CN116070870A - Shale gas drilling site selection method based on geographic information system and remote sensing data - Google Patents
Shale gas drilling site selection method based on geographic information system and remote sensing data Download PDFInfo
- Publication number
- CN116070870A CN116070870A CN202310150534.1A CN202310150534A CN116070870A CN 116070870 A CN116070870 A CN 116070870A CN 202310150534 A CN202310150534 A CN 202310150534A CN 116070870 A CN116070870 A CN 116070870A
- Authority
- CN
- China
- Prior art keywords
- shale gas
- parameter
- weight
- evaluation
- distance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000005553 drilling Methods 0.000 title claims abstract description 45
- 238000010187 selection method Methods 0.000 title claims abstract description 16
- 238000011156 evaluation Methods 0.000 claims abstract description 77
- 230000009286 beneficial effect Effects 0.000 claims abstract description 32
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 29
- 238000004364 calculation method Methods 0.000 claims abstract description 24
- 238000000034 method Methods 0.000 claims abstract description 21
- 238000012876 topography Methods 0.000 claims abstract description 17
- 230000010354 integration Effects 0.000 claims description 2
- 238000012502 risk assessment Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 13
- 238000011161 development Methods 0.000 description 11
- 238000011160 research Methods 0.000 description 10
- 241000282414 Homo sapiens Species 0.000 description 4
- 238000010276 construction Methods 0.000 description 4
- 238000000605 extraction Methods 0.000 description 4
- 241000132092 Aster Species 0.000 description 3
- 230000002349 favourable effect Effects 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 238000005065 mining Methods 0.000 description 3
- 241001465754 Metazoa Species 0.000 description 2
- 238000009792 diffusion process Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 239000003673 groundwater Substances 0.000 description 2
- 239000007788 liquid Substances 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000002689 soil Substances 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000013467 fragmentation Methods 0.000 description 1
- 238000006062 fragmentation reaction Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 239000011435 rock Substances 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 239000002352 surface water Substances 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
- 239000002351 wastewater Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/188—Vegetation
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Databases & Information Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Animal Husbandry (AREA)
- Mining & Mineral Resources (AREA)
- Marine Sciences & Fisheries (AREA)
- Agronomy & Crop Science (AREA)
- Primary Health Care (AREA)
- Biodiversity & Conservation Biology (AREA)
- Remote Sensing (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention provides a shale gas drilling site selection method based on a geographic information system and remote sensing data, which specifically comprises the following steps: calculating four parameters of surface topography, land utilization condition, distance from an effective water source and distance from a road network based on an open source high-precision geographic information system and remote sensing data; evaluating the risk level according to the distribution condition of the value range of the calculation result of each evaluation parameter and giving a rating weight; calculating a shale gas exploitation comprehensive index by integrating the grading weight results of all the parameters; determining a threshold value according to the calculation result of the shale gas exploitation comprehensive index of the position where the existing well drilling is located and delineating a shale gas exploitation potential beneficial area; identifying a connecting effective area meeting the well site size requirement in a defined shale gas exploitation potential beneficial area, and finally determining a potential shale gas drilling well site; the method provided by the invention has the beneficial effects that: and comprehensive, quantitative, objective, credible, convenient and economic risk assessment is carried out on the region with complex surface conditions, so that the evaluation efficiency of shale gas exploitation target selection is improved.
Description
Technical Field
The invention relates to the field of shale gas exploration and development, in particular to a shale gas drilling site selection method based on a geographic information system and remote sensing data.
Background
The commercial exploitation of shale gas in China has been over ten years, and makes an important contribution to the guarantee of national energy supply safety. Shale gas resource exploration and development are used as key targets in modern energy system planning. Currently, the exploration beneficial zone delineation work of each primary shale gas destination layer is basically completed. How to define shale gas exploitation targets from a shale gas exploitation beneficial area efficiently, and avoiding risks as much as possible are key problems of the current shale gas exploitation and development work.
Early practice shows that the main shale gas exploration beneficial area in the present stage of China is located in mountainous and hilly areas, the surface elevation, the relative fluctuation and the gradient are large, the vegetation coverage rate is high, and engineering development is very difficult. In addition, existing researches reveal that shale gas exploitation has significant influence on regional water quality, air quality, soil, vegetation, wild animal habitat, human daily life and the like. And various natural protection areas and densely populated areas are widely distributed in the existing exploration beneficial areas, so that the ecological environment is protected seriously. Meanwhile, shale gas exploitation has large water resource demand, large well site occupation area and extremely high dependence on road network.
The currently commonly used shale gas drilling site selection is mainly based on an operation method of indoor preliminary screening-field investigation, namely, firstly, the well site is initially screened on the basis of a topographic map with a larger scale for a delineated geological beneficial area, then, the field exploration is carried out on a potential target, comprehensive evaluation in various aspects such as oil gas engineering, geotechnical engineering, safety engineering, environmental protection, manufacturing cost and the like is carried out, and finally, the well site selection is finalized. However, indoor operation only relies on the manual work to screen the topography vantage district that is fit for developing shale gas exploitation on regional scale drawing spare, and whole work load is big, and inefficiency. Meanwhile, the above operation does not consider the influence of ecological environmental protection and economic factors, and inevitably leads to the inclusion of a large number of areas unsuitable for development of engineering in the defined submerged target, and as a result, many on-site stepping surveys are practically unnecessary, so that manpower, material resources and time are wasted greatly, and the evaluation cost-effectiveness ratio is further reduced.
Disclosure of Invention
Therefore, the shale gas drilling site selection method based on the geographic information system and the remote sensing data can conveniently, efficiently and quantitatively realize comprehensive evaluation of factors of engineering, ecology and economy on the shale gas exploration favorable region with complex surface conditions, so that the evaluation efficiency and the credibility of shale gas exploitation target selection are improved.
A shale gas drilling site selection method based on a geographic information system and remote sensing data comprises the following steps:
s1: acquiring the values of nine evaluation parameters including the surface elevation, the topography relief, the gradient, the surface coverage type, the vegetation coverage NDVI index, the distance from a densely populated area, the distance from an effective water source, the distance from national roads and county roads at different plane positions in the area to be evaluated;
s2, setting a rating assignment standard according to the distribution range of each evaluation parameter value, and calculating a parameter weight corresponding to each evaluation parameter value;
s3, calculating shale gas exploitation comprehensive indexes of different plane positions in the region to be evaluated according to the parameter weights of all the evaluation parameters in S2, setting a delineating threshold value of a shale gas exploitation potential beneficial region by combining the shale gas exploitation comprehensive index calculation result of the position where the existing well drilling is located in the region to be evaluated, and delineating the shale gas exploitation potential beneficial region;
s4, identifying a connecting effective area meeting the well site size requirement in the defined shale gas exploitation potential beneficial area, and finally determining the potential shale gas drilling well site.
Further, in S2, the parameter weights include a secondary parameter weight and a primary parameter weight.
Further, the specific steps of calculating the parameter weight of each evaluation parameter are as follows:
minimum value (M of each evaluation parameter n min ) To its maximum value (M) n max ) Arranging, sequentially selecting 10%, 25%, 50%, 75% and 90% of the value ranges as nodes, respectively denoted as M n (10) 、M n (25) 、M n (50) 、M n (75) M and M n (90) And giving 100, 10, 1, 0.1, 0.01 and 0 six grading weights, setting rating assignment standards of all the evaluation parameters, and calculating to obtain a secondary parameter weight and a primary parameter weight corresponding to all the evaluation parameter values in the region to be evaluated.
Further, according to the evaluation parameter values, a range of grading weight intervals is correspondingly obtained, assignment is carried out according to formulas (1) - (2), and a secondary parameter weight R corresponding to each evaluation parameter value in the region to be evaluated is calculated n The method comprises the steps of carrying out a first treatment on the surface of the Assignment of altitude or topography relief to use (1)
Evaluation of other parameters by evaluation of the parameters (2)
At the same time, define when M 3 ≥35°、M 6 ≤0.5km、M 7 ≤0.1km、M 8 Less than or equal to 0.2km and M 9 When the distance is less than or equal to 0.1km, the corresponding weight of the secondary parameter is 0, wherein M 3 Is of gradient, M 6 To be distant from densely populated areas, M 7 To be distant from the effective water source, M 8 For the distance sum M from national road and province road 9 Distance from county and rural roads.
Further, the nine secondary parameter weights are integrated into the topography (Q 1 ) Gradient (Q) 2 ) Land use condition (Q) 3 ) Distance from effective water source (Q) 4 ) Distance to road network (Q) 5 ) The integration method comprises the following steps:
Q 1 ＝R 1 ×R 2 (3)
Q 2 ＝R 3 (4)
Q 3 ＝(R 4 +R 5 )×R 6 (5)
Q 4 ＝R 7 (6)
wherein R is 1 Is the weight of the secondary parameter of the altitude, R 2 Is the weight of the secondary parameter of the relief of the topography, R 3 Is the weight of the secondary parameter of the gradient, R 4 A secondary parameter weight for the land cover type, R 5 A secondary parameter weight for the vegetation coverage NDVI index, R 6 For the secondary parameter weight distance from the densely populated area, R 7 For the weight of the secondary parameter distant from the effective water source, R 8 The weight of the secondary parameter is R which is the distance from the national province 9 Is a secondary parameter weight for the distance from county roads and villages.
Further, in S3, the method for calculating the shale gas exploitation comprehensive index of different plane positions in the region to be evaluated includes:
in the above, Q n For the corresponding nth level parameter weight, Q n In the range of [0,100 ]]。
Further, in S3, the minimum value of the shale gas exploitation comprehensive index of the existing drilling position in the region to be evaluated is used as a lower limit threshold value for delineating a potentially beneficial shale gas exploitation region.
Further, in S4, after binarizing the shale gas exploitation potential beneficial area defined in the step S3 into an image, traversing the whole area by using the lower limit size as a convolution kernel, judging pixels which are coincident with the convolution kernel in the image and outputting the positions of the pixels, so as to obtain a connected effective area which meets the condition and serve as a final potential shale gas well site evaluation result.
The technical scheme provided by the invention has the beneficial effects that: and comprehensive, quantitative, objective, credible, convenient and economic risk assessment is carried out on the region with complex surface conditions, so that the evaluation efficiency of shale gas exploitation target selection is improved.
Drawings
FIG. 1 is a flow chart of a shale gas drilling and locating method based on a geographic information system and remote sensing data of the invention;
FIG. 2 is a schematic representation of digital altitude model data used in an embodiment of the present invention;
FIG. 3 is a schematic view of the result of the calculation of the relief of the terrain according to an embodiment of the present invention;
FIG. 4 is a graph showing the gradient calculation results according to the embodiment of the present invention;
FIG. 5 is a global land cover data schematic used in an embodiment of the present invention;
FIG. 6 is a schematic view of a visible band image of multispectral remote sensing data used in an embodiment of the present invention;
FIG. 7 is a schematic diagram of a calculation result of a regional normalized vegetation index according to an embodiment of the present invention;
FIG. 8 is a graph of normalized vegetation index calculation for a vegetation coverage according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of the distance calculation from a populated area according to an embodiment of the present invention;
FIG. 10 is a schematic diagram of the calculation of the distance from the effective water source according to an embodiment of the present invention;
FIG. 11 is a schematic diagram of road network data used in an embodiment of the present invention;
FIG. 12 is a diagram showing the calculation result of the road-saving distance from national roads according to the embodiment of the invention;
FIG. 13 is a schematic diagram of the calculation result of the distance from county roads and rural roads according to the embodiment of the invention;
FIG. 14 is a schematic diagram of the results of the weight of each secondary evaluation parameter according to the embodiment of the present invention;
FIG. 15 is a schematic diagram of the weight results of the evaluation parameters of each stage according to the embodiment of the invention;
FIG. 16 is a schematic diagram of a shale gas exploitation comprehensive index calculation result according to an embodiment of the invention;
FIG. 17 is a schematic diagram of a potential beneficial zone delineation result for shale gas mining in accordance with an embodiment of the present invention.
FIG. 18 is a schematic diagram of a method for identifying a valid area of a patch according to the present invention;
FIG. 19 is a schematic diagram of potential beneficial zones and potential well site delineation results for shale gas extraction in key areas in accordance with embodiments of the present invention;
FIG. 20 is a schematic representation of the well location delineation results of a shale gas drilling well that is potentially advantageous in accordance with embodiments of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be further described with reference to the accompanying drawings.
Referring to fig. 1, the shale gas drilling site selection method based on a geographic information system and remote sensing data provided by the invention comprises the following steps:
s1: based on an open source high-precision geographic information system and remote sensing data, computing four nine evaluation parameters of surface topography, land utilization conditions, effective water source distance and road network distance of different plane positions in an area to be evaluated, wherein the nine evaluation parameters are sequentially as follows: elevation of earth (M) 1 ) Relief degree (M) 2 ) Gradient (M) 3 ) Type of earth surface coverage (M 4 ) NDVI index of vegetation coverage (M 5 ) Distance from densely populated area (M 6 ) Distance from effective water source (M 7 ) Distance from national road and provincial road (M 8 ) And distance from county and rural roads (M 9 )；
Based on the existing theoretical basis and exploration practice, analyzing engineering, ecological and economic factor connotations and influence of each evaluation parameter on shale gas exploitation:
1) Topographic parameters (M) 1 -M 3 )
The elevation and the topography relief jointly reflect the surface topography characteristics, and the greater the elevation and the topography relief, the greater the difficulty in the construction of well sites and auxiliary facilities and the transportation of various materials of shale gas exploration and development activities. The slope then directly influences well site construction, and in addition, the slope is too big can restrict vegetation development, leads to geological disasters such as landslide indirectly, increases diffusion rate such as various pollution liquids simultaneously. Therefore, shale engineering development difficulty, cost and risk are all significantly increased with gradient.
2) Type of land cover (M 4 )
Shale gas exploitation can cause forest fragmentation, destroy wild animal habitat, pollute water body, influence daily life of human beings, occupy bad influence such as planning land, and the land coverage type is an important parameter in shale gas exploitation well site selection. The existing exploration examples show that farmlands and low coverage vegetation areas (such as grasslands) are the optimal well site selection type, and shale gas exploitation in high coverage vegetation areas, various water bodies and densely populated areas is avoided as much as possible.
3) Vegetation coverage NDVI index (M 5 )
The NDVI index is the most commonly used index for reflecting vegetation growth state and coverage rate, the value range is-1 to 1, and when the index is smaller than 0, the index indicates that the ground is covered by cloud, water, snow and the like; indicating that the earth surface is rock or bare when the index is approximately 0; and when the index is greater than 0, the surface development vegetation is indicated, and the larger the value is, the larger the vegetation coverage is. The greater the NDVI index, the greater the shale gas exploitation engineering difficulty, and the stronger the damage to vegetation protection areas such as forests.
4) Distance class parameter (M) 6 -M 9 )
Shale gas extraction affects human daily life and therefore needs to be far away from the population gathering area, namely M 6 It is necessary to be greater than a certain range. Meanwhile, because the water consumption in the hydraulic fracturing process in shale gas exploitation is huge, and the hydraulic fracturing process is highly dependent on materials required by road transportation exploitation, namely M 7 -M 9 The smaller the water taking cost and the lower the road construction cost in shale gas exploitation are.
Wherein, according to the existing research results and industry standards, the evaluation of part of parameter intervals is assigned as 0:
1) Research shows that the construction difficulty is extremely high when the surface gradient is larger than 35 degrees, and the water loss and soil loss are serious, thus being not suitable for engineering development, namely M 3 Should be less than 35 °;
2) Industry standards specify that the drilling platform be no less than 500M from the populated area, i.e., M 6 Should be greater than 0.5km.
3) The existing research considers that the hydraulic fracturing process of deep shale gas exploitation has little influence on the shallow groundwater, but the wastewater generated in the exploitation process may have leakage and diffusion risks, thereby influencing the surface water quality. In addition, the fracture and small-scale fracture length generated by hydraulic fracturing are mostly 10 to 20m, but may extend to more than 100m in rare cases. Thus, when working with a shallow target layer, there is still a possibility that the surface groundwater layer will be contaminated by the waste liquid generated during mining. Accordingly, this evaluation considers M 7 Should be greater than 0.1km.
4) In order to avoid the influence of mining on roadbed and vehicle driving safety, the distance between the drilling platform and the common road should be more than 100M, and the distance between the drilling platform and the expressway should be more than 200M, namely M 8 And M is as follows 9 Should be greater than 0.2km and 0.1km, respectively.
Specifically, the surface elevation (M 1 ) Relief degree (M) 2 ) Gradient (M) 3 ) The operation steps of (a) are as follows:
s11, collecting data of a digital elevation model (digital elevationmodel, DEM for short) in the region to be evaluated, and extracting the surface elevation (M) of different plane positions in the region to be evaluated 1 ) And calculates the relief of the terrain (M based on the data 2 ) And gradient (M) 3 ) Specifically, the calculation mode of the topographic relief degree is as follows:
s110: dividing the region to be evaluated by taking 39 times of grid node spacing multiplied by 39 times of grid node spacing as a basic calculation unit;
s111: and calculating the difference between the maximum value and the minimum value of the surface elevation of different plane positions in the basic calculation unit, endowing the calculation unit with a center point, and interpolating by using a natural nearest neighbor method to obtain a terrain relief parameter grid of different plane positions.
S12, collecting global land coverage (global land cover, GLC) data in the region to be evaluated, and extracting surface coverage types (M) of different plane positions in the region to be evaluated 4 ) Data;
s13, quantitatively estimating the vegetation coverage of the whole area by using a normalized vegetation index (normalized difference vegetation index, NDVI for short) according to multispectral remote sensing image data in the area to be evaluated, wherein a calculation formula is as follows
Wherein: p (NIR) is near infrared band reflectance and P (RED) is RED band reflectance; normalizing the maximum calculated value of the NDVI in the whole time period of each pixel; meanwhile, the surface coverage type (M 4 ) Integrating the vegetation coverage areas in the area to be evaluated to obtain the NDVI (M) index of the vegetation coverage areas at different plane positions in the area to be evaluated 5 )；
S14, calculating distances (M) between different plane positions in the region to be evaluated and the population dense region based on GLC data indication and the population dense region and the effective water source distribution in the region to be evaluated 6 ) And distance from the effective water source (M 7 )；
S15, obtaining road network data of the region to be evaluated, extracting four-level road distribution of national roads, provincial roads, county roads and rural roads, and calculating distances (M) between different plane positions in the region to be evaluated and the national roads and the provincial roads 8 ) And distance from county and rural roads (M 9 )。
S2: setting a rating assignment standard according to the distribution range of each evaluation parameter value, and calculating a parameter weight corresponding to each evaluation parameter value, wherein the parameter weight comprises a secondary parameter weight and a primary parameter weight, and the specific operation is as follows:
s21, pair M 1 -M 9 The nine parameters are rasterized by using a nearest neighbor method, the holding ranges of grids are the same, and the distances between grid nodes are equal and not less than 30m;
s22, setting a rating assignment standard according to the distribution range of each evaluation parameter value range, wherein the specific method comprises the following steps: minimum value (M of each evaluation parameter n min ) To its maximum value (M) n max ) Arranging, sequentially selecting 10%, 25%, 50%, 75% and 90% of the value ranges as nodesRespectively denoted as M n (10) 、M n (25) 、M n (50) 、M n (75) M and M n (90) And giving 100, 10, 1, 0.1, 0.01 and 0 six grading weights, setting the rating assignment of each evaluation parameter by combining the six grading weights, wherein the formulas are shown in formulas (1) - (7), and the rating assignment standard of each evaluation parameter obtained by calculation is shown in tables 1-2. According to the evaluation parameter values, correspondingly obtaining a range of graded weight intervals, and calculating to obtain secondary parameter weights corresponding to the evaluation parameter values in the region to be evaluated, thereby obtaining the altitude (R 1 ) Relief degree (R) 2 ) Gradient (R) 3 ) Type of land cover (R) 4 ) NDVI index (R) 5 ) Distance from densely populated area (R 6 ) Distance from effective water source (R 7 ) Distance from national road province (R) 8 ) Distance from county road and county road (R) 9 ) Nine secondary parameter weights; the rating of the partial evaluation parameter interval is assigned to 0 according to the existing research results and industry standards.
For altitude M 1 Or relief of topography M 2 The assignment is as follows:
other evaluation parameter assignments were as follows:
in addition, an evaluation weight value of 0 is given to a part of the secondary parameter value range section:
R 3 ＝0，M 3 ≥35° (3)
R 6 ＝0，M 6 ≤0.5km (4)
R 7 ＝0，M 7 ≤0.1km (5)
R 8 ＝0，M 8 ≤0.2km (6)
R 9 ＝0，M 9 ≤0.1km (7)
TABLE 1
| Evaluation parameter | R n ＝10 | R n ∈[1，10) | R n ∈[0.1，1) | R n ∈[0，0.1) |
| M 1 (m) | [M 1 min ，M 1 (25) ] | (M 1 (25) ，M 1 (50) ] | (M 1 (50) ，M 1 (75) ] | (M 1 (75) ，M 1 max ] |
| M 2 (m) | [M 2 min ，M 2 (25) ] | (M 2 (25) ，M 2 (50) ] | (M 2 (50) ，M 2 (75) ] | (M 2 (75) ，M 2 max ] |
TABLE 2
S23, integrating the weight values of the nine secondary parameters into a topography (Q) 1 ) Gradient (Q) 2 ) Land use condition (Q) 3 ) Distance from effective water source (Q) 4 ) Distance to road network (Q) 5 ) Five primary parameter weights, wherein:
Q 1 ＝R 1 ×R 2 (8)
Q 2 ＝R 3 (9)
Q 3 ＝(R 4 +R 5 )×R 6 (10)
Q 4 ＝R 7 (11)
s3: calculating shale gas exploitation comprehensive indexes of different plane positions in the region to be evaluated according to the parameter weight of each evaluation parameter in the S2, setting a delineating threshold value of a shale gas exploitation potential beneficial region by combining the shale gas exploitation comprehensive index calculation result of the position where the existing well drilling is located in the region to be evaluated, and delineating the shale gas exploitation potential beneficial region; specifically, the minimum value of the shale gas exploitation comprehensive index of the existing drilling position in the region to be evaluated is used as a lower limit threshold value for delineating a potential beneficial region of shale gas exploitation.
As another embodiment of the invention delineating a potentially beneficial zone of shale gas extraction, the specific operations are:
s31, dividing a shale gas exploitation potential favorable region into three grades of a barely suitable region, a more suitable region and an extremely suitable region;
s32, calculating shale gas exploitation comprehensive indexes of different plane positions in the region to be evaluated and shale gas exploitation comprehensive indexes of the position where the existing well drilling is located in the region to be evaluated according to the formula 13;
in the above, Q n For the corresponding nth level parameter weight, Q n In the range of [0,100 ]]。
S33, counting shale gas exploitation comprehensive index data of the position of the existing drilling well in the region to be evaluated, circling the minimum value of the shale gas exploitation comprehensive index of the position of the existing drilling well in the region to be evaluated as a lower limit threshold value of a barely suitable region, selecting the shale gas exploitation comprehensive index value of the corresponding drilling well which is larger than 1/4 of the sum of the existing drilling well numbers as the lower limit threshold value of the circling more suitable region, and selecting the shale gas exploitation comprehensive index value of the corresponding drilling well which is larger than 1/2 of the sum of the existing drilling well numbers as the lower limit threshold value of the circling most suitable region.
In practical application, the division level of the shale gas exploitation potential beneficial zone and the setting of the lower limit threshold corresponding to each level can be adjusted according to practical application.
S4: and identifying a continuous effective area meeting the size requirement of the well site in the defined shale gas exploitation potential beneficial area, and finally determining the potential shale gas well drilling well site, wherein the reason is that the comprehensive index only reflects the suitability degree of shale gas exploitation, and the exploitation well site is provided with a drilling machine and various auxiliary equipment, and a certain effective use area, namely the continuous effective area with the length and width size meeting the lower limit of the practical requirement, is required to be met.
The length and width of well sites required by drilling machines at all levels of oil and gas exploitation specified by petroleum industry standards are 60-120 m, so that the continuous effective area is not less than 60m multiplied by 60m, 90m multiplied by 90m and 120m multiplied by 120m respectively, and are used as delineation standards of well sites of marginal suitable areas, more suitable areas and most suitable areas.
The specific operation is as follows:
the length and width dimensions of 60m multiplied by 60m, 90m multiplied by 90m and 120m multiplied by 120m are taken as the lower limit dimensions of three potential well sites of marginal suitable area, more suitable area and most suitable area; and (3) binarizing the range of the potential beneficial zone of shale gas exploitation defined in the step (S3) into an image, traversing the whole area by using the lower limit size as a convolution kernel, judging pixels which are coincident with the convolution kernel in the image, and outputting the positions of the pixels so as to obtain a connected effective area which meets the condition and serve as a final potential shale gas well site evaluation result.
Example 1]
The method of the embodiment extracts the surface element information required by evaluation based on the following four types of open source data:
(1) DEM data: the data of an advanced satellite-borne heat emission and reflection radiometer global digital elevation model (Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model, ASTER GDEM for short, second edition; https:// eartxplore. Usgs. Gov /) "published by the United states aviation administration and Japanese industrial economy is selected. The data uses satellite live-action stereo images (ASTER GDEM Validation Team, 2011), the spatial resolution can reach 1 radian second (about 30 m), and high-precision ground surface topography details can be provided for the research.
(2) GLC data: the 2017 GLC dataset (http:// data. Ess. Tsinghua. Edu. Cn) was chosen. The data comprehensively realizes 10 m-level land surface stability classification (Gong et al, 2019) based on various remote sensing technologies, and can provide the distribution information of main land types such as land surface farmlands, vegetation, water systems, towns and the like in a research area for the evaluation.
(3) Multispectral remote sensing data: based on a spectrum data set (https:// scihub.copernicus.eu /) acquired by a sentinel-2A satellite multispectral imager emitted by European space agency, visible light wave Band (Band 2-4) and near infrared wave Band (Band 8) data with a resolution of 10m from 5 months to 9 months in 2019 are selected and used for calculating normalized vegetation indexes (normalized difference vegetation index, NDVI for short).
(4) Road network data: road network data of roads at each level of a research area are obtained based on a Goldmap platform (https:// lbs. amap. Com /), and are further sorted into distribution conditions of high-level roads (national roads and provinces) and low-level roads (county roads and rural roads).
The embodiment is positioned in the transition area between the inner basin edge and the outer basin edge of the Sichuan basin, the geographic range is 107-108 DEG E, 29-30 DEG N, and the administrative division belongs to Chongqing city longevity area, nanchuan area and Wu LongThe total area of the areas is approximately 1.1X10 4 km 2 . Three shale gas fields of Fuling, nanchuan and Wu Long are found in the area, and the deployment of the shale gas exploratory well 28 is an important shale gas exploratory area in the current stage and the future of China. The northwest part of the area is mainly a plain area, and the land coverage type is mainly farmland; the rest areas are mainly mountainous and hilly land features, and the vegetation coverage rate is extremely high. Besides the main rivers and lakes, the water bodies in the areas are only distributed sporadically, and the water sources are relatively scarce. The roads in the high altitude area other than the plain area in the northwest are relatively few. The natural geographical background of the earth surface causes great difficulty in exploitation and utilization of shale gas in a research area, and the cost is obviously higher than that of a plain area. In addition, most of the regional ecology systems in the region are fragile and spread over densely populated areas, so that restrictions of ecology, environment, humanity and the like are not negligible. Accordingly, the target selection of shale gas development in the zone is restricted by the combination of various factors on the earth surface.
The embodiment takes the area as an object to develop shale gas drilling site selection based on a geographic information system and remote sensing data, and the invention provides a shale gas drilling site selection method based on the geographic information system and the remote sensing data, which comprises the following steps:
s1, collecting 30M-level ASTER GDEM (v 2) data of a region to be evaluated, and converting original data into a current evaluation custom coordinate system (WGS 84 coordinate system, plane orthogonal projection, scale 1:200000 and central meridian 108 DEG E) from a geographic coordinate system projection as a digital elevation model for evaluation and an evaluation parameter M 1 As shown in fig. 2.
Based on the digital elevation model data, calculating an evaluation parameter M 2 And M 3 As shown in fig. 3 and 4.
S2, collecting 2017 grade 10m full-sphere land coverage data [ Science Bulletin, volume 64 in 2019, pages 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 ] of a region to be evaluated, which is processed based on various remote sensing data by Gong et al]Converting the original data projection into a custom coordinate system for the evaluation, and determining farmland,Four types of land coverage ranges of vegetation (forests, shrubs, grasslands and the like), water systems (rivers, lakes and wetlands) and population dense areas (artificial impermeable surfaces) are used as the full-sphere land coverage and evaluation parameters M 4 As shown in fig. 5.
S3, collecting multispectral remote sensing image data of 10 m-level sentinel-2A satellites in the 2019 5 month to 9 month of the region to be evaluated, performing cloud removal and mosaic treatment, and then performing projection conversion to the current evaluation custom coordinate system to serve as multispectral remote sensing data for evaluation, wherein the multispectral remote sensing data is shown in FIG. 6.
Calculating vegetation coverage of each pixel in different periods by using normalized vegetation index (NDVI) with the formula of
Wherein: p (NIR) is near infrared band reflectance and P (RED) is RED band reflectance; the maximum calculated NDVI value for each phase element over the full period is normalized to within the [ -1,1] interval, as shown in fig. 7.
And (3) obtaining a vegetation coverage NDVI calculation result of the area to be evaluated according to the vegetation range extracted in the step (S2), as shown in fig. 8.
S4, calculating evaluation parameters M of different plane positions in the region according to the population density region and the water system range extracted in the S2 6 And M 7 As shown in fig. 9 and 10.
S5, collecting road network data of the Hide map of the region 2020 to be evaluated, arranging the road network data into high-level roads (national roads and provinces) and low-level roads (county roads and rural roads), and converting the road network data into a current evaluation custom coordinate system by projection, wherein the road network data are used for evaluation, as shown in FIG. 11.
Based on the road network data, calculating different plane position evaluation parameters M in the region 8 And M 9 As shown in fig. 12 and 13.
S6, for parameter M 1 -M 9 The data were rasterized using the nearest neighbor method, with each grid remaining the same range, with a grid pitch of 30m x 30m, resulting in a parametric grid series.
And S7, counting the value range distribution range and nodes of each evaluation parameter, as shown in table 3.
TABLE 3 Table 3
| Parameters (parameters) | M n min | M n (10) | M n (25) | M n (50) | M n (75) | M n (90) | M n max |
| M 1 (m) | 22.0 | 305.2 | 451.4 | 722.8 | 1073.6 | 1405.4 | 2227.1 |
| M 2 (m) | 33.4 | 118.9 | 184.4 | 282.2 | 392.9 | 518.0 | 1131.3 |
| M 3 (°) | 0 | 6.43 | 10.13 | 16.04 | 23.74 | 31.96 | 74.55 |
| M 5 | 0.278 | 0.583 | 0.754 | 0.836 | 0.882 | 0.910 | 1 |
| M 6 (km) | 0 | 0.80 | 1.92 | 3.56 | 5.62 | 8.12 | 13.10 |
| M 7 (km) | 0 | 0.60 | 1.58 | 3.38 | 6.68 | 10.78 | 27.06 |
| M 8 (km) | 0 | 0.47 | 1.36 | 3.29 | 6.33 | 9.79 | 21.09 |
| M 9 (km) | 0 | 0.25 | 0.70 | 1.57 | 2.82 | 4.22 | 14.71 |
S8, according to the standards shown in tables 1 and 2, is the parameter M 1 -M 9 Giving corresponding grading weight to obtain R 1 、R 2 、R 3 、R 4 、R 5 、R 6 、R 7 、R 8 、R 9 Nine secondary parameter weights, a weight grid is compiled as shown in fig. 14.
S9, using the weight value R of the secondary parameter 1 And R is 2 Calculating the first-level parameter weight Q 1 The formula is
Q 1 ＝R 1 ×R 2
By the second-level parameter weight R 4 、R 5 And R is 6 Calculating the first-level parameter weightValue Q 3 The formula is
Q 3 ＝(R 4 +R 5 )×R 6
By the weight R of the secondary parameter 8 And R is 9 Calculating the first-level parameter weight Q 5 The formula is
Will Q 1 -Q 5 And each is independently used as a first-level parameter weight, and a weight grid is compiled, as shown in fig. 15.
S10, calculating shale gas exploitation comprehensive index based on the first-level parameter weight, wherein the formula is
Q in n For the corresponding nth level parameter weight, Q n In the range of [0,100 ]]。
The calculation result of the comprehensive shale gas extraction index of the whole region to be evaluated in the embodiment is shown in fig. 16.
S11, calculating results of nine evaluation parameters according to positions of 28 shale gas exploratory wells in the region to be evaluated, wherein the results are shown in a table 4.
TABLE 4 Table 4
| Well number | M 1 (m) | M 2 (m) | M 3 (°) | M 4 | M 5 | M 6 (km) | M 7 (km) | M 8 (km) | M 9 (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 | Farm land | / | 2.35 | 1.37 | 1.636 | 4.24 |
| FL3 | 423.3 | 236.9 | 9.29 | Farm land | / | 0.85 | 1.90 | 1.132 | 3.12 |
| FL4 | 526.5 | 177.1 | 15.73 | Farm land | / | 4.61 | 1.49 | 2.386 | 1.29 |
| FL5 | 314.6 | 223.6 | 7.01 | Farm land | / | 5.34 | 5.47 | 6.502 | 0.48 |
| FL6 | 456.9 | 175.7 | 5.66 | Farm land | / | 2.37 | 4.35 | 9.650 | 0.80 |
| FL7 | 517.1 | 225.0 | 3.15 | Farm land | / | 2.64 | 6.75 | 4.720 | 0.23 |
| FL8 | 429.9 | 174.6 | 6.80 | Farm land | / | 0.67 | 2.82 | 10.556 | 1.29 |
| FL9 | 783.0 | 108.1 | 7.00 | Farm land | / | 4.05 | 7.46 | 0.663 | 2.68 |
| FL10 | 484.6 | 189.6 | 9.24 | Farm land | / | 3.77 | 7.07 | 4.002 | 0.34 |
| FL11 | 745.1 | 184.5 | 7.25 | Farm land | / | 1.88 | 8.05 | 0.338 | 1.57 |
| FL12 | 611.3 | 162.7 | 5.36 | Farm land | / | 4.69 | 4.48 | 5.839 | 1.19 |
| FL13 | 678.7 | 149.2 | 7.95 | Farm land | / | 0.56 | 6.33 | 0.447 | 0.46 |
| FL14 | 560.5 | 173.7 | 6.72 | Farm land | / | 0.91 | 7.56 | 2.409 | 0.21 |
| FL15 | 972.8 | 206.7 | 5.98 | Farm land | / | 1.19 | 2.41 | 1.668 | 2.79 |
| NC1 | 741.2 | 175.6 | 6.55 | Farm land | / | 1.20 | 6.41 | 6.338 | 0.33 |
| NC2 | 707.9 | 150.1 | 5.91 | Farm land | / | 2.42 | 5.25 | 6.200 | 0.39 |
| NC3 | 709.3 | 135.8 | 8.37 | Farm land | / | 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 | Farm land | / | 3.10 | 3.14 | 0.327 | 1.50 |
| NC6 | 561.8 | 170.0 | 2.03 | Farm land | / | 2.00 | 3.87 | 3.061 | 2.32 |
| NC7 | 557.6 | 247.8 | 3.83 | Farm land | / | 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 | Farm land | / | 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 | Farm land | / | 5.10 | 3.91 | 4.124 | 0.28 |
According to the assignment criteria of tables 1 and 2, the primary and secondary parameter weights and the shale gas exploitation comprehensive indexes of the positions of 28 shale gas exploratory wells are calculated, as shown in table 5.
TABLE 5
S12, according to the calculation result of the shale gas exploitation comprehensive index of the position where the 28-hole drilling is located, the shale gas exploitation comprehensive indexes 5.06, 5.44 and 6.10 are respectively used as lower limit thresholds, and the beneficial areas of barely suitable, more suitable and most suitable shale gas exploitation are defined, and the result is shown in figure 17.
S13, further identifying a continuous effective area in the shale gas exploitation potential beneficial area defined in the previous step, and finally determining the potential shale gas well site, wherein the specific identification method of the continuous effective area is as follows: and (3) binarizing the shale gas exploitation potential beneficial area defined in the step (S12) into an image, traversing the whole area by using the lower limit size as a convolution kernel, judging pixels which are coincident with the convolution kernel in the image, and outputting the positions of the pixels to obtain a connected effective area which meets the condition and serve as a final potential shale gas well site evaluation result. Fig. 18 is a schematic diagram of a process of identifying a patch effective area using 90m×90m as a lower-limit-of-size criterion. FIG. 19 is a plot of a highly desirable zone and a highly desirable well site for shale gas production in a region of interest to be evaluated.
As shown in FIG. 20, the comprehensive evaluation results are shown to define a marginal suitable area, a more suitable area and an extremely suitable area as 580.52km of a shale gas exploitation well site 2 、908.86km 2 1313.06km 2 Each accounting for 4.86%, 7.61% and 10.99% of the total area of the study area, i.e., approximately 90% of the area unsuitable for use as a well site is excluded. The evaluation result shows that the method can effectively remove unfavorable areas occupying most areas in the areas, and can play the roles of reducing cost and risk and improving the exploitation and utilization efficiency of shale gas.
The evaluation method carries out rating assignment according to the value range distribution condition of each parameter, and determines the delineating threshold value of the favorable region based on the existing drilling comprehensive index calculation result, so that the evaluation method is also suitable for research regions with other different parameter distribution characteristics, human subjective intervention in the key steps is avoided more effectively, objectivity and operability are strong, and the evaluation method has good application prospect.
The technical scheme provided by the invention has the beneficial effects that: and comprehensive, quantitative, objective, credible, convenient and economic risk assessment is carried out on the region with complex surface conditions, so that the evaluation efficiency of shale gas exploitation target selection is improved.
The embodiments described above and features of the embodiments herein may be combined with each other without conflict.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (8)
1. A shale gas drilling site selection method based on a geographic information system and remote sensing data is characterized by comprising the following steps:
s1: acquiring the values of nine evaluation parameters including the surface elevation, the topography relief, the gradient, the surface coverage type, the vegetation coverage NDVI index, the distance from a densely populated area, the distance from an effective water source, the distance from national roads and county roads at different plane positions in the area to be evaluated;
s2, setting a rating assignment standard according to the distribution range of each evaluation parameter value, and calculating a parameter weight corresponding to each evaluation parameter value;
s3, calculating shale gas exploitation comprehensive indexes of different plane positions in the region to be evaluated according to the parameter weights of all the evaluation parameters in S2, setting a delineating threshold value of a shale gas exploitation potential beneficial region by combining the shale gas exploitation comprehensive index calculation result of the position where the existing well drilling is located in the region to be evaluated, and delineating the shale gas exploitation potential beneficial region;
s4, identifying a connecting effective area meeting the well site size requirement in the defined shale gas exploitation potential beneficial area, and finally determining the potential shale gas drilling well site.
2. The shale gas drilling site selection method based on the geographic information system and the remote sensing data according to claim 1, wherein in the step S2, the parameter weights comprise a secondary parameter weight and a primary parameter weight.
3. The shale gas drilling site selection method based on the geographic information system and the remote sensing data as claimed in claim 2, wherein the specific steps of calculating the parameter weight of each evaluation parameter are as follows:
minimum value (M of each evaluation parameter n min ) To its maximum value (M) n max ) Arranging, sequentially selecting 10%, 25%, 50%, 75% and 90% of the value ranges as nodes, respectively denoted as M n (10) 、M n (25) 、M n (50) 、M n (75) M and M n (90) And giving 100, 10, 1, 0.1, 0.01 and 0 six grading weights, setting rating assignment standards of all the evaluation parameters, and calculating to obtain a secondary parameter weight and a primary parameter weight corresponding to all the evaluation parameter values in the region to be evaluated.
4. The shale gas drilling site selection method based on the geographic information system and the remote sensing data as claimed in claim 3, wherein the range of the grading weight interval is correspondingly obtained according to the evaluation parameter values, the values are assigned according to the formulas (1) - (2), and the secondary parameter weight R corresponding to each evaluation parameter value in the region to be evaluated is calculated n ；
Assignment of altitude or topography relief to use (1)
Evaluation of other parameters by evaluation of the parameters (2)
At the same time, define when M 3 ≥35°、M 6 ≤0.5km、M 7 ≤0.1km、M 8 Less than or equal to 0.2km and M 9 When the distance is less than or equal to 0.1km, the corresponding weight of the secondary parameter is 0, wherein M 3 Is of gradient, M 6 To be distant from densely populated areas, M 7 To be distant from the effective water source, M 8 For the distance sum M from national road and province road 9 Distance from county and rural roads.
5. A shale gas drilling site selection method based on a geographic information system and remote sensing data as claimed in claim 3, wherein nine secondary parameter weights are integrated into a topography (Q) 1 ) Gradient (Q) 2 ) Land use condition (Q) 3 ) Distance from effective water source (Q4), distance from road network (Q) 5 ) The integration method comprises the following steps:
Q 1 ＝R 1 ×R 2 (3)
Q 2 ＝R 3 (4)
Q 3 ＝(R 4 +R 5 )×R 6 (5)
Q 4 ＝R 7 (6)
wherein R is 1 Is the weight of the secondary parameter of the altitude, R 2 Is the weight of the secondary parameter of the relief of the topography, R 3 Is the weight of the secondary parameter of the gradient, R 4 A secondary parameter weight for the land cover type, R 5 A secondary parameter weight for the vegetation coverage NDVI index, R 6 For the secondary parameter weight distance from the densely populated area, R 7 For the weight of the secondary parameter distant from the effective water source, R 8 The weight of the secondary parameter is R which is the distance from the national province 9 Is a secondary parameter weight for the distance from county roads and villages.
6. The shale gas drilling site selection method based on the geographic information system and the remote sensing data, according to claim 5, wherein in S3, the method for calculating the shale gas exploitation comprehensive indexes of different plane positions in the region to be evaluated is as follows:
in the above, Q n For the corresponding nth level parameter weight, Q n In the range of [0,100 ]]。
7. The shale gas drilling site selection method based on the geographic information system and the remote sensing data, according to claim 1, wherein in the step S3, the minimum value of the shale gas exploitation comprehensive index of the existing drilling position in the region to be evaluated is used as a lower limit threshold value for defining a shale gas exploitation potential beneficial region.
8. The shale gas drilling site selection method based on the geographic information system and the remote sensing data, according to claim 7, is characterized in that in S4, after the shale gas exploitation potential beneficial area defined in the step S3 is binarized into an image, a lower limit size is used as a convolution kernel to traverse the whole area, pixels which are coincident with the convolution kernel in the image are judged, and the positions of the pixels are output, so that a continuous effective area meeting the condition is obtained, and the continuous effective area is used as a final potential shale gas well site evaluation result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310150534.1A CN116070870A (en) | 2023-02-22 | 2023-02-22 | Shale gas drilling site selection method based on geographic information system and remote sensing data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310150534.1A CN116070870A (en) | 2023-02-22 | 2023-02-22 | Shale gas drilling site selection method based on geographic information system and remote sensing data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116070870A true CN116070870A (en) | 2023-05-05 |
Family
ID=86174841
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310150534.1A Pending CN116070870A (en) | 2023-02-22 | 2023-02-22 | Shale gas drilling site selection method based on geographic information system and remote sensing data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116070870A (en) |
-
2023
- 2023-02-22 CN CN202310150534.1A patent/CN116070870A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gill et al. | Characterising the urban environment of UK cities and towns: A template for landscape planning | |
| Lakshmi et al. | Identification of groundwater potential zones using GIS and remote sensing | |
| Degerli et al. | Using the remote sensing method to simulate the land change in the year 2030 | |
| Flügel et al. | Integrating geographical information systems, remote sensing, ground truthing and modelling approaches for regional erosion classification of semi‐arid catchments in South Africa | |
| Sarkar Chaudhuri et al. | Assessment of impervious surface growth in urban environment through remote sensing estimates | |
| Hamdani et al. | Characterization of groundwater potential zones using analytic hierarchy process and integrated geomatic techniques in Central Middle Atlas (Morocco) | |
| Huiping et al. | Landscape and water quality change detection in urban wetland: A post-classification comparison method with IKONOS data | |
| Dereli et al. | Comparison of GIS-based surrogate weighting methods for multi-directional landfill site selection in West Mediterranean Planning Region in Turkey | |
| Shrestha et al. | Fusion of Sentinel-1 and Sentinel-2 data in mapping the impervious surfaces at city scale | |
| Weng | Techniques and methods in urban remote sensing | |
| Li et al. | Habitat quality assessment of mining cities based on InVEST model—A case study of Yanshan County, Jiangxi Province | |
| Bai et al. | Eco-service value evaluation based on eco-economic functional regionalization in a typical basin of northwest arid area, China | |
| Ramachandra et al. | Land use land cover dynamics in a rapidly urbanising landscape | |
| CN113240257A (en) | Territorial space partitioning method and device based on minimum cumulative resistance model | |
| Sekulović et al. | Landfill site selection using GIS technology and the Analytic Hierarchy Process | |
| Xu et al. | Fine-resolution mapping of the circumpolar Arctic Man-made impervious areas (CAMI) using sentinels, OpenStreetMap and ArcticDEM | |
| Shalby et al. | Geospatial model for allocating favorable plots for groundwater-dependent cultivation activities in Egypt | |
| CN113327032B (en) | Land-sea overall planning-based dual-evaluation man-machine interaction adjustment method for territorial space planning | |
| CN116070870A (en) | Shale gas drilling site selection method based on geographic information system and remote sensing data | |
| Pugin | Locating the rock art of the Maloti-Drakensberg: identifying areas of higher likelihood using remote sensing | |
| Ghahremanlou et al. | Mechanism of the effect of urban form and land use on transportation and air pollution in Tehran | |
| Shafie et al. | An analysis of the landscape structure changes as an ecological approach to achieve sustainable regional planning (case study: Latian Dam watershed) | |
| Drzyzga et al. | Spatial and temporal dynamics of ownership parcels and forest cover in three counties of Northern Lower Michigan USA, ca. 1970 to 1990 | |
| Mohammad et al. | Detecting Land use/cover dynamics and land suitability mapping for Irbid governorate using an integrated approach | |
| Sayl et al. | Research article minimizing the impacts of desertification in an arid region: A case study of the West Desert of Iraq |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |