Coal seam roof and floor weak oil-bearing layer comprehensive treatment effect evaluation method and system

Abstract

The present application provides a coal seam roof and floor weak oil-bearing layer comprehensive treatment effect evaluation method and system, belonging to the technical field of coal mine geology. The method comprises: carrying out evaluation grid division along the advancing direction of the mining working face, obtaining multi-source monitoring data from before treatment to after treatment; carrying out comprehensive evaluation of the treatment effect of each evaluation grid from multiple preset evaluation dimensions; screening out evaluation grids whose comprehensive evaluation results do not satisfy the treatment qualified conditions, determining the score contribution rate, generating and executing a remediation scheme, updating the comprehensive evaluation results of the evaluation grids, repeatedly executing the comprehensive evaluation result updating of the evaluation grids that do not satisfy the treatment qualified conditions until each evaluation grid satisfies the treatment qualified conditions; and generating a treatment effect evaluation report based on the process data of each evaluation grid from before treatment to satisfying the treatment qualified conditions. Thus, the target area is subjected to multi-dimensional, dynamic, accurate, closed-loop and visualized treatment effect evaluation, the treatment reliability is improved, and the mining risk is reduced.

Description

Technical Field

[0001] This invention relates to the field of coal mine geological technology, specifically to a method for evaluating the comprehensive treatment effect of weak oil-bearing strata in the roof and floor of a coal seam, a system for evaluating the comprehensive treatment effect of weak oil-bearing strata in the roof and floor of a coal seam, a machine-readable storage medium, and an electronic device. Background Technology

[0002] Currently, weak oil-bearing layers are developed in the roof and floor of mines, and a combination of directional drainage, low-pressure fracturing, and oil-resistant grouting is commonly used for treatment. Existing methods for evaluating the effectiveness of treatment of weak oil-bearing layers have the following shortcomings: 1. Single indicator: Only water inflow is measured, ignoring oil seepage, oil and gas explosions, and mining-induced damage; 2. Static acceptance: Only one test is conducted after construction, without tracking dynamic changes caused by mining. 3. No oil resistance evaluation: The ability of the slurry to resist oil and seal oil stains is not tested, which makes it easy for it to re-seep in later. 4. No closed-loop system: If the assessment is unqualified, the system cannot automatically locate the problem or perform automatic remedial treatment. 5. Lack of resource evaluation: The benefits of oil and water recovery are not evaluated, resulting in poor economic viability.

[0003] Therefore, how to achieve multi-dimensional, dynamic, visualized, and closed-loop evaluation of the treatment effect of weak oil-bearing reservoirs in order to improve the reliability of treatment and reduce the risk of recovery is an urgent problem to be solved. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for evaluating the comprehensive treatment effect of weak oil-bearing strata in the roof and floor of coal seams, so as to at least solve the problems mentioned above in the existing treatment of weak oil-bearing strata, which relies on single-point indicators, static evaluation, low accuracy, and no closed-loop feedback.

[0005] To achieve the above objectives, the first aspect of the present invention provides a method for evaluating the comprehensive treatment effect of weak oil-bearing strata in the roof and floor of coal seams, comprising: The target area is dynamically divided into assessment grids along the direction of the mining face advance, and multi-source monitoring data of each assessment grid from before to after treatment are obtained. Based on multi-source monitoring data of the same assessment grid, the governance effect of the current assessment grid is comprehensively evaluated from multiple preset assessment dimensions to obtain the comprehensive assessment result of the corresponding assessment grid; The evaluation grids that do not meet the governance qualification conditions are screened out. The contribution rate of the same evaluation grid in each preset evaluation dimension is determined. Based on the determined contribution rate, the corresponding remedial treatment plan is generated and executed. The comprehensive evaluation result of the corresponding evaluation grid is updated based on the multi-source monitoring data of the remedial evaluation grid. The comprehensive evaluation result update of the evaluation grids that do not meet the governance qualification conditions is repeated until all evaluation grids meet the governance qualification conditions. Based on the process data of each assessment grid from before governance to when the governance qualification conditions are met, a governance effectiveness assessment report is generated to generate a structured governance archive that characterizes the target area.

[0006] Optionally, each preset evaluation dimension includes at least one evaluation indicator; Based on multi-source monitoring data from the same assessment grid, a comprehensive evaluation of the governance effectiveness of the current assessment grid is conducted from multiple preset assessment dimensions, including: Numerical simulations of rock mechanical parameters before and after treatment were performed on each evaluation grid based on multi-source monitoring data of each evaluation grid, and the numerical simulation results of each evaluation grid were obtained. After coupling the numerical simulation results and multi-source monitoring data of the same evaluation grid, the coupling data of the corresponding evaluation grid for each evaluation index is extracted from the coupling results. Based on the preset weights of each evaluation indicator, the coupled data of each evaluation indicator in the same evaluation grid under the same preset evaluation dimension are weighted and summed to obtain the evaluation input data of the corresponding evaluation grid in the corresponding preset evaluation dimension. Based on the preset dimension weights of each preset evaluation dimension, the evaluation input data of each preset evaluation dimension in the same evaluation grid are weighted and summed to obtain the comprehensive evaluation result of the corresponding evaluation grid.

[0007] Optionally, the rules for determining the preset weights of each evaluation indicator and the preset weights of each preset evaluation dimension include: Based on the correlation between each assessment indicator and the degree of governance compliance, each assessment indicator is determined to be a positive or negative indicator, and the range is normalized according to the results of the positive or negative indicator determination. Among them, assessment indicators whose corresponding values ​​are positively correlated with the degree of governance compliance are determined to be positive indicators, and assessment indicators whose corresponding values ​​are negatively correlated with the degree of governance compliance are determined to be negative indicators. Each evaluation indicator after range normalization is assigned a preset indicator weight that couples a subjective weight vector representing the proportion of indicator values ​​and an objective weight vector representing the dispersion of indicator values. The preset indicator weights of each evaluation indicator in the same preset evaluation dimension are then added together to obtain the preset dimension weights of the corresponding preset evaluation dimension.

[0008] Optionally, the preset rules for allocating indicator weights include: The percentage ratio of each evaluation indicator value is determined based on the analytic hierarchy process, and the subjective weight vector of each evaluation indicator is allocated based on the determined percentage ratio. The degree of dispersion and information entropy of the numerical values ​​of each evaluation indicator are determined based on the entropy weight method, and the objective weight vector of each evaluation indicator is assigned based on the determined degree of dispersion. The subjective weight vector and objective weight vector of the same evaluation indicator are combined to obtain the combined result. The weight preference coefficient, which is determined by the difference between the information entropy of each evaluation indicator value, is used to adjust the combined result to obtain the preset indicator weight of the corresponding evaluation indicator.

[0009] Optionally, determine the contribution rate of the selected evaluation grid to the score loss in each preset evaluation dimension, including: Obtain the evaluation scores of the same selected evaluation grid in each preset evaluation dimension; wherein, the evaluation scores of the preset evaluation dimensions are obtained by weighting the corresponding evaluation input data based on the preset dimension weights of the corresponding preset evaluation dimensions; The difference between the preset full score and the evaluation score of the same preset evaluation dimension of the same evaluation grid is taken as the absolute score loss of the corresponding evaluation grid in the corresponding preset evaluation dimension. The total score loss of the corresponding evaluation grid is obtained by combining the absolute score loss of the same evaluation grid in each preset evaluation dimension. The contribution rate of the current evaluation grid to the score loss in each preset evaluation dimension is determined based on the proportion of the absolute score loss of the same evaluation grid in each preset evaluation dimension to the total score loss of the current evaluation grid.

[0010] Optionally, process data includes multi-source monitoring data, remedial treatment plan records, and corresponding comprehensive assessment results for each assessment grid during each round of remedial treatment from before treatment to when the treatment qualification conditions are met; The rules for generating governance effectiveness evaluation reports include: For each assessment grid, a governance evolution data chain is constructed in chronological order from before governance to when the governance qualification conditions are met; the governance evolution data chain is used to record the dynamic evolution process of the same assessment grid from the comprehensive assessment results after the initial governance to the comprehensive assessment results after each round of supplementary governance; Based on the current comprehensive evaluation results of each evaluation grid in the same time period, spatial interpolation is performed on the unknown points in each evaluation grid to generate a heat map of the governance effect of the target area in the current time period based on the spatial interpolation results of each evaluation grid. By spatially linking the heatmaps of all governance effects in the target area from before to after governance with the governance evolution data chains of each assessment grid, a governance effect assessment report is formed to characterize the structured governance archives of the target area.

[0011] Optional rules for acquiring multi-source monitoring data include: According to the time sequence from before to after treatment, multi-source monitoring data of the same assessment grid were acquired; among them, the multi-source monitoring data included the original geological parameters in the assessment grid before treatment, the borehole parameters, grouting parameters, drainage parameters and physical field online monitoring data in the assessment grid during treatment, and the sampling test data in the assessment grid after treatment. By associating the spatial index coordinates of the same evaluation grid with multi-source monitoring data, a multi-dimensional data matrix is ​​constructed with the evaluation grid as the smallest unit and time sequence as the axis.

[0012] A second aspect of the present invention provides a comprehensive treatment effect evaluation system for weak oil-bearing strata in the roof and floor of coal seams, comprising: The evaluation grid division module is used to dynamically divide the target area into evaluation grids along the advance direction of the mining face, and to acquire multi-source monitoring data of each evaluation grid from before to after treatment. The comprehensive evaluation module for governance effectiveness is used to comprehensively evaluate the governance effectiveness of the current evaluation grid from multiple preset evaluation dimensions based on multi-source monitoring data of the same evaluation grid, and obtain the comprehensive evaluation result of the corresponding evaluation grid. The comprehensive assessment result update module is used to filter out assessment grids whose comprehensive assessment results do not meet the governance qualification conditions, determine the contribution rate of the same assessment grid in each preset assessment dimension, generate and execute the corresponding remedial treatment plan based on the determined contribution rate, update the comprehensive assessment result of the corresponding assessment grid based on the multi-source monitoring data of the remedial assessment grid, and repeat the comprehensive assessment result update of assessment grids that do not meet the governance qualification conditions until all assessment grids meet the governance qualification conditions. The governance effectiveness evaluation report generation module is used to generate a structured governance archive to characterize the target area based on the process data of each evaluation grid from before governance to when the governance qualification conditions are met.

[0013] In a third aspect, the present invention provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the aforementioned method for evaluating the comprehensive treatment effect of weak oil-bearing strata in the roof and floor of a coal seam.

[0014] In a fourth aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the aforementioned method for evaluating the comprehensive treatment effect of weak oil-bearing strata in the roof and floor of a coal seam.

[0015] The above technical solution provides a method and system for evaluating the comprehensive treatment effect of weak oil-bearing strata in the roof and floor of coal seams. The method assesses the treatment effect of the target area along the advance direction of the mining face, and automatically executes the evaluation process once every preset distance the mining face advances, achieving dynamic, ongoing evaluation. After each preset distance advance, the target area is dynamically divided into evaluation grids along the advance direction of the mining face, ensuring that the evaluation range always closely matches the already mined area, avoiding evaluation deviations caused by misalignment between the evaluation range and the actual working area. After acquiring multi-source monitoring data from before to after treatment for each divided evaluation grid, a comprehensive evaluation of the treatment effect is performed on each grid from multiple preset evaluation dimensions, yielding the comprehensive evaluation results for each grid. This comprehensive evaluation across multiple preset evaluation dimensions not only ensures the comprehensiveness of the treatment effect assessment but also reflects the actual effect of the comprehensive treatment, effectively solving the evaluation deviation problem caused by the single evaluation indicator in existing technologies. The system identifies assessment grids whose comprehensive evaluation results do not meet the governance qualification criteria. It determines the contribution rate of each selected assessment grid's score deduction in each preset assessment dimension. Based on this contribution rate, a corresponding remedial treatment plan is generated and implemented. The comprehensive evaluation results of the corresponding assessment grids are updated based on the multi-source monitoring data of the remediated grids. This process of updating the comprehensive evaluation results of grids that do not meet the governance qualification criteria is repeated until all assessment grids meet the criteria. This achieves dynamic closed-loop management of assessment grid evaluation, remediation, and reassessment. Finally, based on the process data of each assessment grid from before governance to meeting the governance qualification criteria, a governance effectiveness evaluation report is generated to characterize the target area's structured governance archive. This achieves the goal of multi-dimensional, dynamic, accurate, closed-loop, and visualized governance effectiveness evaluation of the target area, improving governance reliability and reducing the risk of data re-collection.

[0016] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a method for evaluating the comprehensive treatment effect of weak oil-bearing strata in the roof and floor of a coal seam, provided by one embodiment of the present invention; Figure 2 This is a cross-sectional structural diagram of a weak oil-bearing layer treatment effect assessment in the top and bottom plates of a coal seam provided by one embodiment of the present invention; Figure 3 This is a flowchart of a five-dimensional closed-loop evaluation provided by one embodiment of the present invention; Figure 4 This is a block diagram of a comprehensive treatment effect evaluation system for weak oil-bearing strata in the roof and floor of a coal seam, provided by one embodiment of the present invention. Detailed Implementation

[0018] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0019] Figure 1 This is a flowchart illustrating a method for evaluating the comprehensive treatment effect of weak oil-bearing strata in the roof and floor of a coal seam, provided by one embodiment of the present invention. Figure 1 As shown, this invention provides a method for evaluating the comprehensive treatment effect of weak oil-bearing strata in the roof and floor of coal seams, comprising: S110: Dynamically divide the target area into assessment grids along the direction of the mining face advance, and acquire multi-source monitoring data of each assessment grid from before to after treatment; The target area can be a weak oil-bearing layer in the roof and floor of a coal seam. The collected multi-source monitoring data includes aquifer inflow data, oil seepage in the target area, oil and gas concentration, and stress and deformation data of the roof and floor rock mass. This ensures that the collected multi-source monitoring data covers the four types of risks that need to be prevented and controlled after the treatment of weak oil-bearing layers: water hazards, oil seepage pollution, oil and gas explosions, and roof and floor instability. This makes up for the shortcomings of traditional single inflow assessment, which cannot cover all risk types.

[0020] Specifically, this method evaluates the treatment effect of the target area along the advance direction of the mining face, and automatically executes the evaluation process every 200 m of advance of the mining face to achieve dynamic, ongoing evaluation. Within each evaluation cycle, the evaluation grid for the target area can be updated according to a preset grid step size along the advance direction of the mining face, ensuring that the evaluation range always closely matches the already mined area and avoiding evaluation deviations caused by misalignment between the evaluation range and the actual working area.

[0021] The size of the evaluation grid can be dynamically adjusted according to the degree of rock fracture development and grout diffusion radius. Specifically, in areas with intact rock mass and large grout diffusion radius, the grid size is set to 30m×30m; in fault fracture zones or fracture development areas, the grid size is densified to 10m×10m to 15m×15m.

[0022] In some embodiments of this example, the acquisition rules for multi-source monitoring data include: acquiring multi-source monitoring data of the same assessment grid in chronological order from before to after treatment; wherein, the multi-source monitoring data includes the original geological parameters in the assessment grid before treatment, the borehole parameters, grouting parameters, drainage parameters and physical field online monitoring data in the assessment grid during treatment, and the sampling test data in the assessment grid after treatment; associating the spatial index coordinates of the same assessment grid with the multi-source monitoring data to construct a multi-dimensional data matrix with the assessment grid as the smallest unit and the time sequence as the sequence axis.

[0023] Specifically, for each assessment grid, geological parameters, borehole parameters, grouting parameters, drainage parameters, online monitoring data, and sampling test data (obtained through borehole water pressure tests, core sampling, and laboratory oil resistance tests) are collected before, during, and after treatment. Then, the spatial index coordinates of the same assessment grid and multi-source monitoring data are correlated to construct a multi-dimensional data matrix with the assessment grid as the smallest unit and time sequence as the axis. This facilitates targeted assessment and remediation for each assessment grid and ensures that all monitoring data can be traced back to their corresponding spatial location and time point.

[0024] S120: Based on multi-source monitoring data of the same assessment grid, the governance effect of the current assessment grid is comprehensively evaluated from multiple preset assessment dimensions to obtain the comprehensive assessment result of the corresponding assessment grid; Specifically, after acquiring multi-source monitoring data, the governance effect of each assessment grid is comprehensively evaluated from multiple preset assessment dimensions. The comprehensive evaluation from multiple preset assessment dimensions not only ensures the comprehensiveness of the governance effect evaluation, but also reflects the actual effect of comprehensive governance by combining multiple preset assessment dimensions, effectively solving the problem of assessment bias caused by the single assessment indicator in the existing technology.

[0025] Furthermore, this method constructs a five-dimensional integrated evaluation index system encompassing all preset evaluation dimensions, so as to intuitively reflect the evaluation indicators of each preset evaluation dimension through this five-dimensional integrated evaluation index system. The preset evaluation dimensions include the seepage prevention and water blocking dimension (which includes four secondary indicators: residual water pressure, permeability, inflow rate, and pressurized water permeability), the oil suppression and plugging dimension (which includes four secondary indicators: residual oil content, oil seepage, slurry adhesion rate, and fracture sealing degree), the anti-mining stability dimension (which includes four secondary indicators: consolidation strength, mining deformation, fracture propagation length, and aquitard integrity), the safety and control dimension (which includes four secondary indicators: oil and gas concentration, gas concentration, spontaneous combustion risk, and corrosion rate), and the resource utilization dimension (which includes four secondary indicators: oil and water recovery rate, water resource utilization rate, oil and gas gathering and transportation efficiency, and material utilization rate), thus setting 5 primary indicators and 20 secondary indicators.

[0026] For example, the 20 secondary indicators can be specifically defined as follows: the seepage prevention and water blocking dimension includes: the reduction rate of permeability coefficient after grouting, the permeability rate of borehole pressure water test, the grouting filling rate, and the attenuation rate of residual inflow; the oil suppression and plugging dimension includes: the reduction rate of oil saturation in the core, the mass loss rate of indoor oil scour test, the oil droplet precipitation inhibition rate, and the contact angle change rate of the plugging agent; the anti-mining stability dimension includes: the volume ratio of the plastic zone obtained from FLAC3D numerical simulation, the approach rate of the top and bottom plates, the energy release rate of microseismic events, and the stress concentration factor of mining; the safety control dimension includes: the oil content concentration of inflow water, the hydrogen sulfide concentration during drainage, the deformation acceleration rate of the surrounding rock, and the change rate of the advanced support load; the resource utilization dimension includes: the oil-water separation efficiency of drainage water, the recycling rate of separated water, the stabilization rate of heavy metals in the precipitate, and the comprehensive energy saving rate of the system.

[0027] In some implementations of this embodiment, each preset evaluation dimension includes at least one evaluation index; based on multi-source monitoring data of the same evaluation grid, a comprehensive evaluation of the treatment effect of the current evaluation grid is performed from multiple preset evaluation dimensions, including: performing numerical simulation of the rock mechanical parameters of each evaluation grid before / after treatment based on the multi-source monitoring data of each evaluation grid, and obtaining the numerical simulation results of each evaluation grid; after coupling the numerical simulation results of the same evaluation grid with the multi-source monitoring data, extracting the coupling data of the corresponding evaluation grid for each evaluation index from the coupling results; Specifically, firstly, multi-source monitoring data from each assessment grid is used to perform numerical simulations of the rock mechanics parameters of each assessment grid before and after treatment. Then, the numerical simulation results and multi-source monitoring data of the same assessment grid are dynamically and interactively corrected (for example, using the data on the approach of the top and bottom plates and the change in water level obtained from online real-time monitoring, the boundary conditions and rock mechanics parameters of the FLAC3D numerical model are continuously corrected, and the plastic zone volume ratio and mining stress concentration factor obtained from the forward modeling of the corrected model are used as the coupling data of the corresponding assessment indicators for scoring). Thus, the calculation input values ​​of the corresponding assessment grid for each assessment indicator (i.e., the coupling data corresponding to each assessment indicator) are obtained by coupling multi-source monitoring data obtained from drilling water pressure tests, core testing, online real-time monitoring, indoor oil resistance tests and other means with numerical simulation results.

[0028] Based on the preset weights of each evaluation indicator, the coupled data of each evaluation indicator under the same preset evaluation dimension of the same evaluation grid are weighted and summed to obtain the evaluation input data of the corresponding evaluation grid in the corresponding preset evaluation dimension; based on the preset dimension weights of each preset evaluation dimension, the evaluation input data of each preset evaluation dimension of the same evaluation grid are weighted and summed to obtain the comprehensive evaluation result of the corresponding evaluation grid.

[0029] Specifically, for each assessment grid, the coupled data of each assessment indicator under the same preset assessment dimension are first weighted and summed according to the preset weight of each assessment indicator to obtain the assessment input data for each preset assessment dimension. Then, the assessment input data of each preset assessment dimension of the same assessment grid are weighted and summed according to the preset weight of each preset assessment dimension. Thus, the corresponding comprehensive assessment results can be obtained for different assessment grids, and then the comprehensive assessment results of the target area can be obtained. This solves the problem that the existing assessment method only focuses on the single indicator of water inflow and cannot simultaneously cover core safety dimensions such as oil seepage risk, oil and gas explosion risk, and mining damage degree. It can more comprehensively and accurately reflect the actual safety status of weak oil-bearing strata in the roof and floor of the coal seam after treatment.

[0030] In some embodiments of this example, the rules for determining the preset weights of each evaluation indicator and the preset weights of each preset evaluation dimension include: determining the positive or negative indicators of each evaluation indicator based on the correlation between each evaluation indicator and the degree of governance compliance, and performing range normalization processing on each evaluation indicator based on the results of the positive or negative indicator determination; wherein, evaluation indicators whose corresponding indicator values ​​are positively correlated with the degree of governance compliance are determined as positive indicators, and evaluation indicators whose corresponding indicator values ​​are negatively correlated with the degree of governance compliance are determined as negative indicators. Specifically, for each evaluation indicator in the preset evaluation indicator system, it is determined whether the increase of its indicator value is conducive to the governance goal of weak oil-bearing strata in the roof and floor of the coal seam (i.e., whether the increase of the indicator value will achieve a better governance level); if it is conducive to the governance goal, the evaluation indicator is determined to be a positive indicator; if it is contrary to the governance goal, it is determined to be a negative indicator.

[0031] Furthermore, for evaluation indicators judged as positive indicators, according to the formula... Perform range normalization. For evaluation indicators judged as negative, use the formula... Range normalization is performed. To evaluate the actual monitored values ​​or simulated predicted values ​​of the indicators, This represents the optimal ideal value for the evaluation index across all evaluation grids. This represents the worst-case ideal value for this evaluation indicator across all evaluation grids. Normalization is then performed separately for positive and negative indicators.

[0032] Each evaluation indicator after range normalization is assigned a preset indicator weight that couples a subjective weight vector representing the proportion of indicator values ​​and an objective weight vector representing the dispersion of indicator values. The preset indicator weights of each evaluation indicator in the same preset evaluation dimension are then added together to obtain the preset dimension weights of the corresponding preset evaluation dimension.

[0033] Specifically, by combining a subjective weight vector representing the proportion of indicator values ​​to characterize the evaluation indicators and an objective weight vector representing the dispersion of indicator values ​​to characterize the evaluation indicators, the method of assigning weights to each evaluation indicator can effectively eliminate human error.

[0034] In some embodiments of this example, the rules for allocating the preset indicator weights include: determining the proportion of each evaluation indicator's value based on the analytic hierarchy process (AHP), and allocating the subjective weight vector of each evaluation indicator based on the determined proportion; determining the dispersion and information entropy of each evaluation indicator's value based on the entropy weight method, and allocating the objective weight vector of each evaluation indicator based on the determined dispersion; combining the subjective weight vector and objective weight vector of the same evaluation indicator to obtain a combination result, and adjusting the combination result using a weight preference coefficient determined based on the difference between the information entropy of each evaluation indicator's value, to obtain the preset indicator weights of the corresponding evaluation indicators.

[0035] Specifically, the subjective weight vector of each evaluation indicator is first determined by the analytic hierarchy process (AHP). Then, the objective weight vector is obtained by using the entropy weight method based on the dispersion of the indicator values. Finally, the two weights are combined with the weight preference coefficient to obtain the preset indicator weights that take into account both expert experience and the objective differences in the data itself. This avoids the bias caused by a single weighting method and makes the final evaluation results more in line with the actual situation on site.

[0036] S130: Screen out the assessment grids whose comprehensive assessment results do not meet the governance qualification conditions, determine the contribution rate of the same assessment grid in each preset assessment dimension, generate and execute the corresponding remedial treatment plan based on the determined contribution rate, update the comprehensive assessment results of the corresponding assessment grid based on the multi-source monitoring data of the remedial assessment grid, and repeat the update of the comprehensive assessment results of the assessment grids that do not meet the governance qualification conditions until all assessment grids meet the governance qualification conditions. Specifically, a threshold for the overall assessment results is first preset. If the overall assessment result of an assessment grid is lower than this threshold, the grid is deemed not to meet the treatment criteria, thus identifying grids with overall assessment results below the threshold that require remediation. Next, the percentage of points lost in each preset assessment dimension within the overall assessment result (i.e., contribution rate) is calculated for each grid requiring remediation. These contribution rates are then ranked from highest to lowest to determine the most prominent weakness in the grid. A targeted remediation plan is then generated (the remediation plan includes at least one of directional borehole filling, re-grouting, and secondary drainage. For example, if the seepage prevention and water blocking dimension has the highest contribution rate, targeted grouting and densification drilling are performed; if the oil suppression and sealing dimension has the highest contribution rate, the sealing agent ratio is adjusted for secondary filling; if the anti-mining stability dimension has the highest contribution rate, reinforced anchor cable support or reinforced grouting are performed; if the safety and control dimension has the highest contribution rate, the drainage and ventilation plan is optimized). After completing the supplementary treatment using the generated treatment plan, multi-source monitoring data for the assessment grid are collected again, and the above assessment steps are repeated to update the comprehensive assessment results until the comprehensive assessment results of the assessment grid meet the treatment qualification conditions. This ultimately achieves dynamic closed-loop management of assessment, supplementary treatment, and reassessment, ensuring that all assessment grids in the target area meet the treatment requirements.

[0037] Furthermore, this method presets three-level grading thresholds and judges the effectiveness of the three-level grading assessment based on the comprehensive evaluation results. Specifically, according to the preset three-level grading thresholds, evaluation grids with a comprehensive evaluation score ≥80 are judged as qualified, evaluation grids with a comprehensive evaluation score between 60 and 80 are judged as basically qualified, and evaluation grids with a comprehensive evaluation score <60 are judged as unqualified. This identifies unqualified grid areas, allowing for targeted filling, re-injection, secondary drainage, and re-evaluation, achieving closed-loop treatment.

[0038] In some embodiments of this example, determining the contribution rate of the selected same evaluation grid in each preset evaluation dimension includes: obtaining the evaluation score of the selected same evaluation grid in each preset evaluation dimension; wherein, the evaluation score of the preset evaluation dimension is obtained by weighting the corresponding evaluation input data based on the preset dimension weight of the corresponding preset evaluation dimension; taking the difference between the preset full score value and the evaluation score of the same evaluation grid in the same preset evaluation dimension as the absolute loss value of the corresponding evaluation grid in the corresponding preset evaluation dimension, and combining the absolute loss values ​​of the same evaluation grid in each preset evaluation dimension to obtain the total loss value of the corresponding evaluation grid; and determining the contribution rate of the current evaluation grid in each preset evaluation dimension based on the proportion of the absolute loss value of the same evaluation grid in each preset evaluation dimension to the total loss value of the current evaluation grid.

[0039] S140: Based on the process data of each assessment grid from before governance to when the governance qualification conditions are met, generate a governance effectiveness assessment report to characterize the structured governance archive of the target area.

[0040] Specifically, this method evaluates the treatment effect of the target area along the advance direction of the mining face, and automatically executes the evaluation process once every preset distance the mining face advances, thus achieving dynamic, ongoing evaluation. After each preset distance advance, the target area is dynamically divided into evaluation grids along the advance direction of the mining face, ensuring that the evaluation range always closely matches the already mined area and avoiding evaluation deviations caused by misalignment between the evaluation range and the actual working area. After acquiring multi-source monitoring data from before to after treatment for each divided evaluation grid, a comprehensive evaluation of the treatment effect is performed on each grid from multiple preset evaluation dimensions, yielding a comprehensive evaluation result for each grid. This comprehensive evaluation across multiple preset evaluation dimensions not only ensures the comprehensiveness of the treatment effect assessment but also reflects the actual effect of the comprehensive treatment, effectively solving the evaluation deviation problem caused by a single evaluation indicator in existing technologies. The system identifies assessment grids whose comprehensive evaluation results do not meet the governance qualification criteria. It determines the contribution rate of each selected assessment grid's score deduction in each preset assessment dimension. Based on this contribution rate, a corresponding remedial treatment plan is generated and implemented. The comprehensive evaluation results of the corresponding assessment grids are updated based on the multi-source monitoring data of the remediated grids. This process of updating the comprehensive evaluation results of grids that do not meet the governance qualification criteria is repeated until all assessment grids meet the criteria. This achieves dynamic closed-loop management of assessment grid evaluation, remediation, and reassessment. Finally, based on the process data of each assessment grid from before governance to meeting the governance qualification criteria, a governance effectiveness evaluation report is generated to characterize the target area's structured governance archive. This achieves the goal of multi-dimensional, dynamic, accurate, closed-loop, and visualized governance effectiveness evaluation of the target area, improving governance reliability and reducing the risk of data re-collection.

[0041] In the aforementioned implementation process, this method establishes 20 quantitative indicators covering water, oil, stress, safety, and efficiency through a five-dimensional integrated evaluation index system. All indicator data are thresholded, enabling a comprehensive and quantitative assessment of governance effectiveness under different indicators. The method employs a combination of the analytic hierarchy process (AHP) and entropy weighting to ensure objective and fair weighting of each evaluation indicator. A three-tiered evaluation system accurately identifies unqualified evaluation grids. Through simultaneous data collection and evaluation, simultaneous evaluation and treatment, and a closed-loop monitoring mechanism, the method achieves dynamic updates to the governance effectiveness evaluation results.

[0042] Furthermore, the method also includes visualization and archiving output, specifically including: generating heat maps of governance effects, evaluation reports, and archived records, and linking them with the early warning system. The visualization heat maps intuitively display the governance effects, facilitating the intelligent implementation of governance solutions.

[0043] Please refer to Figure 3 , Figure 3This invention provides a five-dimensional closed-loop evaluation flowchart according to one embodiment. Addressing the problems of existing weak oil-bearing strata treatment methods relying solely on single-point indicators, static evaluation, low accuracy, and lack of closed-loop feedback, this application provides a comprehensive treatment effect evaluation method for weak oil-bearing strata in the roof and floor of coal seams. Specifically, it includes the following steps: First, data is collected; then, five-dimensional indicators are constructed, and each indicator is weighted through a combination weighting method; then, the treatment effect is scored through multi-method coupling; based on the treatment effect score results, a three-level graded effect judgment is performed to identify areas where treatment is unqualified; closed-loop remedial treatment is carried out on the identified unqualified areas; and the treatment effect score results are dynamically updated based on the data of the remediated areas. Specifically, this method constructs five evaluation dimensions: seepage prevention and water blocking effect, oil suppression and sealing effect, anti-mining stability effect, safety control effect, and resource utilization effect. It integrates multiple methods such as on-site testing, online monitoring, numerical simulation, and indoor experiments for coupled scoring; establishes a graded evaluation standard, classifying areas into three levels: qualified, basically qualified, and unqualified; and automatically identifies, targets, and dynamically retests unqualified areas, forming a closed-loop system encompassing "prediction, treatment, evaluation, and treatment." This method offers comprehensive, high-precision, dynamically visible evaluation indicators and is adaptable to intelligent mines. It is suitable for the acceptance and post-evaluation of various treatment projects, such as drainage, grouting, and fracturing of weak oil-bearing strata in the roof and floor of coal seams.

[0044] Please refer to Figure 2 , Figure 2 This is a cross-sectional structural diagram illustrating the effectiveness evaluation of treatment for weak oil-bearing strata in the roof and floor of a coal seam, according to one embodiment of the present invention. Taking coal seam A as an example, this comprehensive treatment effectiveness evaluation method for weak oil-bearing strata in the roof and floor of a coal seam is applied to coal seam A, where both the roof and floor have weak oil-bearing strata. Treatment involves drainage and oil-resistant grouting. The specific process of applying this method is as follows: Step 1: Collect data on grouting volume, drainage volume, core samples, water pressure, oil and gas concentration, and stress monitoring. Step 2: Import the pre-built five-dimensional 20-item indicator model; the pre-built five-dimensional integrated evaluation indicator system includes the dimensions of seepage prevention and water blocking, oil suppression and plugging, anti-mining stability, safety control, and resource utilization, with 5 primary indicators and 20 secondary indicators. Step 3: Indicator normalization and combined weighting: The entropy weight-analytic hierarchy process is used to combine weights to eliminate human error and normalize positive and negative indicators. Step 4: Combine water pressure test, core sample analysis, numerical simulation, and online monitoring for comprehensive scoring, achieving integrated scoring through multi-method coupling; Step 5: Divide the area into three zones: high-score qualified zone, medium-score zone, and low-score risk zone; Step 6: Perform directional injection in the low-level zone and retest to ensure compliance; Step 7: Generate a thermal assessment map and connect it to the smart platform.

[0045] During the above process, there was no oil inrush, no water inrush, and no oil and gas exceeding the limits throughout the entire extraction process, and the assessment was reliable.

[0046] In some implementations of this embodiment, the process data includes multi-source monitoring data, remediation plan records, and corresponding comprehensive evaluation results for each evaluation grid during each round of remediation from before remediation to when the remediation qualification conditions are met. The generation rules for the remediation effect evaluation report include: constructing a remediation evolution data chain for each evaluation grid in chronological order from before remediation to when the remediation qualification conditions are met; wherein, the remediation evolution data chain is used to record the dynamic evolution process of the same evaluation grid from the comprehensive evaluation results after the initial remediation to the comprehensive evaluation results after each round of remediation; based on the current comprehensive evaluation results of each evaluation grid in the same time period, spatial interpolation is performed on the unknown points in each evaluation grid to generate a heat map of the remediation effect of the target area in the current time period based on the spatial interpolation results of each evaluation grid; and spatially correlates all the heat maps of the remediation effect of the target area from before remediation to after remediation with the remediation evolution data chain of each evaluation grid to form a remediation effect evaluation report that represents the structured remediation archive of the target area.

[0047] Figure 4 This is a block diagram of a comprehensive treatment effect evaluation system for weak oil-bearing strata in the roof and floor of a coal seam, provided by one embodiment of the present invention. Figure 4 As shown, this invention provides a comprehensive treatment effect evaluation system for weak oil-bearing strata in the roof and floor of coal seams, comprising: The evaluation grid division module is used to dynamically divide the target area into evaluation grids along the advance direction of the mining face, and to acquire multi-source monitoring data of each evaluation grid from before to after treatment. The comprehensive evaluation module for governance effectiveness is used to comprehensively evaluate the governance effectiveness of the current evaluation grid from multiple preset evaluation dimensions based on multi-source monitoring data of the same evaluation grid, and obtain the comprehensive evaluation result of the corresponding evaluation grid. The comprehensive assessment result update module is used to filter out assessment grids whose comprehensive assessment results do not meet the governance qualification conditions, determine the contribution rate of the same assessment grid in each preset assessment dimension, generate and execute the corresponding remedial treatment plan based on the determined contribution rate, update the comprehensive assessment result of the corresponding assessment grid based on the multi-source monitoring data of the remedial assessment grid, and repeat the comprehensive assessment result update of assessment grids that do not meet the governance qualification conditions until all assessment grids meet the governance qualification conditions. The governance effectiveness evaluation report generation module is used to generate a structured governance archive to characterize the target area based on the process data of each evaluation grid from before governance to when the governance qualification conditions are met.

[0048] Specifically, the system evaluates the treatment effect of the target area along the advance direction of the mining face, and automatically executes the evaluation process once every preset distance the mining face advances, thus achieving dynamic, ongoing evaluation. After each preset distance of advance, the target area is dynamically divided into evaluation grids along the advance direction of the mining face, ensuring that the evaluation range always closely matches the already mined area and avoiding evaluation deviations caused by misalignment between the evaluation range and the actual working area. After acquiring multi-source monitoring data from before to after treatment for each divided evaluation grid, the system performs a comprehensive evaluation of the treatment effect of each grid from multiple preset evaluation dimensions, obtaining the comprehensive evaluation result for each grid. This comprehensive evaluation across multiple preset evaluation dimensions not only ensures the comprehensiveness of the treatment effect assessment but also reflects the actual effect of the comprehensive treatment, effectively solving the evaluation deviation problem caused by the single evaluation indicator in existing technologies. The system identifies assessment grids whose comprehensive evaluation results do not meet the governance qualification criteria. It determines the contribution rate of each selected assessment grid's score deduction in each preset assessment dimension. Based on this contribution rate, a corresponding remedial treatment plan is generated and implemented. The comprehensive evaluation results of the corresponding assessment grids are updated based on the multi-source monitoring data of the remediated grids. This process of updating the comprehensive evaluation results of grids that do not meet the governance qualification criteria is repeated until all assessment grids meet the criteria. This achieves dynamic closed-loop management of assessment grid evaluation, remediation, and reassessment. Finally, based on the process data of each assessment grid from before governance to meeting the governance qualification criteria, a governance effectiveness evaluation report is generated to characterize the target area's structured governance archive. This achieves the goal of multi-dimensional, dynamic, accurate, closed-loop, and visualized governance effectiveness evaluation of the target area, improving governance reliability and reducing the risk of data re-collection.

[0049] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention.

[0050] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not describe the various possible combinations separately.

[0051] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0052] Furthermore, various different implementations of the present invention can be combined arbitrarily, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed in the present invention.

Claims

1. A method for evaluating the comprehensive treatment effect of weak oil-bearing strata in the roof and floor of a coal seam, characterized in that, include: The target area is dynamically divided into assessment grids along the direction of the mining face advance, and multi-source monitoring data of each assessment grid from before to after treatment are obtained. Based on multi-source monitoring data of the same assessment grid, the governance effect of the current assessment grid is comprehensively evaluated from multiple preset assessment dimensions to obtain the comprehensive assessment result of the corresponding assessment grid; The evaluation grids that do not meet the governance qualification conditions are screened out. The contribution rate of the same evaluation grid in each preset evaluation dimension is determined. Based on the determined contribution rate, the corresponding remedial treatment plan is generated and executed. The comprehensive evaluation result of the corresponding evaluation grid is updated based on the multi-source monitoring data of the remedial evaluation grid. The comprehensive evaluation result update of the evaluation grids that do not meet the governance qualification conditions is repeated until all evaluation grids meet the governance qualification conditions. Based on the process data of each assessment grid from before governance to when the governance qualification conditions are met, a governance effectiveness assessment report is generated to generate a structured governance archive that characterizes the target area.

2. The method for evaluating the comprehensive treatment effect of weak oil-bearing strata in the roof and floor of coal seams according to claim 1, characterized in that, Each preset evaluation dimension includes at least one evaluation indicator; The multi-source monitoring data based on the same evaluation grid comprehensively evaluates the governance effect of the current evaluation grid from multiple preset evaluation dimensions, including: Numerical simulations of rock mechanical parameters before and after treatment were performed on each evaluation grid based on multi-source monitoring data of each evaluation grid, and the numerical simulation results of each evaluation grid were obtained. After coupling the numerical simulation results and multi-source monitoring data of the same evaluation grid, the coupling data of the corresponding evaluation grid for each evaluation index is extracted from the coupling results. Based on the preset weights of each evaluation indicator, the coupled data of each evaluation indicator in the same evaluation grid under the same preset evaluation dimension are weighted and summed to obtain the evaluation input data of the corresponding evaluation grid in the corresponding preset evaluation dimension. Based on the preset dimension weights of each preset evaluation dimension, the evaluation input data of each preset evaluation dimension in the same evaluation grid are weighted and summed to obtain the comprehensive evaluation result of the corresponding evaluation grid.

3. The method for evaluating the comprehensive treatment effect of weak oil-bearing strata in the roof and floor of coal seams according to claim 2, characterized in that, The rules for determining the preset weights of each evaluation indicator and the preset weights of each preset evaluation dimension include: Based on the correlation between each assessment indicator and the degree of governance compliance, each assessment indicator is determined to be a positive or negative indicator, and the range is normalized according to the results of the positive or negative indicator determination. Among them, assessment indicators whose corresponding values ​​are positively correlated with the degree of governance compliance are determined to be positive indicators, and assessment indicators whose corresponding values ​​are negatively correlated with the degree of governance compliance are determined to be negative indicators. Each evaluation index after range normalization is assigned a preset index weight that is coupled with a subjective weight vector representing the proportion of the index value and an objective weight vector representing the dispersion of the index value. The preset index weights of each evaluation index in the same preset evaluation dimension are added together to obtain the preset dimension weight of the corresponding preset evaluation dimension.

4. The method for evaluating the comprehensive treatment effect of weak oil-bearing strata in the roof and floor of coal seams according to claim 3, characterized in that, The rules for allocating the preset index weights include: The percentage ratio of each evaluation indicator value is determined based on the analytic hierarchy process, and the subjective weight vector of each evaluation indicator is allocated based on the determined percentage ratio. The degree of dispersion and information entropy of the numerical values ​​of each evaluation indicator are determined based on the entropy weight method, and the objective weight vector of each evaluation indicator is assigned based on the determined degree of dispersion. The subjective weight vector and objective weight vector of the same evaluation indicator are combined to obtain the combined result. The weight preference coefficient, which is determined by the difference between the information entropy of each evaluation indicator value, is used to adjust the combined result to obtain the preset indicator weight of the corresponding evaluation indicator.

5. The method for evaluating the comprehensive treatment effect of weak oil-bearing strata in the roof and floor of coal seams according to claim 1, characterized in that, The determination of the contribution rate of the selected evaluation grid in each preset evaluation dimension includes: Obtain the evaluation scores of the same selected evaluation grid in each preset evaluation dimension; wherein, the evaluation scores of the preset evaluation dimensions are obtained by weighting the corresponding evaluation input data based on the preset dimension weights of the corresponding preset evaluation dimensions; The difference between the preset full score and the evaluation score of the same preset evaluation dimension of the same evaluation grid is taken as the absolute score loss of the corresponding evaluation grid in the corresponding preset evaluation dimension. The total score loss of the corresponding evaluation grid is obtained by combining the absolute score loss of the same evaluation grid in each preset evaluation dimension. The contribution rate of the current evaluation grid to the score loss in each preset evaluation dimension is determined based on the proportion of the absolute score loss of the same evaluation grid in each preset evaluation dimension to the total score loss of the current evaluation grid.

6. The method for evaluating the comprehensive treatment effect of weak oil-bearing strata in the roof and floor of coal seams according to claim 1, characterized in that, The process data includes multi-source monitoring data, remedial treatment plan records, and corresponding comprehensive evaluation results for each assessment grid during each round of remedial treatment from before treatment to when the treatment qualification conditions are met; The rules for generating the governance effectiveness evaluation report include: A governance evolution data chain is constructed for each assessment grid, ordered by a timeline from before governance to when the governance qualification conditions are met; wherein, the governance evolution data chain is used to record the dynamic evolution process of the same assessment grid from the comprehensive assessment results after the initial governance to the comprehensive assessment results after each round of supplementary governance; Based on the current comprehensive evaluation results of each evaluation grid in the same time period, spatial interpolation is performed on the unknown points in each evaluation grid to generate a heat map of the governance effect of the target area in the current time period based on the spatial interpolation results of each evaluation grid. By spatially linking the heatmaps of all governance effects in the target area from before to after governance with the governance evolution data chains of each assessment grid, a governance effect assessment report is formed to characterize the structured governance archives of the target area.

7. The method for evaluating the comprehensive treatment effect of weak oil-bearing strata in the roof and floor of coal seams according to claim 1, characterized in that, The rules for acquiring the multi-source monitoring data include: According to the time sequence from before to after treatment, multi-source monitoring data of the same assessment grid are obtained; wherein, the multi-source monitoring data includes the original geological parameters in the assessment grid before treatment, the borehole parameters, grouting parameters, drainage parameters and physical field online monitoring data in the assessment grid during treatment, and the sampling test data in the assessment grid after treatment. By associating the spatial index coordinates of the same evaluation grid with multi-source monitoring data, a multi-dimensional data matrix is ​​constructed with the evaluation grid as the smallest unit and time sequence as the axis.

8. A comprehensive treatment effect evaluation system for weak oil-bearing strata in the roof and floor of coal seams, characterized in that, include: The evaluation grid division module is used to dynamically divide the target area into evaluation grids along the advance direction of the mining face, and to acquire multi-source monitoring data of each evaluation grid from before to after treatment. The comprehensive evaluation module for governance effectiveness is used to comprehensively evaluate the governance effectiveness of the current evaluation grid from multiple preset evaluation dimensions based on multi-source monitoring data of the same evaluation grid, and obtain the comprehensive evaluation result of the corresponding evaluation grid. The comprehensive assessment result update module is used to filter out assessment grids whose comprehensive assessment results do not meet the governance qualification conditions, determine the contribution rate of the same assessment grid in each preset assessment dimension, generate and execute the corresponding remedial treatment plan based on the determined contribution rate, update the comprehensive assessment result of the corresponding assessment grid based on the multi-source monitoring data of the remedial assessment grid, and repeat the comprehensive assessment result update of assessment grids that do not meet the governance qualification conditions until all assessment grids meet the governance qualification conditions. The governance effectiveness evaluation report generation module is used to generate a structured governance archive to characterize the target area based on the process data of each evaluation grid from before governance to when the governance qualification conditions are met.

9. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by the processor, the instruction causes the processor to be configured to perform the comprehensive treatment effect evaluation method for weak oil-bearing strata in the roof and floor of the coal seam as described in any one of claims 1 to 7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the comprehensive treatment effect evaluation method for weak oil-bearing strata in the roof and floor of the coal seam as described in any one of claims 1 to 7.

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