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All Journal JOURNAL ONLINE OF PHYSICS Scientific Contributions Oil and Gas
Abdul Latiff, Abdul Halim
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Chemical Engineering, Chemistry & Bioengineering Energy Physics

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Machine Learning for Reservoir Characterization: Lithology Prediction Using Support Vector Machine (SVM) in The "VISA" Field, East Kalimantan Muhammad Faiz Nugraha; Eki Komara; Abdul Latiff, Abdul Halim; Wien Lestari; Edy Wijanarko
Scientific Contributions Oil and Gas Vol 49 No 1 (2026)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v49i1.2032

Abstract

This research was conducted in the "VISA" field of the Balikpapan Formation, located in the Kutai Basin, one of Indonesia's largest hydrocarbon basins. The lithology of this formation is primarily Sandstone and shale, which are significant for hydrocarbon exploration and production. Determining the initial lithology is an essential step for understanding the characteristics of the well data during processing. Consequently, the Support Vector Machine (SVM) algorithm was implemented in this study to predict lithology using well data. This investigation employs data from four wells: VISA-9, VISA-13, VISA-36, and VISA-39. The prediction results are subsequently visualized as well as logs and lithology distribution histograms to make the results easier to interpret based on three interpreted lithology categories: Sandstone, shale, and Coal. Performance evaluations indicate that limitations remain in the SVM classification. The error range obtained in Experiments 1 and 2 was 11–22% compared to the actual lithology. However, Experiment 3 demonstrated substantial improvement by utilizing three training datasets, which reduced the error rate to 5% (a 7% improvement from previous experiments). Overall, the SVM method can effectively classify rock lithology; however, the model still requires optimization to minimize residual errors during the prediction process. Ultimately, this investigation demonstrates that SVM can be successfully applied to predict lithology using well log parameters.
DISTRIBUTED ACOUSTIC SENSING (DAS) FOR GEOTHERMAL EXPLORATION: A CASE STUDY OF THE KIZILDERE GEOTHERMAL FIELD Putra, Ahmad Dedi; Abdul Latiff, Abdul Halim; Mohd Noh, Khairul Arifin; Aparajita, Made Jnanaparama; Pascaloa, M. Rafif; Naufal, Harish Hartsa
JOURNAL ONLINE OF PHYSICS Vol. 11 No. 3 (2026): JOP (Journal Online of Physics) Vol 11 No 3
Publisher : Prodi Fisika FST UNJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jop.v11i3.48530

Abstract

Geothermal resources have been popular around the world as a cleaner type of alternative energy source to hydrocarbons. Geothermal resources can be used for many purposes such as power generation, direct use, and more. In exploring geothermal resources, details and extensive investigations are required. Distributed acoustic sensing (DAS) is an advanced technology that is becoming more widely used in geoscientific studies, particularly for geothermal exploration. DAS records seismic signals using fiber optic cable instead of geophones, offering advantages over conventional methods in terms of cost, ease of deployment, and capability for continuous real-time monitoring. In this paper, we present a DAS data simulation to study the potential of DAS in geothermal exploration. This study involved several steps, including constructing a geological model based on the Kizildere Geothermal Field Model, simulating seismic data for DAS, and analyzing the DAS data in terms of signal quality and subsurface imaging resolution. The results demonstrated that DAS effectively imaged the subsurface and produced structural images comparable to those from geophones. Although DAS amplitudes are lower, the geological features remain clearly visible, suggesting that DAS is a viable and promising technology for geothermal exploration.  
Co-Authors Aparajita, Made Jnanaparama Edy Wijanarko Eki Komara Mohd Noh, Khairul Arifin Muhammad Faiz Nugraha Naufal, Harish Hartsa Pascaloa, M. Rafif Putra, Ahmad Dedi Wien Lestari