Earticle

초록

영어

Coal structure not only is a significant physical parameter of the coal reservoirs, but also has an effect on the coalbed methane (CBM) productivity during coal mining. In this paper, the clustering method is used to evaluate the coal structures quantitatively. For better application on the identification process of the coal structures, the clustering method is modified twice by ascertaining initial clustering centers and calibrating Euclidean distances, respectively. The quantitative method was applied to evaluate coal structures in 16 CBM wells in the Zhengzhuang field, Southern Qinshui Basin, North China. Results show that coal structures by the twice modified clustering method has good accordance with that by the coring samples. Therefore, coal structure identification using clustering method with the aid of geophysical logging data is feasible in the study area. The thickness proportions of coal structures have positive/ negative relationships with the corresponding average logging data. The multiple linear regression method was employed to predict the thickness proportions of coal structures. The predicted results show that prediction models of thickness proportions of coal structures are feasible to predict the distribution of coal structures fast in spite of the limited errors.

목차

Abstract
 1. Introduction
 2. The Proposed Method
  2.1 Well Logging Data
  2.2 Methodology
 3. Results and Discussion
  3.1 Calculated Results of Typical Well
  3.3 Thickness Proportion Prediction Models 
 4. Conclusions
 Acknowledgements 
 References

키워드

저자

• Teng Juan [ Coal Reservoir Laboratory of National CBM Engineering Center, School of Energy Resources, China University of Geosciences, Beijing 100083, China ]
• Liu Dameng [ Coal Reservoir Laboratory of National CBM Engineering Center, School of Energy Resources, China University of Geosciences, Beijing 100083, China ]
• Yao Yanbin [ Coal Reservoir Laboratory of National CBM Engineering Center, School of Energy Resources, China University of Geosciences, Beijing 100083, China ]
• Cai Yidong [ Coal Reservoir Laboratory of National CBM Engineering Center, School of Energy Resources, China University of Geosciences, Beijing 100083, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605