基于模糊评价-BP神经网络的煤炭地下气化选址模型研究以新疆阜康矿区为例

Research on site selection model for underground coal gasification based on fuzzy evaluation and BP neural network: A case study of Fukang Mining Area, Xinjiang

  • 摘要: 煤炭地下气化(Underground Coal Gasification,UCG)技术作为我国天然气增储升产的战略替代方案,其安全高效开发高度依赖于精准的地质评价与选址,但传统UCG选址方法主观性强、时效性差。针对上述问题,以新疆阜康矿区26个煤矿的162组煤层数据为研究对象,在全面总结前人概括的UCG地质影响因素基础上,构建了涵盖建炉可行性、过程易控性等4项一级指标;井壁稳定性、轨迹可控性等9项二级指标和坚固性系数、煤层厚度等30项三级指标的评价指标体系;同时利用层次分析法和模糊评价法对各组煤层进行了综合地质评价,识别出了各煤矿中具备UCG开发潜力的最有利煤层;基于各组煤层的评价结果,引入BP神经网络构建了模糊评价-BP神经网络UCG选址模型;最终为验证模型的可靠性与高效性,进一步选取阜康矿区康龙煤矿作为典型案例进行实证分析。研究结果表明:煤层厚度(权重占比17.99%)、煤层倾角(7.33%)、褶皱复杂程度(7.26%)等地质因素是影响UCG选址的关键指标;35-36号煤在研究区UCG有利煤层中占比最大,达19.23%;模糊评价-BP神经网络模型通过输入标准化的地质参数数据,可快速生成选址适宜性评估结果,实现对其他复杂地质条件的空白评价区域进行快速准确的UCG选址。研究结果有望丰富UCG选址体系与选址方法,并为新疆难开采煤炭资源的清洁开发提供思路。

     

    Abstract: As a strategic alternative to enhance natural gas reserves and production in China, Underground Coal Gasification (UCG) technology relies heavily on precise geological evaluation and site selection for safe and efficient development. However, traditional UCG site selection methods suffer from strong subjectivity and poor timeliness. To address these issues, this study took 162 sets of coal seam data from 26 coal mines in the Fukang Mining Area, Xinjiang, as the research object. Based on a comprehensive review of previous geological factors influencing UCG, an evaluation index system was constructed, encompassing four primary indicators (e.g., furnace construction feasibility, process controllability), nine secondary indicators (e.g., wellbore stability, trajectory controllability), and 30 tertiary indicators (e.g., rock hardness coefficient, coal seam thickness). Using the Analytic Hierarchy Process (AHP) and fuzzy evaluation method, a comprehensive geological assessment of each coal seam was conducted to identify the most favorable coal seams with UCG development potential in each mine. Building on these evaluation results, a fuzzy evaluation-Backpropagation Neural Network UCG site selection model was introduced. Finally, to validate the model's reliability and efficiency, a case study was conducted on Kanglong Coal Mine in the Fukang Mining Area. The research findings indicate that geological factors such as coal seam thickness (weight proportion: 17.99%), coal seam dip angle (7.33%), and fold complexity (7.26%) are key indicators affecting UCG site selection. The 35-36# coal constitutes the largest share of UCG-favorable coal seams in the study area, reaching 19.23%. The model can rapidly generate site suitability assessment results by inputting standardized geological parameter data, enabling quick and accurate UCG site selection in blank evaluation areas with complex geological conditions. These findings are expected to enrich the UCG site selection system and methodologies, providing insights for the clean development of hard-to-mine coal resources in Xinjiang.

     

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