A Shale Gas Drilling Well Site Selection Method Based on Geographic Information System and Remote Sensing Data

Abstract

This invention provides a shale gas drilling site selection method based on geographic information systems and remote sensing data. Specifically, it includes: calculating four types of parameters based on open-source high-precision geographic information systems and remote sensing data: surface topography, land use, distance to effective water sources, and distance to road networks; assessing the risk level and assigning rating weights based on the value range distribution of each parameter's calculation results; calculating a comprehensive shale gas extraction index based on the combined rating weights of each parameter; determining a threshold and delineating potentially favorable shale gas extraction areas based on the calculated comprehensive shale gas extraction index for existing well locations; identifying contiguous effective areas within the delineated potentially favorable shale gas extraction areas that meet well site size requirements, and ultimately determining potential shale gas drilling sites. The beneficial effects of this method are: providing a comprehensive, quantitative, objective, reliable, convenient, and economical risk assessment for areas with complex surface conditions, thereby improving the evaluation efficiency of shale gas extraction target selection.

Description

Technical Field

[0001] This invention relates to the field of shale gas exploration and development, and in particular to a method for shale gas well site selection based on geographic information systems and remote sensing data. Background Technology

[0002] my country has been commercially exploiting shale gas for over a decade, making significant contributions to ensuring national energy security. The exploration, development, and utilization of shale gas resources are a key objective in the planning of a modern energy system. Currently, the delineation of favorable exploration areas for major shale gas target formations has been largely completed. The key challenge in current shale gas exploration and development work is how to efficiently delineate shale gas extraction targets from these favorable exploration areas while minimizing risks.

[0003] Previous experience has shown that most of my country's current favorable shale gas exploration areas are located in mountainous and hilly regions with significant elevation differences, relative undulations, and slopes, as well as high vegetation cover, making engineering development extremely difficult. Furthermore, existing research reveals that shale gas extraction has a significant impact on regional water quality, air quality, soil, vegetation, wildlife habitats, and human daily life. The existing favorable exploration areas are widely distributed within various nature reserves and densely populated areas, resulting in a severe ecological and environmental protection situation. At the same time, shale gas development requires large amounts of water resources, large well sites, and is highly dependent on road networks.

[0004] Currently, the commonly used shale gas drilling site selection method is mainly based on "indoor preliminary screening - field reconnaissance." This involves first screening well sites in geologically favorable areas using large-scale topographic maps, followed by on-site exploration of potential targets, conducting comprehensive assessments of oil and gas engineering, geotechnical engineering, safety engineering, environmental protection, and cost, and finally finalizing the well site selection. However, indoor operations rely solely on manual screening of suitable topographical areas for shale gas extraction on regional-scale maps, resulting in a large workload and low efficiency. Furthermore, this approach does not consider ecological, environmental, and economic factors, inevitably leading to a large number of unsuitable areas included in the identified potential targets. Consequently, many field reconnaissances are unnecessary, resulting in a significant waste of manpower, resources, and time, further reducing the cost-effectiveness of the evaluation. Summary of the Invention

[0005] In view of this, a shale gas drilling site selection method based on geographic information systems and remote sensing data can conveniently, efficiently, and quantitatively conduct a comprehensive assessment of engineering, ecological, and economic factors in shale gas exploration favorable areas with complex surface conditions, thereby improving the evaluation efficiency and credibility of shale gas extraction target selection.

[0006] A shale gas drilling site selection method based on geographic information systems and remote sensing data includes the following steps:

[0007] S1: Obtain the values ​​of nine evaluation parameters at different planar locations within the area to be evaluated, including surface elevation, topographic relief, slope, surface cover type, NDVI index of vegetation cover area, distance from densely populated areas, distance from effective water sources, distance from national and provincial highways, and distance from county and township roads.

[0008] S2. Based on the distribution range of each evaluation parameter value, set the rating assignment standard and calculate the parameter weight corresponding to each evaluation parameter value;

[0009] S3. Calculate the comprehensive shale gas extraction index at different planar locations within the evaluation area based on the parameter weights of each evaluation parameter in S2. Combine the calculation results of the comprehensive shale gas extraction index at the locations of existing wells within the evaluation area to set the delineation threshold for potential favorable shale gas extraction areas and delineate potential favorable shale gas extraction areas.

[0010] S4. Identify contiguous effective areas that meet the well site size requirements within the delineated potential favorable areas for shale gas extraction, and finally determine the potential shale gas drilling sites.

[0011] Furthermore, in S2, the parameter weights include secondary parameter weights and primary parameter weights.

[0012] Furthermore, the specific steps for calculating the parameter weights of each evaluation parameter are as follows:

[0013] The minimum value of each evaluation parameter (M) n min ) to its maximum value (M) n max Arrange the values ​​and select 10%, 25%, 50%, 75%, and 90% of their range as nodes, denoted as M respectively. n (10) M n (25) M n (50) M n (75) and M n (90) Six graded weights of 100, 10, 1, 0.1, 0.01, and 0 are assigned, and rating standards for each evaluation parameter are set. The secondary parameter weights and primary parameter weights corresponding to each evaluation parameter value in the evaluation area are calculated.

[0014] Furthermore, based on the evaluation parameter values, the corresponding range of graded weight values ​​is obtained. Values ​​are assigned according to equations (1)-(2), and the secondary parameter weights R corresponding to each evaluation parameter value within the evaluation area are calculated. n The altitude or topographic relief is assigned using equation (1).

[0015]

[0016] Equation (2) is used to assign values ​​to other evaluation parameters.

[0017]

[0018] Meanwhile, when M3≥35°, M6≤0.5km, M7≤0.1km, M8≤0.2km and M9≤0.1km, the corresponding secondary parameter weights are all 0. Among them, M3 is the slope, M6 is the distance from densely populated areas, M7 is the distance from effective water sources, M8 is the distance from national and provincial highways and M9 is the distance from county and township roads.

[0019] Furthermore, the weights of the nine secondary parameters are integrated into terrain (Q1), slope (Q2), land use (Q3), distance to effective water source (Q4), and distance to road network (Q5). The integration method is as follows:

[0020] Q1=R1×R2 (3)

[0021] Q2 = R3 (4)

[0022] Q3=(R4+R5)×R6 (5)

[0023] Q4 = R7 (6)

[0024]

[0025] Among them, R1 is the secondary parameter weight of altitude, R2 is the secondary parameter weight of topographic relief, R3 is the secondary parameter weight of slope, R4 is the secondary parameter weight of land cover type, R5 is the secondary parameter weight of NDVI index of vegetation cover area, R6 is the secondary parameter weight of distance from densely populated areas, R7 is the secondary parameter weight of distance from effective water source, R8 is the secondary parameter weight of distance from national and provincial highways, and R9 is the secondary parameter weight of distance from county and township roads.

[0026] Furthermore, in S3, the method for calculating the comprehensive shale gas extraction index at different planar locations within the evaluation area is as follows:

[0027]

[0028] In the above formula, Q n Q is the weight of the corresponding nth first-level parameter. n The range is [0, 100].

[0029] Furthermore, in S3, the minimum value of the comprehensive shale gas extraction index at the location of existing wells in the area to be evaluated is used as the lower limit threshold for delineating potentially favorable areas for shale gas extraction.

[0030] Furthermore, in S4, after binarizing the distribution range of the potential favorable shale gas extraction area delineated in step S3 into an image, the lower limit size is used as the convolution kernel to traverse the entire area, determine the pixels in the image that coincide with the convolution kernel and output their positions, so as to obtain the contiguous effective area that meets the conditions and use it as the final evaluation result of the potential shale gas well site.

[0031] The beneficial effects of the technical solution provided by this invention are: to conduct comprehensive, quantitative, objective, reliable, convenient and economical risk assessments of areas with complex surface conditions, thereby improving the evaluation efficiency of shale gas extraction target selection. Attached Figure Description

[0032] Figure 1 This is a flowchart of a shale gas drilling site selection method based on geographic information system and remote sensing data according to the present invention;

[0033] Figure 2 This is a schematic diagram of the digital altitude model data used in an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of the terrain relief calculation results according to an embodiment of the present invention;

[0035] Figure 4 This is a schematic diagram of the slope calculation results according to an embodiment of the present invention;

[0036] Figure 5 This is a schematic diagram of global land cover data used in embodiments of the present invention;

[0037] Figure 6 This is a schematic diagram of the visible light band image of multispectral remote sensing data used in an embodiment of the present invention;

[0038] Figure 7 This is a schematic diagram of the calculation results of the regional normalized vegetation index according to an embodiment of the present invention;

[0039] Figure 8 This is a schematic diagram of the calculation results of the normalized vegetation index of the vegetation cover area according to an embodiment of the present invention;

[0040] Figure 9 This is a schematic diagram illustrating the distance calculation results from densely populated areas according to an embodiment of the present invention;

[0041] Figure 10 This is a schematic diagram illustrating the calculation results of the distance to an effective water source according to an embodiment of the present invention;

[0042] Figure 11 This is a schematic diagram of road network data used in an embodiment of the present invention;

[0043] Figure 12 This is a schematic diagram illustrating the calculation results of distances from national and provincial highways according to an embodiment of the present invention;

[0044] Figure 13 This is a schematic diagram illustrating the calculation results of the distance from county roads and township roads according to an embodiment of the present invention;

[0045] Figure 14 This is a schematic diagram of the weighting results of each secondary evaluation parameter in an embodiment of the present invention;

[0046] Figure 15 This is a schematic diagram of the weighting results of each primary evaluation parameter in an embodiment of the present invention;

[0047] Figure 16 This is a schematic diagram illustrating the calculation results of the comprehensive shale gas extraction index in an embodiment of the present invention;

[0048] Figure 17 This is a schematic diagram showing the delineation results of potential favorable areas for shale gas extraction according to an embodiment of the present invention.

[0049] Figure 18 This is a schematic diagram of the contiguous effective area identification method involved in the present invention;

[0050] Figure 19 This is a schematic diagram showing the delineation results of potential favorable areas and potential well sites for shale gas extraction in key areas according to an embodiment of the present invention;

[0051] Figure 20 This is a schematic diagram of the delineation results of potential advantageous shale gas drilling well locations according to an embodiment of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0053] Please refer to Figure 1 The present invention provides a shale gas drilling site selection method based on geographic information system and remote sensing data, comprising the following steps:

[0054] S1: Based on open-source high-precision geographic information systems and remote sensing data, calculate nine evaluation parameters in four categories for different planar locations within the evaluation area: surface topography, land use, distance from effective water sources, and distance from road networks. The nine evaluation parameters are as follows: surface elevation (M1), topographic relief (M2), slope (M3), surface cover type (M4), NDVI index of vegetation cover area (M5), distance from densely populated areas (M6), distance from effective water sources (M7), distance from national and provincial highways (M8), and distance from county and township roads (M9).

[0055] Based on existing theoretical foundations and exploration practices, this paper analyzes the engineering, ecological, and economic factors and their impacts related to shale gas extraction for each evaluation parameter:

[0056] 1) Terrain parameters (M1-M3)

[0057] Elevation and topographic relief together reflect the characteristics of the Earth's surface. The greater both are, the more difficult it is to construct well sites and ancillary facilities, as well as transport various materials, for shale gas exploration and development activities. Slope directly affects well site construction. In addition, excessive slope restricts vegetation development, indirectly leading to geological disasters such as landslides, and also increases the diffusion rate of various polluting liquids. Therefore, the difficulty, cost, and risk of shale engineering development all increase significantly with slope.

[0058] 2) Land cover type (M4)

[0059] Shale gas extraction can cause adverse effects such as deforestation, destruction of wildlife habitats, water pollution, disruption of daily life, and occupation of planned land. Land cover type is an important parameter in the site selection of shale gas extraction wells. Existing exploration examples show that farmland and low-coverage vegetation areas (such as grasslands) are the best site types for well sites, and shale gas extraction should be avoided as much as possible in high-coverage vegetation areas, various water bodies, and densely populated areas.

[0060] 3) NDVI index (M5) of vegetation cover area

[0061] The NDVI index is the most commonly used indicator to reflect vegetation growth status and coverage. Its value ranges from -1 to 1. An index less than 0 indicates that the ground is covered by clouds, water, snow, etc.; an index close to 0 indicates that the surface is rocky or bare; and an index greater than 0 indicates that vegetation has developed on the surface, with a higher value indicating greater vegetation coverage. In other words, the higher the NDVI index, the more difficult the shale gas extraction project is, and the greater the damage to forests and other vegetation protection areas.

[0062] 4) Distance parameters (M6-M9)

[0063] Shale gas extraction can impact daily life, therefore it must be located far from densely populated areas, meaning M6 needs to be greater than a certain range. Furthermore, because the hydraulic fracturing process in shale gas extraction consumes a huge amount of water and is highly dependent on road transportation for extraction materials, smaller M7-M9 values ​​result in lower water extraction and road construction costs.

[0064] Based on existing research findings and industry standards, the evaluation value for some parameter ranges will be assigned to 0:

[0065] 1) Studies have shown that when the surface slope is greater than 35°, construction is extremely difficult and soil erosion is severe, making it unsuitable for engineering development. Therefore, M3 should be less than 35°.

[0066] 2) Industry standards stipulate that the distance between drilling platforms and densely populated areas shall not be less than 500m, that is, M6 should be greater than 0.5km.

[0067] 3) Existing research suggests that the hydraulic fracturing process in deep shale gas extraction has minimal impact on shallow groundwater. However, wastewater generated during extraction may pose a risk of leakage and diffusion, thus affecting surface water quality. Furthermore, the length of fractures and small-scale faults generated by hydraulic fracturing is mostly 10–20 m, but in rare cases, it may extend to over 100 m. Therefore, if operations are carried out in shallower target formations, the wastewater generated during extraction may still contaminate shallow groundwater layers. Based on this, this assessment deems M7 to be greater than 0.1 km.

[0068] 4) To avoid the mining operations affecting the roadbed and vehicle driving safety, the distance between the drilling platform and ordinary roads should be greater than 100m, and the distance from highways should be greater than 200m, that is, M8 and M9 should be greater than 0.2km and 0.1km respectively.

[0069] Specifically, the steps for calculating the surface elevation (M1), topographic relief (M2), and slope (M3) at different planar locations within the area to be evaluated are as follows:

[0070] S11. Collect digital elevation model (DEM) data for the area to be evaluated, extract the surface elevation (M1) at different planar locations within the area to be evaluated, and calculate the topographic relief (M2) and slope (M3) based on this data. Specifically, the calculation method for the topographic relief is as follows:

[0071] S110: The area to be evaluated is divided using 39 times the grid node spacing × 39 times the grid node spacing as the basic calculation unit;

[0072] S111: Calculate the difference between the maximum and minimum surface elevation values ​​at different planar locations within the above basic calculation unit, assign the center point of the calculation unit, and use the natural nearest neighbor method to interpolate to obtain the terrain relief parameter raster at different planar locations.

[0073] S12. Collect global land cover (GLC) data for the area to be evaluated and extract land cover type (M4) data for different planar locations within the area to be evaluated.

[0074] S13. Based on the multispectral remote sensing image data of the area to be evaluated, the vegetation cover of the entire area is quantitatively estimated using the normalized difference vegetation index (NDVI). The calculation formula is as follows:

[0075]

[0076] In the formula: P(NIR) is the reflectance of the near-infrared band, and P(RED) is the reflectance of the red band; the maximum calculated value of NDVI for each pixel throughout the entire time period is normalized; at the same time, based on the land cover type (M4) obtained in S12, the vegetation cover area within the area to be evaluated is integrated to obtain the NDVI index (M5) of the vegetation cover area at different planar locations within the area to be evaluated.

[0077] S14. Based on the distribution of densely populated areas and effective water sources in the area to be evaluated indicated by GLC data, calculate the distance (M6) from the densely populated areas and the distance (M7) from the effective water sources at different planar locations in the area to be evaluated.

[0078] S15. Obtain road network data for the area to be evaluated, extract the distribution of national highways, provincial highways, county roads, and township roads, and calculate the distances (M8) from national highways and provincial highways and the distances (M9) from county roads and township roads at different planar locations within the area to be evaluated.

[0079] S2: Based on the distribution range of each evaluation parameter value, set the rating assignment standard, and calculate the parameter weight corresponding to each evaluation parameter value. The parameter weight includes secondary parameter weight and primary parameter weight. The specific operation is as follows:

[0080] S21. Rasterize the nine parameters M1-M9 using the nearest neighbor method, ensuring that each grid has the same range and that the grid node spacing is equal and not less than 30m.

[0081] S22. Based on the distribution range of each evaluation parameter's value range, set the rating assignment standard. The specific method is as follows: set the minimum value (M) of each evaluation parameter as the minimum value (M) of each evaluation parameter. n min ) to its maximum value (M) n max Arrange the values ​​and select 10%, 25%, 50%, 75%, and 90% of their range as nodes, denoted as M respectively. n (10) M n (25) M n (50) M n (75) and M n (90)Six graded weights of 100, 10, 1, 0.1, 0.01, and 0 are assigned. Based on these six graded weights, the rating values ​​of each evaluation parameter are set, as shown in formulas (1)-(7). The rating values ​​of each evaluation parameter are calculated and shown in Table 1-2. According to the evaluation parameter values, the corresponding graded weight range is obtained, and the secondary parameter weights corresponding to each evaluation parameter value in the area to be evaluated are calculated. Thus, nine secondary parameter weights are obtained: altitude (R1), topographic relief (R2), slope (R3), land cover type (R4), NDVI index of vegetation cover area (R5), distance from densely populated areas (R6), distance from effective water sources (R7), distance from national and provincial highways (R8), and distance from county and township roads (R9). It should be noted that, based on existing research results and industry standards, the rating value of some evaluation parameter ranges is assigned to 0 as appropriate.

[0082] The following formula is used to assign values ​​to elevation M1 or topographic relief M2:

[0083]

[0084] Other evaluation parameters are assigned values ​​using the following formula:

[0085]

[0086] In addition, a weight of 0 is assigned to some of the secondary parameter value ranges.

[0087] R3=0,M3≥35° (3)

[0088] R6=0, M6≤0.5km (4)

[0089] R7=0, M7≤0.1km (5)

[0090] R8=0, M8≤0.2km (6)

[0091] R9=0, M9≤0.1km (7)

[0092] Table 1

[0093] Evaluation parameters <![CDATA[R n =10]]> <![CDATA[R n ∈[1,10)]]> <![CDATA[R n ∈[0.1,1)]]> <![CDATA[R n ∈[0,0.1)]]> <![CDATA[M1(m)]]> <![CDATA[[M1 min ,M1 (25) ]]]> <![CDATA[(M1 (25) ,M1 (50) ]]]> <![CDATA[(M1 (50) ,M1 (75) ]]]> <![CDATA[(M1 (75) ,M1 max ]]]> <![CDATA[M2(m)]]> <![CDATA[[M2 min M2 (25) ]]]> <![CDATA[(M2 (25) M2 (50) ]]]> <![CDATA[(M2 (50) M2 (75) ]]]> <![CDATA[(M2 (75) M2 max ]]]>

[0094] Table 2

[0095]

[0096] S23. The weights of the above nine secondary parameters are integrated into five primary parameters: terrain (Q1), slope (Q2), land use (Q3), distance to effective water source (Q4), and distance to road network (Q5).

[0097] Q1=R1×R2 (8)

[0098] Q2 = R3 (9)

[0099] Q3=(R4+R5)×R6 (10)

[0100] Q4 = R7 (11)

[0101]

[0102] S3: Calculate the comprehensive shale gas extraction index at different planar locations within the evaluation area based on the parameter weights of each evaluation parameter in S2. Combine the calculation results of the comprehensive shale gas extraction index at the locations of existing wells within the evaluation area to set a threshold for delineating potentially favorable shale gas extraction areas and delineate these areas. Specifically, this invention uses the minimum value of the comprehensive shale gas extraction index at the locations of existing wells within the evaluation area as the lower limit threshold for delineating potentially favorable shale gas extraction areas.

[0103] As another embodiment of the present invention for delineating potentially favorable areas for shale gas extraction, the specific operation is as follows:

[0104] S31. The potential favorable areas for shale gas extraction are divided into three levels: barely suitable areas, relatively suitable areas, and extremely suitable areas.

[0105] S32. According to Equation 13, calculate the comprehensive shale gas extraction index at different planar locations within the area to be evaluated, as well as the comprehensive shale gas extraction index at the locations of existing wells within the area to be evaluated.

[0106]

[0107] In the above formula, Q n Q is the weight of the corresponding nth first-level parameter. n The range is [0, 100].

[0108] S33. Statistically analyze the shale gas extraction comprehensive index data of the locations of existing wells in the evaluation area. Determine the minimum value of the shale gas extraction comprehensive index of the locations of existing wells in the evaluation area as the lower limit threshold for the barely suitable area. Select the shale gas extraction comprehensive index value of the corresponding wells that is greater than 1 / 4 of the total number of existing wells as the lower limit threshold for the relatively suitable area. Select the shale gas extraction comprehensive index value of the corresponding wells that is greater than 1 / 2 of the total number of existing wells as the lower limit threshold for the extremely suitable area.

[0109] It should be noted that, in practical applications, the classification of potentially favorable shale gas extraction areas and the setting of corresponding lower thresholds for each level can be adjusted according to the actual application.

[0110] S4: Identify contiguous effective areas that meet the well site size requirements within the delineated potential favorable areas for shale gas extraction, and finally determine the potential shale gas drilling sites. The reason is that the comprehensive index only reflects the suitability of shale gas extraction, while the extraction well site is equipped with drilling rigs and various auxiliary equipment, and also needs to meet a certain effective usable area, that is, a contiguous effective area whose length and width dimensions meet the lower limit of actual needs.

[0111] The petroleum industry standards stipulate that the length and width of the well site required for each level of drilling rig for oil and gas extraction are between 60 and 120m. Therefore, the standards for delineating well sites in barely suitable, moderately suitable, and extremely suitable areas are respectively based on a contiguous effective area of ​​not less than 60m×60m, 90m×90m, and 120m×120m.

[0112] The specific steps are as follows:

[0113] The length and width dimensions of 60m×60m, 90m×90m, and 120m×120m are used as the lower limit dimensions for three categories of potential well sites: barely suitable, relatively suitable, and extremely suitable. After binarizing the distribution range of the potential favorable shale gas extraction area delineated in step S3 into an image, the lower limit dimensions are used as the convolution kernel to traverse the entire area, determine the pixels in the image that coincide with the convolution kernel, and output their positions to obtain contiguous effective areas that meet the conditions and use them as the final evaluation results of potential shale gas well sites.

[0114] <Example 1>

[0115] The method described in this embodiment extracts the surface feature information required for evaluation based on the following four types of open-source data:

[0116] (1) DEM Data: Data from the "Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM, Version 2; https: / / earthexplorer.usgs.gov / )" published by NASA and the Japanese Ministry of Economy, Trade and Industry was selected. This data uses satellite stereo imagery (ASTER GDEMValidation Team, 2011) with a spatial resolution of up to 1 radian-second (approximately 30 m), providing high-precision details of the surface topography for this study.

[0117] (2) GLC Data: The 2017 GLC dataset (http: / / data.ess.tsinghua.edu.cn) was selected. This data, based on a combination of remote sensing technologies, achieves a 10m-level land stability classification (Gong et al., 2019), which can provide the distribution information of major land types such as farmland, vegetation, water systems and towns in the study area for this evaluation.

[0118] (3) Multispectral remote sensing data: Based on the spectral dataset collected by the Sentinel-2A satellite multispectral imager launched by the European Space Agency (https: / / scihub.copernicus.eu / ), 10m resolution visible light band (Band 2-4) and near-infrared band (Band 8) data from May to September 2019 were selected for the calculation of normalized difference vegetation index (NDVI).

[0119] (4) Road network data: Based on the Gaode Map platform (https: / / lbs.amap.com / ), the road network data of all levels of roads in the study area were obtained and further organized into the distribution of high-level highways (national highways and provincial highways) and low-level highways (county roads and township roads).

[0120] This embodiment is located in the transitional area between the inner and outer edges of the Sichuan Basin, geographically ranging from 107° to 108°E and 29° to 30°N. Administratively, it belongs to Changshou District, Nanchuan District, Wulong District, Fuling County, and Fengdu County of Chongqing Municipality, with a total area of ​​approximately 1.1 × 10⁻⁶. 4 km 2 Three shale gas fields—Fuling, Nanchuan, and Wulong—have been discovered in the area, with 28 shale gas exploration wells deployed, making it a key shale gas exploration area in my country at present and in the future. The northwestern part of the area is mainly plains, with farmland as the primary land cover; the remaining areas are mainly mountainous and hilly terrain with extremely high vegetation cover. Apart from major rivers and lakes, water bodies are only scattered throughout the area, making water resources relatively scarce. Roads are also relatively few in the high-altitude areas outside the northwestern plains. These natural geographical features make shale gas extraction and utilization in the study area more difficult and costly than in plains areas. Furthermore, most areas in the area have fragile ecosystems and are densely populated, so ecological, environmental, and human constraints cannot be ignored. Therefore, the selection of shale gas development targets in the area is subject to the combined constraints of multiple surface factors.

[0121] This embodiment focuses on the aforementioned region and conducts shale gas well site selection based on geographic information systems and remote sensing data. The present invention provides a shale gas well site selection method based on geographic information systems and remote sensing data, comprising the following steps:

[0122] S1. Collect 30m ASTER GDEM (v2) data for the area to be evaluated, and transform the raw data from the geographic coordinate system to the custom coordinate system used in this evaluation (WGS84 coordinate system, plane rectangular projection, scale 1:200000, central meridian 108°E) to serve as the data for the digital elevation model and evaluation parameter M1 used in the evaluation. Figure 2 As shown.

[0123] Based on the above digital elevation model data, the evaluation parameters M2 and M3 are calculated, as follows: Figure 3 and Figure 4 As shown.

[0124] S2. Collect 2017 10-m resolution global land cover data for the area to be evaluated, obtained by Gong et al. based on various remote sensing data processing [Science Bulletin, 2019, Vol. 64, pp. 370-373, Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017]. Project and transform the raw data to the custom coordinate system for this evaluation, and determine the extent of four land cover types: farmland, vegetation (forests, shrubs, grasslands, etc.), water systems (rivers, lakes, and wetlands), and densely populated areas (artificial impermeable surfaces). These will serve as data for the global land cover and evaluation parameter M4. Figure 5 As shown.

[0125] S3. Collect 10m-level Sentinel-2A satellite multispectral remote sensing image data of the area to be evaluated from May to September 2019. After cloud removal and mosaicking, project and transform the data to the custom coordinate system for this evaluation, and use it as the multispectral remote sensing data for the evaluation. Figure 6 As shown.

[0126] The vegetation cover of each pixel at different time periods was calculated using the Normalized Difference Vegetation Index (NDVI), and the formula is as follows:

[0127]

[0128] In the formula: P(NIR) is the near-infrared reflectance, and P(RED) is the red reflectance; the maximum calculated NDVI value of each phase pixel throughout the entire time period is standardized to the interval [-1,1], such as... Figure 7 As shown.

[0129] Based on the vegetation extent extracted in S2, the NDVI calculation results of the vegetation cover area in the area to be evaluated are obtained, such as... Figure 8 As shown.

[0130] S4. Based on the densely populated areas and water system ranges extracted in S2, calculate the distribution of evaluation parameters M6 and M7 at different planar locations within the area, such as... Figure 9 and Figure 10 As shown.

[0131] S5. Collect the 2020 Gaode Map road network data for the area to be evaluated, organize it into high-level highways (national and provincial highways) and low-level highways (county and township roads), and project and transform it to the custom coordinate system for this evaluation as the road network data used in the evaluation. Figure 11 As shown.

[0132] Based on the above road network data, the evaluation parameters M8 and M9 for different planar locations within the area are calculated, such as... Figure 12 and Figure 13 As shown.

[0133] S6. Rasterize the data of parameters M1-M9 using the nearest neighbor method, keeping the range of each grid the same, with a grid spacing of 30m×30m, to obtain the parameter grid series.

[0134] S7. Statistical analysis of the value range and nodes of each evaluation parameter, as shown in Table 3.

[0135] Table 3

[0136] parameter <![CDATA[M n min ]]> <![CDATA[M n (10) ]]> <![CDATA[M n (25) ]]> <![CDATA[M n (50) ]]> <![CDATA[M n (75) ]]> <![CDATA[M n (90) ]]> <![CDATA[M n max ]]> <![CDATA[M1(m)]]> 22.0 305.2 451.4 722.8 1073.6 1405.4 2227.1 <![CDATA[M2(m)]]> 33.4 118.9 184.4 282.2 392.9 518.0 1131.3 <![CDATA[M3(°)]]> 0 6.43 10.13 16.04 23.74 31.96 74.55 <![CDATA[M5]]> 0.278 0.583 0.754 0.836 0.882 0.910 1 <![CDATA[M6(km)]]> 0 0.80 1.92 3.56 5.62 8.12 13.10 <![CDATA[M7(km)]]> 0 0.60 1.58 3.38 6.68 10.78 27.06 <![CDATA[M8(km)]]> 0 0.47 1.36 3.29 6.33 9.79 21.09 <![CDATA[M9(km)]]> 0 0.25 0.70 1.57 2.82 4.22 14.71

[0137] S8. Based on the standards shown in Tables 1 and 2, assign corresponding hierarchical weights to parameters M1-M9 to obtain nine secondary parameter weights: R1, R2, R3, R4, R5, R6, R7, R8, and R9. Compile a weight grid, as follows: Figure 14 As shown.

[0138] S9. Calculate the first-level parameter weight Q1 using the second-level parameter weights R1 and R2, using the following formula:

[0139] Q1 = R1 × R2

[0140] The first-level parameter weight Q3 is calculated using the second-level parameter weights R4, R5, and R6, using the following formula:

[0141] Q3 = (R4 + R5) × R6

[0142] The first-level parameter weight Q5 is calculated using the second-level parameter weights R8 and R9, using the following formula:

[0143]

[0144] Using Q1-Q5 as independent first-level parameter weights, a weight grid is constructed, such as... Figure 15 As shown.

[0145] S10. Calculate the comprehensive shale gas extraction index based on the weights of the above-mentioned primary parameters. The formula is as follows:

[0146]

[0147] In the formula Q n Q is the weight of the corresponding nth first-level parameter. n The range is [0, 100].

[0148] The calculation results of the comprehensive shale gas extraction index for the entire area to be evaluated in this embodiment are as follows: Figure 16 As shown.

[0149] S11. The calculation results of nine evaluation parameters based on the location of 28 shale gas exploration wells in the evaluation area are shown in Table 4.

[0150] Table 4

[0151] hashtag <![CDATA[M1(m)]]> <![CDATA[M2(m)]]> <![CDATA[M3(°)]]> <![CDATA[M4]]> <![CDATA[M5]]> <![CDATA[M6(km)]]> <![CDATA[M7(km)]]> <![CDATA[M8(km)]]> <![CDATA[M9(km)]]> FL1 465.2 269.4 12.22 vegetation 0.388 0.97 4.83 0.675 1.52 FL2 429.5 208.4 10.65 farmland / 2.35 1.37 1.636 4.24 FL3 423.3 236.9 9.29 farmland / 0.85 1.90 1.132 3.12 FL4 526.5 177.1 15.73 farmland / 4.61 1.49 2.386 1.29 FL5 314.6 223.6 7.01 farmland / 5.34 5.47 6.502 0.48 FL6 456.9 175.7 5.66 farmland / 2.37 4.35 9.650 0.80 FL7 517.1 225.0 3.15 farmland / 2.64 6.75 4.720 0.23 FL8 429.9 174.6 6.80 farmland / 0.67 2.82 10.556 1.29 FL9 783.0 108.1 7.00 farmland / 4.05 7.46 0.663 2.68 FL10 484.6 189.6 9.24 farmland / 3.77 7.07 4.002 0.34 FL11 745.1 184.5 7.25 farmland / 1.88 8.05 0.338 1.57 FL12 611.3 162.7 5.36 farmland / 4.69 4.48 5.839 1.19 FL13 678.7 149.2 7.95 farmland / 0.56 6.33 0.447 0.46 FL14 560.5 173.7 6.72 farmland / 0.91 7.56 2.409 0.21 FL15 972.8 206.7 5.98 farmland / 1.19 2.41 1.668 2.79 NC1 741.2 175.6 6.55 farmland / 1.20 6.41 6.338 0.33 NC2 707.9 150.1 5.91 farmland / 2.42 5.25 6.200 0.39 NC3 709.3 135.8 8.37 farmland / 1.68 6.43 7.176 0.44 NC4 639.3 196.1 7.34 vegetation 0.792 3.14 1.94 0.360 0.63 NC5 611.7 196.1 8.97 farmland / 3.10 3.14 0.327 1.50 NC6 561.8 170.0 2.03 farmland / 2.00 3.87 3.061 2.32 NC7 557.6 247.8 3.83 farmland / 3.74 4.31 1.277 2.09 NC8 532.2 145.0 5.10 vegetation 0.787 2.09 4.03 0.270 2.15 NC9 487.7 295.0 7.77 vegetation 0.358 0.54 4.34 1.325 1.95 NC10 292.8 260.8 8.49 farmland / 2.55 8.80 1.709 0.19 WL1 809.4 193.5 8.48 vegetation 0.547 2.09 2.15 2.168 0.52 WL2 202.2 238.7 10.92 vegetation 0.684 2.64 0.79 1.017 0.27 WL3 325.0 221.6 9.81 farmland / 5.10 3.91 4.124 0.28

[0152] Based on the assignment criteria in Tables 1 and 2, the weights of the primary and secondary parameters of the locations of 28 shale gas exploration wells and the comprehensive index of shale gas production were calculated, as shown in Table 5.

[0153] Table 5

[0154]

[0155]

[0156] S12. Based on the comprehensive shale gas extraction index calculation results of the locations of 28 wells, and using the comprehensive shale gas extraction index of 5.06, 5.44, and 6.10 as the lower limit thresholds, the favorable shale gas extraction areas were delineated as barely suitable, relatively suitable, and extremely suitable. The results are as follows: Figure 17 As shown.

[0157] S13. Further identify contiguous effective areas within the shale gas extraction potential favorable area delineated in the previous step, and finally determine the potential shale gas well sites. The specific method for identifying contiguous effective areas is as follows: After binarizing the distribution range of the shale gas extraction potential favorable area delineated in step S12 into an image, use the lower limit size as the convolution kernel to traverse the entire area, determine the pixels in the image that coincide with the convolution kernel and output their positions, so as to obtain the contiguous effective areas that meet the conditions and use them as the final evaluation results of potential shale gas well sites. Figure 18 This is a schematic diagram illustrating the process of identifying contiguous effective areas using 90m×90m as the lower limit standard for size. Figure 19 The results show the delineation of highly suitable and favorable areas for shale gas extraction and highly suitable well sites in key areas within the evaluation zone.

[0158] Comprehensive evaluation results as follows Figure 20 As shown, a total area of ​​580.52 km² was delineated as barely suitable, moderately suitable, and extremely suitable zones for shale gas drilling sites. 2 908.86km 2 and 1313.06km 2 These areas, representing 4.86%, 7.61%, and 10.99% of the total study area respectively, effectively excluded nearly 90% of the unsuitable areas for well sites. This evaluation result demonstrates that this method can effectively eliminate the majority of unfavorable areas within the study area, thereby reducing costs and risks and improving the efficiency of shale gas extraction and utilization.

[0159] This evaluation method assigns ratings based on the distribution of each parameter's value range, and determines the threshold for delineating favorable areas based on existing drilling comprehensive index calculation results. Therefore, it is also applicable to study areas with different parameter distribution characteristics, and more effectively avoids subjective human intervention in the above key steps. It is highly objective and operable, and has good application prospects.

[0160] The beneficial effects of the technical solution provided by this invention are: to conduct comprehensive, quantitative, objective, reliable, convenient and economical risk assessments of areas with complex surface conditions, thereby improving the evaluation efficiency of shale gas extraction target selection.

[0161] Where there is no conflict, the above embodiments and features described herein can be combined with each other.

[0162] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A shale gas well site selection method based on geographic information systems and remote sensing data, characterized in that, Includes the following steps: S1: Obtain the values ​​of nine evaluation parameters at different planar locations within the area to be evaluated, including surface elevation, topographic relief, slope, surface cover type, NDVI index of vegetation cover area, distance from densely populated areas, distance from effective water sources, distance from national and provincial highways, and distance from county and township roads. S2. Based on the distribution range of each evaluation parameter value, set the rating assignment standard and calculate the parameter weight corresponding to each evaluation parameter value; S3. Calculate the comprehensive shale gas extraction index at different planar locations within the evaluation area based on the parameter weights of each evaluation parameter in S2. Combine the calculation results of the comprehensive shale gas extraction index at the locations of existing wells within the evaluation area to set the delineation threshold for potential favorable shale gas extraction areas and delineate potential favorable shale gas extraction areas. S4. Identify contiguous effective areas that meet the well site size requirements within the delineated potential favorable areas for shale gas development, and finally determine the potential shale gas drilling sites. Specifically, based on the evaluation parameter values, the corresponding range of graded weight values ​​is obtained. Values ​​are assigned according to equations (1)-(2), and the secondary parameter weights corresponding to each evaluation parameter value within the evaluation area are calculated. R n ; Equation (1) is used to assign values ​​to altitude or topographic relief. （1） Equation (2) is used to assign values ​​to other evaluation parameters. （2） At the same time, define when M 3≥35° M 6≤0.5km M 7≤0.1km M 8≤0.2km and M When 9 ≤ 0.1 km, the corresponding weights of the second-order parameters are all 0, where, M 3 represents the slope. M 6 represents the distance from densely populated areas. M 7 represents the distance from an effective water source. M 8 represents the distance from national and provincial highways and M 9 represents the distance from county roads and township roads; The weights of nine secondary parameters are integrated into the terrain value. Q 1. Slope Q 2. Land use situation Q 3. Distance from effective water source Q 4. Distance from road network Q 5. The integration method is as follows: （3） （4） （5） （6） （7） in, R 1 represents the weight of the secondary parameter of altitude. R 2 represents the weights of the secondary parameters of terrain relief. R 3 represents the weight of the secondary parameter of slope. R 4 represents the weights of the secondary parameters for land cover type. R 5 represents the weight of the secondary parameter of the NDVI index in vegetation cover areas. R 6 represents the weight of the secondary parameter for distance from densely populated areas. R 7 represents the weight of the secondary parameter for distance from an effective water source. R 8 represents the weight of the secondary parameter for distance from national and provincial highways. R 9 represents the weight of the secondary parameter for distance from county and township roads; In S3, the method for calculating the comprehensive shale gas extraction index at different planar locations within the evaluation area is as follows: （8） In the above formula, Q n For the corresponding nth first-level parameter weight, Q n The range is [0, 100].

2. The shale gas drilling site selection method based on geographic information system and remote sensing data according to claim 1, characterized in that, In S2, the parameter weights include secondary parameter weights and primary parameter weights.

3. The shale gas drilling site selection method based on geographic information system and remote sensing data according to claim 2, characterized in that, The specific steps for calculating the parameter weights of each evaluation parameter are as follows: The minimum value of each evaluation parameter M n min up to its maximum value M n max Arrange the values ​​and select 10%, 25%, 50%, 75%, and 90% of their respective ranges as nodes, denoted as _____. M n (10) , M n (25) , M n (50) , M n (75) and M n (90) Six graded weights of 100, 10, 1, 0.1, 0.01, and 0 are assigned, and rating standards for each evaluation parameter are set. The secondary parameter weights and primary parameter weights corresponding to each evaluation parameter value in the evaluation area are calculated.

4. The shale gas drilling site selection method based on geographic information system and remote sensing data according to claim 1, characterized in that, In S3, the minimum value of the comprehensive shale gas extraction index at the location of existing wells in the area to be evaluated is used as the lower limit threshold for delineating potentially favorable areas for shale gas extraction.

5. The shale gas drilling site selection method based on geographic information system and remote sensing data according to claim 4, characterized in that, In S4, after binarizing the distribution range of the potential favorable shale gas extraction area delineated in step S3 into an image, the lower limit size is used as the convolution kernel to traverse the entire area, determine the pixels in the image that coincide with the convolution kernel and output their positions, so as to obtain the contiguous effective area that meets the conditions and use it as the final evaluation result of the potential shale gas well site.

Images