1.College of Physics and Petroleum Resources, Yangtze University, Wuhan 430100, China 2.Key Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education, Yangtze University, Wuhan 430100, China 3.CNOOC Research Institute, Beijing 100027, China
The content of coalbed methane is an important parameter in evaluating coalbed reservoir. In this paper, the gray system was applied to the coalbed logging curve, the improved slope correlation method was used to analyze the logging curve series which are sensitive to the coalbed gas content. The gray multivariate static model GM (0,N) was used to predict the coalbed methane content in the sequence of positive correlation logging curves. Taking Qinshui Coal Field as an example, the authors compared the gray multivariate static model prediction results with the results of the multiple regression model analysis, and studied and analyzed the practicability of the gray multivariate static model. The results show that the improved association analysis of gray incidence can fully develop the relationship between logging curve and coalbed methane content, and that the GM(0,N) prediction model is more accurate and more robust than the multiple regression model in that it can effectively predict the coalbed methane content curve when the sample data is relatively small. The result is reliable and has practical application value.
Meng Z P, Tian Y D, Lei Y. Prediction models of coalbed gas content based on BP neural networks and its applications[J]. Journal of China University of Mining & Technology, 2008,37(4):28-33.
Lian C B, Zhao Y J, Li H L, et al. Main controlling factors analysis and prediction of coalbed gas content[J]. Journal of China Coal Societ, 2005,30(6):726-729.
Gao B, Ma Y Z, Tao M X, et al. Main controlling factors analysis of enrichment condition of coalbed methane[J]. Acta sedimentologica Sinica, 2003,21(2):345-349.
Wu Y P, Li Z D, Wang Y C. The formation mechanisms of abnormal pressure and factor in control of the coal bed gas in Qinshui Basin[J]. Journal of China Coal Society, 2006,31(4):475-479.
Ye J P, Wu Q, Wang Z H. Controlled characteristics of hydrogeological conditions on the coalbed methane migration and accumulation[J]. Journal of China Coal Society, 2001,26(5):63-67.
Dong H, Hou J S, Li N G, et al. The logging evaluation method for coal quality and methane[J]. Geophysical and Geochemical Exploration, 2001,25(2):138-143.
Li G H, Zhang H, Cui Y J, et al. A predictive model of gas content in coal reservoirs based on multiple stepwise regression analysis: a case study from Qinshui Basin[J]. Coal Geology & Exploration, 2005,33(2):22-25.
[14]
Kim A G. Estimating methane content of bituminous coal beds from adsorption data[R]. United States Department of the Interior, Report of Investigations-Bureau of Mines 8245, 1977, 1-11.
[15]
Ahmed U, Johnston D, Colson L. An advanced and integrated approach to coal formation evaluation[C]// SPE22736, 1991: 755-770.
[16]
Hawkins J M, Schraufnagel R A, Olszewsk A J. Estimating coalbed gas content and sorption isotherm using well log data[C]// SPE24905, 1992: 491-501.
Meng Z P, Guo Y S, Zhang J X, et al. Application and prediction model of coalbed methane content based on logging parameters[J]. Coal Science and Technology, 2014,42(6):25-30.
Shao X J, Sun Y B, Sun J M, et al. Logging interpretation of coal petrologic parameters: A case study of Hancheng mining area[J]. Petroleum Exploration and Development, 2013,40(5):559-565.
Huang Z H, Zou C C, Yang Y Q, et al. Coal bed methane reservoir evaluation from wireline logs in TS District, southern Qinshui Basin[J]. Geoscience, 2012,26(6):1275-1282.
Jin Z L, Xue H F, Gao H B, et al. Technology for evaluation of CBM reservoir logging and its application[J]. Coal Geology & Exploration, 2013,41(2):42-45.
Cao J T, Zhao J L, Wang Y P, et al. Review of influencing factors and prediction methods of gas content in coal seams and prospect of prediction methods[J]. Journal of Xi’an Shiyou University:Natural Science Edition, 2013,28(4):29-34, 94.
Lian C B, Zhao Y J, Li H L, et al. Prediction of coal bed gas content based on support vector machine regression[J]. Journal Center of Xi’an University of Science and Technology, 2008,28(4):707-709.
Xiao X P, Xie L C, Huang D R. A modified computation method of grey correlation degree and its application[J]. Journal of Applied Statistics and Management, 1995,14(5):27-30.
Zhang S L, Zhang G L. Comparison between computation modles of grey interconnet degree and analysis on their shortages[J]. Systems Engineering, 1996,14(3):45-49.
[30]
李明凉. 灰色关联度新判别准则及其计算公式[J]. 系统工程, 1998,16(1):68-70.
[30]
Li M L. A new descriminant byelaw for grey interconnet degree and its calculation formulas[J]. Systems Engineering, 1998,16(1):68-70.
Tian M, Zhao Y J, Zhuansun P C. Application of grey system theroy in prediction of coalbed methane content[J]. Coal Geology & Exploration, 2008,36(2):24-27.
Zhang C, Jiang Q. The prediction of small sample time-displacement data based on GM(0,N) and RBF[J]. Computer Engineering and Applications, 2005(5):62-64.
Xia H Q, Tan D H, Liang C B, et al. Prediction of formation fracture pressure based on grey artificial neural network logging[J]. Journal of Southwest Petroleum Institute, 1996,18(4):1-8.
Liang Y L, Yuan W T. The prediction of the content and distribution of coalbed gas: a case study in the Qinshui coalfield based on logging[J]. Geophysical and Geochemical Exploration, 2018,42(6):1144-1149.
Jia C Z, Zheng M, Zhang Y F. Unconventional hydrocarbon resources in China and the prospect of exploration and development[J]. Petroleum Exploration and Development, 2012,39(2):129-136.
Ministry of Land and Resources, Strategic Research Center of Oil and Gas Resources. National oil and gas resource assessment[M]. Beijing: China Land Press, 2010.