Identification of the coal structure and prediction of the fracturability in the No. 8 coal reservoir, Gujiao block, China

Abstract

Five boreholes were selected in the Gujiao block, Xishan, Taiyuan, China, as calibration wells to identify the coal structure. The geophysical-log responses of the coal structure in the No. 8 coal reservoir, Gujiao block, were investigated using coal-core logging combined with actual observations in the borehole profile of the coal mine. Natural gamma, density, apparent resistivity, and sonic travel time logs were extracted at 0.15 m intervals from 41 undeformed coal, 29 cataclastic coal, and 48 granulated coal regions. Box plots and Fisher's maximum separation criterion were used to screen and identify sensitive log responses of the coal structure. The coal structure was identified in 31 boreholes using the available logs in the block, and the layered distribution patterns of the coal structure were discussed. The fracturability of the coal structure was divided into types using cluster analysis based on the thickness ratio of the coal structure and the relationship between the coal structure and its permeability. The results show that sensitive log responses for identifying undeformed coal and cataclastic coal are natural gamma, followed by density and apparent resistivity; log responses for identifying cataclastic coal and granulated coalmylonitized coal are sonic travel time, apparent resistivity and natural gamma. The sensitive log response data were integrated and coal structure identification models were constructed based on the principle of amplifying the log responses to identify the coal structure in the No. 8 coal reservoir. The reservoir generally contains two or three dirt bands, and the coal structure is divided into several independent layers, with the cataclastic coal and granulated coal-mylonitized coal distributed in the middle of the reservoir. The coal structure was classified into four types

Prediction of high-quality coalbed methane reservoirs based on the fuzzy gray model: An investigation into coal seam No. 8 in Gujiao, Xishan, North China

“…The granulated coal structure is severely destructed, and the coal is cut into small granules with multiple sliding surfaces (Figure 4(c)). The mylonitized coal is scaly or powdery, and fractures cannot be observed by the naked eye; the coal has crumpled sliding surfaces that are highly developed (Figure 4(d)) (Huang et al, 2018). Based on the chemical and physical differences between the different coal structures, sensitive logging responses of different coal structures were obtained, and a numerical model of the coal structure was established (Huang et al, 2018).…”

“…Coal structure. A numerical model for identifying coal structure was established according to the sensitive parameters of logging response as well as their combinations (Hatherly, 2013;Huang et al, 2018;Teng et al, 2015). Coal structures were quantitatively identified for the 36 boreholes in the Gujiao block using the numerical model, and the layered and regional distributions of the coal structures were also predicted.…”

“…Coal structures were quantitatively identified for the 36 boreholes in the Gujiao block using the numerical model, and the layered and regional distributions of the coal structures were also predicted. The numerical model used to identify primary undeformed and cataclastic coals is shown as follows (Huang et al, 2018)…”

“…Identifying coal structures from their logging responses is more feasible compared with the other methods (Fu et al, 2009;Teng et al, 2013Teng et al, , 2015Wang et al, 2018b). Huang et al (2018) identified the coal structures of the No. 8 coal in the Gujiao block using a quantitative index of coal structure.…”

Classification of Coal Structure Combinations and Their Influence on Hydraulic Fracturing: A Case Study from the Qinshui Basin, China

“…At present, the coal structure cannot meet the actual demand only from the perspective of qualitative analysis and the logging identification method is gradually moving towards quantitative development. Researchers use multiple regression analysis [23], correspondence analysis [28], BP neural networks [35,36], pore structure index methods [37], the intersection method [27,38], principal component analysis [39,40], clustering analysis [41], and the kernel Fisher discriminant analysis method [42] based on data from drilling coring, caliper logging, resistivity logging, density logging, natural gamma ray logging, and acoustic logging to identify coal structure, and any trends therein. At present, although there are many identification methods for coal structure and some logging parameters are affected by geological evolution and other factors, the logging response characteristics are not obvious.…”