| 支持向量机在煤层地应力预测中的应用 |
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| 引用本文： | 冯鹏,李松,汤达祯,陈博,钟广浩.支持向量机在煤层地应力预测中的应用[J].现代地质,2022,36(5):1333-1340. |
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| 作者姓名： | 冯鹏 李松 汤达祯 陈博 钟广浩 |
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| 作者单位： | 1.中国地质大学(北京) 能源学院,北京 1000832.煤层气开发利用国家工程研究中心中国地质大学(北京) 煤储层物性实验室,北京 100083 |
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| 基金项目： | 国家自然科学基金项目(41802180);中央高校基本科研业务费专项(2652019211);中央高校基本科研业务费专项(2652018270) |
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| 摘 要： | 为了探讨煤层地应力的有效预测方法,将支持向量机回归方法用于计算煤层最小水平主应力,进而得到最大水平主应力,结合垂向主应力的求取,最终构建地应力的地质模型,实现地应力场的三维可视化。利用灰色关联法筛选出与煤层最小水平主应力关联度最好的测井参数;结果表明,井径测井(CAL)、补偿中子测井(CNL)、自然伽马测井(GR)、密度测井(DEN)和深浅侧向电阻率测井值的平均值(R)与煤层最小水平主应力关联度较好。以这5个测井参数作为训练因子,利用支持向量机回归方法建立煤层最小水平主应力预测模型。基于该模型,对鄂尔多斯盆地韩城区块H3井组煤层地应力进行计算,发现研究区内三个方向的地应力随埋深的增加呈现递增的趋势,应力场状态也随着埋深的变化发生转换,由浅部的大地动力型逐渐转换为大地静力型,煤储层所处的应力环境也相应地由挤压带过渡为伸张带。
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| 关 键 词： | 地应力 支持向量机 测井 灰色关联 地质建模 |
| 收稿时间： | 2020-04-28 |
| 修稿时间： | 2020-10-13 |
Application of Support Vector Machine in Prediction of Coal Seam Stress |
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| FENG Peng,LI Song,TANG Dazhen,CHEN Bo,ZHONG Guanghao.Application of Support Vector Machine in Prediction of Coal Seam Stress[J].Geoscience——Journal of Graduate School,China University of Geosciences,2022,36(5):1333-1340. |
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| Authors: | FENG Peng LI Song TANG Dazhen CHEN Bo ZHONG Guanghao |
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| Institution: | 1. School of Energy Resources, China University of Geosciences, Beijing 100083, China2. Coal Reservoir Laboratory of National Engineering Research Center of CBM Development & Utilization, China University of Geosciences, Beijing 100083, China |
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| Abstract: | In order to explore the effective prediction method of coal seam in-situ stress, the Support Vector Machine (SVM) regression method was used to calculate the minimum horizontal principal stress. Combined with the other two directions of stress calculation, the in-situ geological stress model was constructed, and the three-dimensional in-situ stress field was visualized. The grey correlation method was used to screen out the logging parameters that are best correlated with the minimum horizontal principal stress, including caliper logging (CAL), compensated neutron logging (CNL), natural gamma-ray logging (GR), density logging (DEN), and the mean deep and shallow lateral resistivity logging (R). With these five training factors, the prediction model of the minimum horizontal principal stress was established with the SVM regression method. And the H3 well group in Hancheng block in the Eastern Erdos Basin was used as an example to calculate the coal seam stress. The results indicate that the in-situ stress in three directions of the study area has an increasing trend with increasing burial depth, and the stress field also changes from the shallow geodynamic type to the deep geostatic type, and the stress environment of the coal reservoir correspondingly transformed from the extrusion to the extensional zone. |
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| Keywords: | in-situ stress support vector machine logging gray correlation geological modeling |
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