A deep geothermal resource exploration method and system based on big data

Abstract

The present invention discloses a deep geothermal resource exploration method and system based on big data, and relates to the field of geothermal resource exploration technology. The deep geothermal resource exploration method based on big data includes the following steps: obtaining fault layer measurement data, and determining the fault structure layer based on the fault layer measurement data; obtaining fault inclination measurement data, based on the determined fault structure layer, determining the fault structure layer inclination through the fault inclination measurement data, and determining the fault hanging plate area based on the fault structure layer inclination; determining the exploration hole position on the determined fault hanging plate area, drilling and logging at the exploration hole position according to the set drilling depth, and obtaining the logging result; performing coincidence analysis on the logging result and the geophysical interpretation to determine the geothermal energy reservoir, which solves the problems of inaccurate positioning, high exploration cost and low efficiency caused by the complex structure of the fault layer in the traditional geothermal exploration process.

Description

Technical Field

[0001] The present invention relates to the technical field of geothermal resource exploration, and in particular to a deep geothermal resource exploration method and system based on big data. Background Art

[0002] Geothermal resource exploration is a process that uses geological, geophysical, and geochemical techniques to identify and evaluate the potential of geothermal energy. This usually starts with surface geological surveys to understand the geological background and structural characteristics of the area, and then uses geophysical methods such as seismic measurements, resistivity measurements, and magnetic measurements to determine the distribution and characteristics of underground heat sources. Subsequently, geochemical analysis helps confirm the chemical composition and temperature characteristics of geothermal fluids. Ultimately, by combining these data, scientists can determine the most promising areas for geothermal energy development and conduct drilling verification to evaluate the exploitability and economic value of resources. This process requires not only scientific and technological support, but also environmental protection and sustainable development strategies.

[0003] However, due to the complex structure of the fault layer, the traditional geothermal exploration process has problems such as inaccurate positioning, high exploration costs and low efficiency. Summary of the invention

[0004] In view of the shortcomings of the existing technology, the present invention provides a deep geothermal resource exploration method and system based on big data, which solves the problems of inaccurate positioning, high exploration cost and low efficiency caused by the complex structure of the fault layer in the traditional geothermal exploration process.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a deep geothermal resource exploration method based on big data, comprising the following steps: obtaining fault layer measurement data, and determining the fault structure layer based on the fault layer measurement data; obtaining fault dip measurement data, based on the determined fault structure layer, determining the dip of the fault structure layer through the fault dip measurement data, and determining the upper plate area of ​​the fault based on the dip of the fault structure layer; determining the location of the exploration hole in the determined upper plate area of ​​the fault, drilling and logging at the exploration hole location according to the set drilling depth, and obtaining the logging results; performing coincidence analysis on the logging results and the geophysical interpretation to determine the geothermal energy reservoir.

[0006] Furthermore, the fault layer measurement data includes seismic data, downhole logging data and rock mechanical property data.

[0007] Furthermore, determining the fault structure layer based on the fault layer measurement data includes the following steps: performing feature processing on the fault layer measurement data to obtain fault layer measurement feature data, wherein the fault layer measurement feature data includes seismic feature data, downhole logging feature data, and rock mechanical property feature data; performing similarity matching on the fault layer measurement feature data with each fault layer measurement feature matching data stored in a database to obtain a matching coefficient, wherein the fault layer measurement feature matching data includes seismic feature matching data, downhole logging feature matching data, and rock mechanical property feature matching data; and obtaining the fault structure layer stored in the database corresponding to the fault layer measurement feature matching data corresponding to the minimum matching coefficient.

[0008] Furthermore, the method of determining the inclination of the fracture structure layer based on the determined fracture structure layer through fracture inclination measurement data comprises the following steps: obtaining a fracture inclination matching data set corresponding to the fracture structure layer from a database based on the determined fracture structure layer, wherein the fracture inclination matching data set comprises a plurality of fracture inclination matching data; comparing the fracture inclination measurement data with the fracture inclination matching data in the fracture inclination matching data set one by one to obtain a comparison coefficient, wherein the fracture inclination measurement data comprises seismic data, downhole logging data and surface geological data, and the fracture inclination matching data comprises seismic matching data, downhole logging matching data and surface geological matching data; obtaining a fracture layer inclination model corresponding to the fracture inclination matching data with the minimum comparison coefficient; and determining the fracture inclination of the fracture structure layer based on the fracture layer inclination model.

[0009] Furthermore, determining the location of the exploration hole on the determined fault hanging wall area includes the following steps: gridding the fault hanging wall area to determine the grid area; and conducting exploration feasibility judgment on each grid area to determine the optimal exploration hole location.

[0010] Furthermore, the exploration feasibility judgment of each grid area and determination of the optimal exploration hole location include the following steps: obtaining exploration feasibility data for each grid area, the exploration feasibility data including geological stability, geological complexity, drilling cost and drilling equipment scalability; obtaining exploration feasibility parameter data stored in a database, the exploration feasibility parameter data including geological parameter stability, geological parameter complexity, drilling parameter cost and drilling equipment parameter scalability; fusing the exploration feasibility data of each grid area with the exploration feasibility parameter data to obtain a judgment coefficient; obtaining the grid area corresponding to the exploration feasibility data corresponding to the smallest judgment coefficient, and determining the center position of the grid area as the optimal exploration hole location.

[0011] Furthermore, the calculation formula of the judgment coefficient of each grid area is as follows:

[0012] ;

[0013] In the formula, is the number of the grid area, For the The judgment coefficient of the grid area is For geological stability, is the geological complexity, is the drilling cost, For drilling equipment scalability, For geological parameter stability, The complexity of geological parameters is determined by Determine the cost of drilling. Determine the scalability of drilling equipment parameters, is a natural constant.

[0014] Furthermore, the logging results include gamma-ray data, resistivity data, acoustic wave data, temperature data and formation pressure data; the geophysical interpretation includes gamma-ray interpretation data, resistivity interpretation data, acoustic wave interpretation data, temperature interpretation data and formation pressure interpretation data; the determination of geothermal energy reservoirs by performing coincidence analysis between the logging results and the geophysical interpretation includes the following steps: aligning the logging results with the geophysical interpretation at depth or measurement points; establishing a gamma-ray cross map based on the gamma-ray data and the gamma-ray interpretation data; establishing a resistivity cross map based on the resistivity data and the resistivity interpretation data; establishing an acoustic cross map based on the acoustic wave data and the acoustic wave interpretation data; establishing a temperature cross map based on the temperature data and the temperature interpretation data; establishing a formation pressure cross map based on the formation pressure data and the formation pressure interpretation data; and determining geothermal energy reservoirs based on the gamma-ray cross map, the resistivity cross map, the acoustic wave cross map, the temperature cross map and the formation pressure cross map.

[0015] Furthermore, the method of determining geothermal energy reservoirs based on the gamma-ray cross map, the resistivity cross map, the acoustic wave cross map, the temperature cross map and the formation pressure cross map comprises the following steps: determining a gamma-ray anomaly region where the gamma ray exceeds a gamma-ray threshold based on the gamma-ray cross map; determining a resistivity anomaly region where the resistivity exceeds a first resistivity threshold and the resistivity is lower than a second resistivity threshold based on the resistivity cross map; determining a sonic wave anomaly region where the sonic wave is lower than the sonic wave threshold based on the acoustic wave cross map; determining a temperature anomaly region where the temperature exceeds a set temperature threshold based on the temperature cross map; determining a formation pressure anomaly region where the temperature exceeds a set formation pressure threshold based on the formation pressure cross map; superimposing the gamma-ray anomaly region, the resistivity anomaly region, the acoustic wave anomaly region, the temperature anomaly region and the formation pressure anomaly region as geothermal energy reservoir regions, and determining the geothermal energy reservoir based on the geothermal energy reservoir regions.

[0016] A deep geothermal resource exploration system based on big data, comprising a fracture structure layer determination module, an upper plate area determination module, a logging result acquisition module and a geothermal energy reservoir determination module, wherein: the fracture structure layer determination module is used to obtain fracture layer measurement data, and determine the fracture structure layer based on the fracture layer measurement data; the upper plate area determination module is used to obtain fracture inclination measurement data, based on the determined fracture structure layer, determine the fracture structure layer inclination through the fracture inclination measurement data, and determine the upper plate area of ​​the fault based on the fracture structure layer inclination; the logging result acquisition module is used to determine the location of the exploration hole on the determined upper plate area of ​​the fault, drill and log at the exploration hole location according to the set drilling depth, and obtain the logging result; the geothermal energy reservoir determination module is used to determine the geothermal energy reservoir by performing coincidence analysis between the logging result and the geophysical interpretation.

[0017] The present invention has the following beneficial effects:

[0018] This deep geothermal resource exploration method and system based on big data can accurately determine the fault upper wall area and exploration hole location by integrating fault layer measurement data and fault dip measurement data. It combines the coincidence analysis of drilling and logging data with geophysical interpretation to improve the detection accuracy of geothermal resources, reduce unnecessary drilling attempts, greatly improve exploration efficiency and success rate, and thus reduce exploration costs. It can effectively solve the problems of inaccurate positioning, high exploration costs, and low efficiency caused by the complex structure of fault layers in traditional geothermal exploration.

[0019] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of the deep geothermal resource exploration method based on big data of the present invention.

[0021] Figure 2 This is a flowchart of the deep geothermal resource exploration method based on big data of the present invention. DETAILED DESCRIPTION

[0022] The embodiment of the present application uses a deep geothermal resource exploration method and system based on big data to accurately determine the fault upper plate area and exploration hole location by integrating fault layer measurement data and fault dip measurement data, and combines the coincidence analysis of drilling and logging data with geophysical interpretation to improve the detection accuracy of geothermal resources, reduce unnecessary drilling attempts, greatly improve exploration efficiency and success rate, thereby reducing exploration costs, and can effectively solve the problems of inaccurate positioning, high exploration costs, and low efficiency caused by the complex structure of fault layers in traditional geothermal exploration.

[0023] See also Figure 1The embodiment of the present invention provides a technical solution: a deep geothermal resource exploration method based on big data, comprising the following steps: obtaining fault layer measurement data, and determining the fault structure layer based on the fault layer measurement data; obtaining fault inclination measurement data, based on the determined fault structure layer, determining the fault structure layer inclination through the fault inclination measurement data, and determining the fault hanging wall area based on the fault structure layer inclination; determining the exploration hole position on the determined fault hanging wall area, drilling and logging at the exploration hole position according to the set drilling depth, and obtaining the logging result; performing coincidence analysis on the logging result and the geophysical interpretation to determine the geothermal energy reservoir. The fault layer measurement data includes seismic data, downhole logging data and rock mechanical property data.

[0024] The location and characteristics of the fault layer are obtained through seismic data, downhole logging data and rock mechanical property data. These data can help understand the details of the underground structure and provide basic information for subsequent steps. Based on the determined fault structure layer, the specific direction and inclination of the fault are further determined using the dip measurement data, which helps to locate the fault more accurately. Using the dip data of the fault structure layer, the area of ​​the upper wall of the fault is determined, which is an area where geothermal resources may be rich. The best exploration hole location is selected in the determined area of ​​the upper wall of the fault to improve the target hit rate of drilling.

[0025] Drilling is carried out at the selected exploration hole location at the set drilling depth, and logging operations are performed. This step collects geological data during the drilling process, and compares and analyzes the logging results with the geophysical interpretation results to test their consistency and overlap, thereby accurately evaluating the location and quality of geothermal resources.

[0026] Through detailed data analysis and precise fault positioning, the accuracy of exploration drilling is greatly improved, and the risk of blind drilling is reduced. Accurate target positioning can reduce the number and depth of drilling, thus saving cost and time. In-depth understanding of fault characteristics and geothermal resource distribution can improve the success rate of exploration and reduce failed attempts. By combining with geophysical interpretation, the quality and potential of geothermal energy reservoirs can be more accurately evaluated, providing a scientific basis for subsequent development.

[0027] Specifically, determining the fault structure layer based on the fault layer measurement data includes the following steps: performing feature processing on the fault layer measurement data to obtain fault layer measurement feature data, wherein the fault layer measurement feature data includes seismic feature data, downhole logging feature data, and rock mechanical property feature data; performing similarity matching on the fault layer measurement feature data with each fault layer measurement feature matching data stored in a database to obtain a matching coefficient, wherein the fault layer measurement feature matching data includes seismic feature matching data, downhole logging feature matching data, and rock mechanical property feature matching data; and obtaining the fault structure layer stored in the database corresponding to the fault layer measurement feature matching data corresponding to the minimum matching coefficient.

[0028] First, three main data types are collected: seismic data, downhole logging data, and rock mechanics data. Each data type converts raw data into feature data, such as seismic feature data (reflecting the reflection and refraction characteristics between strata), downhole logging feature data (physical and chemical properties of downhole rocks and fluids), and rock mechanics feature data (such as compression strength, tensile strength, etc.). The extracted feature data is matched with the known fault layer feature matching data stored in the database for similarity, including seismic feature matching data, downhole logging feature matching data, and rock mechanics feature matching data. The matching coefficient is obtained, including but not limited to, through similarity calculation (cosine similarity, Euclidean distance, etc.), statistical methods (Pearson correlation coefficient and Spearman rank correlation, etc.).

[0029] The use of comprehensive data analysis, especially the combination of seismic, logging and rock mechanics characteristics, enhances the accuracy of fault layer identification and improves the accuracy of fault location. By accurately locating the fault structure layer, resource exploration and development can be carried out more effectively, aimless exploration can be avoided, time and cost can be saved, and uncertainty and risk in the exploration process can be reduced, because through high-precision data matching and analysis, underground conditions and potential challenges can be better predicted. The smallest value is selected from the calculated matching coefficient, and the corresponding fault layer feature matching data shows the highest consistency, thereby determining the most likely fault structure layer.

[0030] Seismic data records information about underground structures, especially the location, morphology, and continuity of strata and faults, through reflection, refraction, and diffraction of seismic waves. Seismic surveys can cover vast areas, providing a preliminary, large-scale view for identifying strata distribution and fracture structures over a large area. Downhole logging data provide detailed properties of rocks and fluids through physical and chemical parameters (such as resistivity, acoustic velocity, density, etc.) collected during the drilling process. These data provide high-resolution, local to microscopic geological information obtained from the borehole, which helps to refine the interpretation of seismic data. Rock mechanics data include mechanical properties of rocks, such as compressive strength, shear strength, elastic modulus, etc. These data help understand the behavior of rocks under geological forces (such as tectonic stress) and are critical to assessing the stability of fractures and faults and their impact on fluid flow.

[0031] Seismic data provides macroscopic, large-scale fracture and formation information; downhole logging data provides detailed geological records along the drilling path; rock mechanics data provides the behavioral characteristics of geological materials under underground stress conditions. Seismic data has obvious advantages in spatial coverage, but the resolution is low; downhole logging data has a high vertical resolution, but is limited to the vicinity of the borehole; rock mechanics data reflects more of the physical state and mechanical response of the rock essence. By comparing the results of these data, each other's analysis results can be verified and supported, reducing the errors and deviations that may be caused by a single data source.

[0032] Specifically, the method of determining the inclination of the fracture structure layer based on the determined fracture structure layer through fracture inclination measurement data comprises the following steps: obtaining a fracture inclination matching data set corresponding to the fracture structure layer from a database based on the determined fracture structure layer, wherein the fracture inclination matching data set comprises a plurality of fracture inclination matching data; comparing the fracture inclination measurement data with the fracture inclination matching data in the fracture inclination matching data set one by one to obtain a comparison coefficient, wherein the fracture inclination measurement data comprises seismic data, downhole logging data and surface geological data, and the fracture inclination matching data comprises seismic matching data, downhole logging matching data and surface geological matching data; obtaining a fracture layer inclination model corresponding to the fracture inclination matching data with the minimum comparison coefficient; and determining the fracture inclination of the fracture structure layer based on the fracture layer inclination model.

[0033] In this embodiment, first, a fault tendency matching data set related to the determined fault structure layer is extracted from the database. This data set contains data on multiple fault tendencies previously studied and recorded, such as matching data of earthquakes, downhole logging and surface geological data. The current fault tendency measurement data is compared one by one with the data in the fault tendency matching data set. This includes using seismic data, downhole logging data and surface geological data to evaluate their similarity with known tendency models. By calculating the comparison coefficient (which can use the similarity analysis or other statistical methods as above), the degree of match between the current data and each stored model is evaluated. The fault tendency matching data with the smallest comparison coefficient is selected, that is, the tendency model closest to the current measurement data is found. The tendency of the fault structure layer is determined based on this best matching model.

[0034] This method provides a systematic way to determine the dip of a fault by integrating and comparing multiple data sources, reducing misunderstandings and errors that may be caused by a single data source. It determines the dip of a fault based on actual data and previous matching cases, making geological analysis more objective and scientific, and relying on data-driven decisions rather than just empirical judgment. Accurate fault dip analysis can help better plan drilling and mining activities, avoid unnecessary drilling and reduce risks, thereby optimizing resource allocation and utilization.

[0035] Seismic data depicts the approximate location and morphology of strata and faults by transmitting and recording the reflection and refraction of seismic waves in the subsurface. This data provides a large-scale view of the subsurface and can cover a large area. Seismic exploration is mainly used to initially identify geological structures, help determine potential exploration target areas and approximate stratigraphic continuity. Downhole logging data is obtained during the drilling process and includes various physical and chemical measurements such as resistivity, porosity, permeability, etc. These data can provide detailed stratigraphic and lithological information near the borehole. Downhole logging is a supplement to seismic data, providing a high-resolution vertical geological record for accurate identification and evaluation of the geological characteristics of a specific location. Surface geological data is obtained through the analysis of surface rocks, soils, and the study of topography and landforms. This includes rock types, structures, fossil content, etc. Surface geological surveys help determine the exposure and geological history of strata and provide key surface references for interpreting subsurface data.

[0036] Seismic data can show the continuity of strata and faults and how they extend in space, including the angle of inclination. By analyzing the propagation paths of reflected and refracted waves, seismic images can reveal the morphology and inclination of strata and faults. Providing a wide range of geological structural views, it is possible to preliminarily identify and locate faults over a wide area. Downhole logging provides detailed information about the rock formations around the borehole, including the angle of inclination of the rock formations. Specific logging techniques, such as inclination measurements and azimuth measurements, can directly measure and record the direction and angle of inclination of strata and faults. Providing very precise local stratigraphic information helps refine the interpretation of seismic data. Surface geological surveys provide direct evidence of the inclination of strata by observing the exposure of rock formations on the surface. For example, by measuring the inclination angle of rock formations on the surface, the extension and inclination trend of underground structures can be inferred. Providing direct surface verification helps understand and correct interpretations in seismic and well logging data.

[0037] The macro-trends provided by seismic data can be verified and corrected by downhole logging and surface geological data. For example, seismic data may show the approximate dip of a fault, while downhole logging data can provide more precise angle measurements at the location where the fault crosses the borehole. Combining these three types of data can build a three-dimensional geological model, where each type of data provides verification and details of different parts of the model. This multi-data source approach improves the accuracy and reliability of geological interpretation. Although seismic data has a wide range, its resolution limits its accuracy; downhole logging provides high-resolution local data; and surface geological surveys provide data support from another perspective.

[0038] Specifically, determining the location of the exploration hole on the determined fault hanging wall area includes the following steps: gridding the fault hanging wall area to determine the grid area; and judging the exploration feasibility of each grid area to determine the optimal exploration hole location.

[0039] The method of conducting exploration feasibility judgment on each grid area and determining the optimal exploration hole location includes the following steps: obtaining exploration feasibility data for each grid area, wherein the exploration feasibility data includes geological stability, geological complexity, drilling cost and drilling equipment scalability; obtaining exploration feasibility parameter data stored in a database, wherein the exploration feasibility parameter data includes geological parameter stability, geological parameter complexity, drilling parameter cost and drilling equipment parameter scalability; fusing the exploration feasibility data of each grid area with the exploration feasibility parameter data to obtain a judgment coefficient; obtaining the grid area corresponding to the exploration feasibility data corresponding to the minimum judgment coefficient, and determining the center position of the grid area as the optimal exploration hole location.

[0040] The fault hanging wall area is divided into multiple small areas or grids, so that the characteristics and exploration feasibility of each area can be systematically evaluated. Data on geological stability, geological complexity, drilling costs and drilling equipment scalability are collected for each grid area. This data can help evaluate the difficulty and cost-effectiveness of exploration in each area. The collected data is compared with the parameter data stored in the database, which represents the ideal or acceptable conditions. The actual data is fused with the parameter data to calculate a judgment coefficient, which indicates the exploration feasibility of each grid area. The grid area with the smallest judgment coefficient is selected, that is, the conditions in this area are closest to the ideal state, and then the center position of the grid is determined as the best exploration hole location.

[0041] This method systematizes the process of selecting the location of the exploration hole, and by quantifying the impact of various factors, it reduces the interference of subjective judgment. Determining the optimal location of the exploration hole can minimize drilling costs and increase the success rate, because the selected location is based on the results of a comprehensive evaluation, including economic and technical feasibility. Considering geological stability and complexity can help predict and manage the risks that may be encountered during drilling, such as the risk of formation sliding or drilling into complex geological structures.

[0042] Geological stability is evaluated through geological surveys, seismic data analysis, and historical geological activity records (counting the number of fault activities, earthquakes, and soil liquefaction events within a set time period, adding the weighted sum of the number of fault activities, earthquakes, and soil liquefaction events to 1 and then calculating the inverse. The larger the value, the better the geological stability). Geological stability involves fault activity, earthquake-prone areas, and structural stability of rock layers. Geological complexity is determined through detailed geological mapping, core analysis, and downhole logging data (obtaining fault density and average rock hardness, weighted summing the fault density and average rock hardness, and the larger the value, the higher the geological complexity). Geological complexity involves the sequence of rock layers, the distribution of faults, and the type and variation of rocks. The scalability of drilling equipment parameters is determined based on the sum of the inverses of the geological stability and geological complexity of the grid areas adjacent to the grid area. The larger the value of the scalability of drilling equipment parameters, the better the scalability of drilling equipment parameters, and the easier it is to increase the number of equipment.

[0043] The calculation formula of the judgment coefficient of each grid area is as follows:

[0044] ;

[0045] In the formula, is the number of the grid area, For the The judgment coefficient of the grid area is For geological stability, is the geological complexity, is the drilling cost, For drilling equipment scalability, For geological parameter stability, The complexity of geological parameters is determined by Determine the cost of drilling. Determine the scalability of drilling equipment parameters, is a natural constant.

[0046] In this embodiment, the formula amplifies the influence of geological stability, geological complexity and scalability of drilling equipment parameters through an exponential function, and evaluates the feasibility of the drilling location by integrating geological data and economic data.

[0047] Specifically, the logging results include gamma-ray data, resistivity data, acoustic wave data, temperature data and formation pressure data; the geophysical interpretation includes gamma-ray interpretation data, resistivity interpretation data, acoustic wave interpretation data, temperature interpretation data and formation pressure interpretation data; the determination of geothermal energy reservoirs by performing coincidence analysis between the logging results and the geophysical interpretation includes the following steps: aligning the logging results with the geophysical interpretation at depth or measurement points; establishing a gamma-ray cross map based on the gamma-ray data and the gamma-ray interpretation data; establishing a resistivity cross map based on the resistivity data and the resistivity interpretation data; establishing an acoustic cross map based on the acoustic wave data and the acoustic wave interpretation data; establishing a temperature cross map based on the temperature data and the temperature interpretation data; establishing a formation pressure cross map based on the formation pressure data and the formation pressure interpretation data; and determining geothermal energy reservoirs based on the gamma-ray cross map, the resistivity cross map, the acoustic wave cross map, the temperature cross map and the formation pressure cross map.

[0048] First, align the logging data with the geophysical interpretation data at depth or measurement point. This is a basic step to ensure that corresponding measurements in all data sets correspond to each other so that they can be accurately compared and analyzed. Create cross plots for each type of data (gamma ray, resistivity, sonic, temperature, formation pressure). These cross plots show the relationship and difference between the actual measurement results and the interpreted data, so that the degree of match between the two can be intuitively seen. For example, the process of creating a gamma ray cross is to select the appropriate chart type based on the characteristics of the data and the purpose of the comparison, and compare the logged gamma ray data with its corresponding geophysical interpretation data. Scatter plots can be used to display the logging values ​​and interpretation values ​​for each measurement point. Observe whether the data points are arranged around a linear trend, which can help identify direct correlations or systematic deviations between the data. Use statistical methods such as Pearson's correlation coefficient or Spearman's rank correlation coefficient to quantify the strength of the relationship between the two sets of data. Make sure all charts have clear titles, axis labels, legends, and data point markers to help understand and interpret the charts.

[0049] Gamma ray data is often used to identify the radioactivity of rock formations, resistivity data helps identify the conductivity of rocks, acoustic wave data reflects the speed of sound in rock formations (related to rock density), and temperature data and formation pressure data are directly related to geothermal energy indicators.

[0050] Specifically, the method of determining geothermal energy reservoirs based on a gamma-ray cross map, a resistivity cross map, an acoustic wave cross map, a temperature cross map and a formation pressure cross map comprises the following steps: determining a gamma-ray anomaly region where gamma rays exceed a gamma-ray threshold based on a gamma-ray cross map; determining a resistivity anomaly region where resistivity exceeds a first resistivity threshold and resistivity is lower than a second resistivity threshold based on a resistivity cross map; determining a sonic wave anomaly region where sonic waves are lower than a sonic wave threshold based on a acoustic wave cross map; determining a temperature anomaly region where temperature exceeds a set temperature threshold based on a temperature cross map; determining a formation pressure anomaly region where temperature exceeds a set formation pressure threshold based on a formation pressure cross map; superimposing gamma-ray anomaly regions, resistivity anomaly regions, acoustic wave anomaly regions, temperature anomaly regions and formation pressure anomaly regions as geothermal energy reservoir regions, and determining geothermal energy reservoirs based on the geothermal energy reservoir regions.

[0051] In this embodiment, abnormal areas where gamma ray readings exceed a preset threshold are identified. Gamma rays are often used to identify the radioactivity of rocks, and their abnormally high values ​​may indicate specific lithology or mineral content, which may indicate the presence of heat source rocks in geothermal exploration. Identify abnormal areas where the resistivity is significantly higher than a first threshold or lower than a second threshold. In geothermal systems, high resistivity may indicate dry, hot rocks or a lack of fluids, while low resistivity may indicate the presence of conductive hot fluids. Find abnormal areas where the acoustic velocity is below a specific threshold. Low acoustic velocity may indicate areas with developed fractures, which is beneficial for the flow of geothermal fluids. Identify abnormally high temperature areas where the temperature exceeds a set threshold. High temperature in geothermal energy systems is a direct energy indicator. Identify areas of abnormal formation pressure that exceed a preset threshold. High formation pressure may be associated with geothermal activity, indicating high deep fluid pressure, which may be caused by high temperature. All identified abnormal areas are superimposed to comprehensively determine the most likely location of geothermal energy reservoirs. This multi-parameter superposition analysis provides a more comprehensive and reliable method to determine the location of geothermal resources.

[0052] A deep geothermal resource exploration system based on big data, such as Figure 2 As shown, it includes a fault structure layer determination module, an upper plate area determination module, a logging result acquisition module and a geothermal energy reservoir determination module, wherein: the fault structure layer determination module is used to obtain fault layer measurement data, and determine the fault structure layer based on the fault layer measurement data; the upper plate area determination module is used to obtain fault inclination measurement data, based on the determined fault structure layer, determine the fault structure layer inclination through the fault inclination measurement data, and determine the upper plate area of ​​the fault based on the fault structure layer inclination; the logging result acquisition module is used to determine the exploration hole position on the determined upper plate area of ​​the fault, drill and log at the exploration hole position according to the set drilling depth, and obtain the logging result; the geothermal energy reservoir determination module is used to determine the geothermal energy reservoir by performing coincidence analysis between the logging result and the geophysical interpretation.

[0053] An electronic device comprises: a processor and a memory, wherein computer program instructions are stored in the memory, and when the computer program instructions are executed by the processor, the processor executes the deep geothermal resource exploration method based on big data as described above.

[0054] A computer-readable storage medium is used to store a program, which, when executed by a processor, implements the deep geothermal resource exploration method based on big data as described above.

[0055] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0056] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0057] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0059] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0060] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A deep geothermal resource exploration method based on big data, characterized in that: The following steps are involved: Acquire fracture layer measurement data, and determine the fracture structure layer based on the fracture layer measurement data; Acquire fault dip measurement data, determine the dip of the fault structure layer based on the determined fault structure layer through the fault dip measurement data, and determine the hanging wall area of ​​the fault based on the dip of the fault structure layer; Determine the location of the exploration hole in the determined fault hanging wall area, drill and log the well at the exploration hole location according to the set drilling depth, and obtain the logging results; Analyze the coincidence between logging results and geophysical interpretation to determine geothermal reservoirs; The fault layer measurement data includes seismic data, downhole logging data and rock mechanical property data; Determining the fracture structure layer based on the fracture layer measurement data includes the following steps: Performing feature processing on the fault layer measurement data to obtain fault layer measurement feature data, wherein the fault layer measurement feature data includes seismic feature data, downhole logging feature data, and rock mechanics property feature data; Perform similarity matching between the fault layer measurement feature data and each fault layer measurement feature matching data stored in the database to obtain a matching coefficient, wherein the fault layer measurement feature matching data includes seismic feature matching data, downhole logging feature matching data, and rock mechanics property feature matching data; Obtaining the fracture layer measurement feature matching data corresponding to the minimum matching coefficient corresponding to the fracture structure layer stored in the database; The method of determining the fracture structure layer inclination based on the determined fracture structure layer by fracture inclination measurement data comprises the following steps: Based on the determined fracture structure layer, a fracture tendency matching data set corresponding to the fracture structure layer is obtained from a database, wherein the fracture tendency matching data set includes a plurality of fracture tendency matching data; Comparing the fault tendency measurement data with the fault tendency matching data in the fault tendency matching data set one by one to obtain a comparison coefficient, wherein the fault tendency measurement data includes seismic data, downhole well logging data and surface geological data, and the fault tendency matching data includes seismic matching data, downhole well logging matching data and surface geological matching data; Obtaining the fault tendency matching data corresponding to the minimum comparison coefficient corresponding to the fault layer tendency model stored in the database; Determine the fracture inclination of the fracture structure layer based on the fracture layer inclination model; Determining the location of the exploration hole on the determined fault hanging wall area comprises the following steps: Grid the fault hanging wall area and determine the grid area; Conduct exploration feasibility assessment on each grid area and determine the best exploration hole location; The process of determining the feasibility of exploration for each grid area and determining the optimal exploration hole location comprises the following steps: Obtaining exploration feasibility data for each grid area, the exploration feasibility data including geological stability, geological complexity, drilling cost, and drilling equipment scalability; Acquiring exploration feasibility parameter data stored in a database, wherein the exploration feasibility parameter data includes geological parameter stability, geological parameter complexity, drilling parameter cost, and drilling equipment parameter scalability; The exploration feasibility data of each grid area are respectively integrated with the exploration feasibility parameter data to obtain the judgment coefficient; The grid area corresponding to the exploration feasibility data corresponding to the minimum judgment coefficient is obtained, and the center position of the grid area is determined as the optimal exploration hole position.

2. The deep geothermal resource exploration method based on big data according to claim 1 is characterized in that: The calculation formula of the judgment coefficient of each grid area is as follows: ； In the formula, is the number of the grid area, For the The judgment coefficient of the grid area is For geological stability, is the geological complexity, is the drilling cost, For drilling equipment scalability, To determine the stability of the geological parameters, The complexity of geological parameters is determined by Determine the cost of drilling. Determine the scalability of drilling equipment parameters, is a natural constant.

3. The deep geothermal resource exploration method based on big data according to claim 1 is characterized in that: The logging results include gamma ray data, resistivity data, acoustic wave data, temperature data and formation pressure data; The geophysical interpretation includes gamma ray interpretation data, resistivity interpretation data, acoustic wave interpretation data, temperature interpretation data and formation pressure interpretation data; The method of analyzing the coincidence of the logging results and the geophysical interpretation to determine the geothermal energy reservoir comprises the following steps: Align logging results with geophysical interpretation at depth or measurement point; Establishing a gamma-ray cross diagram based on gamma-ray data and gamma-ray interpretation data; Establishing resistivity cross-plot based on resistivity data and resistivity interpretation data; Establishing a sonic cross-plot based on sonic data and sonic interpretation data; Establishing a temperature cross diagram based on temperature data and temperature interpretation data; Establishing a formation pressure cross diagram based on formation pressure data and formation pressure interpretation data; Geothermal reservoirs are identified based on gamma ray cross plots, resistivity cross plots, acoustic wave cross plots, temperature cross plots and formation pressure cross plots.

4. A deep geothermal resource exploration method based on big data according to claim 3, characterized in that: The method of determining geothermal energy reservoirs based on a gamma ray cross map, a resistivity cross map, a sonic wave cross map, a temperature cross map and a formation pressure cross map comprises the following steps: determining a gamma ray anomaly region where gamma rays exceed a gamma ray threshold based on the gamma ray crossover diagram; Determine resistivity anomaly areas having resistivity exceeding a first resistivity threshold and resistivity below a second resistivity threshold based on the resistivity cross map; Determining an acoustic abnormality region where the acoustic wave is below the acoustic wave threshold based on the acoustic wave cross-plot; Determine temperature abnormality areas where the temperature exceeds a set temperature threshold based on the temperature cross graph; Determine an abnormal formation pressure area exceeding a set formation pressure threshold based on the formation pressure cross map; The superimposed gamma-ray anomaly area, resistivity anomaly area, acoustic wave anomaly area, temperature anomaly area and formation pressure anomaly area are geothermal energy storage areas, and the geothermal energy storage is determined based on the geothermal energy storage areas.

5. A deep geothermal resource exploration system based on big data, applying the deep geothermal resource exploration method based on big data according to any one of claims 1 to 4, characterized in that: It includes a fault structure layer determination module, a hanging wall area determination module, a logging result acquisition module and a geothermal energy reservoir determination module, among which: The fracture structure layer determination module is used to obtain fracture layer measurement data and determine the fracture structure layer based on the fracture layer measurement data; The hanging wall region determination module is used to obtain fault inclination measurement data, determine the fault structural layer inclination through the fault inclination measurement data based on the determined fault structural layer, and determine the fault hanging wall region based on the fault structural layer inclination; The logging result acquisition module is used to determine the location of the exploration hole in the determined fault hanging wall area, drill and log the exploration hole at the location of the exploration hole according to the set drilling depth, and obtain the logging result; The geothermal energy reservoir determination module is used to determine the geothermal energy reservoir by analyzing the coincidence between the logging results and the geophysical interpretation.

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