Coal Structure Prediction Based on Type-2 Fuzzy Inference System for Multi-Attribute Fusion: A Case Study in South Hengling Block, Qinshui Basin, China

Abstract

The accurate prediction of coal structure is important to guide the exploration and development of coal reservoirs. Most prediction models are interpreted for a single sensitive coal seam, and the selection of sensitive parameters is correlated with the coal structure, but they ignore the interactions between different attributes. Part of it introduces the concept of the geological strength index (GSI) of coal rocks in order to achieve a multi-element macroscopic description and quantitative characterization of coal structure; however, the determination of coal structure involves some uncertainties among the properties of coal, such as lithology, gas content and tectonic fracture, due to their complex nature. Fuzzy inference systems provide a knowledge discovery process to handle uncertainty. The study shows that a type-2 fuzzy inference system (T2-FIS) with multi-attribute fusion is used to effectively fuse pre-stack and post-stack seismic inversion reservoir parameters and azimuthal seismic attribute parameters in order to produce more accurate prediction results for the Hengling block in the Shanxi area. The fuzzy set rules generated in this paper can provide a more reliable prediction of coal structure in the GSI system. The proposed system has been tested on various datasets and the results show that it is capable of providing reliable and high-quality coal structure predictions.