A method for evaluating a favorable zone of oil-rich coal underground in-situ pyrolysis development

Abstract

This invention relates to the field of coal geological engineering technology, specifically a method for selecting and evaluating favorable areas for in-situ underground pyrolysis development of rich oil and coal. The method first collects multi-source geological data to construct an evaluation database; second, it establishes an evaluation system comprising five primary and multiple secondary indicators, including coal quality, coal seam occurrence, surrounding rock, hydrogeology, and environment; then, it classifies each indicator and constructs fuzzy membership functions for suitability prediction; further, it uses the analytic hierarchy process (AHP) to determine subjective weights, the entropy weight method to determine objective weights, and game theory to obtain the comprehensive weight; finally, it uses an improved grey relational analysis-ideal solution method to calculate the relative proximity of each evaluation object, and combines this with the natural discontinuity method to classify favorable, relatively favorable, and unfavorable areas. This invention enables unified expression of multi-source geological information, comprehensive weighting of indicators, and quantitative ranking of development potential, providing a scientific basis for the selection of pilot test areas and well site deployment for in-situ underground pyrolysis of rich oil and coal.

Description

Technical Field

[0001] This invention relates to the field of coal geological engineering technology, specifically a method for the selection and evaluation of favorable areas for the underground in-situ pyrolysis development of oil-rich coal. Background Technology

[0002] Oil-rich coal is a type of coal-based oil and gas resource that combines the attributes of coal, oil, and gas. It can produce tar, pyrolysis gas, and semi-coke through medium- and low-temperature pyrolysis. my country has abundant reserves of oil-rich coal, mainly distributed in western regions such as Xinjiang and Shaanxi. Promoting its green development and efficient conversion is of great significance to ensuring national energy security.

[0003] Compared to surface pyrolysis, underground in-situ pyrolysis technology can reduce surface disturbance and improve the development level of deep coal seam resources, showing great promise in the field of refined utilization of deep coal resources. However, the underground in-situ pyrolysis process is constrained by a variety of factors, including coal quality characteristics, coal seam occurrence conditions, surrounding rock sealing, hydrogeological conditions, environmental risks, and engineering economics. Its suitability for development exhibits strong geological dependence and multi-factor coupling characteristics. Currently, this technology is generally in the pilot testing stage, lacking a unified and mature method for evaluating favorable areas.

[0004] Although some explorations have been carried out in existing research, such as using the analytic hierarchy process or fuzzy mathematics to evaluate resource potential, the following shortcomings still exist: (1) The evaluation index system does not adequately consider key engineering constraints such as the sealing capacity of the surrounding rock and hydrogeological risks; (2) The weight determination method is singular, with subjective or objective weighting each having its own biases, making it difficult to take into account both expert experience and the inherent laws of data; (3) There is insufficient expression of the fuzziness of the index boundary under complex geological conditions, and the quantitative characterization ability is limited; (4) The evaluation results mostly remain at the qualitative level and are not closely connected with the engineering deployment.

[0005] Therefore, there is an urgent need to develop a method for selecting and evaluating favorable areas for the underground in-situ pyrolysis development of oil-rich coal, in order to overcome the shortcomings in current practical applications. Summary of the Invention

[0006] The purpose of this invention is to provide a method for evaluating the optimal area for underground in-situ pyrolysis development of oil-rich coal, in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for selecting and evaluating favorable areas for underground in-situ pyrolysis development of oil-rich coal includes the following steps:

[0009] S1: Collect multi-source data of the target coal seam in the study area and construct an evaluation object database; the multi-source data includes coal quality test data, coal seam occurrence data, roof and floor lithology data, hydrogeological data, environmental geochemical data and spatial location data;

[0010] S2: Establish a multi-level evaluation index system, which includes five primary indicators: coal quality conditions, coal seam occurrence conditions, surrounding rock conditions, hydrogeological conditions, and environmental conditions, as well as multiple secondary indicators under each primary indicator.

[0011] S3: Based on the direction of influence of each secondary indicator on the suitability of underground in-situ pyrolysis development of oil-rich coal, they are classified into cost-type indicators, interval-type indicators, or benefit-type indicators, and the grading boundary values ​​of each indicator are determined.

[0012] S4: Based on the aforementioned grading threshold, construct a trapezoidal membership function, calculate the membership degree of each evaluation object to the three levels of favorable, relatively favorable, and unfavorable at each secondary index, and make a preliminary judgment on the suitability of the project based on the principle of maximum membership degree;

[0013] S5: Use the analytic hierarchy process (AHP) to determine the subjective weights of each secondary indicator;

[0014] S6: The objective weights of each secondary indicator are determined using the entropy weight method;

[0015] S7: Using a game theory-based weighting method, coordinate the differences between the subjective and objective weights, and calculate the comprehensive weight of each secondary indicator;

[0016] S8: Based on the comprehensive weight, the relative proximity of each evaluation object is calculated using the improved grey relational degree-ideal solution method. The relative proximity is used to quantitatively characterize the size of its development potential.

[0017] S9: Based on the relative proximity of each evaluation object, the study area is divided into favorable, relatively favorable and unfavorable areas using the natural breakpoint method, and a distribution map of favorable areas for underground in-situ pyrolysis development of oil-rich coal is generated by combining spatial location data.

[0018] As a further aspect of the present invention: the multiple secondary indicators in step S2 specifically include: coal quality conditions including: moisture, ash, volatile matter and total sulfur; coal seam occurrence conditions including: coal seam thickness, coal seam burial depth and number of interbedded rock layers; surrounding rock conditions including: roof lithology, floor lithology and geothermal gradient; hydrogeological conditions including: roof aquitard thickness and distance between the coal seam and the roof aquifer; environmental conditions including: fluorine content, arsenic content and chlorine content.

[0019] As a further aspect of the present invention: In step S2, the lithology of the top plate and the bottom plate is quantitatively processed using engineering assignment rules: Based on the permeability, density and sealing stability of the surrounding rock, the lithology of the top plate and the bottom plate is assigned engineering values, and the higher the value, the stronger the sealing ability of the surrounding rock.

[0020] As a further aspect of the present invention: in step S3: cost-related indicators include: moisture, ash, total sulfur, number of interbedded rock layers, fluorine content, arsenic content, and chlorine content; range-related indicators include: coal seam thickness and coal seam burial depth; benefit-related indicators include: volatile matter, roof lithology, floor lithology, geothermal gradient, roof aquitard thickness, and distance between the coal seam and the roof aquifer.

[0021] As a further aspect of the present invention: in step S4, the original index values ​​of different dimensions and attributes are uniformly converted into membership degree vectors within the interval [0,1] using a trapezoidal membership function. ,in , , These represent the strength of the indicator value, belonging to one of the three levels: favorable, fairly favorable, and unfavorable.

[0022] As a further aspect of the present invention, step S7 specifically includes: constructing a weight vector set composed of subjective weight vectors and objective weight vectors; obtaining the optimal weight combination coefficient by solving an optimization model with the objective of minimizing the total deviation between the combined weight vector and each basic weight vector; and then calculating the comprehensive weight.

[0023] As a further aspect of the present invention, the improved grey relational degree-ideal solution method in step S8 specifically includes: constructing a standardized weighted decision matrix to determine the positive ideal solution and the negative ideal solution; calculating the Euclidean distance and grey relational degree between each evaluation object and the positive and negative ideal solutions respectively; after processing the Euclidean distance and grey relational degree to be in the same direction and dimensionless, constructing a grey Euclidean fusion measure, and finally calculating the relative closeness of each evaluation object.

[0024] As a further aspect of the present invention: in step S9, the grading results of each evaluation object are coupled with spatial location data by means of Kriging spatial interpolation, contour plotting or block mapping to form the favorable area distribution map.

[0025] Compared with the prior art, the beneficial effects of the present invention are:

[0026] (1) Comprehensive evaluation system. The evaluation framework integrates coal quality conditions, coal seam occurrence conditions, surrounding rock conditions, hydrogeological conditions and environmental conditions, overcoming the problem that existing methods focus on single resource conditions and do not adequately reflect engineering constraints.

[0027] (2) Strong data fusion capability. Through lithological engineering assignment and fuzzy membership degree conversion, a unified quantitative expression of multi-source, heterogeneous, and different-dimensional data is achieved, reducing the boundary mutation error caused by rigid threshold division.

[0028] (3) Reasonable weight allocation. The AHP-entropy weight method-game theory combination weighting is adopted, which takes into account both expert experience and objective data laws, and avoids the deviation of single subjective or objective weighting.

[0029] (4) The evaluation results are more refined. An improved Grey-TOPSIS method is adopted, which considers both location distance and curve similarity to obtain more stable ranking results of development potential.

[0030] (5) The project is highly practical. The evaluation results directly output a distribution map of favorable areas, which can be used for the selection of pilot test areas, well location deployment and development sequence arrangement. Attached Figure Description

[0031] Figure 1 This is a technical flowchart of a method for selecting and evaluating favorable areas for underground in-situ pyrolysis development of oil-rich coal according to the present invention.

[0032] Figure 2 This is a schematic diagram of the evaluation index system of the present invention.

[0033] Figure 3 The diagram shows the fuzzy suitability prediction results of the first-level indicators in this embodiment of the invention. (a) to (e) correspond to the prediction results of coal quality conditions, coal seam occurrence conditions, surrounding rock conditions, hydrogeological conditions, and environmental conditions, respectively.

[0034] Figure 4 This is a schematic diagram showing the distribution of favorable areas for underground in-situ pyrolysis development of oil-rich coal generated in an embodiment of the present invention. Detailed Implementation

[0035] The technical solution of this application will be further described in detail below with reference to specific embodiments.

[0036] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0037] Please see Figure 1 This invention provides a method for selecting and evaluating favorable areas for underground in-situ pyrolysis development of oil-rich coal, comprising the following steps:

[0038] Step S1: Build the evaluation object database.

[0039] Data on coal quality testing, coal seam occurrence, roof and floor lithology, hydrogeological data, environmental geochemical data, and spatial location of the target coal seam in the study area were collected to construct the original evaluation matrix X.

[0040] ;

[0041] Where, x ij This represents the original value of the i-th evaluation object on the j-th evaluation index, where i = 1, 2, ..., m, j = 1, 2, ..., n; m is the number of evaluation objects, and n is the number of evaluation indicators. The evaluation objects can be boreholes, evaluation units, grid units, or geological blocks. The data sources include mining area exploration reports, borehole columnar data, coal sample experimental test results, well logging interpretation results, core logging data, hydrogeological data, and geological maps.

[0042] Step S2: Construction of the evaluation index system.

[0043] Based on the requirements for oil production potential, coal seam exploitability, surrounding rock sealing, hydrogeological safety, and environmental risk control during the in-situ pyrolysis development of oil-rich coal, an evaluation index system is established, comprising 5 primary indicators and 15 secondary indicators. The primary indicators include coal quality conditions (U1), coal seam occurrence conditions (U2), surrounding rock conditions (U3), hydrogeological conditions (U4), and environmental conditions (U5). Among the secondary indicators, coal quality conditions include moisture (U11), ash content (U12), volatile matter (U13), and total sulfur (U14); coal seam occurrence conditions include coal seam thickness (U21), coal seam depth (U22), and number of interbedded rock layers (U23); surrounding rock conditions include roof lithology (U31), floor lithology (U32), and geothermal gradient (U33); hydrogeological conditions include roof aquitard thickness (U41) and distance between the coal seam and the roof aquifer (U42); and environmental conditions include fluorine content (U51), arsenic content (U52), and chlorine content (U53).

[0044] The lithology of the top plate (U31) and the bottom plate (U32) was quantitatively processed using an engineering-based value assignment rule. Based on the permeability, density, and sealing stability of the surrounding rock, the lithologies of the top and bottom plates were divided into five levels: limestone and mudstone were assigned a value of 5, sandy mudstone and siltstone a value of 4, fine sandstone a value of 3, medium sandstone a value of 2, and coarse sandstone and conglomerate a value of 1. A higher value indicates a denser surrounding rock, lower permeability, and stronger sealing stability, which is more conducive to maintaining the temperature and pressure fields during in-situ underground pyrolysis and reducing the risk of oil and gas escape and groundwater intrusion. This value assignment method can be comprehensively determined based on borehole core logging, well logging interpretation, rock property testing, and fracture development characteristics.

[0045] Step S3: Determine the classification and grading thresholds for indicator attributes.

[0046] Based on the direction of each evaluation indicator's effect on the suitability of underground in-situ pyrolysis development of oil-rich coal, the evaluation indicators are divided into cost-type indicators, interval-type indicators, and benefit-type indicators.

[0047] Cost-related indicators are those where a lower value is more advantageous, including moisture (U11), ash (U12), total sulfur (U14), number of interbedded rock layers (U23), fluorine content (U51), arsenic content (U52), and chlorine content (U53).

[0048] Interval-type indicators are those with an optimal value interval or optimal value center, including coal seam thickness U21 and coal seam burial depth U22. Insufficient coal seam thickness reduces effective pyrolysis resources and engineering economics, while excessive coal seam thickness may increase the difficulty of pyrolysis control; insufficient coal seam burial depth is not conducive to sealing the pyrolysis cavity, while excessive coal seam burial depth increases the difficulty of drilling, completion, heat injection, and recovery.

[0049] Benefit-type indicators are those with higher values ​​that are more advantageous, including volatile matter U13, roof lithology U31, floor lithology U32, geothermal gradient U33, roof aquifer thickness U41, and distance between coal seam and roof aquifer U42.

[0050] Based on the engineering suitability requirements for in-situ underground pyrolysis development of oil-rich coal, the geological conditions of the target coal seam, coal quality test results, surrounding rock sealing conditions, hydrogeological constraints, and environmental risk control requirements, the graded boundary values ​​for each evaluation index are determined. For cost-type and benefit-type indicators, three graded boundary values ​​(u1, u2, u3) are set; for interval-type indicators, five graded boundary values ​​(u1, u′, u2, u″, u3) are set. u1 and u3 represent the two unfavorable boundaries, u′ and u″ represent the two boundaries of the favorable interval, and u2 represents the optimal value center. Each graded boundary value is used to subsequently construct a trapezoidal membership function, converting the original index values ​​into membership degrees at three levels: favorable, relatively favorable, and unfavorable. The above graded boundary values ​​can be determined based on existing engineering experience, industry standards, mining area exploration data, experimental test results, and expert judgment results, and can be adjusted according to the geological conditions and development constraints of different mining areas.

[0051] Step S4: Fuzzy membership degree calculation and engineering suitability judgment.

[0052] To characterize the gradual change of evaluation indicators for underground in-situ pyrolysis development of oil-rich coal among different suitability levels, a trapezoidal membership function is used to transform the membership degree of each evaluation indicator.

[0053] For the i-th evaluation object with the j-th evaluation index, its original index value x ij After membership function transformation, the membership degree vector r is obtained. ij :

[0054] ;

[0055] where r ij1 , r ij2 , r ij3 respectively represent the membership degrees of the index value belonging to the three levels of favorable, relatively favorable, and unfavorable. This step is used to convert the original index values with different dimensions and attributes into the grade membership degrees within the interval [0, 1].

[0056] For cost-type indicators, let the grading boundary values be u1, u2, u3, and u1 < u2 < u3. Then, the membership degrees of their favorable level, relatively favorable level, and unfavorable level are calculated according to the following formulas respectively.

[0057] Membership degree of favorable level r ij1

[0058] ;

[0059] Membership degree of relatively favorable level r ij2 :

[0060] ;

[0061] Membership degree of unfavorable level r ij3 :

[0062] ;

[0063] For interval-type indicators, let the grading boundary values be u1, u′, u2, u″, u3, and u1 < u′ < u2 < u″ < u3. Among them, from u′ to u″ is the favorable interval, u2 is the optimal value center, u′ and u″ are the transition boundaries on both sides of the favorable interval respectively, and u1 and u3 are the unfavorable boundaries at both ends.

[0064] Membership degree of favorable level r ij1 Calculated according to the middle trigonometric function:

[0065] ;

[0066] Membership degree of relatively favorable level r ij2 Composed of the left and right trigonometric functions:

[0067] ;

[0068] Membership degree of unfavorable level r ij3 Calculated according to the two-end shoulder function:

[0069] ;

[0070] For benefit-type indicators, let the grading boundary values be u1, u2, u3, and u1 < u2 < u3. Then, the membership degrees of their favorable level, relatively favorable level, and unfavorable level are calculated according to the following formulas respectively.

[0071] Favorable membership degree r ij1 :

[0072] ;

[0073] More favorable membership degree r ij2 :

[0074] ;

[0075] Unfavorable membership degree r ij3 :

[0076] ;

[0077] Based on the membership degree of each evaluation object, the suitability of coal quality conditions, coal seam occurrence conditions, surrounding rock conditions, hydrogeological conditions, and environmental conditions is predicted according to the principle of maximum membership degree. This prediction is used to identify the advantageous and limiting factors for in-situ pyrolysis development in the study area. The prediction results serve as the basis for interpreting the subsequent comprehensive evaluation results. The final favorable area division is based on the relative proximity D calculated by the improved Grey-TOPSIS. i It is determined by combining the natural breakpoint method.

[0078] Step S5: Calculation of subjective weights using the Analytic Hierarchy Process (AHP).

[0079] The analytic hierarchy process (AHP) was used to determine the subjective weights of each evaluation indicator. Based on the impact of each evaluation indicator on pyrolysis efficiency, engineering safety, surrounding rock sealing, hydrogeological risks, and environmental constraints during the underground in-situ pyrolysis development of oil-rich coal, the Saaty 1-9 scaling method was used to perform pairwise comparisons of the evaluation indicators within the same level, establishing a judgment matrix A.

[0080] ;

[0081] in, Indicators relative to indicators The importance of n is the number of evaluation indicators within the same level.

[0082] Normalize the judgment matrix A column-wise to obtain the normalized matrix B:

[0083] ;

[0084] Among them, b ij represents the normalized value of each column element of matrix A.

[0085] Based on the normalized matrix B, the subjective weight w of the i-th evaluation index is calculated using the sum-product method. i d:

[0086] ;

[0087] This yields the subjective weight vector W. d :

[0088] ;

[0089] To verify the consistency of the judgment matrix, the largest eigenvalue λ is calculated. max :

[0090] ;

[0091] Further calculate the consistency index CI and the consistency ratio CR:

[0092] ;

[0093] ;

[0094] Wherein, RI is the random consistency index. When CR < 0.1, the judgment matrix meets the consistency requirement; when CR ≥ 0.1, the judgment matrix is ​​adjusted until the consistency requirement is met.

[0095] Step S6: Calculation of objective weights using the Entropy Weight Method (EWM).

[0096] The objective weights of each evaluation index are determined using the entropy weight method. First, the original evaluation matrix X is standardized to obtain the standardized matrix Y:

[0097] ;

[0098] For efficiency-type indicators:

[0099] ;

[0100] For cost-related indicators:

[0101] ;

[0102] For interval-type indicators, standardization is performed based on how close the indicator value is to the optimal interval. Let the optimal representative value of the interval-type indicator be x0, then:

[0103] ;

[0104] After obtaining the standardized matrix Y, calculate the weight p of the i-th evaluation object under the j-th indicator. ij :

[0105] ;

[0106] Calculate the information entropy e of the j-th indicatorj :

[0107] ;

[0108] in, is the normalization constant, and m is the number of evaluation objects.

[0109] Calculate the objective weight w of the j-th indicator j f:

[0110] ;

[0111] This yields the objective weight vector W. f :

[0112] .

[0113] Step S7: Game theory combinatorial weighting.

[0114] To reconcile the differences between the subjective weights of AHP and the objective weights of EWM, a game-theoretic combination weighting method is used to obtain the comprehensive weights. This method aims to minimize the overall deviation between subjective and objective weights, thereby achieving a balance between expert experience judgment and the objective laws governing sample data.

[0115] Let the weight vector set be:

[0116] ;

[0117] Where K=2, corresponding to the subjective weights W d and objective weight W f Linear combination of different weight vectors:

[0118] ;

[0119] Where, α k These are the weighted combination coefficients.

[0120] An optimization model is established with the objective of minimizing the total deviation between the combined weight vector w and each basic weight vector:

[0121] ;

[0122] The combination coefficient α is obtained by solving the problem. k Then, normalize it:

[0123] ;

[0124] The final comprehensive weight w is obtained c :

[0125] .

[0126] Step S8: Improve the overall evaluation of Grey-TOPSIS.

[0127] In this invention, the improved grey relational degree-ideal solution method is also known as the improved Grey-TOPSIS method.

[0128] After obtaining the comprehensive weight w c Then, the improved Grey-TOPSIS method was used to calculate the development potential of each evaluation object.

[0129] First, construct the standardized weighted decision matrix Z:

[0130] ;

[0131] Among them, z ij w is the weighted standardized value of the i-th evaluation object on the j-th indicator. c j is the comprehensive weight of the j-th indicator.

[0132] Determine the ideal solution Z + and negative ideal solution Z − :

[0133] ;

[0134] ;

[0135] Among them, the ideal solution Z + Z represents the set of optimal values ​​among all evaluation indicators, and the negative ideal solution. − This represents the set of worst values ​​among all evaluation indicators.

[0136] Calculate the Euclidean distance between the i-th evaluation object and the positive and negative ideal solutions:

[0137] ;

[0138] ;

[0139] Calculate the grey relational coefficients between the i-th evaluation object and the positive and negative ideal solutions:

[0140] ;

[0141] ;

[0142] in, This represents the grey relational coefficient of the i-th evaluation object relative to the positive ideal solution on the j-th evaluation index. ρ represents the grey relational coefficient of the i-th evaluation object relative to the negative ideal solution on the j-th evaluation index; ρ is the resolution coefficient, preferably 0.5. It is the minimum absolute value of the difference between all evaluated objects and the ideal solution; It represents the maximum absolute value of the difference between all evaluated objects and the ideal solution.

[0143] Calculate the grey relational degree:

[0144] ;

[0145] ;

[0146] To simultaneously characterize the positional distance and curve similarity between the evaluation object and the ideal solution, the Euclidean distance and grey relational degree are homogenized and dimensionless, and an improved grey Euclidean fusion measure is constructed:

[0147] ;

[0148] ;

[0149] in, This represents the dimensionless distance of the i-th evaluation object from the positive ideal solution. This represents the dimensionless distance between the i-th evaluation object and the negative ideal solution; This represents the dimensionless grey relational degree of the i-th evaluation object as it approaches the positive ideal solution. λ represents the dimensionless grey relational degree of the i-th evaluation object that is close to the negative ideal solution; λ is the adjustment coefficient of Euclidean distance and grey relational degree, preferably 0.5.

[0150] Calculate the relative closeness D of the i-th evaluation object. i :

[0151] ;

[0152] D i The larger the value, the closer the evaluated object is to the ideal development state, and the higher its potential for in-situ underground pyrolysis development of oil-rich coal.

[0153] Step S9: Facilitate the differentiation of levels and spatial output.

[0154] Based on the relative closeness D of each evaluation object i The study area was classified into three categories: favorable area, relatively favorable area, and unfavorable area, using the natural breakpoint method.

[0155] Among them, the favorable areas are those with relatively high proximity, superior comprehensive geological conditions, and suitable for priority implementation of pilot tests and engineering deployment of underground in-situ pyrolysis of oil-rich coal; the relatively favorable areas are those with certain development potential, but with limiting factors in some local indicators; and the unfavorable areas are those with strong limitations due to coal seam occurrence, hydrogeology, surrounding rock sealing or environmental conditions, and are not suitable for priority deployment of in-situ pyrolysis projects in the near future.

[0156] Finally, the grading results of each evaluation object are coupled with spatial location data, and a distribution map of favorable areas for in-situ pyrolysis development of oil-rich coal is formed by Kriging spatial interpolation, contour plotting, or block mapping, providing a basis for target area selection, well location deployment, and development sequence arrangement.

[0157] The present invention will be further explained below using the selection of a favorable area for coal seam M in coalfield A of a certain oil-rich coal mining area in western China as an example.

[0158] (I) Conduct basic geological surveys and data collection for the in-situ underground pyrolysis development of oil-rich coal in the study area, and obtain data on regional geological background, coal seam occurrence conditions, coal quality characteristics, surrounding rock conditions, hydrogeological conditions, environmental conditions, and spatial location. Regional geological background includes stratigraphic development, structural pattern, coal series distribution, and target coal seam distribution; coal seam occurrence conditions include coal seam thickness, burial depth, number of interbedded rock layers, and coal seam stability; coal quality characteristics include moisture, ash, volatile matter, total sulfur, and tar yield; surrounding rock conditions include roof and floor lithology and geothermal gradient; hydrogeological conditions include roof aquitard thickness, distance between coal seam and roof aquifer, aquifer type, water level, water pressure, and permeability; environmental conditions include the content of environmentally sensitive components such as fluorine, arsenic, and chlorine in the coal.

[0159] The above data were obtained through regional geological data, geological mapping, exploration boreholes, borehole column data, well logging interpretation, core logging, coal sample testing, elemental analysis, and hydrogeological experiments. Based on the collected data, controlling factors affecting the suitability of in-situ pyrolysis development of oil-rich coal, such as oil production potential, surrounding rock sealing, aquitard conditions, and environmental risks, were identified. A database of evaluation objects for the study area was established to provide basic data for subsequent indicator classification, membership calculation, combined weighting, and selection of favorable areas.

[0160] (II) Selection of evaluation objects within the study area. Evaluation objects can be borehole points, regular grid cells, or geological evaluation units obtained through spatial interpolation. This embodiment uses borehole evaluation objects as an example. For each evaluation object, coal quality test data, coal seam occurrence data, roof and floor lithology data, hydrogeological data, environmental data, and spatial location data are collected, and an original evaluation matrix X is constructed:

[0161] ;

[0162] In this embodiment, m=30 and n=15.

[0163] Evaluation index system such as Figure 2 As shown in Table 1, an evaluation index system comprising 5 primary indicators and 15 secondary indicators is established based on the requirements for oil production potential, coal seam exploitability, surrounding rock sealing, hydrogeological safety, and environmental risk control during the in-situ pyrolysis development of oil-rich coal. The graded threshold values ​​for each evaluation index are shown in Table 2.

[0164] Table 1 Evaluation Index System for In-situ Pyrolysis Development of Oil-Rich Coal Underground

[0165]

[0166] Table 2. Grading Boundary Values ​​for Evaluation Indicators of Oil-Rich Coal Underground In-Situ Pyrolysis Development

[0167]

[0168] In this embodiment, the top lithology U31 and bottom lithology U32 are quantitatively processed using engineering-based value assignment rules. Based on the permeability, density, fracture development, and sealing stability of the surrounding rock, the top and bottom lithologies are divided into five levels: dense and intact limestone and mudstone are assigned a value of 5; sandy mudstone and siltstone are assigned a value of 4; fine sandstone is assigned a value of 3; medium sandstone is assigned a value of 2; and coarse sandstone, conglomerate, or fractured rock strata are assigned a value of 1. If significant fractures, dissolution cavities, tectonic fracture zones, or highly permeable channels are developed in the limestone, sandstone, or other rock strata, the value is reduced by 1 to 3 levels based on the degree of fracture development and permeability. A higher value indicates a denser surrounding rock, lower permeability, and stronger sealing ability, which is more conducive to maintaining the temperature and pressure fields during in-situ underground pyrolysis and reducing the risk of oil and gas escape and groundwater intrusion.

[0169] (III) Based on the indicator types and grading thresholds determined in Table 2, the trapezoidal membership functions corresponding to cost-type, interval-type, and benefit-type indicators were used to transform the membership degrees of the 15 evaluation indicators for the 30 evaluation objects, respectively, to obtain the membership degree r of each indicator to the three levels of favorable, relatively favorable, and unfavorable. ij1 r ij2 and r ij3 This step converts geological parameters of different dimensions, attributes, and data sources into membership results within the [0,1] interval.

[0170] In this embodiment, the engineering geological suitability is predicted for each primary indicator based on the principle of maximum membership. The results are as follows: Figure 3 As shown, the overall coal quality and environmental conditions are relatively good, the coal seam occurrence conditions are mainly favorable and relatively favorable, the surrounding rock conditions are generally feasible for engineering, and the hydrogeological conditions are a relatively sensitive factor. This preliminary result is used to identify the advantageous and limiting factors in the in-situ underground pyrolysis development of oil-rich coal in the study area, and is not the sole basis for the final favorable classification.

[0171] (iv) The subjective weights of each evaluation index are calculated using the analytic hierarchy process (AHP), the objective weights are calculated using the entropy weight method, and the comprehensive weights are obtained using a game theory-based combined weighting method, as shown in Table 3. All judgment matrices must pass a consistency test; a CR value less than 0.1 indicates that the judgment matrix meets the consistency requirements. In this embodiment, the combined weighting results show that the surrounding rock condition U3 and the coal seam occurrence condition U2 are the main controlling factors affecting the underground in-situ pyrolysis development potential of the target coal seam, followed by the coal quality condition U1 and the hydrogeological condition U4, while the environmental condition U5 has a relatively low weight. This result indicates that the suitability for underground in-situ pyrolysis development of the target coal seam is jointly controlled by oil production potential, coal seam spatial occurrence, surrounding rock sealing capacity, and hydrogeological conditions, with surrounding rock conditions and coal seam occurrence conditions being the main controlling factors.

[0172] Table 3 Comprehensive Weight Table of Evaluation Indicators

[0173]

[0174] (v) Obtaining the comprehensive weight W c Then, a standardized weighted decision matrix Z is constructed for the 30 evaluation objects to determine the positive ideal solution Z. + and negative ideal solution Z − Calculate the Euclidean distance E between the evaluation object and the positive and negative ideal solutions, respectively. i Grey relational coefficient γ ij Grey relational degree R i And construct the grey Euclidean fusion measure S i Finally, the relative closeness D of each evaluation object is obtained. i D i The larger the value, the closer the evaluated object is to the ideal development state, and the higher the development potential of oil-rich coal through underground in-situ pyrolysis. (D) i The smaller the value, the more obvious the limitations imposed on the evaluation object by coal quality, occurrence, surrounding rock conditions, hydrogeological or environmental conditions, and the lower the development priority. The calculation results are shown in Table 4.

[0175] Table 4. Statistical Table of Development Potential Classification of Evaluation Objects

[0176]

[0177] (vi) Based on the relative closeness D of the 30 evaluation objects i The natural breakpoint method is used to determine the grading thresholds. In this embodiment, the area is divided into three levels with grading thresholds of 0.4714 and 0.5123, classifying the study area into three categories: favorable area, relatively favorable area, and unfavorable area. When D i When ≥0.5123, it is classified as a favorable region; when 0.4714≤D iWhen D < 0.5123, it is classified as a more favorable region; when D i When the value is less than 0.4714, it is classified as an unfavorable area. In this embodiment, among the 30 evaluation objects, there are 7 favorable areas, 10 relatively favorable areas, and 13 unfavorable areas. Based on the relative spatial relationships of the evaluation objects, spatial interpolation and block mapping are performed on their classification results. The resulting schematic diagram of the distribution of favorable areas for underground in-situ pyrolysis development of oil-rich coal seams is shown below. Figure 4 .

[0178] (vii) To further verify the rationality of the evaluation results of the present invention, the favorable zone classification results can be compared with the spatial distribution of coal seam tar yield. If the overall coal seam tar yield in the favorable zone is relatively high and has a good spatial correspondence with the high tar yield zone, it indicates that the favorable zone selection results of the present invention can better reflect the resource base and geological conditions required for the underground in-situ pyrolysis development of oil-rich coal.

[0179] This embodiment demonstrates that the present invention can transform coal quality test data, coal seam occurrence data, roof and floor lithology information, hydrogeological data, environmental parameters, and spatial location data into calculable, sortable, and zoned development potential evaluation results. Through this method, favorable, relatively favorable, and unfavorable areas for in-situ underground pyrolysis development of oil-rich coal can be comprehensively identified without relying on a single indicator. This provides a technical basis for the selection of pilot test areas, well location deployment, development sequence arrangement, and determination of non-priority development areas.

[0180] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, and these should also be considered within the scope of protection of the present invention. These will not affect the effectiveness of the implementation of the present invention or the practicality of the patent.

Claims

1. A method for the preferential evaluation of a favorable zone for the development of underground in situ pyrolysis of oil-rich coal, characterized in that, Includes the following steps: S1: Collect multi-source data of the target coal seam in the study area and construct an evaluation object database; the multi-source data includes coal quality test data, coal seam occurrence data, roof and floor lithology data, hydrogeological data, environmental geochemical data and spatial location data; S2: Establish a multi-level evaluation index system, which includes five primary indicators: coal quality conditions, coal seam occurrence conditions, surrounding rock conditions, hydrogeological conditions, and environmental conditions, as well as multiple secondary indicators under each primary indicator. S3: Based on the direction of influence of each secondary indicator on the suitability of underground in-situ pyrolysis development of oil-rich coal, they are classified into cost-type indicators, interval-type indicators, or benefit-type indicators, and the grading boundary values ​​of each indicator are determined. S4: Based on the aforementioned grading threshold, construct a trapezoidal membership function, calculate the membership degree of each evaluation object to the three levels of favorable, relatively favorable, and unfavorable at each secondary index, and make a preliminary judgment on the suitability of the project based on the principle of maximum membership degree; S5: Use the analytic hierarchy process (AHP) to determine the subjective weights of each secondary indicator; S6: The objective weights of each secondary indicator are determined using the entropy weight method; S7: Using a game theory-based weighting method, coordinate the differences between the subjective and objective weights, and calculate the comprehensive weight of each secondary indicator; S8: Based on the comprehensive weight, the relative proximity of each evaluation object is calculated using the improved grey relational degree-ideal solution method. The relative proximity is used to quantitatively characterize the size of its development potential. S9: Based on the relative proximity of each evaluation object, the study area is divided into favorable, relatively favorable and unfavorable areas using the natural breakpoint method, and a distribution map of favorable areas for underground in-situ pyrolysis development of oil-rich coal is generated by combining spatial location data.

2. The method for the preferred evaluation of the favorable zone for the development of the underground in-situ pyrolysis of the oil-rich coal, according to claim 1, characterized in that, The specific secondary indicators in step S2 are as follows: coal quality conditions include moisture, ash, volatile matter, and total sulfur; coal seam occurrence conditions include coal seam thickness, coal seam burial depth, and number of interbedded rock layers; surrounding rock conditions include roof lithology, floor lithology, and geothermal gradient; hydrogeological conditions include roof aquitard thickness and distance between the coal seam and the roof aquifer; and environmental conditions include fluorine content, arsenic content, and chlorine content.

3. The method for selecting and evaluating favorable areas for underground in-situ pyrolysis development of oil-rich coal as described in claim 2, characterized in that, In step S2, the lithology of the top and bottom plates is quantitatively processed using engineering assignment rules: based on the permeability, density and sealing stability of the surrounding rock, engineering values ​​are assigned to the lithology of the top and bottom plates, and the higher the value, the stronger the sealing ability of the surrounding rock.

4. The method for selecting and evaluating favorable areas for underground in-situ pyrolysis development of oil-rich coal as described in claim 1, characterized in that, In step S3: cost-related indicators include: moisture, ash, total sulfur, number of interbedded rock layers, fluorine content, arsenic content, and chlorine content; range-related indicators include: coal seam thickness and coal seam burial depth; benefit-related indicators include: volatile matter, roof lithology, floor lithology, geothermal gradient, roof aquitard thickness, and distance between the coal seam and the roof aquifer.

5. The method for selecting and evaluating favorable areas for underground in-situ pyrolysis development of oil-rich coal as described in claim 1, characterized in that, In step S4, the original index values ​​of different dimensions and attributes are uniformly converted into membership degree vectors in the interval [0,1] using a trapezoidal membership function. ,in , , These represent the degree of membership of the indicator value to the three levels of favorable, relatively favorable, and unfavorable, respectively.

6. The method for selecting and evaluating favorable areas for underground in-situ pyrolysis development of oil-rich coal according to claim 1, characterized in that, Step S7 specifically includes: constructing a weight vector set composed of subjective weight vectors and objective weight vectors; obtaining the optimal weight combination coefficient by solving an optimization model with the objective of minimizing the total deviation between the combined weight vector and each basic weight vector; and then calculating the comprehensive weight.

7. The method for selecting and evaluating favorable areas for underground in-situ pyrolysis development of oil-rich coal as described in claim 1, characterized in that, The improved grey relational degree-ideal solution method in step S8 specifically includes: constructing a standardized weighted decision matrix to determine the positive ideal solution and the negative ideal solution; calculating the Euclidean distance and grey relational degree between each evaluation object and the positive and negative ideal solutions respectively; after processing the Euclidean distance and grey relational degree to be in the same direction and dimensionless, constructing a grey Euclidean fusion measure, and finally calculating the relative closeness of each evaluation object.

8. The method for selecting and evaluating favorable areas for underground in-situ pyrolysis development of oil-rich coal according to claim 1, characterized in that, In step S9, the grading results of each evaluation object are coupled with spatial location data through Kriging spatial interpolation, contour plotting, or block mapping to form the favorable area distribution map.

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