A method and system for remote sensing evaluation of surface conditions for oil and gas exploration

Abstract

The application discloses an oil and gas exploration ground condition remote sensing evaluation method and system, and relates to the field of oil and gas exploration. The oil and gas exploration ground condition remote sensing evaluation method comprises the following steps: obtaining remote sensing data, calculating an unusable digital elevation model area, removing the unusable digital elevation model area from the remote sensing data to obtain an available digital elevation model area, performing multi-directional convolution terrain relief analysis on the available digital elevation model area to obtain relief analysis results, and grading the relief analysis results to obtain ground construction difficulty grading evaluation results. The application fully utilizes digital elevation information and spectral information in remote sensing images, introduces remote sensing and geographic information analysis technology into the field of exploration deployment, accurately obtains the boundary range of an unusable ground area, grades and evaluates the difficulty of exploration ground construction operation, greatly saves the time cost and labor cost of a traditional ground investigation method, and extends the traditional method from point to surface and from qualitative to quantitative.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas exploration deployment technology, and specifically relates to a remote sensing evaluation method and system for oil and gas exploration ground conditions. Background Technology

[0002] China has a wide distribution of oil and gas resources. Petroleum geologists have summarized a series of oil and gas exploration evaluation methods, including oil and gas zone evaluation, reservoir element evaluation, resource evaluation and reserve assessment. These evaluation methods mainly focus on the geological evaluation of oil and gas exploration.

[0003] The exploration and development of oil and gas resources depends not only on underground geological evaluation and reserve assessment, but also on surface exploration and development conditions.

[0004] Therefore, there is an urgent need for an efficient and accurate method for evaluating surface conditions in oil and gas exploration, as an effective supplement to traditional geological evaluation, to support decisions on the entry and exit of oil and gas mineral rights and the deployment of exploration on the surface. Summary of the Invention

[0005] To address the above problems, this invention discloses a remote sensing evaluation method for surface conditions in oil and gas exploration, comprising:

[0006] Acquire remote sensing data, calculate unusable digital elevation model (DEM) areas, and remove unusable DEM areas from the remote sensing data to obtain usable DEM areas;

[0007] Multi-directional convolutional terrain relief analysis was performed on the available digital elevation model area to obtain relief analysis results;

[0008] The results of the undulation analysis were graded to obtain the evaluation results of the difficulty of ground construction.

[0009] Furthermore, the process of acquiring remote sensing data, calculating unusable digital elevation model (DEM) areas, and removing unusable DEM areas from the remote sensing data to obtain usable DEM areas includes the following steps:

[0010] High-resolution digital elevation model data, optical remote sensing image data, and basic geographic data are collected and preprocessed. The basic geographic data includes basic vector data of roads, railways, ecological red line areas, water sources, and transmission pipelines.

[0011] The NDBI index method and the NDWI index method were used to extract the boundaries of urban construction areas and river and lake water areas, respectively.

[0012] The catchment area of ​​a water source was determined using surface runoff analysis.

[0013] By removing urban construction areas, river and lake areas, water source catchment areas, ecological red line areas, road areas, railway areas, and transmission pipeline areas from the digital elevation model data, usable digital elevation model areas are obtained.

[0014] Furthermore, the preprocessing of the digital elevation model data includes peak reduction and depression filling.

[0015] Furthermore, the preprocessing of the optical remote sensing image data includes atmospheric correction and geometric correction.

[0016] Furthermore, the specific steps for extracting the catchment area of ​​a water source using surface runoff analysis are as follows:

[0017] The direction of water flow is determined by the steepest descent method in multiple directions, resulting in a flow direction grid.

[0018] The flow rate grid is obtained by calculating the cumulative flow of each grid cell based on the flow direction grid.

[0019] By using flow direction grids and flow rate grids, and dividing the catchment area into catchment areas according to the set catchment area threshold, a catchment area grouping grid is obtained.

[0020] The catchment area is grouped into raster data and converted into vector data. This data is then spatially overlaid with the water source area in the basic geographic data to obtain the catchment area of ​​the water source.

[0021] Furthermore, the step of performing multi-directional convolutional terrain undulation analysis on the available digital elevation model area to obtain the undulation analysis results includes the following steps:

[0022] Construct the basic convolution window matrix;

[0023] By rotating the basic convolution window matrix, we can construct convolution rotation window matrices in multiple directions.

[0024] The undulation is calculated by performing convolution on each convolutional rotation window matrix to obtain the undulation raster results in each direction;

[0025] The undulation raster results from each direction are merged into the undulation analysis results.

[0026] Furthermore, the grading criteria for the fluctuation analysis results are as follows:

[0027] Low difficulty zone: fluctuation < a;

[0028] Medium difficulty zone: a ≤ variability < b;

[0029] High difficulty zone: b ≤ undulation.

[0030] A remote sensing evaluation system for surface conditions in oil and gas exploration includes:

[0031] The determination unit is used to acquire remote sensing data, calculate unusable digital elevation model areas, and remove unusable digital elevation model areas from the remote sensing data to obtain usable digital elevation model areas.

[0032] A convolutional unit is used to perform multi-directional convolutional terrain relief analysis on the available digital elevation model area to obtain relief analysis results.

[0033] The evaluation unit is used to classify the undulation analysis results and obtain the evaluation results of the difficulty of ground construction.

[0034] Furthermore, the determining unit is specifically used for:

[0035] High-resolution digital elevation model data, optical remote sensing image data, and basic geographic data are collected and preprocessed. The basic geographic data includes basic vector data of roads, railways, ecological red line areas, water sources, and transmission pipelines.

[0036] The NDBI index method and the NDWI index method were used to extract the boundaries of urban construction areas and river and lake water areas, respectively.

[0037] The catchment area of ​​a water source was determined using surface runoff analysis.

[0038] By removing urban construction areas, river and lake areas, water source catchment areas, ecological red line areas, road areas, railway areas, and transmission pipeline areas from the digital elevation model data, usable digital elevation model areas are obtained.

[0039] Furthermore, the determining unit is specifically used for:

[0040] The direction of water flow is determined by the steepest descent method in multiple directions, resulting in a flow direction grid.

[0041] The flow rate grid is obtained by calculating the cumulative flow of each grid cell based on the flow direction grid.

[0042] By using flow direction grids and flow rate grids, and dividing the catchment area into catchment areas according to the set catchment area threshold, a catchment area grouping grid is obtained.

[0043] The catchment area is grouped into raster data and converted into vector data. This data is then spatially overlaid with the water source area in the basic geographic data to obtain the catchment area of ​​the water source.

[0044] Furthermore, the convolutional unit is specifically used for:

[0045] Construct the basic convolution window matrix;

[0046] By rotating the basic convolution window matrix, we can construct convolution rotation window matrices in multiple directions.

[0047] The undulation is calculated by performing convolution on each convolutional rotation window matrix to obtain the undulation raster results in each direction;

[0048] The undulation raster results from each direction are merged into the undulation analysis results.

[0049] Compared with the prior art, the embodiments of the present invention have at least the following advantages:

[0050] 1) Fully utilize digital elevation and spectral information in remote sensing images, introduce remote sensing and geographic information analysis technology into the field of exploration deployment, accurately obtain the boundary range of unusable areas on the ground, evaluate the difficulty of exploration ground construction operations in a graded manner, greatly save the time and manpower costs of traditional ground survey methods, and expand the traditional methods from point to area and from qualitative to quantitative.

[0051] 2) It is applicable to the rapid evaluation of surface conditions in new exploration and development blocks. Its application effect and value are more obvious in complex mountain and canyon areas. It can be promoted and applied to oil and gas fields in basins across the country, providing a reliable basis for the decision-making on the entry and exit of oil and gas exploration and mining rights and the surface deployment of oil and gas exploration and development.

[0052] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention can be realized and obtained by means of the structures pointed out in the description and the drawings. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 A flowchart of a remote sensing evaluation method for oil and gas exploration surface conditions according to an embodiment of the present invention is shown;

[0055] Figure 2 A flowchart of a surface runoff analysis method according to an embodiment of the present invention is shown;

[0056] Figure 3 A flowchart of multi-directional convolutional terrain undulation analysis according to an embodiment of the present invention is shown;

[0057] Figure 4 An optical remote sensing image of the study area according to an embodiment of the present invention is shown;

[0058] Figure 5This shows a DEM topographic remote sensing image of the study area according to an embodiment of the present invention;

[0059] Figure 6 The map showing the NDBI urban construction area extraction results of the study area according to an embodiment of the present invention is illustrated.

[0060] Figure 7 The image shows the NDWI river and lake water area extraction results of the study area according to an embodiment of the present invention;

[0061] Figure 8 The diagram shows the flow direction (a) and flow rate (b) of the DEM in the study area according to an embodiment of the present invention;

[0062] Figure 9 The diagram shows a grid (a) and a vector (b) diagram of the catchment area grouping in the study area according to an embodiment of the present invention;

[0063] Figure 10 A diagram showing the analysis results of the water source catchment area in the study area according to an embodiment of the present invention is provided.

[0064] Figure 11 A DEM distribution map of the available area of ​​the study area according to an embodiment of the present invention is shown;

[0065] Figure 12 The following diagram shows the results of multi-directional convolutional terrain undulation analysis according to an embodiment of the present invention;

[0066] Figure 13 A comprehensive undulation distribution map of the study area according to an embodiment of the present invention is shown;

[0067] Figure 14 A graph showing the evaluation results of the difficulty level of ground construction in the study area according to an embodiment of the present invention is provided. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0069] Figure 1 A flowchart of a remote sensing evaluation method for surface conditions in oil and gas exploration according to an embodiment of the present invention is shown. Figure 1 As shown, the present invention proposes a remote sensing evaluation method for surface conditions in oil and gas exploration, comprising the following steps:

[0070] Step S1: Acquire remote sensing data of the study area, calculate the unusable digital elevation model (DEM) areas, and remove the unusable DEM areas from the remote sensing data to obtain the usable DEM areas;

[0071] Step S101: Collect high-resolution digital elevation model data, optical remote sensing image data and basic geographic data of the study area, and perform data preprocessing;

[0072] For example, the study area is located between 31°57′ and 32°5′ north latitude and 104°51′ and 105°9′ east longitude, in the northwestern part of the Sichuan Basin. The area is characterized by large elevation differences in the mountainous region and a cloudy and rainy climate. To meet the needs of ground condition assessment, medium-resolution optical remote sensing images (DOM) and digital elevation model (DEM) images of the study area were collected and preprocessed.

[0073] Optical remote sensing images are photographs captured and processed by optical sensors. Common types of remote sensing images include panchromatic images, visible light images, and multispectral images.

[0074] The optical remote sensing imagery used is Sentinel-2 multispectral imagery data from August 9, 2020. It includes one aerosol band, three visible light bands (blue, green, and red), three bands within the red border, two near-infrared bands, one water vapor band, and three shortwave infrared bands, totaling 13 bands. The highest resolution of each band is 10 meters, and the coordinate system is WGS_1984_UTM_Zone_48N. The imagery underwent atmospheric and geometric correction preprocessing to obtain a high-precision positioning and high-resolution image (e.g., [image of image]). Figure 4 ).

[0075] Topographic remote sensing imagery, also known as digital elevation model (DEM), is a discrete mathematical representation of the undulating shape of the Earth's surface.

[0076] The topographic remote sensing imagery uses 5-meter resolution ZY-3 satellite stereo relative elevation data. After data peak reduction and depression filling, the values ​​of nearest neighbor cells are used to replace depressions and peaks, effectively avoiding the possibility that peaks and depressions in the original DEM data could lead to fragmentation of water systems and errors in water flow direction during extraction. This results in a superior image (e.g., Figure 5 ).

[0077] The relevant basic geographic data includes basic vector data such as roads, railways, environmentally sensitive areas, ecological red line areas, water sources, and transmission pipelines. All data is converted into a unified WGS_1984_UTM_Zone_48N coordinate system projection. Subsequent data analysis and information extraction are based on the preprocessed image data.

[0078] Preprocessing of optical remote sensing image data includes atmospheric correction and geometric correction. Geometric correction unifies data from different sources into the same coordinate reference and achieves geometric registration between data under certain standard image and control point constraints.

[0079] Preprocessing of topographic remote sensing imagery (digital elevation model data) includes peak reduction and depression filling. In the DEM matrix, a depression cell is a cell whose elevation is no lower than that of its eight adjacent cells, and a peak cell is a cell whose elevation is no higher than that of its eight adjacent cells. Depression filling and peak reduction replace depression and peak cells with the values ​​of their nearest neighbors to avoid the depressions and peaks in the original DEM data causing breaks in the extracted water system and errors in water flow direction.

[0080] Step S102: Use the NDBI index method and the NDWI index method to extract the boundaries of urban construction areas and river and lake water areas, respectively;

[0081] NDBI stands for Normalized Difference Building Index. It can accurately reflect information about built-up land use. A higher value indicates a higher proportion of built-up land and a higher building density. The calculation formula is as follows:

[0082]

[0083] Wherein, MIR stands for mid-infrared band and NIR stands for near-infrared band. The urban built-up area is extracted by setting an NDBI index threshold. For example, the NDBI index threshold is 0.

[0084] Using Sentinel-2 remote sensing imagery of the study area as the data source, MIR refers to the 11th band (shortwave infrared band, wavelength range: 1565-1655nm) of the Sentinel-2 data, and NIR refers to the 8th band (near-infrared band, wavelength range: 785-900nm) of the Sentinel-2 data. The calculation results are as follows: Figure 6 As shown, the black area has an NDBI value greater than 0, indicating an urban construction area; the white area has an NDBI value less than 0, indicating other land use types.

[0085] NDWI, short for Normalized Difference Water Index, primarily utilizes the characteristic that water bodies have strong absorption and almost no reflection in the near-infrared band, while vegetation has high reflectivity. By suppressing vegetation and highlighting water bodies, it extracts water information from images. NDWI reflects the intensity of surface water vapor evaporation; the higher the NDWI value, the greater the surface humidity. The calculation formula is:

[0086]

[0087] Wherein, Green represents the green band and NIR represents the near-infrared band. The range of river and lake water areas is extracted by setting the NDWI index threshold. For example, the NDWI index threshold is 0.

[0088] Using Sentinel-2 remote sensing imagery of the study area as the data source, Green represents the third band of the Sentinel-2 data (green band, wavelength range: 543-578nm), and NIR represents the eighth band of the Sentinel-2 data (near-infrared band, wavelength range: 785-900nm). The calculation results are as follows: Figure 7 As shown, the black areas with NDWI values ​​greater than 0 represent water bodies such as rivers and lakes; the white areas with NDWI values ​​less than 0 represent other land use types.

[0089] Step S103: Use surface runoff analysis to extract the catchment area of ​​the water source and designate the catchment area of ​​the water source as an area unsuitable for environmental protection;

[0090] like Figure 2 As shown, surface runoff analysis is a collective term for a series of spatial algorithms that can generate various types of river-related spatial data. Surface runoff analysis requires calculating the flow direction, flow rate, and catchment area of ​​the study area sequentially, and then vectorizing the catchment area to extract the catchment area of ​​the water source.

[0091] Flow direction calculation: The D8 algorithm, based on the steepest descent method in eight directions, is used to determine the flow direction for DEM data, as shown below. Figure 8 (a) shows the flow grid. Each color in the diagram represents a direction.

[0092] Flow calculation: Assuming each raster cell has 1 unit of precipitation, the cumulative runoff of each raster cell is calculated based on the flow direction obtained from the flow direction calculation, as shown below. Figure 8 (b) shows a flow grid where larger white pixel values ​​indicate locations with higher flow accumulation, and smaller black pixel values ​​indicate locations with lower flow accumulation.

[0093] Catchment area calculation: Catchment areas are divided using flow direction grids, flow rate grids, and by specifying a catchment area threshold, as shown below. Figure 9 (a) shows a grid of catchment areas, where the catchment area threshold is set to 500 using an empirical threshold. This means that each catchment area should be greater than 12,500 square meters, and catchment areas smaller than this area will be merged. The catchment area threshold refers to the critical catchment area that can form and maintain a river channel, and can be determined by the river network density method or by using an empirical threshold.

[0094] Water catchment area vectorization: Converting the grouped raster data of the water catchment area into vector data, resulting in data such as... Figure 9 (b) shows the vector data of the catchment area.

[0095] Source catchment area extraction: Spatially overlay the vector data of the catchment area with the source area range in the basic geographic data to filter out the source catchment area (black area), such as... Figure 10 As shown.

[0096] Step S104: Remove urban construction areas, river and lake water areas, water source catchment areas, ecological red line areas, road areas, railway areas and transmission pipeline areas from the digital elevation model data to obtain usable digital elevation model areas.

[0097] Based on regional characteristics, a certain buffer threshold is selected to calculate the area of ​​unusable digital elevation model (DEM) data. The buffer threshold is 100 meters for urban construction areas, 10 meters for river and lake areas, and no buffer threshold is set for water source catchment areas or ecological red line areas. The buffer threshold is 10 meters for road and railway areas and 200 meters for transmission pipelines. After removing unusable DEM areas from the DEM data space, the following results are obtained: Figure 11 The available DEM area is shown. Unavailable DEM areas include urban construction areas, river and lake areas, water source catchment areas, ecological red line areas, road areas, railway areas, and pipeline areas. It should be noted that a study area may only contain a portion of the unavailable DEM areas. For example, the first study area only contains urban construction areas, river and lake areas, and water source catchment areas, with no other unavailable DEM areas. In practical applications, the available DEM areas can be obtained simply by removing urban construction areas, river and lake areas, and water source catchment areas from the DEM data.

[0098] Step S2: Perform multi-directional convolutional terrain undulation analysis on the available digital elevation model area to obtain the undulation analysis results;

[0099] like Figure 3 As shown, construct the basic convolution window matrix;

[0100] By rotating the basic convolution window matrix, we can construct convolution rotation window matrices in multiple directions.

[0101] The undulation is calculated by performing convolution on each convolutional rotation window matrix to obtain the undulation raster results in each direction;

[0102] The undulation raster results from each direction are merged into the undulation analysis results.

[0103] Convolution is a general raster computation method. It sets a window matrix, calculates the cell values ​​contained in the window according to a certain formula, uses the result as the result value of the center cell, and then traverses the raster by translating the window to obtain the result of each cell, forming the result raster.

[0104] The formula for calculating terrain relief is:

[0105] R = H max -H min

[0106] Where R represents the topographic relief, H max H represents the maximum elevation value per unit area. min This represents the minimum elevation value per unit area.

[0107] Oil and gas exploration and development well sites are typically rectangular areas. Within these areas, it is necessary to calculate the undulation difference. The smaller the difference, the easier the construction work and the more suitable the surface conditions are for exploration and development activities. Since exploration and development well sites do not have orientation requirements, the working area window can be rotated in multiple directions. Each rotation yields a new window matrix. The undulation is then calculated by convolution based on each window matrix, and the minimum value from multiple calculations is combined into a single grid to obtain a quantitative distribution map of the area's undulation.

[0108] Specifically, a convolutional topographic relief analysis was performed on the available DEM area in eight directions. The convolution window was 100 meters * 45 meters, and the rotation angles in the eight directions were 0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, and 157.5°, respectively, yielding the following results: Figure 12 The results of the multi-directional topographic relief analysis are shown.

[0109] Eight terrain relief grids were merged into one grid by taking the minimum value of pixels at the same location, resulting in the following: Figure 13 The map shown is a comprehensive topographic relief distribution map.

[0110] Step S3: Classify the undulation analysis results to obtain the evaluation results of the ground construction difficulty classification.

[0111] Based on the difficulty classification standard of ground undulation within the well site for oil and gas well site construction operations, the regional undulation distribution map is displayed in a graded manner. This allows for the evaluation results of the ground construction difficulty classification of the available area within the study range, and the determination of the usable site selection area.

[0112] The grading criteria are as follows:

[0113] Low difficulty zone: fluctuation < a;

[0114] Medium difficulty zone: a ≤ variability < b;

[0115] High difficulty zone: b ≤ undulation.

[0116] In some embodiments, a is 10m and b is 15m. The result is as follows: Figure 14The results of the ground construction difficulty classification are shown below, where black represents low difficulty areas, dark black represents medium difficulty areas, light black represents high difficulty areas, and white represents areas that cannot be constructed.

[0117] Although the above description uses 10m and 15m as grading standards as examples, the present invention is not limited to these and can employ different grading standards, such as 11m and 16m. Those skilled in the art can consider the grading principle of the present invention and practical applications, and make any design that achieves the principle of the present invention.

[0118] Based on the above-mentioned remote sensing evaluation method for oil and gas exploration surface conditions, this embodiment proposes a remote sensing evaluation system for oil and gas exploration surface conditions, including:

[0119] The determination unit is used to acquire remote sensing data, calculate unusable digital elevation model areas, and remove unusable digital elevation model areas from the remote sensing data to obtain usable digital elevation model areas.

[0120] A convolutional unit is used to perform multi-directional convolutional terrain relief analysis on the available digital elevation model area to obtain relief analysis results.

[0121] The evaluation unit is used to classify the undulation analysis results and obtain the evaluation results of the difficulty of ground construction.

[0122] Determine the unit, specifically for:

[0123] High-resolution digital elevation model data, optical remote sensing image data, and basic geographic data are collected and preprocessed. The basic geographic data includes basic vector data of roads, railways, ecological red line areas, water sources, and transmission pipelines.

[0124] The NDBI index method and the NDWI index method were used to extract the boundaries of urban construction areas and river and lake water areas, respectively.

[0125] The catchment area of ​​a water source was determined using surface runoff analysis.

[0126] By removing urban construction areas, river and lake areas, water source catchment areas, ecological red line areas, road areas, railway areas, and transmission pipeline areas from the digital elevation model data, usable digital elevation model areas are obtained.

[0127] Determine the unit, specifically for:

[0128] The direction of water flow is determined by the steepest descent method in multiple directions, resulting in a flow direction grid.

[0129] The flow rate grid is obtained by calculating the cumulative flow of each grid cell based on the flow direction grid.

[0130] By using flow direction grids and flow rate grids, and dividing the catchment area into catchment areas according to the set catchment area threshold, a catchment area grouping grid is obtained.

[0131] The catchment area is grouped into raster data and converted into vector data. This data is then spatially overlaid with the water source area in the basic geographic data to obtain the catchment area of ​​the water source.

[0132] Convolutional units are specifically used for:

[0133] Construct the basic convolution window matrix;

[0134] By rotating the basic convolution window matrix, we can construct convolution rotation window matrices in multiple directions.

[0135] The undulation is calculated by performing convolution on each convolutional rotation window matrix to obtain the undulation raster results in each direction;

[0136] The undulation raster results from each direction are merged into the undulation analysis results.

[0137] This invention proposes a remote sensing evaluation method and system for oil and gas exploration surface conditions. It fully utilizes digital elevation and spectral information from remote sensing images, introducing remote sensing and geographic information analysis technologies into the exploration deployment field. This accurately identifies the boundaries of unusable areas on the ground, and provides a graded evaluation of the difficulty of exploration surface construction operations. It significantly reduces the time and labor costs of traditional ground survey methods, expanding upon traditional methods from point to area and from qualitative to quantitative approaches. It is suitable for rapid evaluation of surface conditions in entirely new exploration and development blocks, with particularly significant application effects and value in complex mountainous and canyon areas. It can be extended to oil and gas fields in twelve basins across China, providing a reliable basis for decisions on the entry and exit of oil and gas exploration and mining rights and for the ground deployment of oil and gas exploration and development.

[0138] Although the present invention 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 of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote sensing evaluation method for surface conditions in oil and gas exploration, characterized in that, include: Acquire remote sensing data, calculate unusable digital elevation model (DEM) areas, and remove unusable DEM areas from the remote sensing data to obtain usable DEM areas; Multi-directional convolutional terrain relief analysis was performed on the available digital elevation model area to obtain the relief analysis results. The results of the undulation analysis are graded to obtain the evaluation results of the difficulty of ground construction. The process of acquiring remote sensing data, calculating unusable digital elevation model (DEM) areas, and removing unusable DEM areas from the remote sensing data to obtain usable DEM areas includes the following steps: High-resolution digital elevation model data, optical remote sensing image data, and basic geographic data are collected and preprocessed. The basic geographic data includes basic vector data of roads, railways, ecological red line areas, water sources, and transmission pipelines. The NDBI index method and the NDWI index method were used to extract the boundaries of urban construction areas and river and lake water areas, respectively. The catchment area of ​​a water source was determined using surface runoff analysis. By removing urban construction areas, river and lake areas, water source catchment areas, ecological red line areas, road areas, railway areas, and transmission pipeline areas from the digital elevation model data, usable digital elevation model areas are obtained. The process of performing multi-directional convolutional terrain undulation analysis on the available digital elevation model area to obtain the undulation analysis results includes the following steps: Construct the basic convolution window matrix; By rotating the basic convolution window matrix, we can construct convolution rotation window matrices in multiple directions. The undulation is calculated by performing convolution on each convolutional rotation window matrix to obtain the undulation raster results in each direction; The undulation raster results from each direction are merged into the undulation analysis results.

2. The remote sensing evaluation method for oil and gas exploration surface conditions according to claim 1, characterized in that, The preprocessing of the digital elevation model data includes peak reduction and depression filling.

3. The remote sensing evaluation method for oil and gas exploration surface conditions according to claim 1 or 2, characterized in that, The preprocessing of the optical remote sensing image data includes atmospheric correction and geometric correction.

4. The remote sensing evaluation method for oil and gas exploration surface conditions according to claim 1, characterized in that, The specific steps for using surface runoff analysis to extract the catchment area of ​​a water source are as follows: The direction of water flow is determined by the steepest descent method in multiple directions, resulting in a flow direction grid. The flow rate grid is obtained by calculating the cumulative flow of each grid cell based on the flow direction grid. By using flow direction grids and flow rate grids, and dividing the catchment area into catchment areas according to the set catchment area threshold, a catchment area grouping grid is obtained. The catchment area is grouped into raster data and converted into vector data. This data is then spatially overlaid with the water source area in the basic geographic data to obtain the catchment area of ​​the water source.

5. The remote sensing evaluation method for oil and gas exploration surface conditions according to claim 1, characterized in that, The grading criteria for the fluctuation analysis results are as follows: Low difficulty zone: fluctuation < a; Medium difficulty zone: a ≤ variability < b; High difficulty zone: b ≤ undulation.

6. A remote sensing evaluation system for oil and gas exploration surface conditions, used to execute the remote sensing evaluation method for oil and gas exploration surface conditions according to any one of claims 1-5, characterized in that, include: The determination unit is used to acquire remote sensing data, calculate unusable digital elevation model areas, and remove unusable digital elevation model areas from the remote sensing data to obtain usable digital elevation model areas. A convolutional unit is used to perform multi-directional convolutional terrain relief analysis on the available digital elevation model area to obtain relief analysis results. The evaluation unit is used to classify the undulation analysis results and obtain the evaluation results of the difficulty of ground construction. The determining unit is specifically used for: High-resolution digital elevation model data, optical remote sensing image data, and basic geographic data are collected and preprocessed. The basic geographic data includes basic vector data of roads, railways, ecological red line areas, water sources, and transmission pipelines. The NDBI index method and the NDWI index method were used to extract the boundaries of urban construction areas and river and lake water areas, respectively. The catchment area of ​​a water source was determined using surface runoff analysis. By removing urban construction areas, river and lake areas, water source catchment areas, ecological red line areas, road areas, railway areas, and transmission pipeline areas from the digital elevation model data, usable digital elevation model areas are obtained. The convolutional unit is specifically used for: Construct the basic convolution window matrix; By rotating the basic convolution window matrix, we can construct convolution rotation window matrices in multiple directions. The undulation is calculated by performing convolution on each convolutional rotation window matrix to obtain the undulation raster results in each direction; The undulation raster results from each direction are merged into the undulation analysis results.

7. The remote sensing evaluation system for oil and gas exploration surface conditions according to claim 6, characterized in that, The determining unit is specifically used for: The direction of water flow is determined by the steepest descent method in multiple directions, resulting in a flow direction grid. The flow rate grid is obtained by calculating the cumulative flow of each grid cell based on the flow direction grid. By using flow direction grids and flow rate grids, and dividing the catchment area into catchment areas according to the set catchment area threshold, a catchment area grouping grid is obtained. The catchment area is grouped into raster data and converted into vector data. This data is then spatially overlaid with the water source area in the basic geographic data to obtain the catchment area of ​​the water source.

Images