Electrofacies analysis for coal lithotype profiling based on high-resolution wireline log data. (June 2016)

• Record Type: Journal Article
• Authors: Roslin, A.; Esterle, J.S.
• Is Part Of: Computers & geosciences. Volume 91(2016)
• Page Start: 1
• Page End: 10
• Publication Date: 2016-06
• Subjects: Reservoir characterisation -- Electrofacies analysis -- Artificial networks -- Wireline logs -- Coal lithotype; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2016.03.006
• Languages: English
• ISSNs: 0098-3004

Abstract

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. Highlights: An automated coal lithotypes profiling algorithm has been presented. This method is based on wireline geophysical logs data and does not require any predefined cut-offs or assumptions. Method is based on raw data analysis only and it's immune to interpreter bias. The results of automated coal lithotype profiling were validated by millimetre scale logging data and analysed. Some recommendations were made about which wireline log data are appropriate for coal lithotype profiling.