构造煤厚度定量预测技术新进展

马丽; 陈同俊; 王新; 马国栋

doi:10.3969/j.issn.1001-1986.2018.05.011

构造煤厚度定量预测技术新进展

马丽¹,
陈同俊^2,3, ,,
王新⁴,
马国栋^2,3

1. 国土资源部煤炭资源勘查与综合利用重点实验室，陕西西安 710021;
2. 中国矿业大学煤层气资源与成藏过程教育部重点实验室，江苏徐州 221008;
3. 中国矿业大学资源与地球科学学院，江苏徐州 221116;
4. 中国矿业大学计算机科学学院，江苏徐州 221116

基金项目:

国家自然科学基金项目（41774128，41704115，41430317）；国土资源部煤炭资源勘查与综合利用重点实验室开放基金项目（KF2018-5）

详细信息

作者简介:
马丽,女,1975年生,陕西三原人,高级工程师,从事煤田地震勘探研究.E-mail:mary248@163.com

通讯作者:
陈同俊,男,1977年生,安徽舒城人,博士,教授,从事煤田地震勘探教学与研究.E-mail:tjchen@cumt.edu.cn

中图分类号: P631
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出版历程
- 收稿日期: 2018-05-11
- 发布日期: 2018-10-24

Recent progress of quantitative prediction of tectonic coal thickness

MA Li¹,
CHEN Tongjun^2,3, ,,
WANG Xin⁴,
MA Guodong^2,3

1. Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Land and Resources, Xi'an 710021, China;
2. Key Laboratory of CBM Resource & Reservoir Formation Process, Ministry of Education, Xuzhou 221008, China;
3. School of Resource and Earth Science, China University of Mining and Technology, Xuzhou 221116, China;
4. School of Computer Science, China University of Mining and Technology, Xuzhou 221116, China

Funds:

National Natural Science Foundation of China(41774128,41704115,41430317)

摘要

摘要: 构造煤与原生煤的物性差异明显，是煤层气储层建模需要考虑的关键因素之一。针对构造煤厚度分布预测这一关键问题，以测井曲线和地震属性为数据输入，综合分析了构造煤识别和厚度预测的最新研究进展。相对于交互式测井曲线识别来说，基于小波多尺度分析和聚类分析的构造煤识别方法精度更高、可靠性更好。结合地震属性和机器学习算法，可以获得精度更高的构造煤厚度确定性预测结果。结合地震属性和地质统计学随机模拟，可以获得可靠性更高的构造煤厚度非确定性预测结果。尽管构造煤厚度预测已研究多年，但构造类型和空间位置预测仍然需要进一步研究。
- 构造煤 /
- 识别 /
- 测井曲线 /
- 地震属性 /
- 预测
Abstract: TDCs(Tectonically deformed coal) are obviously different from normal coal in physical characteristics. It is one of the key factors needed to be considered in CBM(coalbed methane) reservoir modeling. In order to figure out the issue of TDC thickness prediction, we analyzed the recent progress in TDC identification and thickness prediction using well logs and seismic attribution as inputs. Compared with the interactive recognition using well logs, the method for identifying TDCs based on wavelet multi-scale analysis and cluster analysis has higher accuracy and better reliability. Combining with seismic attributes and machine learning algorithms, one can produce a deterministic prediction of TDC thickness with more accuracy. Combining with seismic attributes and geostatistical stochastic simulation, one can produce a non-deterministic prediction of TDC thickness with higher reliability. Although the prediction of TDC thickness has been proceeded for many years, the prediction of TDC types and spatial characteristics still need further study.
- tectonic coal /
- identification /
- well log /
- seismic attribute /
- prediction

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