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基于地质分区的煤层含气量预测方法研究
供稿: 张崇崇;王延斌;倪小明 时间: 2018-11-21 次数:

作者:张崇崇王延斌倪小明

作者单位:中国矿业大学(北京)地球科学与测绘工程学院河南理工大学能源科学与工程学院;中原经济区煤层(页岩)气协同创新中心

摘要:为准确预测煤层含气量,解决目前常规测井解释方法适用性差、准确率低等问题,提出了基于地质因素差异对含气性影响的分区预测含气量方法。首先结合大量实验室测试数据,分析了沉积、构造、水动力条件差异对煤层含气性的影响,得出主控因素;其次通过模糊层次分析,量化地质因素差异,实现地质分区;最终在各分区内引入主成分分析,充分利用现场大量常规测井曲线,进行多元回归,建立煤层含气量预测模型。以柿庄南地区3号煤层为研究对象进行计算,结果表明,柿庄南地区3号煤层含气性受到各地质因素的综合影响,平面上受顶板泥岩厚度、构造应力类型、矿化度控制,单井纵向上主要受固定碳含量变化影响;进行地质分区后煤层含气量回归预测值与实测值拟合率达89%,而常规回归预测拟合率17%,故准确率有显著的提高。

基金:国家科技重大专项课题(2011ZX05060);

关键词:含气量预测;地质分区;主成分分析;回归分析;

DOI:10.16186/j.cnki.1673-9787.2017.02.004

分类号:P618.13;P631.81

Abstract:In order to accurately predict the gas content in coal seam, and to solve the problem of poor applicability and low accuracy of conventional logging interpretation methods, a method to district forecast the gas content in coal seam was proposed based on the difference of geological factors on the gas bearing property.First, according to a large number of laboratory test data, the influence of different sedimentary, tectonic and hydrodynamic conditions on the gas bearing properties of coal seams was analyzed and main control factors were obtained.Secondly, according to the fuzzy analytic hierarchy process, the difference of geological factors were quantified, and geological division was realized.Method of principal component analysis in each partition was introduced, fully used of a large number of conventional logging curves, and the prediction model of gas content in coal seam was established according to multivariate regression.The No.3 coal seam in southern area of Shizhuang was calculated as the research object the results show that the gas bearing properties were comprehensively influenced of various geological factors, and roof mudstone thickness, tectonic stress type, mineralization degree on plane, and controlled by fixed carbon content changing vertically.The fitting rate between division regression predictive value and measured value is 89%, the accuracy is fundamentally improved compared with the fitting rate 17% of the conventional regression prediction.

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