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All Journal Advance Sustainable Science, Engineering and Technology (ASSET)
Martadi
Institute Teknologi Nasional Yogyakarta
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Interpretation of Geophysical Well Logging and the Relationship between the Density Log and the Calorific Value of Coal Martadi; Setyo Pambudi; Okki Verdiansyah
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i4.3592

Abstract

Geophysical well logging is widely used in coal exploration to characterize subsurface lithology and coal seams; however, its application for rapid coal quality prediction remains limited in operational mining workflows. This study investigates the quantitative relationship between density log measurements and coal calorific value to develop a predictive approach for coal quality assessment based on geophysical logging data in the Lati and Labanan Formations within the Berau Sub Basin, which is part of the Tarakan Basin. The research uses geophysical well-logging data from 113 exploration boreholes. Bivariate geostatistical analysis and linear regression were applied to evaluate the correlation between density log values and calorific value (air-dried basis). The results show a strong positive relationship between coal density and calorific value, with coefficients of determination ranging from 0.90 to 0.98. These findings indicate that the density log measurements can serve as a reliable proxy for estimating coal energy quality. The proposed relationship model demonstrates the potential of geophysical logging data to support faster coal quality prediction. This approach contributes to the development of data-driven coal quality assessment methods that can complement laboratory testing within integrated digital mining workflows.
Co-Authors Okki Verdiansyah Setyo Pambudi