Accurate Prediction of the Macrolithotype of a High-Rank Coal Reservoir by Logging Interpretation: A Case Study of the Junlian Block, Sichuan Province, China.

Macrolithotypes control the non-homogeneity of coal reservoirs, and it is important to grasp their distribution patterns for the exploration and development of coalbed methane (CBM). Traditional evaluation of macrolithotypes often relies on drilling cores and downhole workings, which are costly and difficult for obtaining comprehensive physical properties of coal seams. In recent years, geophysical logging technology has been commonly applied for evaluating physical properties of coal reservoirs because of its high efficiency and economic features. Taking the Junlian block in Sichuan province as a case study, based on many CBM evaluation wells and data analysis, a logging prediction method for the macrolithotypes of a high-rank coal reservoir is established. The problem of "boundary effect" is solved by the Haar wavelet analysis. Through a Pearson correlation coefficient matrix combined with backpropagation neural network analysis, density, natural gamma ray, acoustic time, compensated neutron, and deep lateral resistivity are extracted as critical parameters for constructing the method of logging interpretation of macrolithotypes. Logging identification models of different macrolithotypes are established by principal components analysis. Based on the results of logging interpretation, the macrolithotypes in the study area are mainly semi-dull coal and semi-bright coal. The genesis of macrolithotypes is analyzed by trace elements and lithologic characteristics. The bright coal in the study area is formed in a sedimentary environment of low paleotemperature, high anaerobic, saline water, and low sea level. In contrast, dull coal is formed in a sedimentary environment of relatively high sea level, brackish water, relatively rich oxygen, and relatively high paleotemperature. [ABSTRACT FROM AUTHOR]