Machine Learning for Reservoir Characterization: Lithology Prediction Using Support Vector Machine (SVM) in The "VISA" Field, East Kalimantan
DOI:
https://doi.org/10.29017/scog.v49i1.2032Keywords:
lithology prediction, support vector machine, radial basis function, accuracyAbstract
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.
References
Abhimantra, S. (2021). Geologi Dan Studi Sikuen Stratigrafi Formasi Balikpapan, Lapangan “Minggiran “Cekungan Kutai, Kalimantan Timur. Jurnal Ilmiah Geologi PANGEA, 2(2).
Asquith, G. B., Krygowski, D., & Gibson, C. R. (2004). Basic well log analysis (Vol. 16, pp. 305-371). Tulsa: American Association of Petroleum Geologists.
Augusto, F. D. O. A., & Martins, J. L. (2009). A well-log regression analysis for P-wave velocity prediction in the namorado oil field, Campos basin. Revista Brasileira de Geofisica, 27, 595-608.
Al Ghaithi, A., & Prasad, M. (2020). Machine learning with artificial neural networks for shear log predictions in the Volve field Norwegian North Sea. In SEG Technical Program Expanded Abstracts 2020 (pp. 450-454). Society of Exploration Geophysicists.
Burges, C. J. (1998). A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2), 121-167.
Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer.
Battu, D. P. (2026). Characterization Of Deltaic Source Rocks And Hydrocarbon Potential In The Lower Kutai Basin. Scientific Contributions Oil and Gas, 49(1), 55-67.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20, 273-297.
Doust, H., & Noble, R. A. (2008). Petroleum systems of Indonesia. Marine and Petroleum Geology, 25(2), 103-129.
Ellis, D. V., & Singer, J. M. (2007). Well logging for earth scientists (Vol. 692). Dordrecht: Springer.
Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O'Reilly Media, Inc.".
Guo, G., Diaz, M. A., Paz, F., Smalley, J., & Waninger, E. A. (2005). Rock typing as an effective tool for permeability and water-saturation modeling: a case study in a clastic reservoir in the Oriente Basin. In SPE Annual Technical Conference and Exhibition? (pp. SPE-97033). SPE.
Horsfall, O. I., Omubo-Pepple, V. B., & Tamunobereton-ari, I. (2013). Correlation analysis between sonic and density logs for porosity determination in the south-eastern part of the Niger Delta Basin of Nigeria. Asian Journal of Science and Technology, 4(1), 1-5.
Husein, S. (2015). Petroleum and Regional Geology of Northeast Java Basin, Indonesia-Excursion Guide Book for Universiti Teknologi Petronas Malaysia. Department of Geological Engineering Universitas Gadjah Mada.
Hidayat, H., Setiawan, J. J. H., Ibrahim, A., Marjiyono, M., & Junursyah, G. L. (2021). Studi Magnetotelurik (MT) untuk Mendelineasi Potensi Regional Gas Serpih Bawah Permukaan Berdasarkan Properti Tahanan Jenis di Cekungan Kutai, Kalimantan Timur. Jurnal Geologi dan Sumberdaya Mineral, 22(2), 107-114.
Handoyo, F., Fourier, D. E. L., Reza, R., & Harnanti, Y. P. (2018). Estimasi Parameter Fisis Batuan Berdasarkan Citra Batuan (Digital Rock Physics) Studi Kasus: Lapangan Minyak Bumi Blok Cepu, Jawa Tengah, Indonesia. Jurnal Geofisika, 16(01), 21-26.
Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer.
Khawarizmy, A., Haryanto, I., Ganjar, R. M., & Firmansyah, Y. (2020). ASIES PENGENDAPAN DAN POROSITAS BATUAN KARBONAT FORMASI KUJUNG I CEKUNGAN JAWA TIMUR UTARA LAPANGAN “TENGAH”. Geoscience Journal, 4(1), 61-67.
Killeen, P. G. (1982). Gamma-ray logging and interpretation. In Developments in Geophysical Exploration Methods—3 (pp. 95-150). Dordrecht: Springer Netherlands.
Lunt, P. (2019). The origin of the East Java Sea basins deduced from sequence stratigraphy. Marine and Petroleum Geology, 105, 17-31.
Moherek, A., Mukherjee*, S., Garrison, N., Caraway, A., Medina, R., & Sarmah, B. B. (2016, August). Application of High-Resolution Blended Resistivity Measurement in Characterization of Unconventional Plays. In Unconventional Resources Technology Conference, San Antonio, Texas, 1-3 August 2016 (pp. 2706-2717). Society of Exploration Geophysicists, American Association of Petroleum Geologists, Society of Petroleum Engineers.
Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9(1), 381-386.
Munadi, S. (2007). The Effect of Oil Content on Sonic Wave Propagation (Analyses from Well Log Data). Scientific Contributions Oil and Gas, 30(2), 33-37. https://doi.org/10.29017/SCOG.30.2.983
Passey, Q. R., Bohacs, K. M., Esch, W. L., Klimentidis, R., & Sinha, S. (2010, June). From oil-prone source rock to gas-producing shale reservoir–geologic and petrophysical characterization of unconventional shale-gas reservoirs. In SPE International Oil and Gas Conference and Exhibition in China (pp. SPE-131350). SPE.
Peng, J., Jury, E. C., Dönnes, P., & Ciurtin, C. (2021). Machine learning techniques for personalized medicine approaches in immune-mediated chronic inflammatory diseases: applications and challenges. Frontiers in pharmacology, 12, 720694.
Qi, Q., Fu, L. Y., Deng, J., & Cao, J. (2021). Attenuation methods for quantifying gas saturation in organic-rich shale and tight gas formations. Geophysics, 86(2), D65-D75.
Serra, O. E. (1983). Fundamentals of well-log interpretation.
Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge university press.
Sunarjanto, D., Suliantara, S., Iskandar, U. P., & Nainggolan, M. T. (2014). Sistem Informasi Geogra untuk Optimasi Eksplorasi dan Pengembangan Wilayah Migas Geographic Information System for Optimization Exploration Oil and Gas Area Development. Lembaran publikasi minyak dan gas bumi, 48(1), 1-12. https://doi.org/10.29017/LPMGB.48.1.225
Sebtosheikh, M. A., & Salehi, A. (2015). Lithology prediction by support vector classifiers using inverted seismic attributes data and petrophysical logs as a new approach and investigation of training data set size effect on its performance in a heterogeneous carbonate reservoir. Journal of Petroleum Science and Engineering, 134, 143-149.
Sukma, R. F. N., Rosid, M. S., & Wijanarko, E. (2025). Telisa Formation Characterization Using Seismic Acoustic Impedance Inversion in the Akasia Area of the Central Sumatra Basin. Scientific Contributions Oil and Gas, 48(2), 29-40. https://doi.org/10.29017/scog.v48i2.1774
Usama, M., Qadir, J., Raza, A., Arif, H., Yau, K. L. A., Elkhatib, Y., ... & Al-Fuqaha, A. (2019). Unsupervised machine learning for networking: Techniques, applications and research challenges. IEEE access, 7, 65579-65615.
Wardhana, S. G., Pakpahan, H. J., Simarmata, K., Pranowo, W., & Purba, H. (2021). Algoritma komputasi machine learning untuk aplikasi prediksi nilai total organic carbon (TOC). Lembaran publikasi minyak dan gas bumi, 55(2), 75-87. https://doi.org/10.29017/LPMGB.55.2.606
Zamri, N., Pairan, M. A., Azman, W. N. A. W., Abas, S. S., Abdullah, L., Naim, S., ... & Gao, M. (2022). A comparison of unsupervised and supervised machine learning algorithms to predict water pollutions. Procedia Computer Science, 204, 172-179.
Zaemi, F. F., Rohmana, R. C., & Atmoko, W. (2022). Uncovering The Potential of Low Resistivity Reservoirs Through Integrated Analysis: A Case Study from The Talang Akar Formation in The South Sumatra Basin. Scientific Contributions Oil and Gas, 45(3), 169-181. https://doi.org/10.29017/SCOG.45.3.1258
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