Deep geothermal resource potential evaluation method, device, equipment and medium

Abstract

The invention discloses a deep geothermal resource potential evaluation method, a deep geothermal resource potential evaluation device, deep geothermal resource potential evaluation equipment and a medium, and relates to the technical field of deep geothermal resource evaluation.The deep geothermal resource potential evaluation method comprises the steps that a research area is divided into five levels of structural units, and the deep geothermal resource potential is evaluated on the basis of deep thermal backgrounds of I-III level structural units and storage cover combination characteristics of IV level structural units; the method comprises the following steps: constructing indexes for characterizing geothermal occurrence conditions of a foundation of a research area, and constructing indexes for characterizing geothermal resource aggregation conditions of the research area on the basis of fracture distribution and protrusions and recesses of a V-level structural unit, so as to carry out step-by-step optimization from a whole area to a local favorable area, thereby emphatically considering a heat enrichment area in the research area; and then a social network analysis method is adopted to establish the influence weight of each index based on the relevance of the influence factors of each index, so that high-accuracy evaluation is carried out on the deep terrestrial heat of the research area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of deep geothermal resource evaluation, and particularly relates to a deep geothermal resource potential evaluation method, device, equipment and medium. BACKGROUND

[0002] The deep geothermal of the research area refers to the high-temperature geothermal resource buried below 3km in the research area, which has huge reserves and is widely distributed, and is the most potential alternative energy. It is of great significance to accelerate the national energy structure adjustment under the background of "carbon peak and carbon neutral". A large amount of research has been done on the evaluation of geothermal resource potential by predecessors. Based on the geothermal geological background of the research area, different evaluation index systems are established. "Source, reservoir, cover and passage" are the main geological factors affecting the occurrence of geothermal resources. Pang Zhonghe et al. consider that the parameters closely related to heat source mainly include terrestrial heat flow value, geothermal gradient, intracrustal depth, low-velocity high-conductivity layer depth, volcanic magmatic activity, etc. The crust-mantle structure affects the distribution of heat flow. The Moho and intracrustal surface often have a positive correlation with the distribution of geothermal field. Magmatic rock, deep fault and low-velocity high-conductivity layer can be used as local heat source to affect the local thermal state. The cover layer is an important geological structure above the reservoir, which prevents heat from escaping. The larger the physical property difference between the reservoir and the cover layer is, the better the heat insulation effect is. The Mesozoic and Cenozoic terrigenous clastic rock has the widest distribution and the largest thickness, and is the best heat insulation cover layer. In addition, because the thermal conductivity of coal seam in sedimentary rock is the lowest, the coal-bearing rock series has stronger heat insulation effect on deep heat flow, and is a better heat insulation cover layer. At present, the reservoirs for deep geothermal development at home and abroad are mainly granite and deep crystalline metamorphic rock, followed by carbonate rock and sandstone, etc.

[0003] At present, the evaluation of deep geothermal in the research area mainly adopts the analytic hierarchy process. It usually establishes indexes for the whole region, and then uses the analytic hierarchy process to distribute the weight of each index to evaluate the deep geothermal. However, when establishing indexes for the whole region, the heat enrichment area in the research area is often ignored. When using the analytic hierarchy process to distribute the weight of each index, the complex correlation between the influencing factors of each index in the actual situation is ignored, so it is difficult to evaluate the deep geothermal in the research area with high accuracy. SUMMARY

[0004] The embodiment of the present application provides a deep geothermal resource potential evaluation method, device, equipment and medium, which can solve the problem in the prior art that the existing method at the present stage often ignores the heat enrichment area in the research area when establishing indexes for the whole region, and ignores the complex correlation between the influencing factors of each index in the actual situation when using the analytic hierarchy process to distribute the weight of each index, so it is difficult to evaluate the deep geothermal in the research area with high accuracy.

[0005] The embodiment of the present application provides a deep geothermal resource potential evaluation method, comprising the following steps: According to the geological structure and stratigraphic combination characteristics of the research area, the research area is divided into different levels of structural units; Among them, the structural unit is five levels, the Ⅰ-Ⅲ level structural unit represents the deep basement structure characteristics of the research area, the Ⅳ level and the Ⅴ level structural unit represents the structure and sedimentary characteristics of the sedimentary cover of the research area; The Ⅳ structural unit is contained in the Ⅰ-Ⅲ level structural unit, and the Ⅴ level structural unit is contained in the Ⅳ structural unit; According to the deep thermal background of the Ⅰ-Ⅲ level structural unit and the reservoir-cap combination characteristics of the Ⅳ level structural unit, the index representing the basic geothermal occurrence of the research area is constructed; according to the fracture distribution and the protrusion and depression of the Ⅴ level structural unit, the index representing the geothermal resource aggregation of the research area is constructed; Each type of index has multiple influencing factors, based on social network analysis method, the relevance between each influencing factor and all other influencing factors is judged, and each influencing factor is given a relevance scale value based on the relevance; according to the relevance scale value of the multiple influencing factors of each type of index, the influence weight of each type of index is obtained, and the deep geothermal resource potential of the research area is evaluated based on the influence weight.

[0006] Preferably, the division of the different levels of structural units comprises: According to the fault structure and basement relief structure of the research area in geology, and the sedimentary cover type and magmatic activity of the research area in stratigraphic combination, the research area is divided into five level structural units; The Ⅰ-Ⅲ level structural unit represents the deep basement structure characteristics of the research area, and is divided based on the tectonic evolution characteristics of the upper lithosphere, basement and sedimentary cover of the research area; The Ⅳ level structural unit represents the structure and sedimentary characteristics of the pre-triassic sedimentary cover of the research area, and is divided based on the large faults as boundaries; The Ⅴ level structural unit represents the structure and sedimentary characteristics of the post-triassic sedimentary cover of the research area, and is divided based on the shallow reservoir, cap rock and small fault distribution.

[0007] Preferably, the index representing the basic geothermal occurrence of the research area is constructed, comprising: The deep thermal background is controlled by the deep thermal evolution of the Ⅰ-Ⅲ level structural unit, and the Ⅳ level structural unit includes the structural rock combination characteristics; According to the deep thermal background of the Ⅰ-Ⅲ level structural unit and the reservoir-cap combination characteristics of the Ⅳ level structural unit, three types of indexes representing the basic geothermal occurrence of the research area are constructed, including crust-mantle structure, geothermal field and reservoir-cap combination; Among them, the basic heat source potential depends on the crust-mantle structure of the research area, the geothermal field reflects the shallow geothermal potential of different areas, and the reservoir and cap rock in the reservoir-cap combination are used to preserve the geological structure of the deep heat.

[0008] Preferably, the index is constructed to represent the geothermal resource accumulation in the study area, including: The Vth-order tectonic unit is a small-scale fault-controlled convex and concave structure, which controls the thickness and distribution of the reservoir and cap rock; the small-scale faults are distributed in the sedimentary cap rock and act as water-conducting channels, controlling the distribution and convection of geothermal water in the reservoir, and the convex and concave structures affect the redistribution of geothermal resources in the shallow surface; According to the fault distribution and convex-concave structure of the Vth-order tectonic unit, four types of indexes representing additional heat sources, fault characteristics, reservoir properties and cap rock properties are constructed to represent the geothermal resource accumulation in the study area; Among them, the additional heat source is a control factor affecting the accumulation of geothermal resources, the fault characteristic represents the trend and scale of geothermal resource flow, the reservoir property represents the distribution range of geothermal resources, and the cap rock property is a geological structure that prevents heat from escaping.

[0009] Preferably, the influence weight of each type of index is obtained, including: Each type of index has multiple influencing factors, and the relevance between each influencing factor and all other influencing factors is determined a ij , and each influencing factor is given a relevance scale value based on the relevance, and an adjacency matrix is constructed A n×n ; According to the relevance scale value of each influencing factor, the degree centrality of each influencing factor is obtained, represented as: ; Among them: v i represents the influencing factor node; According to the degree centrality of each influencing factor, the importance measure value of each influencing factor is obtained, represented as: ; ; Among them: x i is the importance measure value of the influencing factor node v i ; x =[ x 1, x 2, x 3,…, x n ] T ; x represents the eigenvector of the adjacency matrix A ; λ represents the eigenvalue of the adjacency matrix; The degree centrality of each influencing factor is composed of a vectorx (0)=[ C d ( v 1), C d ( v 2), C d ( v 3),…, C d ( v n ], as an adjacency matrix A The initial vector of the principal eigenvectors; the vector is updated using the power iteration method, expressed as: ; Obtain the final feature vector x ( k )=[ b 1, b 2, b 3,…, b n The final feature vector is normalized to obtain the weight of each influencing factor, the weight of multiple influencing factors for each type of indicator, and the influence weight of each type of indicator.

[0010] This invention also provides a device for evaluating the potential of deep geothermal resources, comprising: The structural division module is used to divide the study area into different levels of structural units based on the geological structure and stratigraphic combination characteristics of the study area. The tectonic units are divided into five levels: Level I-III tectonic units characterize the deep basement tectonic features of the study area, while Level IV and Level V tectonic units characterize the tectonic and sedimentary features of the sedimentary cap layer of the study area; Level IV tectonic units are included within Level I-III tectonic units, and Level V tectonic units are included within Level IV tectonic units. The index module is used to construct indices characterizing the basic geothermal occurrence in the study area based on the deep thermal background of Class I-III tectonic units and the reservoir-seal combination characteristics of Class IV tectonic units; and to construct indices characterizing the accumulation of geothermal resources in the study area based on the fault distribution and uplifts and depressions of Class V tectonic units. The evaluation module has multiple influencing factors for each type of indicator. Based on social network analysis, it determines the correlation between each influencing factor and all other influencing factors, and assigns a correlation scale value to each influencing factor based on the correlation. Based on the correlation scale values ​​of the multiple influencing factors for each type of indicator, it obtains the influence weight of each type of indicator, and evaluates the resource potential of deep geothermal energy in the study area based on the influence weight.

[0011] This invention also provides an electronic device, including a memory and a processor; The memory is configured to store the computer program. The processor is configured to implement the steps of the deep geothermal resource potential evaluation method when executing the computer program stored in the memory.

[0012] The embodiment of the present application also provides a computer readable storage medium configured to store a computer program, and the computer program is configured to implement the steps of the deep geothermal resource potential evaluation method when executed by a processor.

[0013] The embodiment of the present application provides a deep geothermal resource potential evaluation method, device, equipment and medium, and has the following beneficial effects compared with the prior art. The embodiment of the present application divides the research area into five levels of structural units, the I-III level structural units represent the deep basement structural characteristics of the research area, the IV-V level structural units represent the structural and sedimentary characteristics of the sedimentary cover of the research area, and the IV structural unit is contained in the I-III level structural units, and the V level structural unit is contained in the IV structural unit, based on the deep thermal background of the I-III level structural units and the storage and cover combination characteristics of the IV level structural units, the index representing the basic geothermal occurrence conditions of the research area is constructed, based on the fracture distribution and the protrusion and depression of the V level structural unit, the index representing the geothermal resource accumulation conditions of the research area is constructed, so as to perform step-by-step optimization from the whole area to the local favorable area, thereby focusing on the thermal enrichment area in the research area, and then using the social network analysis method to judge the correlation of the influencing factors of each index, and establishing the influence weight of each index, thereby performing high-accuracy evaluation on the deep geothermal of the research area. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 An evaluation system schematic diagram of the deep geothermal resource potential evaluation method provided by the embodiment of the present application is shown in the figure. Figure 2 A structural unit division schematic diagram of the research area of the deep geothermal resource potential evaluation method provided by the embodiment of the present application is shown in the figure. Figure 3 A Moho surface burial depth schematic diagram of the research area of the deep geothermal resource potential evaluation method provided by the embodiment of the present application is shown in the figure. Figure 4 A lithospheric surface burial depth schematic diagram of the research area of the deep geothermal resource potential evaluation method provided by the embodiment of the present application is shown in the figure. Figure 5 A geothermal gradient schematic diagram of the research area of the deep geothermal resource potential evaluation method provided by the embodiment of the present application is shown in the figure. Figure 6 A Moho surface burial depth schematic diagram of the research area of the deep geothermal resource potential evaluation method provided by the embodiment of the present application is shown in the figure. Figure 7A deep geothermal resource potential evaluation method provided by the embodiment of the present application is a buried depth schematic diagram of the abundant area of the region; Figure 8 A deep geothermal resource potential evaluation method provided by the embodiment of the present application is a geothermal gradient schematic diagram of the abundant area of the region; Figure 9 A deep geothermal resource potential evaluation method provided by the embodiment of the present application is a deep fault schematic diagram of the abundant area of the region; Figure 10 A deep geothermal resource potential evaluation method provided by the embodiment of the present application is a concealed rock mass schematic diagram of the abundant area of the region; Figure 11 A deep geothermal resource potential evaluation method provided by the embodiment of the present application is an acid rock outcrop schematic diagram of the abundant area of the region; Figure 12 A deep geothermal resource potential evaluation method provided by the embodiment of the present application is a low-velocity anomaly schematic diagram of the abundant area of the region; Figure 13 A deep geothermal resource potential evaluation method provided by the embodiment of the present application is a uplift and depression structure schematic diagram of the abundant area of the region; Figure 14 A deep geothermal resource potential evaluation method provided by the embodiment of the present application is a caprock thickness schematic diagram of the abundant area of the region; Figure 15 A deep geothermal resource potential evaluation method provided by the embodiment of the present application is a reservoir lithology distribution schematic diagram of the abundant area of the region; Figure 16 A deep geothermal resource potential evaluation method provided by the embodiment of the present application is an index correlation network schematic diagram; Figure 17 A deep geothermal resource potential evaluation method provided by the embodiment of the present application is a deep geothermal potential division schematic diagram of the abundant area of the region. DETAILED DESCRIPTION

[0015] In order to make the above objectives, features and advantages of the present application more apparent, specific embodiments of the present application are described in detail below with reference to the accompanying drawings. In the following description, a lot of specific details are set forth in order to fully understand the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the scope of the present application, so the present application is not limited to the specific embodiments disclosed below.

[0016] Reference Figure 1The embodiment of the present application provides a deep geothermal resource potential evaluation method, and the present application divides the research area into different levels of structural units according to the geological structure and stratigraphic combination characteristics of the research area; considering the characteristics of different levels of structural units, two levels are combined to construct an evaluation index system, the evaluation index system includes a basic geothermal occurrence index evaluation system of the whole region and a geothermal resource aggregation index evaluation system in the prospective area, and then a favorable exploration area is selected to evaluate the geothermal resources.

[0017] Specifically, I. Constructing a deep geothermal resource potential evaluation index system.

[0018] The construction principle of the evaluation index system is that the deep geothermal resource system is mainly divided into two parts, one is the deep heat source background, which basically determines whether a region has the potential to develop geothermal resources, and the other is the reservoir and cap rock mainly composed of sedimentary strata since the Phanerozoic and the faults interspersed therein, which affects the occurrence and aggregation of shallow geothermal resources; the two systems are different in action and are nearly independent of each other; therefore, the establishment of the evaluation index system is centered on these two points. Specifically, it includes:

[0019] 1. Corresponding relationship between structural unit division of the research area and deep geothermal resources.

[0020] The present researchers have divided the national I-III level structural units, which mainly reflect the tectonic evolution characteristics of the regional lithosphere, basement and sedimentary cap rock; it can be seen that the I-III level structural units mainly reflect the deep basement tectonic characteristics, which are closely related to the deep thermal state and geothermal field; the IV-V level structural units take regional faults as boundaries and take fault uplifts and fault depressions as basic units, reflecting the tectonic and sedimentary characteristics of different evolution stages of the sedimentary cap rock since the Mesoproterozoic.

[0021] Usually, the scope of the research area covers the IV-V level structural units, and the I-III level structural units are mostly used as the tectonic background for analyzing the deep thermal state of the research area; therefore, when analyzing the differences in the basic geothermal occurrence conditions of the research area, the I-III level structural unit regional thermal background analysis is taken as the basis, the characteristics of the IV level structural unit are considered, and an evaluation index system is constructed with the IV level structural unit as the core, so as to select the IV level structural unit with the best conditions as the prospective area; the V level structural unit is located in the IV level structural unit and controls the distribution of shallow reservoirs, cap rocks and faults, thereby affecting the aggregation of deep geothermal resources therein, and thus an evaluation index system of geothermal resource aggregation conditions is constructed with the V level structural unit as the core.

[0022] 2. Constructing a basic geothermal occurrence condition evaluation index system of the whole region.

[0023] The deep thermal background determines whether the study area has the potential to develop deep geothermal resources, and the reservoir-cap combination structure of the shallow sedimentary strata is an important constraint affecting the occurrence of deep geothermal resources. The evaluation scale and range of deep geothermal occurrence conditions between Ⅳ-level structural units are larger, and the deep heat source is mainly quantitatively evaluated by regional crust-mantle structure, with the distribution of the shallow geothermal field as a reference. The large-scale reservoir-cap characteristics of the shallow part are difficult to quantify, and are mainly analyzed qualitatively, focusing on the control of the combination relationship between the whole area basement, reservoir and cap rock on the occurrence of geothermal resources.

[0024] First, the heat source is the primary control factor for the formation of large-scale geothermal resources, and the potential of the basic heat source depends on the regional crust-mantle structure. The crust-mantle structure includes: lithospheric thickness, Moho depth, and mantle depth. The study area is usually located in a sedimentary basin, and the type of basin is different, and the heat source conditions differ greatly. Among them, the rift basin is formed by the thinning, cracking and subsidence of the crust or lithosphere under the extension background, and generally has a higher geothermal background. The tectonic background of the craton basin is relatively stable, the lithosphere and crust are better, and the thermal background value is lower. Therefore, the crust-mantle structure determines the basic thermal state of the study area.

[0025] Second, the geothermal field distribution can directly reflect the shallow geothermal potential of different regions, and shows a near-positive correlation with the deep heat source. The better the deep heat source conditions, the larger the measured geothermal gradient and terrestrial heat flow value of the shallow surface.

[0026] The reservoir and cap rock are important geological structures for preserving deep heat. The Mesozoic and Cenozoic continental clastic rock has a low thermal conductivity, is widely distributed and stable, and is the best thermal insulation cap rock. In addition, the thermal conductivity of the coal measures occurring in the Carboniferous-Permian strata in some areas is the lowest among sedimentary rocks, and the thermal insulation effect is strong, which can also be used as a thermal insulation cap rock. The Cambrian-Ordovician limestone with good water enrichment, metamorphic basement and locally developed granite body are potential deep geothermal reservoirs. Due to the large difference in geological structure between different tectonic units, there are many combination modes between reservoirs and cap rocks, which affect the conduction of deep heat flow and form different types of geothermal distribution. Therefore, the quantitative range of the crust-mantle structure index, the geothermal field index and the reservoir-cap combination type of the deep geothermal potential distribution area are summarized, and they are divided into four levels from low to high according to their influence on deep geothermal resources, as shown in Table 1.

[0027] Table 1 Evaluation index system of basic geothermal occurrence conditions 3. Construct the evaluation index system of geothermal resource aggregation.

[0028] The prospective area is mainly composed of Ⅴ-level structural units, and the evaluation index system of deep geothermal resource aggregation conditions in the prospective area is constructed around the Ⅴ-level structural units, so as to optimize the exploration and development of favorable areas. Specifically including:

[0029] ①Additional heat source index.

[0030] Additional heat source is the key controlling factor of geothermal resource accumulation in the study area, including uplift and depression structure, magmatic rock and low velocity high conductivity layer.

[0031] Magmatic rock and low velocity high conductivity layer are less distributed, which is difficult to affect the overall thermal background of the study area, but the local area is easy to accumulate geothermal resources controlled by them. Magmatic rock bodies intrude into sedimentary cover along regional faults; low velocity high conductivity layer is caused by partial melting in the crust, which has high temperature properties and greater influence on deep geothermal field; in addition, the development of uplift and depression structure, heat flow is concentrated in the convex structure with high thermal conductivity, which realizes the redistribution of heat flow and has a greater impact on the deep geothermal field of local area.

[0032] ②Fault index.

[0033] The size of fault is different, and the geothermal properties are different. The boundary fault of Ⅳ grade tectonic unit is generally regional fault, which cuts through the crust and even the lithosphere, and communicates with deep mantle source material, which can be used as a local heat source and affect the regional geothermal field. The number of this kind of fault is less; while the boundary fault of Ⅴ grade tectonic unit or small faults inside is smaller in scale, which can only cut through the cover, but has stronger water conductivity, among which the tensile and torsional faults are better, and the compressive faults are worse.

[0034] ③Reservoir index.

[0035] The reservoir of deep geothermal development at present stage is mainly granite and deep crystalline metamorphic rock; carbonate rock is concerned because of its natural fracture development and easy reservoir reconstruction; carbonate rock is mostly developed in sedimentary basin, with multiple sets of superimposed, layered distribution characteristics of Carboniferous-Permian, Cambrian-Ordovician and Proterozoic. Considering that the deep geothermal reservoir is mostly buried below 3000m, and the sandstone is mostly shallow, the potential reservoir is mainly carbonate rock and metamorphic basement; the lithology and its combination characteristics of potential reservoir in the study area are observed and studied, including thickness, lithology combination, direct caprock sealing property, karst fracture development characteristics, etc., to optimize the reservoir layer with best deep geothermal potential.

[0036] ④Caprock index.

[0037] Caprock is an important geological structure above the reservoir, which prevents heat from escaping. The better the physical property difference between caprock and reservoir is, the better the heat insulation effect is. The scale can be divided into regional caprock and direct caprock from large to small. Mesozoic-Cenozoic clastic rock and coal seam are widely distributed in sedimentary basin, which can be used as regional caprock because of its low thermal conductivity; while the rock layer above the reservoir is direct caprock, which has different heat preservation and water sealing properties, and affects the geothermal resource storage in the reservoir; the thickness, distribution and lithology combination characteristics of regional caprock are analyzed, and the heat insulation property of direct caprock is focused on.

[0038] Based on this, the deep geothermal accumulation condition evaluation index system is constructed, and the deep geothermal exploration favorable area is preferably selected; the influence of different indexes of'source, storage, cover and passage' in the deep geothermal potential distribution area on the deep geothermal is summarized from low to high and divided into four levels, as shown in Table 2.

[0039] Table 2 Geothermal resource accumulation evaluation index system and weight assignment II. Index weight distribution

[0040] For the geothermal resource potential evaluation of the area with less geothermal data, the evaluation method combining qualitative analysis and quantitative analysis is usually adopted, and the analytic hierarchy process is mainly used; this method is to make the decision-making process mathematical by using less quantitative information on the essence of complex decision-making problems; the core of the method is to divide the system into levels, only consider the control effect of the upper layer on the lower elements, and consider that the elements in the same level are independent of each other; however, in actual problems, the elements in each level are often related to each other, the number and quality of factors associated with different factors are different, and the influence on the upper factors is different; in view of this problem, the present application provides a new solution idea to reflect the dependence between factors, and greatly reduces the calculation amount.

[0041] The specific idea includes: establishing a quasi-measurement layer and an index layer under the total target; the weight is calculated from bottom to top, each factor in the bottom layer is regarded as a point, if there is a relationship between the factors, the points are connected by lines, the set composed of the points and the lines is called a network, and an adjacency matrix is established, the number of points that can be associated with each point can reflect its influence, then the weight of each element is calculated by using the characteristic vector method. Specifically:

[0042] 1. Influence weight calculation

[0043] ① Construct an adjacency matrix.

[0044] Suppose that there are n factors in the index layer, judge the correlation of each factor with all other factors a ij , define the corresponding scale according to the correlation (as shown in Table 3), and establish the adjacency matrix A n×n ; since different factors belong to different criterion layer indexes, the correlation between the factors in the criterion layer index, and the correlation of the factors under different criterion layer indexes are not consistent; generally speaking, if a factor is associated with other factors under the criterion layer index, the influence is strong; and the correlation between the factors in the criterion layer index or more or less exists, so the influence is weak.

[0045] Table 3 Two-by-two comparison scale table Degree centrality assumes that the more neighbors a node has, the more influential it is, which is the simplest measure of the importance of a node in a network; the degree of a node v i , denoted by k i , is the number of nodes directly connected to v i , which is the most basic static feature of a node; in a weighted network, the degree of a node is also called the strength of the node, and the degree centrality describes the direct influence of a node, which assumes that the greater the degree of a node, the more neighbors it can directly influence, and thus the more important it is. If many other nodes are connected to a node, the latter can be considered important.

[0046] Therefore, the centrality of a node can be measured based on its degree, and for a node v i , the degree centrality is represented as: .

[0047] ② Eigenvector centrality.

[0048] Eigenvector centrality is an important indicator for measuring the global influence of a node, which not only considers the number of neighbor nodes but also considers the importance of neighbor nodes, and considers the influence of a single node as a linear combination of the influence of other nodes.

[0049] Let x i be the importance measure value of node v i , then: .

[0050] where C is a proportional constant, x =[ x 1, x 2, x 3,…, x n ] T , and after multiple iterations to reach a steady state, it can be written in the following matrix form: .

[0051] It can be seen from this that the above formula is a typical eigenvalue equation, where x is the eigenvector of the adjacency matrix A, and λ is the eigenvalue of the adjacency matrix.

[0052] Because the adjacency matrix is complex and difficult to obtain through normal methods, the power iteration method is introduced to calculate the eigenvector x; the power iteration method also uses iterative calculation to obtain an approximate value to approximate the principal eigenvector.

[0053] Suppose we have an adjacency matrix A with principal eigenvalues, and select a non-zero vector composed of the degree centrality of each factor. x (0)=[ C d ( v 1), C d ( v 2), C d ( v 3),…, C d ( v n Let )] be the initial vector of the principal eigenvectors of matrix A. Then, the power iteration algorithm can be expressed as: .

[0054] As the number of iterations increases, numerical overflow may occur in the component elements of the matrix calculation. Therefore, it is necessary to normalize each element, as follows: .

[0055] in: It is the Euclidean norm of the eigenvectors, defined as the square root of the sum of the squares of all the elements in the eigenvectors.

[0056] For the obtained eigenvectors x ( k )=[ b 1, b 2, b 3,…, b n Normalization yields the weight of each factor, expressed as: .

[0057] This invention considers the characteristics of different levels of tectonic units and sets up a two-level index system for evaluating the potential of deep geothermal resources, realizing the optimal selection of study area → prospective area → favorable exploration area. This method requires less basic geothermal data, greatly reducing the requirements for data quality and distribution. At the same time, it considers the correlation between factors, improving the accuracy of the weighting of influencing factors and the reliability of the selection of favorable areas.

[0058] By comprehensively considering geological structural characteristics, constructing a multi-level evaluation index system, and optimizing the index weight allocation method, a systematic deep geothermal evaluation method is provided for areas with limited basic geothermal data. This method significantly reduces the requirements for data quality and distribution, while improving the accuracy and reliability of the evaluation results.

[0059] In order to evaluate the accuracy of social network analysis method for deep geothermal potential evaluation, this paper takes Xuzhou area as an example, on the basis of the index system of deep geothermal potential evaluation proposed in the previous paper, establishes the evaluation index system suitable for the geothermal geological conditions of Xuzhou area, calculates the weight of the index by using social network analysis method, realizes the evaluation of deep geothermal potential of Xuzhou area based on GIS platform, and optimizes the deep geothermal favorable area.

[0060] 1. Regional tectonic unit division.

[0061] Xuzhou area is mainly located in the southeast margin of North China plate; in Late Triassic-Early Jurassic, the oblique collision between Yangtze plate and North China plate, due to the blocking effect of Luxi uplift and Bengbu uplift, formed the westward protruding arc-shaped thrust tectonic belt in Xushu area (such as Figure 2 ), the Fengpei fault block west of Zhangji fault is in the underlying system of Xushu arc structure, and is greatly affected by Luxi uplift, forming the structural pattern of EW-NEE interlaced concave, with the concave showing the characteristics of "north fault and south overstep".

[0062] Based on the regional tectonic evolution and stratigraphic combination, the Ⅳ and Ⅴ grade tectonic units of Xuzhou area can be divided into Fengpei fault block, Huankou sag, Huaxi uplift, Jing'an sag, Xuzhou-Jiawang fault block, Xuzhou-Jiawang uplift, Pizhou-Suining fault block, Pantang sag, Sihu sag, Gaoji sag, Pizhou-Suining uplift, and Tanlu fault zone, as shown in Table 4.

[0063] Table 4 Regional tectonic unit division 2. Prospect area optimization.

[0064] According to the established Ⅳ grade tectonic unit evaluation index system and the data situation of Xuzhou area, the Moho surface burial depth, lithospheric surface burial depth, geothermal gradient and reservoir-cap combination are selected as the indexes for optimizing the deep geothermal prospect area of Xuzhou area; as shown in Figure 3 , Figure 4 and Figure 5 , the Moho surface and lithospheric surface of Fengpei fault block are uplifted, the geothermal gradient is high, and the heat source condition is the best.

[0065] Fengpei area is mainly a fault basin, with Mesozoic-Cenozoic clastic rock and Carboniferous-Permian coal-bearing rock series as deep geothermal caprock, the caprock thickness is large, and the heat insulation property is good. Due to the fact that Fengpei area is in the underlying system of Xushu arc structure, the strata are stable, and the Cambrian-Ordovician carbonate rock is stably developed under the caprock, which is the best deep geothermal reservoir. In summary, Fengpei fault block can be selected as the deep geothermal prospect area.

[0066] 3. Index system establishment and data processing.

[0067] According to the distribution and quality of geothermal geological data in Fengpei area, the evaluation index system is established from three aspects of geothermal background, additional heat source and reservoir and cap rock characteristics, as shown in Table 5; the selected evaluation index layer includes the depth of Moho surface, the depth of lithosphere and the geothermal gradient, as shown in Figure 6 、 Figure 7 and Figure 8 , the distribution of three elements in Fengpei area is obtained by interpolation calculation of original data in GIS; the distribution of deep faults and concealed rock mass in Fengpei area interpreted from gravity and magnetic data, the distribution of intermediate-acid rock and the interpretation result of low-velocity anomaly distribution in Fengpei area interpreted by predecessors are selected as the evaluation index layer of additional heat source, as shown in Figure 9 、 Figure 10 、 Figure 11 and Figure 12 ; in order to quantify the influence of deep faults on geothermal, distance analysis is carried out on the deep faults by using GIS, the closer to the fault, the better the potential; the other three elements are subjected to binary processing, and the value 1 indicates that the element is beneficial to the existence of geothermal, and 0 indicates that the element is not beneficial to the existence of geothermal; three elements of structural uplift and depression, cap rock thickness and reservoir lithology distribution are selected as the index of evaluating reservoir and cap rock characteristics, as shown in Figure 13 、 Figure 14 and Figure 15 ; since the three elements are presented in the form of surface elements in GIS, the reclassification tool is used to assign values to the three elements.

[0068] Table 5 Deep geothermal evaluation index system 4. Index weight calculation.

[0069] The correlation between different indexes is determined by literature collection, expert opinions and deepseek method, and the index correlation network is established (as shown in Figure 16 ). Then, the index correlation network is assigned with weight according to the correlation scale table between indexes, and a 10x10 adjacency matrix A of index network is established; represented as:

[0070] .

[0071] The strength of each index can be calculated according to the adjacency matrix. Taking the strength of the index as the initial vector x 0, the power iteration algorithm is realized by using self-compiled MATLAB program to calculate the characteristic vector. Each component in the characteristic vector is the eigenvector centrality of each index, and finally the index eigenvector centrality is normalized to calculate the weight of each index (as shown in Table 6).

[0072] Table 6 Index weight calculation 5. Deep geothermal potential evaluation in Fengpei area.

[0073] In GIS, according to the index weight obtained above, the deep geothermal potential value of Fengpei area is calculated, the maximum is 0.83, and the minimum is 0.39. The area above the favorable area is basically located in the Huankou depression and Jing'an depression, and the potential gradually increases from east to west in the depression.

[0074] The present application carries out shallow hole temperature measurement on 10 groundwater monitoring wells in Fengpei area, and verifies the accuracy of the prediction result according to the measured steady-state geothermal data, as shown in Figure 17 According to the statistical results of the shallow hole temperature measurement data, the potential area above the medium favorable area is covered by the monitoring well, the highest geothermal gradient of the monitoring well is 4.38 ℃ / 100m, and the lowest is 2.54 ℃ / 100m; Overall, the potential increases from low to high, and the average geothermal gradient shows an increasing trend, which is in good agreement with the prediction result of the potential.

[0075] The above-described embodiments only express several embodiments of the present application, which are described in detail and specifically, but should not be understood as the limitation of the scope of the patent. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method for evaluating deep geothermal resource potential, characterized by, The method comprises the following steps: According to the geological structure and stratigraphic combination characteristics of the study area, the study area is divided into different levels of structural units; Among them, the structural units are five levels, Ⅰ-Ⅲ level structural units represent the deep basement structure characteristics of the study area, Ⅳ level and Ⅴ level structural units represent the structure and sedimentary characteristics of the sedimentary cover of the study area; The Ⅳ structural unit is contained in the Ⅰ-Ⅲ level structural unit, and the Ⅴ level structural unit is contained in the Ⅳ structural unit; According to the deep thermal background of Ⅰ-Ⅲ level structural units and the reservoir-cap combination characteristics of Ⅳ level structural units, the index representing the basic geothermal occurrence in the study area is constructed; According to the fracture distribution and convexity and concavity of Ⅴ level structural units, the index representing the geothermal resource aggregation in the study area is constructed; Each type of index has multiple influencing factors, based on social network analysis method, the relevance between each influencing factor and all other influencing factors is judged, and each influencing factor is given a relevance scale value based on the relevance; According to the relevance scale value of the multiple influencing factors of each type of index, the influence weight of each type of index is obtained, and the resource potential of the deep geothermal in the study area is evaluated based on the influence weight.

2. The method for evaluating the potential of deep geothermal resources according to claim 1, characterized in that, The division of the different levels of structural units comprises: According to the fault structure and basement relief structure of the study area in geology, and the sedimentary cover type and magmatic activity of the study area in stratigraphic combination, the study area is divided into five levels of structural units; Ⅰ-Ⅲ level structural units represent the deep basement structure characteristics of the study area, and are divided based on the tectonic evolution characteristics of the upper lithosphere, basement and sedimentary cover of the study area; Ⅳ level structural units represent the structure and sedimentary characteristics of the pre-Triassic sedimentary cover in the study area, and are divided based on the large faults as boundaries in the study area; Ⅴ level structural units represent the structure and sedimentary characteristics of the post-Triassic sedimentary cover in the study area, and are divided based on the shallow reservoir, cap rock and small fault distribution.

3. The method for evaluating the potential of deep geothermal resources according to claim 1, characterized in that, The construction of the index representing the basic geothermal occurrence in the study area comprises: The deep thermal background is controlled by the deep crust-mantle structure of Ⅰ-Ⅲ level structural units, and the Ⅳ level structural unit includes the structural rock combination characteristics; According to the deep thermal background of Ⅰ-Ⅲ level structural units and the reservoir-cap combination characteristics of Ⅳ level structural units, three types of indexes representing the crust-mantle structure, geothermal field and reservoir-cap combination of the basic geothermal occurrence in the study area are constructed; Among them, the basic heat source potential depends on the crust-mantle structure of the study area, the geothermal field reflects the shallow geothermal potential in different areas, and the reservoir and cap rock in the reservoir-cap combination are used to preserve the geological structure of deep heat.

4. The method for evaluating deep geothermal resource potential according to claim 1, characterized in that, The construction of the index representing the geothermal resource aggregation in the study area comprises: The Ⅴ level structural unit is a convexity and concavity controlled by small faults, which controls the thickness and distribution of the reservoir and cap rock; Small faults are distributed in the sedimentary cover, acting as water channels, controlling the distribution and convection of geothermal water in the reservoir, and convexity and concavity affect the redistribution of geothermal resources on the surface in the area; According to the fracture distribution and convexity and concavity of Ⅴ level structural units, four types of indexes representing the additional heat source, fracture characteristics, reservoir properties and cap rock properties of geothermal resource aggregation in the study area are constructed; Wherein, the additional heat source is a control factor affecting the geothermal resource aggregation, the fracture feature represents the trend and scale of the geothermal resource flow, the reservoir property represents the distribution range of the geothermal resource, and the cap rock property is a geological structure preventing heat from escaping.

5. The method for evaluating the potential of deep geothermal resources according to claim 1, characterized in that, The influence weight of each type of index is obtained by: There are multiple influence factors for each type of index, and the relevance between each influence factor and all other influence factors is determined a ij , and a relevance scale value is assigned to each influence factor based on the relevance, and an adjacency matrix is constructed A n×n ; According to the degree centrality of each influence factor, an importance measure value of each influence factor is obtained, and is represented as: ； wherein: v i represents an influencing factor node; According to the degree centrality of each influence factor, an importance measure value of each influence factor is obtained, and is represented as: ； ； wherein: x i is a measure of importance of the factor node v i ; x [ x 1, x 2, x 3,…, x n ] T ; x is an eigenvector of the adjacency matrix A ; λ is an eigenvalue of the adjacency matrix. with the eigenvector of the adjacency matrix x (0)=[ C d ( v 1), C d ( v 2), C d ( v 3),…, C d ( v n )] as the initial vector of the principal eigenvector of the adjacency matrix A ; the vector is updated by the power iteration method, denoted as: ； Obtaining the final feature vector x ( k )=[ b 1, b 2, b 3,…, b n ] Normalize the final feature vector to obtain the weight of each influencing factor, the weight of multiple influencing factors of each index category, and obtain the influence weight of each index category.

6. A device for evaluating deep geothermal resource potential, characterized by, Comprise: The division module is configured to divide the research area into different levels of tectonic units according to the geological structure and stratigraphic combination characteristics of the research area; Wherein, the tectonic units are five levels, the I-III level tectonic units represent the deep basement structure characteristics of the research area, the IV and V level tectonic units represent the structure and sedimentary characteristics of the sedimentary cap rock of the research area; The IV tectonic unit is contained in the I-III level tectonic unit, and the V level tectonic unit is contained in the IV tectonic unit; The index module is configured to construct indexes representing the basic geothermal occurrence in the research area according to the deep thermal background of the I-III level tectonic units and the reservoir-cap combination characteristics of the IV level tectonic units, and to construct indexes representing the geothermal resource aggregation in the research area according to the fracture distribution and protrusions and depressions of the V level tectonic units; The evaluation module has multiple influence factors for each type of index, determines the relevance between each influence factor and all other influence factors based on social network analysis, and assigns a relevance scale value to each influence factor based on the relevance; According to the relevance scale value of the multiple influence factors of each type of index, the influence weight of each type of index is obtained, and the deep geothermal resource potential of the research area is evaluated based on the influence weight.

7. An electronic device, comprising: Comprise: Memory and processor; The memory is configured to store a computer program; The processor is configured to execute the computer program stored in the memory to realize the steps of the deep geothermal resource potential evaluation method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, A computer program is stored, and the computer program is executed by the processor to realize the steps of the deep geothermal resource potential evaluation method according to any one of claims 1-5.

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