Identification of Coal Distribution Pattern Using Well Logging Method Based on Gamma Ray Log Data and Log Density in Area X PT PMC Site Sungai Lilin

Identification of coal distribution pattern

Authors

  • Puja Prana Kesumah University of Jambi
  • Sarwo Sucitra Amin University of Jambi
  • Jarot Wiratama Universitas Jambi
  • Bagus Adhitya University of Jambi

DOI:

https://doi.org/10.22437/bigme.v3i2.30785

Keywords:

Muara Enim Formation, , Well Logging, Coal Distribution

Abstract

Physiographically, the research area is included in the South Sumatra Basin, precisely in the Muara Enim Formation, a coal-bearing formation. In this study, secondary data is used in the form of LAS file data from recording well logging data as many as 7 wells which are then processed using WellCAD 5.5 software to obtain log sheet results, namely gamma ray logs and density logs which are then interpreted for subsurface lithology by reading the log curve deflection, from the results of reading the log curve, the lithology in the research area is obtained in the form of claystone, siltstone, carbonaceous claystone, and coal. Coal is characterized by low gamma ray log response (<10 CPS) and high density log response (>900 CPS). To determine the distribution of coal seams in the study area, structural correlation and stratigraphic correlation of coal seams on strike (northwest - southeast) and cross strike (southwest - northeast) were found to be 1 coal seam, namely seam A. Seam A experienced splitting into seam A1 and seam A2 due to structural influence. From the correlation results, it is found that the coal slope is towards the southwest - northeast by experiencing thickening and thinning layers.

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Published

2023-09-30

How to Cite

Kesumah, P. P., Amin , S. S., Wiratama, J., & Adhitya, B. . (2023). Identification of Coal Distribution Pattern Using Well Logging Method Based on Gamma Ray Log Data and Log Density in Area X PT PMC Site Sungai Lilin: Identification of coal distribution pattern. Jurnal Bio-Geo Material Dan Energi, 3(2), 47-59. https://doi.org/10.22437/bigme.v3i2.30785