Identification of coal structures using geophysical logging data in Qinshui Basin, China: Investigation by kernel Fisher discriminant analysis

Shi, Jinxiong; Zeng, Lianbo; Dong, Shaoqun; Wang, Jipeng; Zhang, Yunzhao

doi:10.1016/j.coal.2019.103314

Cited by 35 publications

(24 citation statements)

References 51 publications

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“…In the above formula, the correlation coefficient R is .799. Fracture identification is a typical nonlinear problem 55,56 . In this paper, we first dealt with the four parameters AC, DEN, GR, and RD based on the fracture probabilistic model, and this process is nonlinear.…”

Section: Discussionmentioning

confidence: 99%

“…Fracture identification is a typical nonlinear problem. 55,56 In this paper, we first dealt with the four parameters AC, DEN, GR, and RD based on the fracture probabilistic model, and this process is nonlinear. Although the processing results of these four parameters are inconsistent for the identification of fractures, however, they belong to the same level of parameters related to slope (or they are parallel parameters).…”

Section: Construction Of a New Fracture Index (F C )mentioning

confidence: 99%

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A new method for identifying fractures in tight sandstone of a gentle structural area using well logs

Guo

Wang²,

2020

Energy Science & Engineering

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Section: Discussionmentioning

confidence: 99%

Section: Construction Of a New Fracture Index (F C )mentioning

confidence: 99%

A new method for identifying fractures in tight sandstone of a gentle structural area using well logs

Guo

Wang²,

2020

Energy Science & Engineering

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“…Traditionally, borehole observations through drilling and identification under coal mine are the two primary and effective methods to accurately delineate coal structures. It is pointed out that both methods are expensive, and for the area where coal cores cannot be obtained by drilling or without mining work, because of which determination of coal structures has become technically infeasible . Geophysical well log data are widely available through geological survey and have recently been used to evaluate the reservoir properties of coals such as gas content, mechanical properties, macrolithotypes, permeability, and coal texture by analyzing the responses in terms of magnetic, acoustic, electrical, and nuclear methods from geological well log data. − Coal structure identification with the help of well log data have been studied and it can accurately characterize the distribution of coal structures .…”

Section: Introductionmentioning

confidence: 99%

Analysis of Logging Identification and Methane Diffusion Properties of Coals with Various Deformation Structures

Zhang

Meng

Li³

2021

Energy Fuels

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Methane diffusion in coals with different deformations is important for gas resource evaluation. In this study, the adsorption/ desorption and diffusion behavior in different coal structures were investigated, and the multi-logging parameter model of backpropagation (BP) neural network was employed to identify coal structures, as well as the effects of diffusion changes on gas contents in the study area, were revealed. The results indicate that with the increase of coal deformation intensity, the Langmuir V L increases, and both specific surface area and pore volume increase as well. Diffusion coefficients in coals decrease gradually with the decrease of pressures, and the development of meso/macropores and micropores in tectonic coals promotes gas migration, which increase the diffusion efficiency, especially the fast and reduction diffusion stages (S1 and S2).Comparisons between intact and tectonically deformed coals in terms of gas diffusion behaviors modeled by the unipore and bidisperse numerical methods show that the analytical solution of the bidisperse model is more appropriate to describe diffusion behaviors in tectonic coals due to the enhanced complexity of mesopores and micropores in coals. Different coal structures exhibiting similar values in well logs lead to multiple interpretations of coal structures. The proposed BP model is an effective way to identify the nonlinear relationships between well logs and coal structures by extracting key information from logging datasets. The modeled results show that the degree of heterogeneity of the gas content in different coal structures is high and the higher diffusion rate in granulated coal zone somewhat reduced the measured gas content.

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“…Sun et al utilized the apparent resistivity as the main characteristic and the γ–γ curve as an auxiliary characteristic to effectively distinguish coal structures. Numerous mathematical methods have also been applied to the quantitatively evaluate coal structure, including clustering methodology, slope-variance and probability statistics, and kernel Fisher discriminant analysis, with the goal of automating the identification of TDCs. ,, A number of empirical formulas have been proposed based on various logging response parameters. − Furthermore, other geophysical evaluation methods such as ultrasonic P- and S-wave tests were tentatively applied to the qualitative characterization of coal structure. , Microscopic evaluation has been performed using scanning electron microscopy (SEM), ,− transmission electron microscopy (TEM), and atomic force microscopy (AFM) to observe the microsurface morphology of TDCs with various structures. Microstructural analytical techniques such as X-ray diffraction (XRD) and nuclear magnetic resonance (NMR) have been used to study the basic structural unit (BSU) and transformation of chemical structure, ,, and reveal the intrinsic relationship between the evolution of macromolecular structure and the change in the maximum vitrinite reflectance ( R o,m ). ,− Laser Raman spectrosocpy (LRS) and Fourier transformation infrared spectroscopy (FTIR) − have been applied to understand the pore structure and deformation mechanisms of coals.…”

Section: Introductionmentioning

confidence: 99%

Coal Structure and Its Implications for Coalbed Methane Exploitation: A Review

Liu

Cai

et al. 2020

Energy Fuels

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Coal structure refers to the internal structural characteristics of coals including the degree of macroscopic and microscopic deformation, pore structure, and mechanical properties after various geothermal and geological stresses, which have substantial impacts on coal mining safety and coalbed methane (CBM) exploitation. The coal structure is an indicator of coal mechanical strength, which determines the petrophysical properties of CBM reservoirs and gas extraction or CBM production efficiency from coals. Coals with different structures are divided into primary coals and tectonically deformed coals (TDCs). To explore the macro- and microscopic petrophysics of TDCs, a wide range of techniques including geophysical logging, seismic inversion, amplitude variation with offset (AVO), scanning electron microscopy (SEM), nuclear magnetic resonance (NMR), and Fourier transform infrared spectroscopy (FTIR) have been employed. From a macroscopic perspective, the geological and geophysical methods are generally used to distinguish the degree of deformation of whole coal seams. Microscopic methods including SEM, NMR, and FTIR, are normally used to investigate the internal petrophysics (e.g., pore and fracture, macromolecular structure, and fluid performance) of TDCs. Herein, the macro- and micro-petrophysical properties of TDCs and their impacts on CBM exploration and exploitation are systematically reviewed and potential opportunities of future directions are recommended. Extensive studies have shown that the development zone of TDCs in the brittle series, notably cataclastic coals, has high potential for CBM exploration and exploitation, while that of TDCs in the ductile series is a dangerous region for coal mining and subject to gas outbursts. This review aims to provide background on the coal structure, summarize evaluation methods, highlight its geological drivers, and explain its significance in CBM exploitation. We will end with proposals for future research directions.

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Identification of coal structures using geophysical logging data in Qinshui Basin, China: Investigation by kernel Fisher discriminant analysis

Cited by 35 publications

References 51 publications

A new method for identifying fractures in tight sandstone of a gentle structural area using well logs

A new method for identifying fractures in tight sandstone of a gentle structural area using well logs

Analysis of Logging Identification and Methane Diffusion Properties of Coals with Various Deformation Structures

Coal Structure and Its Implications for Coalbed Methane Exploitation: A Review

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