Identification of coal seam strata from geophysical logs of borehole using Adaptive Neuro-Fuzzy Inference System

Yegireddi, Satyanarayana; Bhaskar, G. Uday

doi:10.1016/j.jappgeo.2008.08.009

Cited by 26 publications

(11 citation statements)

References 8 publications

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“…However, they cannot be applied to complex geometry distributions of formation resistivity where other measurement systems provide meaningful results (Kozhevnikov et al, 2008;Martinez et al, 2010;Orlando and Pelliccioni, 2010;Schmutz et al, 2009;Yegireddi and Bhaskar, 2009). In addition, simulation of TCR measurements via numerical methods becomes rather challenging due to the high electrical conductivity contrast, small thickness of casing and multiplex formation layers.…”

Section: Post-processing Methodsmentioning

confidence: 99%

New post-processing method for interpretation of through casing resistivity (TCR) measurements

Chen

Pardo

et al. 2011

Journal of Applied Geophysics

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Section: Post-processing Methodsmentioning

confidence: 99%

New post-processing method for interpretation of through casing resistivity (TCR) measurements

Chen

Pardo

et al. 2011

Journal of Applied Geophysics

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“…Simulation of TCR measurements via numerical methods is rather challenging due to the high electrical conductivity contrast and small thickness of casing (Chen et al, 2010;Chen et al, 2011;Cozzolino and da Silva, 2007;Han et al, 2009;Kozhevnikov and Antonov, 2008;Martinez et al, 2010;Nam, 2010;Yegireddi and Bhaskar, 2009). Here, we utilize a 2D axially symmetric numerical method based on a selfadaptive goal oriented hp-finite-element method (FEM) that accurately simulates such logging measurements.…”

Section: Hp-fem Simulationsmentioning

confidence: 99%

Compensation effect analysis in DIE method for through-casing measuring formation resistivity

Chen¹,

Pardo²,

Li³

et al. 2011

Journal of Applied Geophysics

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“…The methods of identification of coal structure mainly include observation of manually collected specimens and geophysical information identification. Among them, geophysical information identification methods mainly include logging [22][23][24][25][26][27][28][29][30] and seismic methods [31][32][33]. Compared with the seismic method, the well-logging method offers higher resolution and accuracy and can provide continuous data allowing better identification of coal structures.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Wang

et al. 2020

Energies

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Coal structure directly correlates to permeability and hydraulic fracturing effects. Underground coal mining indicates that a single coal section generally contains multiple coal structures in superposition, making how to recognise the coal structure combination and predict its influence on coal permeability a challenging problem. Based on well-drilling sampled cores, the geological strength index (GSI), and well-logging data, the DEN, GR, CALX, and CALY were selected to establish a model to predict GSI by multiple regression to identify coal structure from 100 coalbed methane wells. Based on fitting GSI and corresponding permeability test values, injection fall-off (IFO) testing, and hydraulic fracturing results, permeability prediction models for pre- and post-fracturing behaviour were established, respectively. The fracturing effect was evaluated by the difference in permeability. The results show that a reservoir can be classified into one of nine types by different coal structure thickness proportion (and combinations thereof) and the fracturing curves can be classified into four categories (and eight sub-categories) by the pressure curve. Up-down type I and type II reservoirs (proportion of hard coal >60%) and intervening interval type I reservoir (proportion of hard coal >70%) are prone to form stable and descending fracturing curves and the fracturing effects are optimal. Intervening interval type II (hard coal:soft coal:hard coal or soft coal:hard coal:soft coal ≈1:1:1) and up-down type III (hard coal:soft coal =1:1) form descending type II, rising type I and fluctuating type I fracturing curves and fracturing effect ranks second; up-down type IV and V (proportion of hard coal <40%), interval type III (proportion of hard coal <30%), and multi-layer superposition-type reservoirs readily form fluctuating and rising fracturing curves and fracturing effects therein are poor. The research results provide guidance for the targeted stimulation measured under different coal structure combinations.

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Identification of coal seam strata from geophysical logs of borehole using Adaptive Neuro-Fuzzy Inference System

Cited by 26 publications

References 8 publications

New post-processing method for interpretation of through casing resistivity (TCR) measurements

New post-processing method for interpretation of through casing resistivity (TCR) measurements

Compensation effect analysis in DIE method for through-casing measuring formation resistivity

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

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