation of the Q value.

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

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

[0064] 2. In the present invention, by introducing more factors affecting mudstone smearing, such as fluid properties, pressure conditions, and temperature, and combining numerical simulation and laboratory experiments for verification, the accuracy and reliability of the calculation results are significantly improved, which helps to more accurately evaluate the lateral sealing of the fault and provide a more scientific basis for oil and gas exploration and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1This is a method step diagram for evaluating the lateral sealing of shallow faults proposed by the present invention. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] See also Figure 1 As shown, the present invention is a method for evaluating the lateral sealing of a shallow fault, comprising the following steps:

[0068] S1: Data collection and preprocessing: Collect lithology data of the upper and lower walls of the fault in the study area, oil and gas layer depth data D, fault distance data T, as well as fluid properties, pressure conditions, and temperature;

[0069] S2: Calculate the mudstone smear ASGRHih of the upper wall of the fault to the study point: take the point with depth D as the study point, and the depth segment [D, D+T] as the study range, where T represents a depth range, and count the total number n of mudstones in the upper wall of the fault, as well as the thickness, position, fluid properties, pressure conditions, and temperature of each mudstone. The points at different depths in the mudstone Hih are regarded as independent calculation points, and each calculation point has a different distance r from the study point; the distance from the study point to the top of the mudstone Hih is recorded as rih, and the value range of r is [rih, rih+ΔHih]. Calculate the mudstone smear ASGRHih of all calculation points of a single mudstone Hih to the study point, and superimpose the mudstone smears of all mudstones in the upper wall of the fault to the study point to obtain ASGRh;

[0070] S3: Calculate the mudstone smear ASGRHif of the fault footwall mudstone to the study point: Take the point with depth D as the study point and the depth segment [DT, D] as the study range, count the total number m of mudstones in the fault footwall, as well as the thickness, position, fluid properties, pressure conditions, and temperature of each mudstone, and treat the points at different depths in the mudstone Hif as independent calculation points, so that each calculation point has a different distance r from the study point; record the distance from the study point to the bottom of the mudstone Hif as rif, then the value range of r is [rif, rif+ΔHif], calculate the mudstone smear ASGRHif of all calculation points of a single mudstone Hif to the study point, and superimpose the mudstone smear of all mudstones in the fault footwall to the study point to obtain ASGRf;

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

[0072] S5: Calculate the fault lateral sealing evaluation result of the research point: combine ASGRh and ASGRf to obtain the fault lateral sealing evaluation result ASGR of the research point;

[0073] S6: Evaluate the lateral closure of the fault at the non-depth D research point: According to 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 the non-depth D research point. If the ASGR value of the non-depth D research point is greater than or equal to Q, the research point has the lateral closure of the fault; otherwise, it is considered that the research point does not have the lateral closure of the fault;

[0074] S7: Multi-scale analysis and verification: Combine macroscopic geological structures and microscopic geological characteristics to conduct multi-scale analysis and verify the evaluation results to ensure the accuracy and reliability of the evaluation.

[0075] In one embodiment, for the above S1, in S1, the lithology data includes cores and cuttings. The cores 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 layer depth data obtains physical and chemical property information of underground rocks through logging technology, thereby determining the depth and thickness of the oil and gas layer. The logging technology includes sonic logging, resistivity logging, natural gamma logging, etc. The fluid properties, pressure conditions, and temperature are also obtained through logging technology. For example, by measuring parameters such as rock resistivity, sonic wave velocity and density, the type and properties of underground fluids can be inferred.

[0076] In one embodiment, for the above S1, in S1, after collecting the original data, data cleaning is performed to remove erroneous, duplicate or inconsistent data, the data is normalized or standardized to eliminate the influence of different dimensions, and the data is logarithmically transformed and square root transformed to improve the distribution characteristics of the data.

[0077] In one embodiment, for the above S2, in S2, considering the 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, wherein the smearing efficiency function describes the smearing contribution of different depth points r in the mudstone to the research point, wherein 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 calculation formula of mudstone smear ASGRHih is:

[0079]

[0080] Among them, rih is the distance from the study 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, in S3, considering the 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, wherein the smearing efficiency function describes the smearing contribution of different depth points r in the mudstone to the research point, wherein 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 calculation formula of mudstone smear ASGRHif is:

[0084]

[0085] Among them, rif is the distance from the study point to the top of the mudstone Hif, and ΔHif is the thickness of the mudstone;

[0086] ASGRf=∑(ASGRHif).

[0087] In one embodiment, for the above S4, in S4, the threshold Q is determined by cluster analysis, specifically in the following steps:

[0088] Data Collection:

[0089] Collect geological, geophysical and geochemical data in the study area, including fault distribution, lithology information, oil and gas reservoir characteristics, etc.;

[0090] Data preprocessing:

[0091] Standardize the data to eliminate the influence of different dimensions, construct a relationship matrix, and describe the similarity or closeness between different data points;

[0092] Determine the number of clusters:

[0093] Use the Silhouette Coefficient and Dispersion Score to evaluate the effects of different numbers of clusters, and choose the number of clusters with a Silhouette Coefficient close to 1 or a small dispersion as the optimal solution;

[0094] Perform cluster analysis:

[0095] K-means is used to cluster the data. After the clustering results are obtained, the characteristics of cluster center, cluster size and cluster density are described.

[0096] Observation clustering diagram:

[0097] Use cluster diagrams (such as cluster trees, scatter plots, etc.) to observe the separation between different clusters and determine the threshold T so that the distance between classes does not exceed the T value;

[0098] Calculate the threshold Q:

[0099] Determine the threshold Q based on the clustering results and geological background information;

[0100] Consider the geological context:

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

[0102] Consider the fault type:

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

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

[0105] According to the characteristics of the oil and gas reservoir (such as reservoir type, fluid properties, pressure conditions, etc.), the threshold Q is adjusted. For example, in a high-pressure oil and gas reservoir, the threshold Q may need to be increased to more accurately identify the boundary of the oil and gas reservoir;

[0106] Suppose we have a study area that contains multiple faults and multiple oil and gas reservoirs. We collected geological, geophysical, and geochemical data in the area and standardized them. Then, we selected the K-means clustering algorithm to cluster the data and determined that the optimal number of clusters was 3. Next, we observed the clustering graph and determined a reasonable threshold value Q = 0.5 based on the distance between cluster centers and the compactness of samples within the cluster. Finally, we customized the threshold value Q according to the geological background, fault type, and oil and gas reservoir characteristics of the study area, and obtained the final threshold value Q' = 0.45. This threshold value 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 impact of faults.

[0107] In one embodiment, for the above-mentioned cluster analysis, in the cluster analysis, the specific steps of applying K-means to perform cluster analysis on data are:

[0108] Choose the number of clusters (K):

[0109] Specify how many clusters to divide the data into, which is usually a parameter provided by the user before the algorithm is applied;

[0110] Initialize the cluster centers:

[0111] Randomly select K data points as initial cluster centers (centroids), which represent the center of each cluster;

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

[0113] For each data point, calculate its distance from each cluster center and assign it to the cluster with the closest cluster center.

[0114] Update cluster centers:

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

[0116] Iteration:

[0117] Repeat the process of assigning data points to the nearest cluster center and updating the cluster center until the cluster center no longer changes significantly or the predetermined number of iterations is reached.

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

[0119] In one embodiment, for the above S5, in S7, the multi-scale analysis is performed by combining macroscopic geological structures and microscopic geological features, and the specific steps are:

[0120] Macro-geological structural analysis: Collect geological structural data of the study area, including information on stratum distribution, fault strike, dip and inclination, and use geological modeling software or manually draw geological structural maps 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 that there is a fault in the study area that dips to the northwest and has an inclination of about 45°, through the analysis of geological structural maps, it is found that there are obvious thickness differences and lithology changes in the strata on both sides of the fault, which may affect the lateral closure of the fault;

[0122] Micro-geological characteristics analysis: Collect rock samples from both sides of the fault, conduct thin section identification, scanning electron microscope observation, X-ray diffraction analysis and other experiments to reveal information such as the rock's microstructure, mineral composition and cement characteristics, and evaluate the mechanical properties and sealing capacity of the rocks on both sides of the fault based on this information;

[0123] Through thin section identification of rocks on both sides of the fault, it was found that the rocks on one side of the fault are rich in clay minerals and have low porosity, which indicates that the rocks on this side have good sealing ability. On the other side, the rocks have high porosity and contain more brittle minerals, which may lead to poor sealing of the fault on this side.

[0124] In one embodiment, for the above S2-S6, in S2-S6, if the distance between a single mudstone and the research point is r=0, it is considered that the mudstone must have the function of laterally sealing oil and gas, and the ASGR value of the research point is not involved in the formulation of the Q value.

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

Claims

1. A method for evaluating the lateral sealing of a shallow fault, characterized in that: The following steps are involved: S1: Data collection and preprocessing: Collect lithology data of the upper and lower walls of the fault in the study area, oil and gas layer depth data D, fault distance data T, as well as fluid properties, pressure conditions, and temperature; S2: Calculate the mudstone smear ASGRHih of the fault hanging wall mudstone to the study point: take the point with depth D as the study point, and the depth segment [D, D+T] as the study range, where T represents a depth range, count the total number n of mudstones in the fault hanging wall, as well as the thickness, position, fluid properties, pressure conditions, and temperature of each mudstone, calculate the mudstone smear ASGRHih of all calculation points of a single mudstone Hih to the study point, and superimpose the mudstone smears of all mudstones in the fault hanging wall to the study point to obtain ASGRh; S3: Calculate the mudstone smear ASGRHif of the fault footwall mudstone on the study point: Take the point with depth D as the study point and the depth segment [DT, D] as the study range, count the total number m of mudstones in the fault footwall, as well as the thickness, position, fluid properties, pressure conditions, and temperature of each mudstone, calculate the mudstone smear ASGRHif of all calculation points of a single mudstone Hif on the study point, and superimpose the mudstone smear of all mudstones in the fault footwall on the study point to obtain ASGRf; S4: Multi-source data fusion and comprehensive evaluation: K-means is used to determine the threshold Q, and the adjustment is made considering the geological background, fault type, and oil and gas reservoir characteristics of the study area; S5: Calculate the fault lateral sealing evaluation result of the research point: combine ASGRh and ASGRf to obtain the fault lateral sealing evaluation result ASGR of the research point; S6: Evaluate the lateral closure of the fault at the non-depth D research point: According to 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 the non-depth D research point; S7: Multi-scale analysis and verification: Combine macroscopic geological structures and microscopic geological characteristics to conduct multi-scale analysis and verify the evaluation results.

2. A method for evaluating lateral sealing of shallow faults according to claim 1, characterized in that: In S1, the lithology data includes cores and cuttings. The cores 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 layer depth data obtains the physical and chemical properties of underground rocks through logging technology, thereby determining the depth and thickness of the oil and gas layer. The fluid properties, pressure conditions, and temperature are also obtained through logging technology.

3. A method for evaluating lateral sealing of shallow faults according to claim 1, characterized in that: In S1, after collecting the raw data, data cleaning is performed to remove erroneous, duplicated or inconsistent data, the data is normalized or standardized to eliminate the influence of different dimensions, and the data is logarithmically transformed and square root transformed to improve the distribution characteristics of the data.

4. A method for evaluating lateral sealing of shallow faults according to claim 1, characterized in that: In S2, considering the 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 smearing contribution of different depth points r in the mudstone to the research point, where g(r) is a function describing the influence of distance, h(fluid properties), i(pressure conditions), and j(temperature) are functions describing the influence of fluid properties, pressure conditions, and temperature, respectively; The calculation formula of mudstone smear ASGRHih is: Among them, rih is the distance from the study point to the top of the mudstone Hih, and ΔHih is the thickness of the mudstone; ASGRh=∑(ASGRHih).

5. A method for evaluating lateral sealing of shallow faults according to claim 1, characterized in that: In S3, considering the 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 smearing contribution of different depth points r in the mudstone to the research point, where g(r) is a function describing the influence of distance, h(fluid properties), i(pressure conditions), and j(temperature) are functions describing the influence of fluid properties, pressure conditions, and temperature, respectively; The calculation formula of mudstone smear ASGRHif is: Among them, rif is the distance from the study point to the top of the mudstone Hif, and ΔHif is the thickness of the mudstone; ASGRf=∑(ASGRHif).

6. A method for evaluating lateral sealing of shallow faults according to claim 1, characterized in that: In S4, the threshold Q is determined by using K-means, specifically in the following steps: Data Collection: Collect geological, geophysical and geochemical data in the study area; Data preprocessing: Standardize the data to eliminate the influence of different dimensions, construct a relationship matrix, and describe the similarity or closeness between different data points; Determine the number of clusters: Use silhouette coefficient and dispersion to evaluate the effect of different numbers of clusters, and choose the number of clusters with silhouette coefficient close to 1 or small dispersion as the optimal solution; Perform cluster analysis: K-means is used to cluster the data. After the clustering results are obtained, the characteristics of cluster center, cluster size and cluster density are described. Observation clustering diagram: Use the clustering diagram to observe the separation between different clusters and determine a threshold T so that the distance between classes does not exceed the T value; Calculate the threshold Q: Determine a threshold Q based on the clustering results and geological background information; Consider the geological context: According to the geological background information of the study area, the threshold Q is adjusted; Consider the fault type: According to the fault type, the threshold Q is adjusted; Consider the characteristics of oil and gas reservoirs: The threshold Q is adjusted according to the characteristics of the oil and gas reservoir.

7. A method for evaluating lateral sealing of shallow faults according to claim 6, characterized in that: In performing cluster analysis, the specific steps of applying K-means to perform cluster analysis on data are: Choose the number of clusters (K): Specify how many clusters to divide the data into; Initialize the cluster centers: Randomly select K data points 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 from each cluster center and assign it to the cluster with the closest cluster center. Update cluster centers: For each cluster, calculate the average value of all data points in the cluster and use this average value as the new cluster center; Iteration: Repeat the process of assigning data points to the nearest cluster center and updating the cluster center until the cluster center no longer changes significantly or the predetermined number of iterations is reached.

8. A method for evaluating lateral sealing of shallow faults according to claim 1, characterized in that: In S5, the lateral closure evaluation result of the fault at the study point is calculated: ASGRh and ASGRf are combined to obtain the lateral closure evaluation result ASGR of the fault at the study point, ASGR = ASGRh + ASGRf = ∑ (ASGRHih) + ∑ (ASGRHif).

9. A method for evaluating lateral sealing of shallow faults according to claim 1, characterized in that: In S7, the macroscopic geological structure and microscopic geological characteristics are combined to perform multi-scale analysis, and the specific steps are as follows: Macroscopic geological structure analysis: Collect geological structure data of the study area and use geological modeling software or manually draw geological structure maps to show the spatial relationship between faults and surrounding strata; Analysis of microscopic geological characteristics: Collect rock samples from both sides of the fault, conduct experiments to reveal rock information, and evaluate the mechanical properties and sealing capacity of the rocks on both sides of the fault based on the information.

10. A method for evaluating lateral sealing of shallow faults according to claim 1, characterized in that: In S2-S6, if the distance between a single mudstone and the research point is r = 0, it is considered that the mudstone must have the function of laterally sealing oil and gas, and the ASGR value of the research point is not involved in the formulation of the Q value.

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