An intelligent exploration method for deep coal resources based on a tectonic stress field

Abstract

The present application relates to the technical field of coal resource exploration, and particularly relates to a deep coal resource intelligent exploration method based on tectonic stress field, comprising: arranging a detection drill hole in an exploration target area, synchronously collecting five types of parameters of tectonic stress, ground temperature, ground pressure, fluid migration rate and coal porosity by using a deep integrated detection device, and gathering and constructing a multi-field basic data sample library of each parameter; calculating a tectonic stress intensity index based on the multi-field basic data sample library, dividing the exploration target area into a stress concentration area, a transition area and a stable area according to the index, and determining a regulation coefficient of tectonic stress on porosity and fluid migration. In the present application, a multi-field coupling correction model dominated by tectonic stress is constructed, and then the real formation physical property parameters are restored, so that the traditional exploration which mostly uses single physical field detection is improved. Since the nonlinear regulation of tectonic stress on multi-physical field is ignored, the problem that the tectonic coal and gas enrichment area cannot be accurately identified in the deep stress concentration area is caused.

Description

Technical Field

[0001] This invention relates to the field of coal resource exploration technology, and in particular to an intelligent exploration method for deep coal resources based on tectonic stress fields. Background Technology

[0002] With the depletion of shallow coal resources, coal resource development is gradually extending to deeper areas exceeding 1000 meters in depth. The environment in which deep coal resources are located is characterized by significant high ground stress, high ground temperature, and high ground pressure, and the distribution of tectonic stress field is extremely uneven due to the influence of geological tectonic movements.

[0003] However, traditional exploration methods mostly use single physical field detection. Because they ignore the nonlinear control of tectonic stress on multiple physical fields, they cannot accurately identify tectonic coal and gas enrichment areas in deep stress concentration areas. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides an intelligent exploration method for deep coal resources based on tectonic stress fields. It aims to improve the problem that traditional exploration methods mostly use single physical field detection, which ignores the nonlinear control of tectonic stress on multiple physical fields, resulting in the inability to accurately identify tectonic coal and gas-rich areas in deep stress concentration areas.

[0005] This invention provides the following technical solution: an intelligent exploration method for deep coal resources based on tectonic stress fields, comprising the following steps:

[0006] S1. Detection boreholes are set up in the exploration target area, and five types of parameters, namely tectonic stress, ground temperature, ground pressure, fluid migration rate and coal porosity, are collected simultaneously using a deep integrated detection device. The parameters are then aggregated to build a multi-field basic data sample library.

[0007] S2. Calculate the tectonic stress intensity index based on the multi-field basic data sample library. Based on this index, divide the exploration target area into stress concentration zone, transition zone and stable zone, and determine the control coefficient of tectonic stress on porosity and fluid migration.

[0008] S3. Substitute the sample database data into the multi-field parameter coupling correction model, use the tectonic stress parameter to correct the coal porosity parameter, use the geothermal and geopressure parameters to correct the fluid migration rate parameter, and construct the multi-field coupling correlation matrix after removing abnormal data.

[0009] S4. Based on the multi-field coupling correlation matrix and the preset exploration evaluation threshold, the exploration target area is divided into resource blocks of different levels, and the structural coal development area and gas enrichment area are identified simultaneously.

[0010] S5. Set up verification boreholes to obtain measured data, compare them with the resource block division and identification results. When the consistency is lower than the preset standard, use the comparison deviation to reverse calibrate the control coefficient, and re-execute the multi-field parameter coupling correction and resource block division steps until the preset standard is met and the exploration report is output.

[0011] By adopting the above technical solutions, a multi-field coupling correction model dominated by tectonic stress is constructed, thereby restoring the true physical property parameters of the strata. This improves the problem that traditional exploration mostly uses single physical field detection, which ignores the nonlinear control of tectonic stress on multi-physical fields, resulting in the inability to accurately identify tectonic coal and gas enrichment areas in deep stress concentration areas.

[0012] Preferably, in S1, the step of aggregating the parameters to construct a multi-field basic data sample library includes:

[0013] The device is equipped with a deep integrated detection device, which integrates a three-dimensional hydraulic fracturing stress sensing module, a thermocouple temperature and pressure detection module, and an acoustic logging module.

[0014] The device is controlled to move along the detection borehole, and the maximum horizontal principal stress data is obtained as structural stress parameters using the three-dimensional hydraulic fracturing stress sensing module.

[0015] Thermocouple temperature and pressure detection modules are used to obtain wellbore rock temperature and hydrostatic pressure data as geothermal and geopressure parameters.

[0016] The coal porosity parameters were obtained by using an acoustic logging module to acquire acoustic time difference data and then inverting the calculation.

[0017] The above parameters are transmitted to the surface via downhole storage or mud pulse, and the parameters are spatiotemporally aligned according to the depth index tags to generate a multi-field basic data sample library.

[0018] Preferably, in S2, the calculation of the structural stress intensity index based on a multi-field basic data sample library, and the division of the exploration target area into stress concentration zone, transition zone, and stable zone based on this index, includes:

[0019] Read the measured maximum structural stress value from the sample library and call the preset critical value of uniaxial compressive strength of deep coal and rock;

[0020] Calculate the ratio of the maximum tectonic stress value to the critical value of the uniaxial compressive strength of deep coal and rock, and define this ratio as the tectonic stress intensity index characterizing the degree of formation compression;

[0021] Retrieve preset stress concentration and stress stability thresholds;

[0022] The stress intensity index is compared with the stress concentration determination threshold. If it is greater than or equal to the threshold, it is determined to be a stress concentration area.

[0023] The stress intensity index is compared with the stress stability determination threshold. If it is less than the threshold, it is determined to be a stress stable region.

[0024] If the stress intensity index is between two thresholds, it is determined to be a stress transition zone.

[0025] Preferably, in S2, the factor for determining the effect of structural stress on porosity and fluid transport includes:

[0026] Establish a geomechanical evolution model of the exploration target area and import historical rock mechanics experimental data;

[0027] Through multivariate regression analysis, the linear correlation weight between the tectonic stress increment and the porosity attenuation was extracted, and this weight was set as the stress-porosity control coefficient.

[0028] Through multiphysics fluid dynamics simulation, the driving factor of the temperature and pressure product term on the fluid velocity is extracted, and this driving factor is set as the temperature-pressure-fluid regulation coefficient.

[0029] Preferably, in S3, the correction of coal porosity parameters using tectonic stress parameters includes:

[0030] The preset structural stress-porosity coupling correction model is invoked, which assumes that porosity changes linearly with the increase of structural stress.

[0031] The product of the measured structural stress parameters and the stress-porosity control coefficient is calculated to obtain the stress-porosity compression correction amount.

[0032] Obtain the original porosity benchmark value of the coal body under stress-free conditions, and superimpose the compression correction amount on the benchmark value to obtain the calibrated porosity parameters that can reflect the deep in-situ environment.

[0033] Preferably, in S3, the correction of the fluid migration rate parameter using geothermal and geopressure parameters includes:

[0034] The preset temperature-pressure-fluid coupling correction model is invoked. This model sets the fluid transport rate to be positively correlated with the thermodynamic state parameters and negatively correlated with the fluid viscosity characteristics.

[0035] Calculate the product of the measured geothermal parameters and the geopressure parameters, and multiply the product by the temperature-pressure-fluid regulation coefficient to obtain the driving force correction term;

[0036] The dynamic viscosity value of the fluid under the current temperature and pressure conditions is queried, and the dynamic viscosity value is normalized using the driving force correction term to obtain the calibrated fluid transport rate parameter.

[0037] Preferably, in S3, constructing the multi-field coupling correlation matrix after removing outlier data includes:

[0038] Trend fitting is performed on the calibrated parameters in the multi-field coupling correlation matrix to generate regional geological parameter trend lines;

[0039] Calculate the residual value of each discrete sampling point relative to the trend line of the geological parameters of the region;

[0040] Determine whether the absolute value of the residual exceeds a preset statistical outlier threshold;

[0041] When the residual value exceeds the threshold, the sampling point is determined to be affected by environmental noise, marked as invalid data, and removed from the matrix.

[0042] If the residual value does not exceed the threshold, the data of the sampling point is deemed valid and retained.

[0043] Preferably, in S4, the division of the exploration target area into resource blocks of different levels and the simultaneous identification of tectonic coal development zones and gas-rich zones includes:

[0044] Retrieve preset thresholds for tectonic coal development rate, porosity evaluation threshold, and permeability evaluation threshold;

[0045] By traversing the multi-field coupling correlation matrix, regions that simultaneously satisfy the conditions of tectonic coal development rate being lower than the tectonic coal development rate threshold, calibrated porosity parameter being higher than the porosity evaluation threshold, and permeability being higher than the permeability evaluation threshold are selected and marked as favorable exploration blocks rich in coal and suitable for mining.

[0046] Regions with tectonic stress intensity index in stress concentration zone and tectonic coal development rate higher than the tectonic coal development rate threshold are selected and marked as gas-rich areas with potential gas outburst risks.

[0047] Preferably, in S5, the step of setting up verification boreholes to obtain measured data includes:

[0048] Based on the resource block distribution map, verification well locations were determined in favorable exploration blocks and gas-rich areas.

[0049] Configure the number of verification boreholes according to the preset density ratio, and perform full-section coring operations on the verification boreholes.

[0050] The extracted rock cores were subjected to coal and petrographic micro-component analysis and gas content desorption tests to obtain the true values ​​of coal seam thickness, coal body failure type and gas content as measured data.

[0051] Preferably, in S5, the step of reversely calibrating the control coefficient using the comparison bias includes:

[0052] Calculate the consistency matching rate between the measured data of the borehole and the output recognition results;

[0053] If the matching rate does not reach the preset validity standard, then calculate the deviation vector between the measured true value and the recognition result;

[0054] The stress-porosity control coefficient and the temperature-pressure-fluid control coefficient are reversed based on the deviation vector using the least squares method.

[0055] The corrected control coefficients are updated into the multi-field parameter coupling correction model, and the system is triggered to re-execute the multi-field parameter coupling matching, calibration and hierarchical identification steps.

[0056] The present invention has the following beneficial effects:

[0057] 1. In this invention, by constructing a multi-field coupling correction model dominated by tectonic stress, the true physical property parameters of the strata are restored, thereby improving the problem that traditional exploration mostly uses single physical field detection, which ignores the nonlinear control of tectonic stress on multi-physical fields, resulting in the inability to accurately identify tectonic coal and gas enrichment areas in deep stress concentration areas.

[0058] 2. In this invention, a closed-loop iterative mechanism is established to use the verification deviation to calibrate the control coefficient in reverse, and then the model parameters are adaptively optimized based on the measured results. This improves the problem that traditional exploration mostly uses static geological models, which lack dynamic error correction capabilities and are difficult to adapt to the heterogeneity of strata, resulting in low reliability of deep exploration results.

[0059] 3. In this invention, by simultaneously collecting five types of parameters and constructing a sample library, a single detection is used to achieve a synergistic evaluation of coal and gas resources. This improves the problem that traditional exploration mostly adopts independent detection for different mineral types, which requires repeated drilling operations, resulting in long cycles and high costs for deep exploration projects.

[0060] 4. In this invention, by dividing stress functional zones and combining them with coupling matrices to identify structural anomalies, gas enrichment zones can be accurately delineated. This improves upon the problem that traditional exploration methods mostly rely on conventional physical property thresholds for discrimination, which fail to consider the controlling effect of structural stress on disaster development, resulting in unclear identification of deep gas outburst risk zones. Attached Figure Description

[0061] Figure 1 This is a flowchart of an intelligent exploration method for deep coal resources based on tectonic stress field proposed in this invention;

[0062] Figure 2 This is a flowchart illustrating the tectonic stress field dominance analysis and zoning process of an intelligent exploration method for deep coal resources based on tectonic stress field proposed in this invention.

[0063] Figure 3 This is a flowchart of the multi-field parameter coupling correction and correlation matrix construction of an intelligent exploration method for deep coal resources based on tectonic stress field proposed in this invention.

[0064] Figure 4 This is a flowchart illustrating the closed-loop verification and feedback calibration process of a smart exploration method for deep coal resources based on tectonic stress field proposed in this invention. Detailed Implementation

[0065] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] Example 1:

[0067] In the first embodiment of the present invention, the present invention provides an intelligent exploration method for deep coal resources based on tectonic stress fields, such as... Figures 1-4 As shown, it includes the following steps:

[0068] S1. Detection boreholes are set up in the exploration target area, and five types of parameters, namely tectonic stress, ground temperature, ground pressure, fluid migration rate and coal porosity, are collected simultaneously using a deep integrated detection device. The parameters are then aggregated to build a multi-field basic data sample library.

[0069] Furthermore, in S1, the parameters are aggregated to construct a multi-field basic data sample library, including:

[0070] It is equipped with a deep integrated detection device, which integrates a three-dimensional hydraulic fracturing stress sensing module, a thermocouple temperature and pressure detection module, and an acoustic logging module.

[0071] The control device moves along the probe borehole and uses the three-dimensional hydraulic fracturing stress sensing module to obtain the maximum horizontal principal stress data as structural stress parameters.

[0072] Thermocouple temperature and pressure detection modules are used to obtain wellbore rock temperature and hydrostatic pressure data as geothermal and geopressure parameters.

[0073] The coal porosity parameters were obtained by using an acoustic logging module to acquire acoustic time difference data and then inverting the calculation.

[0074] The above parameters are transmitted to the surface via downhole storage or mud pulse, and the parameters are spatiotemporally aligned according to the depth index tags to generate a multi-field basic data sample library.

[0075] Specifically, this addresses the issues of spatiotemporal asynchrony and data coupling difficulties caused by independent acquisition of single parameters under deep, high-stress, high-temperature, and high-pressure environments. The implementation utilizes a deep multi-parameter integrated detection device. This device physically integrates a three-dimensional hydraulic fracturing stress sensing module, a thermocouple temperature and pressure detection module, and an acoustic logging module, with each module arranged sequentially along the probe axis.

[0076] The detection device moves along the direction of the borehole depth, and each module operates according to the following mechanism:

[0077] Structural stress parameter acquisition: A three-dimensional hydraulic fracturing stress sensing module was used to conduct fracturing tests at predetermined locations on the borehole wall. Tensile fractures were induced in the borehole wall by high-pressure pump injection, and the fracturing pressure and re-tension pressure were recorded. Based on the principles of deep rock mechanics, the maximum horizontal principal stress was determined. The calculation formula is as follows:

[0078] ;

[0079] in This represents the minimum horizontal principal stress, and its value is the shut-in pressure. Indicates formation fracture pressure; Indicates the pressure of fluids in the pores of the rock; This represents the tensile strength of the rock. This formula is used to invert and calculate the in-situ tectonic stress state at the borehole location.

[0080] Ground temperature and pressure parameter acquisition: The thermocouple temperature and pressure detection module is attached to the well wall or placed in the well fluid, and the thermocouple sensor directly senses the downhole ambient temperature. The pressure sensor detects the hydrostatic pressure. .

[0081] Coal porosity parameter acquisition: The acoustic logging module transmits acoustic signals into the coal seam, receives and records the propagation time difference of the acoustic waves in the coal-rock medium. Coal porosity. Calculated based on the acoustic time difference inversion formula: ;

[0082] in Indicates the measured time difference of sound waves; This represents the theoretical value of the acoustic transit time of the coal and rock skeleton. This represents the theoretical value of the acoustic transit time for pore fluids.

[0083] Considering the risk of cable breakage and signal attenuation when the depth exceeds 1000 meters, the raw electrical signals collected are converted into digital signals in real time downhole and stored in a high-temperature resistant storage chip, or uploaded to the surface decoding system in the form of pressure waves through a mud pulse generator.

[0084] After receiving the raw data, the ground processing system performs the following data stream processing. Inputs include: a discretized tectonic stress sequence containing depth information, continuous geothermal curves, geopressure curves, fluid transport velocity sequences, and acoustic transit time sequences. Processing: Based on borehole depth... Using unique index labels, depth correction is performed on data collected from different modules. This eliminates depth lag errors caused by different module installation locations, ensuring that data at the same depth point is corrected. Align the five types of parameters to the same row vector. Output: Generate A multi-dimensional basic data sample library matrix, in which The number of sampling points is represented by 6 columns, which are depths. And five types of physical field parameters.

[0085] This acquisition and processing method eliminates environmental disturbance errors caused by multiple well explorations, ensures strict temporal and spatial correspondence of multi-physics parameters, and provides an accurate data source for subsequent coupling correction.

[0086] S2. Calculate the tectonic stress intensity index based on the multi-field basic data sample library. Based on this index, divide the exploration target area into stress concentration zone, transition zone and stable zone, and determine the control coefficient of tectonic stress on porosity and fluid migration.

[0087] Furthermore, in S2, the structural stress intensity index is calculated based on a multi-field basic data sample library. Based on this index, the exploration target area is divided into stress concentration zones, transition zones, and stable zones, including:

[0088] Read the measured maximum structural stress value from the sample library and call the preset critical value of uniaxial compressive strength of deep coal and rock;

[0089] Calculate the ratio of the maximum tectonic stress value to the critical value of the uniaxial compressive strength of deep coal and rock, and define this ratio as the tectonic stress intensity index characterizing the degree of formation compression;

[0090] Retrieve preset stress concentration and stress stability thresholds;

[0091] The stress intensity index is compared with the stress concentration determination threshold. If it is greater than or equal to the threshold, it is determined to be a stress concentration area.

[0092] The stress intensity index is compared with the stress stability determination threshold. If it is less than the threshold, it is determined to be a stress stable zone.

[0093] If the stress intensity index is between two thresholds, it is determined to be a stress transition zone.

[0094] In S2, the factors determining the moderating effect of tectonic stress on porosity and fluid transport include:

[0095] Establish a geomechanical evolution model of the exploration target area and import historical rock mechanics experimental data;

[0096] Through multivariate regression analysis, the linear correlation weight between the tectonic stress increment and the porosity attenuation was extracted, and this weight was set as the stress-porosity control coefficient.

[0097] Through multiphysics fluid dynamics simulation, the driving factor of the temperature and pressure product term on the fluid velocity is extracted, and this driving factor is set as the temperature-pressure-fluid regulation coefficient.

[0098] Specifically, the multi-field basic data sample library built on S1 aims to transform geomechanical theory into a computable mathematical model, thereby quantifying the control effect of tectonic stress on the deep strata environment.

[0099] The data processing system first retrieves the maximum tectonic stress value data sequence for each sampling point from a multi-field basic data sample library. Simultaneously, the system accesses the critical value of uniaxial compressive strength for deep coal and rock pre-stored in the database. This critical value is obtained through indoor uniaxial compression experiments on core samples of the target coal seam in the exploration target area, and characterizes the stress threshold at which coal and rock mass failure occurs.

[0100] The system calculates the structural stress intensity index at each depth sampling point according to the following formula. :

[0101] ;

[0102] in This represents the measured maximum horizontal principal stress value; This index represents the critical value of uniaxial compressive strength in deep coal and rock. It is a dimensionless parameter used to intuitively reflect the ratio of the tectonic compression force on the current stratum to its own strength.

[0103] Obtaining the full borehole section After the sequence is completed, the system calls the preset stress concentration and stress stability thresholds for point-by-point discrimination. In this embodiment, the stress concentration threshold is set to 2.5, and the stress stability threshold is set to 1.2. The specific logical discrimination process is as follows: If the calculated... A value greater than or equal to 2.5 indicates that the area where the sampling point is located is a stress concentration zone, suggesting a high probability of tectonic coal development and formation energy accumulation in the area; if the calculated value is... If the value is less than 1.2, the area where the sampling point is located is determined to be a stress-stable zone, indicating that the geological structure in this area is relatively intact; if the calculated value is less than 1.2, the area is considered to be a stress-stable zone. The value is between 1.2 and 2.5, indicating that the area where the sampling point is located is a stress transition zone.

[0104] Through the above processing, a discretized stratigraphic stress distribution sequence with geological zoning labels is output, clarifying the spatial background of subsequent multi-field coupling correction.

[0105] To address the issue of nonlinear variations in physical parameters in deep environments, it is necessary to establish a quantitative transmission relationship between tectonic stress and other physical fields.

[0106] Determination of the stress-porosity control coefficient: The system imports historical geological data and accompanying rock mechanics experimental data of the exploration target area to construct a geomechanical evolution model. For the porosity parameter, the system performs multivariate regression analysis. The analysis object is the increment of tectonic stress. With the decrease in coal porosity Based on the compaction effect of deep rocks, increased tectonic stress leads to the closure of primary pores. By using least squares fitting, the linear correlation weight between the two is extracted as the stress-porosity regulation coefficient. This coefficient is used to quantify the compressive effect of a unit increase in tectonic stress on the porosity of the coal body.

[0107] Determination of temperature-pressure-fluid regulation coefficients: For the fluid transport rate parameter, the system runs a multiphysics fluid dynamics simulation program. The simulation is set at a specific temperature. With pressure As the driving variable, fluid transport rate As the response variable, based on the fluid state equation and the extended form of Darcy's law, the product term of temperature and pressure is extracted. The driving factor for fluid flow rate is set as the temperature-pressure-fluid regulation coefficient. This coefficient characterizes the combined stimulating effect of the deep thermo-baric environment on fluid activity.

[0108] Data flow, inputs: multi-field basic data sample library, rock mechanics experimental parameters Historical geological experimental data. Processing: Algebraic calculations. Indices, logistic comparisons, partitioning, statistical regression, and numerical simulation are used to determine the partitioning. and Output: Geological structure sequence including stress zoning identifiers, stress-porosity regulation coefficients. Numerical values, temperature-pressure-fluid control coefficient Numerical values. The above output data are directly used as input parameters for multi-field parameter coupling matching and calibration in S3.

[0109] S3. Substitute the sample database data into the multi-field parameter coupling correction model, use the tectonic stress parameter to correct the coal porosity parameter, use the geothermal and geopressure parameters to correct the fluid migration rate parameter, and construct the multi-field coupling correlation matrix after removing abnormal data.

[0110] Furthermore, in S3, the correction of coal porosity parameters using tectonic stress parameters includes:

[0111] The preset structural stress-porosity coupling correction model is invoked, which assumes that porosity changes linearly with the increase of structural stress.

[0112] The product of the measured structural stress parameters and the stress-porosity control coefficient is calculated to obtain the stress-porosity compression correction amount.

[0113] Obtain the original porosity benchmark value of the coal body under stress-free conditions, and superimpose the compression correction amount on the benchmark value to obtain the calibrated porosity parameter that can reflect the deep in-situ environment.

[0114] In S3, the correction of fluid transport rate parameters using geothermal and geopressure parameters includes:

[0115] The preset temperature-pressure-fluid coupling correction model is invoked. This model sets the fluid transport rate to be positively correlated with the thermodynamic state parameters and negatively correlated with the fluid viscosity characteristics.

[0116] Calculate the product of the measured geothermal parameters and the geopressure parameters, and multiply this product by the temperature-pressure-fluid regulation coefficient to obtain the driving force correction term;

[0117] The dynamic viscosity value of the fluid under the current temperature and pressure conditions is queried, and the dynamic viscosity value is normalized using the driving force correction term to obtain the calibrated fluid transport rate parameter.

[0118] In S3, constructing a multi-field coupling correlation matrix after removing outlier data includes:

[0119] Trend fitting is performed on the calibrated parameters in the multi-field coupling correlation matrix to generate regional geological parameter trend lines;

[0120] Calculate the residual values ​​of each discrete sampling point relative to the regional geological parameter trend line;

[0121] Determine whether the absolute value of the residual exceeds a preset statistical outlier threshold;

[0122] When the residual value exceeds the threshold, the sampling point is determined to be affected by environmental noise, marked as invalid data, and removed from the matrix.

[0123] If the residual value does not exceed the threshold, the data of the sampling point is deemed valid and retained.

[0124] Specifically, by using the dominant control coefficient determined by S2, a quantitative model is used to eliminate the nonlinear interference of the complex deep environment field on the measurement of a single physical parameter, thereby restoring the true physical properties of the formation.

[0125] The system invokes a pre-defined tectonic stress-porosity coupled correction model. This model, based on porosimetry theory, assumes that under deep high-pressure conditions, the porosity variation of coal and rock masses is linearly correlated with the effective tectonic stress. The processing unit first reads the measured tectonic stress parameters. The stress-porosity control coefficient determined by S2 The system performs a multiplication operation to obtain the porosity compression correction caused by stress loading. Then, it retrieves the original porosity baseline value of the coal body under zero stress. The calibrated porosity parameter is calculated by algebraically superimposing the baseline value with the compression correction. The calibration formula is as follows:

[0126] ;

[0127] in This represents the calibrated porosity parameters that reflect the deep in-situ environment. It represents the stress-porosity control coefficient, reflecting the sensitivity of porosity to stress changes; This represents the measured maximum horizontal principal stress; This represents the baseline value of the original porosity of the coal seam. This calculation process transforms the apparent porosity affected by tectonic compression into the true porosity, providing an accurate basis for evaluating the gas storage capacity of the coal seam.

[0128] The system invokes the temperature-pressure-fluid coupling correction model. Based on the principles of fluid thermodynamics and seepage mechanics, this model assumes a positive correlation between the thermal activity of fluid molecules and the product of temperature and pressure, while the flow resistance of the fluid in porous media is negatively correlated with the fluid's viscosity. The calculation module first calculates the measured geothermal parameters. With ground pressure parameters The product of these factors is used, and a temperature-pressure-fluid regulation coefficient is introduced. Generate the thermodynamic driving force term. Simultaneously, query the fluid property database to obtain the fluid dynamic viscosity value under the current temperature and pressure conditions. The viscosity value is then used to normalize the driving force term. The calibration formula is as follows:

[0129] ;

[0130] in This represents the calibrated fluid transport rate parameter; Indicates the temperature-pressure-fluid control coefficient; This indicates the measured temperature of the wellbore rock. This represents the measured hydrostatic pressure. This represents the dynamic viscosity of the fluid under corresponding temperature and pressure conditions. This step eliminates background interference from the ambient temperature and pressure field on fluid velocity measurement, restoring the true seepage capacity of the fluid in coal seam fractures.

[0131] After completing the above physical quantity calibration, the system executes a data cleaning procedure. Least squares trend fitting is performed on the parameter sequences after calibration across the entire borehole section to generate a regional geological parameter trend line reflecting the continuous variation of regional geology. For each discrete sampling point, the residual value relative to the corresponding point on the trend line is calculated.

[0132] The system has a built-in statistical outlier threshold, typically set to three times the standard deviation of the residuals. The following discrimination logic is executed: if the absolute value of the residual at a sampling point exceeds this threshold, the data at that point is determined to deviate from geological patterns, caused by sudden instrumental changes or transient environmental noise interference, and is marked as invalid data and physically deleted from the data sequence. If the residual value does not exceed the threshold, the data at that point is considered valid and retained. After cleaning, the sequences of physical field parameters are aligned by depth and reassembled to construct a multi-field coupling correlation matrix.

[0133] Data flow, input: S1 output contains a multi-field basic data sample library. The control coefficient of S2 output , and Processing: Substitute the values ​​into the coupling correction formula for point-by-point numerical calculation, then perform residual analysis and logical filtering. Output: Includes... , And the multi-field coupling correlation matrix of other field parameters after cleaning. This matrix serves as the direct data basis for S4 to perform block hierarchical identification.

[0134] S4. Based on the multi-field coupling correlation matrix and the preset exploration evaluation threshold, the exploration target area is divided into resource blocks of different levels, and the structural coal development area and gas enrichment area are identified simultaneously.

[0135] Furthermore, in S4, the exploration target area is divided into resource blocks of different levels, and tectonic coal development zones and gas-rich zones are identified simultaneously, including:

[0136] Retrieve preset thresholds for tectonic coal development rate, porosity evaluation threshold, and permeability evaluation threshold;

[0137] By traversing the multi-field coupling correlation matrix, areas that simultaneously meet the following conditions are selected: the tectonic coal development rate is lower than the tectonic coal development rate threshold, the calibrated porosity parameter is higher than the porosity evaluation threshold, and the permeability is higher than the permeability evaluation threshold. These areas are marked as favorable exploration blocks that are rich in coal and suitable for mining.

[0138] Regions with tectonic stress intensity indices in stress concentration zones and tectonic coal development rates exceeding the tectonic coal development rate threshold are selected and marked as gas-rich areas with potential gas outburst risks.

[0139] Specifically, based on real stratigraphic parameters corrected through multi-field coupling, a dual assessment of resource evaluation and risk warning is performed, transforming the pure digital matrix into a geological zoning map usable in engineering.

[0140] The data processing system first retrieves three types of benchmark thresholds from the expert knowledge base: tectonic coal development rate threshold. In this embodiment, the value is set to 30%; porosity evaluation threshold. In this embodiment, the value is taken as 8%; the penetration rate evaluation threshold. The value used in this embodiment .

[0141] Since the multi-field coupling correlation matrix directly contains the calibrated fluid transport rate. The system needs to convert it into permeability according to Darcy's law. The conversion formula is as follows:

[0142] ;

[0143] in Indicates coal seam permeability; This indicates the calibrated fluid transport rate output by S3; Indicates the dynamic viscosity of a fluid; This represents the formation pressure gradient. This calculation transforms dynamic velocity parameters into inherent physical properties characterizing the transport capacity of the coal seam medium, facilitating integration with engineering standards. Perform a direct comparison.

[0144] The system performs multi-condition logical judgments on each spatial sampling point in the matrix. The filtering mechanism is set to a logical AND operation: Condition 1: The tectonic coal development rate corresponding to the sampling point is less than the tectonic coal development rate threshold. Condition 1: The coal structure is not severely damaged and retains its original structure; Condition 2: Porosity parameters after calibration Greater than the porosity evaluation threshold Condition 3: Calculated permeability indicates sufficient gas or coal storage space; Greater than the penetration rate evaluation threshold This indicates that the resource has a good capacity for fluid output.

[0145] When consecutive sampling points within a certain area simultaneously meet the above three conditions, the system marks the spatial coordinate set of that area as a favorable exploration block rich in coal and suitable for mining. This logic excludes low-permeability, fragmented, and inferior resource areas, accurately delineating resource sweet spots with commercial development value.

[0146] The system calls S2 to generate the structural stress intensity index. The partitioning results are used as a pre-filtering condition. First, the structural stress intensity index is selected. The set of sampling points greater than or equal to 2.5 is the locked stress concentration area.

[0147] Under this high-stress background, the tectonic coal development rate was further examined. If the tectonic coal development rate in this area exceeds the tectonic coal development rate threshold... This indicates that the coal body has undergone rheological damage under strong in-situ stress compression, forming mylonite or granular coal. This type of tectonic coal has an extremely high gas adsorption capacity and extremely low permeability, making it highly susceptible to forming high-pressure gas pockets under high-stress sealing conditions. The system marks areas meeting both the conditions of high stress and high tectonic coal development rate as gas-rich areas with potential gas outburst risks, highlighting them as warnings in subsequently generated distribution maps.

[0148] Data flow, inputs: multi-field coupling correlation matrix output by S3, stress zoning results output by S2, and preset evaluation thresholds. Processing: Permeability numerical conversion, multi-parameter logical threshold comparison, and region intersection operation. Output: Spatial distribution map of the exploration target area including classification labels and a list of corresponding coordinate ranges. This output result is directly used to guide subsequent verification borehole layout.

[0149] S5. Set up verification boreholes to obtain measured data, compare them with the resource block division and identification results. When the consistency is lower than the preset standard, use the comparison deviation to reverse calibrate the control coefficient, and re-execute the multi-field parameter coupling correction and resource block division steps until the preset standard is met and the exploration report is output.

[0150] Furthermore, in S5, the deployment of verification boreholes to obtain measured data includes:

[0151] Based on the resource block distribution map, verification well locations were determined in favorable exploration blocks and gas-rich areas.

[0152] Configure the number of verification boreholes according to the preset density ratio, and perform full-section coring operations on the verification boreholes.

[0153] The extracted rock cores were subjected to coal and petrographic micro-component analysis and gas content desorption tests to obtain the true values ​​of coal seam thickness, coal body failure type and gas content as measured data.

[0154] In S5, the reverse calibration of the control coefficient using the comparison bias includes:

[0155] Calculate the consistency matching rate between the measured data of the borehole and the output recognition results;

[0156] If the matching rate does not reach the preset validity standard, then calculate the deviation vector between the measured true value and the recognition result;

[0157] The least squares method is used to reverse the stress-porosity control coefficient and the temperature-pressure-fluid control coefficient based on the deviation vector.

[0158] The corrected control coefficients are updated into the multi-field parameter coupling correction model, and the system is triggered to re-execute the multi-field parameter coupling matching, calibration and hierarchical identification steps.

[0159] Specifically, as a quality control and adaptive optimization link in the entire exploration process, it aims to solve the problem of deviation in the applicability of initial empirical parameters caused by the strong heterogeneity of deep strata. It uses physical measurement data to correct the mathematical model in reverse, thereby achieving iterative convergence of exploration accuracy.

[0160] The data processing system first loads the resource block distribution map output by S4 as a spatial base map. The system then retrieves the spatial coordinate ranges of favorable exploration blocks and gas-rich areas, and automatically generates a list of verification well location coordinates based on a preset verification density ratio.

[0161] Based on this, the on-site work unit performed drilling and full-section coring operations to obtain solid rock core samples. Two key tests were performed on the samples: first, coal and petrographic microscopic component analysis, which involved observing the degree of damage to the coal's microstructure under a microscope to determine the type of coal damage, serving as a true indicator of whether tectonic coal was developed; second, gas content desorption testing, which involved determining the gas content per unit mass of coal through isothermal desorption experiments, serving as a true indicator of the degree of fluid enrichment. The coal seam thickness, coal damage type, and gas content data obtained from these tests were digitized and entered into a measured true value dataset.

[0162] The system retrieves the actual measured dataset and compares it point-by-point with the recognition results output by S4. A consistency matching rate is defined. The formula used to quantify the accuracy of exploration models is as follows:

[0163] ;

[0164] in This indicates the number of sampling points where the predicted result matches the actual measured value. This indicates the total number of verification sampling points.

[0165] If the calculated result If the value is greater than or equal to the preset validity standard, the system determines that the current model parameters are valid and directly generates and outputs the final exploration report. If... If the value is lower than the standard, the system determines that there is a systematic bias in the current model and automatically triggers the parameter inversion procedure.

[0166] When the inversion procedure is triggered, the system first calculates the residual between the measured true value and the recognition result, and constructs a deviation vector. Subsequently, the least squares optimization algorithm is invoked, using the deviation vector... Minimizing the square of the modulus is the objective function for the stress-porosity control coefficient. and temperature-pressure-fluid control coefficient Make optimization adjustments.

[0167] Corrected control coefficient and The data is written back into the multi-field parameter coupling correction model, replacing the original initial coefficients. The system then restarts the calculation process, substituting the basic data from S1 into the updated model, and sequentially re-executes the multi-field parameter coupling correction in S3 and the resource block classification identification in S4. This verification-feedback-correction-recalculation process is repeated until the calculated matching rate is obtained. Meets the preset standards.

[0168] Data flow: Input: Resource block distribution map and identification results output by S4, and the measured true value dataset obtained from verification boreholes. Processing: Consistency comparison operation, least squares parameter inversion, and model coefficient update. Output: Corrected control coefficients. and A final, accurate exploration report for deep coal resources that meets the required precision.

[0169] Example 2:

[0170] In the exploration of deep, complex coalfields with burial depths exceeding 1000 meters, the geological environment is characterized by multi-field nonlinear coupling of high ground temperature, high ground pressure, and strong tectonic stress. Existing exploration technologies face severe challenges in such scenarios: Firstly, the stress shielding effect generated by deep tectonic stress concentration zones forcibly closes coal and rock pores, leading to distortion of physical property parameters obtained by conventional logging methods. This can easily result in high-pressure gas storage tanks being misjudged as dense and stable zones. Furthermore, high confining pressure environments often mask the physical response characteristics of tectonic coal, creating blind spots in the identification of tectonic anomaly zones. Due to the lack of dominant quantitative analysis of the tectonic stress field, it is difficult to avoid the risks of geological disasters such as gas outbursts. Secondly, traditional static geological models based on shallow empirical parameters are ill-suited to the strong heterogeneity of deep strata and lack a closed-loop feedback calibration mechanism based on measured data. This leads to a significant decrease in prediction accuracy with increasing depth. In addition, the traditional single-mineral-type single-exploration model cannot achieve coordinated evaluation of coal and associated oil and gas resources, resulting in technical bottlenecks such as long exploration cycles, high engineering costs, and low reliability of evaluation results in deep resources. To address the aforementioned problems, this invention provides an intelligent exploration method for deep coal resources based on tectonic stress fields, the structure of which is as follows: Figure 1 As shown. The specific implementation process of this method is as follows:

[0171] A deep integrated detection device was used to simultaneously acquire five types of parameters—tectonic stress, geothermal temperature, geopressure, fluid migration rate, and coal porosity—within the same borehole, and a sample library was established based on these parameters. This step, through single-shot, in-situ detection, solved the problems of data spatiotemporal dispersion and depth registration errors caused by traditional multi-stage and multi-device detection, ensuring strict correspondence of multi-physics parameters in both spatial and temporal dimensions. This provides accurate and aligned raw data support for revealing the multi-field coupling mechanism in complex deep geological environments.

[0172] Based on multi-field fundamental data, the tectonic stress intensity index is calculated to delineate stress concentration zones, transition zones, and stable zones, and the regulatory coefficient of tectonic stress on porosity and fluid migration is quantified. This step transforms the abstract control effect of the tectonic stress field into quantifiable evaluation indicators and calculation factors, establishing the dominant role of tectonic stress in deep multi-field environments. It also allows for refined stratification of the exploration area according to stress state, providing necessary zoning basis and mathematical benchmarks for subsequent elimination of stress shielding effects and restoration of true stratigraphic parameters.

[0173] A multi-field parameter coupling correction model was run to correct the compressed and distorted coal porosity using tectonic stress parameters and to correct the fluid migration rate affected by the environment using geothermal and geopressure parameters. After removing outlier data, a correlation matrix was generated. This process, based on the nonlinear interaction logic between physical fields, eliminated the background interference of deep high-stress, high-temperature, and high-pressure environments on single sensor readings, and restored the environmentally affected apparent parameters to the true physical properties of the coal and rock mass in its in-situ state, ensuring the fidelity and reliability of the basic data required for subsequent evaluation.

[0174] By analyzing the multi-field coupling correlation matrix against preset evaluation thresholds, the exploration target area is divided into resource blocks of different levels, and tectonic coal development areas and gas-rich areas are simultaneously identified. This step transforms the corrected high-precision geological parameters into engineering-usable block classification results, enabling simultaneous evaluation of coal resource potential and gas dynamic disaster risks. It directly delineates suitable coal-rich zones for mining and warns of potential gas outburst hazard areas, avoiding the one-sided resource evaluation caused by single-mineral exploration.

[0175] By verifying the identification results using the measured true values ​​from the validation boreholes, the system uses the deviation to reverse-calibrate the control coefficient when the consistency fails to meet the standard, triggering iterative correction and recalculation of the system. This mechanism constructs a dynamic adaptive feedback loop, continuously correcting the empirical deviations of the initial geological model through measured data. This overcomes the limitations of the strong heterogeneity of deep strata on the applicability of static exploration models, ensuring that the final exploration report accurately matches the actual geological conditions of the target area.

[0176] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart exploration method for deep coal resources based on tectonic stress field, characterized in that, Includes the following steps: S1. Detection boreholes are set up in the exploration target area, and five types of parameters, namely tectonic stress, ground temperature, ground pressure, fluid migration rate and coal porosity, are collected simultaneously using a deep integrated detection device. The parameters are then aggregated to build a multi-field basic data sample library. S2. Calculate the tectonic stress intensity index based on the multi-field basic data sample library. Based on this index, divide the exploration target area into stress concentration zone, transition zone and stable zone, and determine the control coefficient of tectonic stress on porosity and fluid migration. S3. Substitute the sample database data into the multi-field parameter coupling correction model, use the tectonic stress parameter to correct the coal porosity parameter, use the geothermal and geopressure parameters to correct the fluid migration rate parameter, and construct the multi-field coupling correlation matrix after removing abnormal data. S4. Based on the multi-field coupling correlation matrix and the preset exploration evaluation threshold, the exploration target area is divided into resource blocks of different levels, and the structural coal development area and gas enrichment area are identified simultaneously. S5. Set up verification boreholes to obtain measured data, compare them with the resource block division and identification results. When the consistency is lower than the preset standard, use the comparison deviation to reverse calibrate the control coefficient, and re-execute the multi-field parameter coupling correction and resource block division steps until the preset standard is met and the exploration report is output.

2. The intelligent exploration method for deep coal resources based on tectonic stress field according to claim 1, characterized in that, In S1, the process of aggregating various parameters to construct a multi-field basic data sample library includes: The device is equipped with a deep integrated detection device, which integrates a three-dimensional hydraulic fracturing stress sensing module, a thermocouple temperature and pressure detection module, and an acoustic logging module. The device is controlled to move along the detection borehole, and the maximum horizontal principal stress data is obtained as structural stress parameters using the three-dimensional hydraulic fracturing stress sensing module. Thermocouple temperature and pressure detection modules are used to obtain wellbore rock temperature and hydrostatic pressure data as geothermal and geopressure parameters. The coal porosity parameters were obtained by using an acoustic logging module to acquire acoustic time difference data and then inverting the calculation. The above parameters are transmitted to the surface via downhole storage or mud pulse, and the parameters are spatiotemporally aligned according to the depth index tags to generate a multi-field basic data sample library.

3. The intelligent exploration method for deep coal resources based on tectonic stress field according to claim 1, characterized in that, In S2, the calculation of the structural stress intensity index based on a multi-field basic data sample library, and the division of the exploration target area into stress concentration zone, transition zone, and stable zone based on this index, includes: Read the measured maximum structural stress value from the sample library and call the preset critical value of uniaxial compressive strength of deep coal and rock; Calculate the ratio of the maximum tectonic stress value to the critical value of the uniaxial compressive strength of deep coal and rock, and define this ratio as the tectonic stress intensity index characterizing the degree of formation compression; Retrieve preset stress concentration and stress stability thresholds; The stress intensity index is compared with the stress concentration determination threshold. If it is greater than or equal to the threshold, it is determined to be a stress concentration area. The stress intensity index is compared with the stress stability determination threshold. If it is less than the threshold, it is determined to be a stress stable region. If the stress intensity index is between two thresholds, it is determined to be a stress transition zone.

4. The intelligent exploration method for deep coal resources based on tectonic stress field according to claim 1, characterized in that, In S2, the coefficients for determining the influence of structural stress on porosity and fluid transport include: Establish a geomechanical evolution model of the exploration target area and import historical rock mechanics experimental data; Through multivariate regression analysis, the linear correlation weight between the tectonic stress increment and the porosity attenuation was extracted, and this weight was set as the stress-porosity control coefficient. Through multiphysics fluid dynamics simulation, the driving factor of the temperature and pressure product term on the fluid velocity is extracted, and this driving factor is set as the temperature-pressure-fluid regulation coefficient.

5. The intelligent exploration method for deep coal resources based on tectonic stress field according to claim 4, characterized in that, In S3, the correction of coal porosity parameters using tectonic stress parameters includes: The preset structural stress-porosity coupling correction model is invoked, which assumes that porosity changes linearly with the increase of structural stress. The product of the measured structural stress parameters and the stress-porosity control coefficient is calculated to obtain the stress-porosity compression correction amount. Obtain the original porosity benchmark value of the coal body under stress-free conditions, and superimpose the compression correction amount on the benchmark value to obtain the calibrated porosity parameters that can reflect the deep in-situ environment.

6. The intelligent exploration method for deep coal resources based on tectonic stress field according to claim 4, characterized in that, In S3, the correction of the fluid transport rate parameters using geothermal and geopressure parameters includes: The preset temperature-pressure-fluid coupling correction model is invoked. This model sets the fluid transport rate to be positively correlated with the thermodynamic state parameters and negatively correlated with the fluid viscosity characteristics. Calculate the product of the measured geothermal parameters and the geopressure parameters, and multiply the product by the temperature-pressure-fluid regulation coefficient to obtain the driving force correction term; The dynamic viscosity value of the fluid under the current temperature and pressure conditions is queried, and the dynamic viscosity value is normalized using the driving force correction term to obtain the calibrated fluid transport rate parameter.

7. The intelligent exploration method for deep coal resources based on tectonic stress field according to claim 1, characterized in that, In S3, constructing the multi-field coupling correlation matrix after removing outlier data includes: Trend fitting is performed on the calibrated parameters in the multi-field coupling correlation matrix to generate regional geological parameter trend lines; Calculate the residual value of each discrete sampling point relative to the trend line of the geological parameters of the region; Determine whether the absolute value of the residual exceeds a preset statistical outlier threshold; When the residual value exceeds the threshold, the sampling point is determined to be affected by environmental noise, marked as invalid data, and removed from the matrix. If the residual value does not exceed the threshold, the data of the sampling point is deemed valid and retained.

8. The intelligent exploration method for deep coal resources based on tectonic stress field according to claim 1, characterized in that, In S4, the process of dividing the exploration target area into resource blocks of different levels and simultaneously identifying tectonic coal development zones and gas-rich zones includes: Retrieve preset thresholds for tectonic coal development rate, porosity evaluation threshold, and permeability evaluation threshold; By traversing the multi-field coupling correlation matrix, regions that simultaneously satisfy the conditions of tectonic coal development rate being lower than the tectonic coal development rate threshold, calibrated porosity parameter being higher than the porosity evaluation threshold, and permeability being higher than the permeability evaluation threshold are selected and marked as favorable exploration blocks rich in coal and suitable for mining. Regions with tectonic stress intensity index in stress concentration zone and tectonic coal development rate higher than the tectonic coal development rate threshold are selected and marked as gas-rich areas with potential gas outburst risks.

9. The intelligent exploration method for deep coal resources based on tectonic stress field according to claim 1, characterized in that, In S5, the deployment of verification boreholes to obtain measured data includes: Based on the resource block distribution map, verification well locations were determined in favorable exploration blocks and gas-rich areas. Configure the number of verification boreholes according to the preset density ratio, and perform full-section coring operations on the verification boreholes. The extracted rock cores were subjected to coal and petrographic micro-component analysis and gas content desorption tests to obtain the true values ​​of coal seam thickness, coal body failure type and gas content as measured data.

10. The intelligent exploration method for deep coal resources based on tectonic stress field according to claim 4, characterized in that, In S5, the reverse calibration of the control coefficient using the comparison bias includes: Calculate the consistency matching rate between the measured data of the borehole and the output recognition results; If the matching rate does not reach the preset validity standard, then calculate the deviation vector between the measured true value and the recognition result; The stress-porosity control coefficient and the temperature-pressure-fluid control coefficient are reversed based on the deviation vector using the least squares method. The corrected control coefficients are updated into the multi-field parameter coupling correction model, and the system is triggered to re-execute the multi-field parameter coupling matching, calibration and hierarchical identification steps.

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