Formation thickness fine characterization method and device based on trend method, medium and equipment

Abstract

The invention discloses a stratum thickness fine characterization method based on a trend method. The method comprises the following steps: carrying out fine interpretation on a top / bottom surface horizon of a target stratum and a fault of the target stratum; weaving a target layer fault polygon, and manufacturing a target layer top / bottom layer surface; according to the top / bottom layer surface of the target layer, through direct subtraction of a top interface and a bottom interface, the initial stratum thickness of the target layer containing the fault interference abnormal value is obtained; initial stratum thickness abnormal value processing of the target stratum is carried out, and target stratum thickness data after abnormal value elimination are generated; a Kriging interpolation fitting algorithm is adopted to generate target stratum thickness trend surface data; calculating the stratum thickness of a target layer by adopting an alternative method; and analyzing the correlation between the target stratum thickness and the actual drilling well stratum thickness data, and obtaining the final target stratum thickness. According to the method, fine and objective representation of the stratum thickness of the complex fault area is achieved, deployment and optimization of a development well pattern are guided, and important technical support is provided for efficient development and scheme adjustment of underground oil and gas reservoirs.

Description

Technical Field

[0001] This invention belongs to the field of geological research technology for offshore oilfield development, and more specifically, it relates to a method, apparatus, medium, and equipment for fine characterization of formation thickness based on the trend method. Background Technology

[0002] In oil and gas field geological research, accurate characterization of formation thickness is one of the most crucial core parameters. It directly relates to the accurate determination of sedimentary facies distribution, affects the accurate assessment of reservoir size, and thus determines the reliability of resource estimation and the scientific validity of development potential assessment. In actual oil and gas exploration and development work, seismic stratigraphic interpretation results are usually used, combined with the time-depth relationship after well-seismic calibration, to calculate the difference between the top and bottom interfaces of the formation to obtain the formation thickness.

[0003] However, in complex fault contexts, traditional methods for calculating stratigraphic thickness face several significant limitations. For example, in areas with densely developed faults, widespread small faults, or wide fault fracture zones, the presence of faults disrupts stratigraphic continuity. This makes the interpretation of the depth at the top and bottom interfaces of the stratigraphic layers near the fault prone to errors. When directly subtracting the depths at the top and bottom interfaces to calculate stratigraphic thickness, significant outliers can occur, specifically manifesting as a "false thickening" phenomenon (a sudden increase in stratigraphic thickness) or a "false thinning" phenomenon (a sudden decrease in stratigraphic thickness) near the fault.

[0004] To address this situation, existing technologies primarily employ two approaches: one relies on manual local correction of outliers, but this method has significant drawbacks, being inefficient and highly subjective, making it unsuitable for the detailed characterization needs of large-scale, complex fault regions. The other approach is a partially algorithm-based outlier handling method. While this method can perform simple outlier removal, it fails to consider the regional variation trends in stratigraphic thickness. Therefore, it easily leads to a lack of stratigraphic thickness information near faults, failing to meet the stringent requirements of high-precision geological research and development decision-making.

[0005] In summary, existing technologies have many shortcomings in calculating formation thickness under complex fault conditions. They cannot effectively address the problems caused by complex fault interference, fully utilize regional formation thickness variation trends, or achieve high-precision formation thickness characterization. Therefore, a new method is urgently needed to overcome the deficiencies of existing technologies and meet the pressing need for accurate formation thickness characterization in oil and gas field geological research and development decisions. Summary of the Invention

[0006] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention provides a method for fine characterization of stratum thickness based on the trend method. It aims to eliminate the interference of faults on stratum thickness calculation by combining mathematical algorithms and geological laws through the core logic of "anomaly identification-trend fitting-anomaly replacement". This enables a fine and objective characterization of stratum thickness in complex fault areas.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for fine characterization of formation thickness based on the trend method, comprising the following steps: By combining 3D seismic data and well logging data from existing wells, and integrating well and seismic data, we can conduct detailed interpretation of the top / bottom horizons and faults of the target layer, and obtain interpretation results of the top / bottom horizons and faults of the target layer. Based on the top / bottom boundary horizons and fault interpretation results of the target layer, compile the fault polygon of the target layer and create the top / bottom surface of the target layer; Based on the top / bottom surfaces of the target layer, without considering the influence of faults, the initial formation thickness of the target layer containing fault interference anomalies is obtained by directly subtracting the top and bottom interfaces. Perform initial formation thickness anomaly processing on the target layer to generate formation thickness data of the target layer after removing anomalies; Based on the target layer thickness data after removing outliers, the Kriging interpolation fitting algorithm is used to generate the target layer thickness trend surface data. Based on the target layer thickness data after removing outliers and the target layer thickness trend surface data, the target layer thickness is determined using an alternative method. Based on the target formation thickness, the correlation between the target formation thickness and the actual well drilling formation thickness data is analyzed to obtain the final target formation thickness.

[0008] As a preferred embodiment, the process of "compiling the target layer fault polygon and creating the top / bottom surface of the target layer" includes the following steps: Based on the top / bottom boundary stratigraphy and fault interpretation results of the target layer, and according to the missing parts of the interpretation stratigraphy and geological understanding, the fault polygon of the target layer is compiled. Based on the top / bottom boundary of the target layer and the interpretation results of faults, and constrained by the boundary of the study area, the minimum curvature method is used to generate the top / bottom surface of the target layer without considering the influence of faults.

[0009] As a preferred embodiment, the "processing of initial formation thickness anomalies in the target layer to generate formation thickness data of the target layer after removing anomalies" includes the following steps: Based on the initial stratigraphic thickness of the target layer, and using the constructed fault polygon of the target layer as a constraint, the outlier values ​​of stratigraphic thickness within the fault polygon are deleted and marked as "null regions", thus obtaining the preliminary stratigraphic thickness of the target layer. Based on the initial processed target layer thickness, an anomaly threshold range is set. Layer thickness values ​​exceeding this range are removed and marked as "null areas". The target layer thickness after removing anomalies is then obtained.

[0010] As a preferred embodiment, the "generating the target layer thickness trend surface data using the Kriging interpolation fitting algorithm" includes the following steps: Based on the target layer thickness after removing outliers, the average method is used for smoothing to obtain the initial target layer thickness trend surface data. Based on the initial target layer thickness trend surface data, and constrained by the study area boundary, the Kriging interpolation method was used to interpolate and obtain the target layer thickness trend surface data.

[0011] As a preferred embodiment, the "using an alternative method to determine the thickness of the target formation" includes the following steps: Based on the target layer thickness after outlier removal, construct a polygon representing the "null value region" of the target layer thickness after outlier removal; Based on the target layer thickness trend surface data, and constrained by the constructed "null value region" polygon, the target layer thickness trend surface data is extracted. The target layer thickness data, after removing outliers, is merged with the target layer thickness trend surface data to generate the target layer thickness.

[0012] As a preferred embodiment, the step of "analyzing the correlation between the target formation thickness and the actual drilled formation thickness data to obtain the final target formation thickness" includes the following steps: Based on actual drilling and stratification data, the formation thickness of the target layer was statistically analyzed using actual drilling data. Based on the target layer thickness, the target layer thickness at the well point is statistically analyzed; The correlation between the actual drilling formation thickness of the target layer and the formation thickness of the target layer at the well point is analyzed to obtain the final formation thickness of the target layer.

[0013] As a preferred option: if the correlation is greater than 80%, the final target layer thickness is obtained directly; If the correlation is less than 80%, adjust the smoothing parameters to optimize the target layer thickness trend surface data and replace it again until the accuracy requirements are met.

[0014] Secondly, the present invention provides a fine formation thickness characterization device based on the trend method, comprising: The first processing unit is used to utilize 3D seismic data and well logging data from drilled wells, combining well and seismic data, to conduct detailed interpretation of the top / bottom horizons and faults of the target layer, and to obtain the interpretation results of the top / bottom horizons and faults of the target layer. The second processing unit is used to compile the fault polygon of the target layer based on the top / bottom boundary of the target layer and the fault interpretation results, and to create the top / bottom surface of the target layer. The third processing unit is used to calculate the initial formation thickness of the target layer, including fault interference anomalies, by directly subtracting the top interface from the bottom interface based on the top / bottom surface of the target layer, without considering the influence of faults. The fourth processing unit is used to process the initial formation thickness anomalies of the target layer and generate formation thickness data of the target layer after removing the anomalies. The fifth processing unit is used to generate target layer formation thickness trend surface data based on the target layer formation thickness data after removing outliers, using the Kriging interpolation fitting algorithm. The sixth processing unit is used to determine the thickness of the target layer by using an alternative method based on the target layer thickness data after removing outliers and the target layer thickness trend surface data. The seventh processing unit is used to analyze the correlation between the target layer thickness and the actual drilled well layer thickness data based on the target layer thickness, and to obtain the final target layer thickness.

[0015] Thirdly, the present invention provides a computer-readable storage medium storing a computer program, which is executed by a processor to control the device where the processor is located to implement the steps of the fine characterization method for formation thickness described in the first aspect of the present invention.

[0016] Fourthly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the fine formation thickness characterization method described in the first aspect of the present invention.

[0017] This invention, by adopting the above technical solution, has the following advantages: This invention discloses a method for fine characterization of formation thickness based on the trend method. Utilizing 3D seismic data and well data, through the core logic of "anomaly identification - trend fitting - anomaly replacement," combined with mathematical algorithms and geological laws, it eliminates the interference of faults on formation thickness calculation, achieving a fine and objective characterization of formation thickness in complex fault areas. This guides the deployment and optimization of development well networks, providing important technical support for the efficient development and scheme adjustment of underground oil and gas reservoirs. Compared with existing technologies, this invention has the following significant beneficial effects: 1. Significantly Improved Characterization Accuracy: This invention employs a process of "anomaly identification - trend fitting - anomaly replacement" to accurately identify and specifically eliminate thickness anomalies near complex faults. Compared to traditional methods, it effectively avoids the misleading effect of "false thickening / thinning" caused by faults on formation thickness, thereby significantly improving the accuracy of formation thickness characterization. Furthermore, this invention incorporates trend surface fitting with the sedimentary geological background, making the formation thickness characterization results more consistent with geological laws, providing a reliable basis for the accurate evaluation and development of oil and gas reservoirs.

[0018] 2. Significantly improves processing efficiency: Compared with the traditional method of manually correcting outliers point by point, this invention uses outlier identification technology and trend surface fitting algorithm to significantly reduce manual operation steps, thereby significantly improving processing efficiency.

[0019] 3. Ensuring objectivity and repeatability: The processing flow of this invention is based on mathematical algorithms and geological laws, which reduces the error of subjective human judgment and ensures the objectivity and repeatability of the stratigraphic thickness characterization results.

[0020] 4. Optimize the deployment of development well networks: This invention provides important technical support for the deployment and optimization of development well networks by accurately characterizing formation thickness.

[0021] In summary, this invention achieves a precise and objective characterization of stratigraphic thickness in complex fault regions, significantly improving characterization accuracy and processing efficiency, ensuring the objectivity and repeatability of the results, and providing important technical support for the efficient development and scheme adjustment of underground oil and gas reservoirs, thus having significant economic and social benefits. Attached Figure Description

[0022] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings: Figure 1 This is a flowchart of the method for fine characterization of formation thickness based on the trend method in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the detailed interpretation of the target layer horizon and faults based on three-dimensional seismic data, provided in an embodiment of the present invention. Figure 3 A polygonal plan view of the target layer fault provided in an embodiment of the present invention; Figure 4 A plan view of the initial formation thickness of the target layer provided for an embodiment of the present invention; Figure 5 A plan view of the target layer thickness after preprocessing, provided for an embodiment of the present invention; Figure 6 The target layer formation thickness trend surface plan view provided in the embodiments of the present invention; Figure 7 A plan view of the target layer thickness provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0024] This invention provides a method for fine characterization of formation thickness based on the trend method, comprising: conducting fine interpretation of the top / bottom horizons and faults of the target layer; compiling the fault polygons of the target layer and creating the top / bottom surfaces of the target layer; obtaining the initial formation thickness of the target layer, including fault interference anomalies, by directly subtracting the top and bottom interfaces from the top / bottom surfaces; processing the anomalies of the initial formation thickness of the target layer to generate formation thickness data after removing anomalies; generating trend surface data of the formation thickness of the target layer using a Kriging interpolation fitting algorithm; obtaining the formation thickness of the target layer using a substitution method; and analyzing the correlation between the formation thickness of the target layer and the formation thickness data of actual drilled wells to obtain the final formation thickness of the target layer. This invention achieves a fine and objective characterization of formation thickness in complex fault areas, thereby guiding the deployment and optimization of development well networks and providing important technical support for the efficient development and scheme adjustment of underground oil and gas reservoirs.

[0025] The following is a detailed description of the method and apparatus for fine characterization of formation thickness based on the trend method provided in the embodiments of the present invention, with reference to the accompanying drawings.

[0026] Example 1: Please see Figure 1 This embodiment provides a method for fine characterization of formation thickness based on the trend method, which includes the following steps: S100. Using 3D seismic data and well logging data from existing wells, combining well and seismic data, and performing cross-sectional and horizontal analysis, we can conduct detailed interpretation of the top / bottom horizons and faults of the target layer, and obtain interpretation results of the top / bottom horizons and faults of the target layer. Depend on Figure 2 It can be seen that, based on 3D seismic data and well logging data from drilled wells, the top / bottom positions of the target and the faults of the target layer can be interpreted.

[0027] S200. Compile the target layer fault polygon and create the top / bottom surface of the target layer. The specific steps are as follows: S201. Taking the target layer SQ1 as an example, based on the interpretation results of the top / bottom boundary strata and faults of SQ1, and according to the missing parts of the interpretation strata and geological understanding, the fault polygon of the top surface of SQ1 is manually compiled. S202. Based on the interpretation results of the top / bottom boundary of SQ1, and with the boundary of the study area as a constraint, the minimum curvature method is used to generate the top / bottom surface of SQ1 without considering the influence of faults. Depend on Figure 3 It can be seen that the target layer SQ1 mainly develops northeast-southwest and near east-west trending faults.

[0028] S300. Based on the top / bottom surfaces of the target layer obtained in step S202, the initial formation thickness of the target layer containing fault interference anomalies is obtained by directly subtracting the top interface from the bottom interface.

[0029] Depend on Figure 4 It can be seen that the thickness of the target layer SQ1 is generally thinner in the south and thicker in the north. In addition, due to the influence of faults, there are obvious anomalies in the thickness of the strata in some local areas.

[0030] S400. Perform initial formation thickness anomaly processing on the target layer to generate the formation thickness of the target layer after removing anomalies. The specific steps are as follows: S401. Obtain the initial stratigraphic thickness of the target layer by subtracting the top and bottom interfaces of SQ1. Using the fault polygon of the top surface of SQ1 compiled in step S201 as a constraint, delete the stratigraphic thickness anomalies in the fault polygon and mark them as "null regions" to obtain the preliminary processed stratigraphic thickness of SQ1. S402. Based on the results of step S401, set an anomaly threshold range, i.e., the formation thickness is greater than 0 (the default formation thickness is less than 0). Remove formation thickness values ​​that exceed this range and mark them as "empty areas". Obtain the SQ1 formation thickness after removing outliers.

[0031] Depend on Figure 5 It can be seen that the thickness of the SQ1 stratum after removing outliers is generally thinner in the south and thicker in the north.

[0032] S500. The Kriging interpolation fitting algorithm is used to generate the formation thickness trend surface of the target layer. The specific steps are as follows: S501. Based on the SQ1 formation thickness obtained in step S402 after removing outliers, smooth it using the averaging method to obtain the initial SQ1 formation thickness trend surface data. S502. Based on the results of step S501, using the boundary of the study area as a constraint, the initial SQ1 stratum thickness trend surface is interpolated using the Kriging interpolation method to obtain the SQ1 stratum thickness trend surface data.

[0033] Depend on Figure 6It can be seen that the thickness trend surface of the SQ1 strata is generally thinner in the south and thicker in the north, and a near-NE trending band of strata thinning is developed in the central and northern parts.

[0034] S600. The thickness of the target formation is determined using an alternative method. The specific steps are as follows: S601. Based on the results of step S402, compile the polygon of the "null value region" of the processed SQ1 formation thickness; S602. Based on the results of step S502, using the SQ1 formation thickness “null value region” polygon compiled in step S501 as a constraint, extract the SQ1 formation thickness trend surface data inside the polygon. S603. Based on the results of steps S402 and S602, merge the SQ1 formation thickness data obtained in step S402 (excluding outliers) with the SQ1 formation thickness trend surface data obtained in step S602 to generate the SQ1 formation thickness.

[0035] Depend on Figure 7 It can be seen that the overall thickness of the SQ1 strata is thinner in the south and thicker in the north. Furthermore, a near-NE trending band of strata thinning is developed in the central and northern parts of the strata. Compared with the SQ1 strata thickness trend surface, it reflects the detailed changes in the thickness of the SQ1 strata more accurately.

[0036] S700. Analyze the correlation between the target formation thickness and the actual drilled formation thickness data (obtained through well logging interpretation) to obtain the final target formation thickness. The specific steps are as follows: S701. Based on actual drilling and stratification data, the formation thickness of SQ1 actual drilling well is statistically analyzed; S702. Based on the results of step S603, calculate the formation thickness of SQ1 at the well point; S703. Based on the results of steps S701 and S702, analyze the correlation between the actual drilling formation thickness of SQ1 and the formation thickness at the well point: if the correlation is greater than 80%, the final formation thickness of SQ1 is obtained directly; if the correlation is less than 80%, return to step S501 to adjust the smoothing parameters to optimize the SQ1 formation thickness trend surface data, and replace the SQ1 formation thickness trend surface data in step S603 until the accuracy requirements are met.

[0037] Example 2: The above-described embodiment 1 provides a method for fine characterization of formation thickness based on the trend method. Correspondingly, this embodiment provides a device for fine characterization of formation thickness based on the trend method. The device for fine characterization of formation thickness provided in this embodiment can implement the method for fine characterization of formation thickness in embodiment 1. This device can be implemented through software, hardware, or a combination of both. For example, the device may include integrated or separate functional modules or units to perform the corresponding steps in the methods of embodiment 1. Since the device for fine characterization of formation thickness in this embodiment is basically similar to the method embodiment, the description process in this embodiment is relatively simple. For relevant details, please refer to the description of embodiment 1. The device for fine characterization of formation thickness in this embodiment is merely illustrative.

[0038] The fine formation thickness characterization device provided in this embodiment includes: The first processing unit is used to utilize 3D seismic data and well logging data from drilled wells, combining well and seismic data, to conduct detailed interpretation of the top / bottom horizons and faults of the target layer, and to obtain the interpretation results of the top / bottom horizons and faults of the target layer. The second processing unit is used to compile the fault polygon of the target layer based on the top / bottom boundary of the target layer and the fault interpretation results, and to create the top / bottom surface of the target layer. The third processing unit is used to calculate the initial formation thickness of the target layer, including fault interference anomalies, by directly subtracting the top interface from the bottom interface based on the top / bottom surface of the target layer, without considering the influence of faults. The fourth processing unit is used to process the initial formation thickness anomalies of the target layer and generate formation thickness data of the target layer after removing the anomalies. The fifth processing unit is used to generate target layer formation thickness trend surface data based on the target layer formation thickness data after removing outliers, using the Kriging interpolation fitting algorithm. The sixth processing unit is used to determine the thickness of the target layer by using an alternative method based on the target layer thickness data after removing outliers and the target layer thickness trend surface data. The seventh processing unit is used to analyze the correlation between the target layer thickness and the actual drilled well layer thickness data based on the target layer thickness, and to obtain the final target layer thickness.

[0039] Example 3: This embodiment provides a processing device for implementing the trend-based fine characterization method for formation thickness provided in Embodiment 1. The processing device can be a client-side processing device, such as a mobile phone, laptop, tablet computer, or desktop computer, to execute the method of Embodiment 1.

[0040] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to enable communication between them. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes the trend-based fine formation thickness characterization method provided in Embodiment 1.

[0041] Preferably, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0042] Preferably, the processor can be any type of general-purpose processor such as a central processing unit (CPU) or a digital signal processor (DSP), and there is no limitation herein.

[0043] Example 4: The trend-based method for fine characterization of formation thickness in Embodiment 1 can be specifically implemented as a computer program product. The computer program product may include a computer-readable storage medium on which computer-readable program instructions for executing the method described in Embodiment 1 are loaded.

[0044] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for fine characterization of formation thickness based on trend analysis, characterized in that, Includes the following steps: By combining 3D seismic data and well logging data from existing wells, and integrating well and seismic data, we can conduct detailed interpretation of the top / bottom horizons and faults of the target layer, and obtain interpretation results of the top / bottom horizons and faults of the target layer. Based on the top / bottom boundary horizons and fault interpretation results of the target layer, compile the fault polygon of the target layer and create the top / bottom surface of the target layer; Based on the top / bottom surfaces of the target layer, without considering the influence of faults, the initial formation thickness of the target layer containing fault interference anomalies is obtained by directly subtracting the top and bottom interfaces. Perform initial formation thickness anomaly processing on the target layer to generate formation thickness data of the target layer after removing anomalies; Based on the target layer thickness data after removing outliers, the Kriging interpolation fitting algorithm is used to generate the target layer thickness trend surface data. Based on the target layer thickness data after removing outliers and the target layer thickness trend surface data, the target layer thickness is determined using an alternative method. Based on the target formation thickness, the correlation between the target formation thickness and the actual well drilling formation thickness data is analyzed to obtain the final target formation thickness.

2. The method for fine characterization of formation thickness according to claim 1, characterized in that, The process of "compiling the target layer fault polygon and creating the top / bottom surface of the target layer" includes the following steps: Based on the top / bottom boundary stratigraphy and fault interpretation results of the target layer, and according to the missing parts of the interpretation stratigraphy and geological understanding, the fault polygon of the target layer is compiled. Based on the top / bottom boundary of the target layer and the interpretation results of faults, and constrained by the boundary of the study area, the minimum curvature method is used to generate the top / bottom surface of the target layer without considering the influence of faults.

3. The method for fine characterization of formation thickness according to claim 2, characterized in that, The process of "processing initial formation thickness anomalies in the target layer to generate formation thickness data of the target layer after removing anomalies" includes the following steps: Based on the initial stratigraphic thickness of the target layer, and using the constructed fault polygon of the target layer as a constraint, the outlier values ​​of stratigraphic thickness within the fault polygon are deleted and marked as "null regions", thus obtaining the preliminary stratigraphic thickness of the target layer. Based on the initial processed target layer thickness, an anomaly threshold range is set. Layer thickness values ​​exceeding this range are removed and marked as "null areas". The target layer thickness after removing anomalies is then obtained.

4. The method for fine characterization of formation thickness according to claim 3, characterized in that, The process of "generating trend surface data of the target layer thickness using the Kriging interpolation fitting algorithm" includes the following steps: Based on the target layer thickness after removing outliers, the average method is used for smoothing to obtain the initial target layer thickness trend surface data. Based on the initial target layer thickness trend surface data, and constrained by the study area boundary, the Kriging interpolation method was used to interpolate and obtain the target layer thickness trend surface data.

5. The method for fine characterization of formation thickness according to claim 4, characterized in that, The process of "using an alternative method to determine the thickness of the target formation" includes the following steps: Based on the target layer thickness after outlier removal, construct a polygon representing the "null value region" of the target layer thickness after outlier removal; Based on the target layer thickness trend surface data, and constrained by the constructed "null value region" polygon, the target layer thickness trend surface data is extracted. The target layer thickness data, after removing outliers, is merged with the target layer thickness trend surface data to generate the target layer thickness.

6. The method for fine characterization of formation thickness according to claim 5, characterized in that, The process of "analyzing the correlation between the target formation thickness and the actual drilled formation thickness data to obtain the final target formation thickness" includes the following steps: Based on actual drilling and stratification data, the formation thickness of the target layer was statistically analyzed using actual drilling data. Based on the target layer thickness, the target layer thickness at the well point is statistically analyzed; The correlation between the actual drilling formation thickness of the target layer and the formation thickness of the target layer at the well point is analyzed to obtain the final formation thickness of the target layer.

7. The method for fine characterization of formation thickness according to claim 6, characterized in that, If the correlation is greater than 80%, the final target layer thickness can be obtained directly. If the correlation is less than 80%, adjust the smoothing parameters to optimize the target layer thickness trend surface data and replace it again until the accuracy requirements are met.

8. A fine-grained formation thickness characterization device based on the trend method, characterized in that, include: The first processing unit is used to utilize 3D seismic data and well logging data from drilled wells, combining well and seismic data, to conduct detailed interpretation of the top / bottom horizons and faults of the target layer, and to obtain interpretation results of the top / bottom horizons and faults of the target layer. The second processing unit is used to compile the fault polygon of the target layer based on the top / bottom boundary of the target layer and the fault interpretation results, and to create the top / bottom surface of the target layer. The third processing unit is used to calculate the initial formation thickness of the target layer, including fault interference anomalies, by directly subtracting the top interface from the bottom interface based on the top / bottom surface of the target layer, without considering the influence of faults. The fourth processing unit is used to process the initial formation thickness anomalies of the target layer and generate formation thickness data of the target layer after removing the anomalies. The fifth processing unit is used to generate target layer formation thickness trend surface data based on the target layer formation thickness data after removing outliers, using the Kriging interpolation fitting algorithm. The sixth processing unit is used to determine the thickness of the target layer by using an alternative method based on the target layer thickness data after removing outliers and the target layer thickness trend surface data. The seventh processing unit is used to analyze the correlation between the target layer thickness and the actual drilled well layer thickness data based on the target layer thickness, and to obtain the final target layer thickness.

9. A computer-readable storage medium, characterized in that, The device contains a computer program that is executed by a processor to control the device where the processor is located to implement the steps of the fine characterization method for formation thickness as described in any one of claims 1 to 7.

10. A computer device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for fine characterization of formation thickness as described in any one of claims 1 to 7.

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