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Efrata, Marojahan Benedict
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Energy Engineering Industrial & Manufacturing Engineering

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Classification of Rock Types Using Machine Learning Efrata, Marojahan Benedict; Spalanzani, Widya; Citrowati, Sekar Ayu
Jurnal Jaring SainTek Vol. 6 No. 2 (204): Oktober 2024
Publisher : Fakultas Teknik, Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/2hfrbw48

Abstract

Determining the petrophysical rock type often excludes measured multiphase flow properties, such as relative permeability curves. This is due to limitations in SCAL experiments or difficulties in correlating relative permeability characteristics with standard rock types. However, with a significant number of relative permeability curves, Machine Learning methods can be applied to automatically and objectively classify rock types based on the shape of these curves. This approach combines principal component analysis with unsupervised clustering schemes and preprocesses relative permeability curve data by integrating irreducible water saturation and residual oil. The methodology was tested on real data from carbonate reservoirs with a substantial number of relative permeability curves, demonstrating successful clustering based on fractional flow curves. The results indicate that this clustering can classify rocks from poor to optimal displacement efficiency. Furthermore, the study highlights the importance of high-quality SCAL experiments for normalizing curves and ensuring consistency between capillary pressure measurements and relative permeability. This Machine Learning approach is also compared with capillary pressure analysis, showing that relative permeability data provides additional information in rock typing studies, affirming the feasibility of Machine Learning for automatic rock type classification.
Pengembangan Model Perhitungan Saturasi Air (Sw) Untuk Pemodelan Saturation Height Function (SHF) Di Lapangan "MBES" Efrata, Marojahan Benedict; Irawan, Dedy; Sinurat, Pahala Dominicus
Jurnal Jaring SainTek Vol. 7 No. 1 (2025): April 2025
Publisher : Fakultas Teknik, Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/0ghc0336

Abstract

The determination of water saturation (Sw) is crucial in reservoir modeling as it significantly impacts hydrocarbon volumetrics calculations. Current methods such as Archie’s and Simandoux approaches often face challenges in capturing the complexities of heterogeneous reservoir rocks, particularly in carbonate formations. This study aims to develop an improved method for calculating Sw that enhances Saturation Height Function (SHF) modeling, especially in carbonate reservoirs, and can be validated with well log data. The proposed model offers a more accurate and efficient alternative by reducing the reliance on core permeability data and addressing the shortcomings of traditional methods. The research was conducted at the MBES field, located offshore in the Java Sea, Indonesia, focusing on the Talang Akar formation. The method integrates well log data and new computational techniques to predict water saturation in transition zones between oil, gas, and water. Results show significant improvements in the accuracy of SHF modeling, ultimately leading to more reliable predictions of hydrocarbon reserves and efficient reservoir management.
Co-Authors Citrowati, Sekar Ayu Irawan, Dedy Sinurat, Pahala Dominicus Spalanzani, Widya