Electrofacies analysis for coal lithotype profiling based on high-resolution wireline log data

Roslin, A. and Esterle, J. S. (2016) Electrofacies analysis for coal lithotype profiling based on high-resolution wireline log data. Computers and Geosciences, 91 1-10. doi:10.1016/j.cageo.2016.03.006


Author Roslin, A.
Esterle, J. S.
Title Electrofacies analysis for coal lithotype profiling based on high-resolution wireline log data
Journal name Computers and Geosciences   Check publisher's open access policy
ISSN 0098-3004
1873-7803
Publication date 2016-06-01
Year available 2016
Sub-type Article (original research)
DOI 10.1016/j.cageo.2016.03.006
Open Access Status Not Open Access
Volume 91
Start page 1
End page 10
Total pages 10
Place of publication Kidlington, Oxford United Kingdom
Publisher Pergamon Press
Language eng
Abstract The traditional approach to coal lithotype analysis is based on a visual characterisation of coal in core, mine or outcrop exposures. As not all wells are fully cored, the petroleum and coal mining industries increasingly use geophysical wireline logs for lithology interpretation.This study demonstrates a method for interpreting coal lithotypes from geophysical wireline logs, and in particular discriminating between bright or banded, and dull coal at similar densities to a decimetre level. The study explores the optimum combination of geophysical log suites for training the coal electrofacies interpretation, using neural network conception, and then propagating the results to wells with fewer wireline data. This approach is objective and has a recordable reproducibility and rule set.In addition to conventional gamma ray and density logs, laterolog resistivity, microresistivity and PEF data were used in the study. Array resistivity data from a compact micro imager (CMI tool) were processed into a single microresistivity curve and integrated with the conventional resistivity data in the cluster analysis. Microresistivity data were tested in the analysis to test the hypothesis that the improved vertical resolution of microresistivity curve can enhance the accuracy of the clustering analysis. The addition of PEF log allowed discrimination between low density bright to banded coal electrofacies and low density inertinite-rich dull electrofacies.The results of clustering analysis were validated statistically and the results of the electrofacies results were compared to manually derived coal lithotype logs.
Keyword Reservoir characterisation
Electrofacies analysis
Artificial networks
Wireline logs
Coal lithotype
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Earth Sciences Publications
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