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All Journal Jurnal Jaring SainTek Petro : Jurnal Ilmiah Teknik Perminyakan Journal of Earth Energy Science, Engineering, and Technology Scientific Contributions Oil and Gas
Pahala Dominicus Sinurat
Institut Teknologi Bandung
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Industrial & Manufacturing Engineering

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Integration of Empirical Methods for Accurate Water Saturation Calculation in Low Resistivity Reservoir Citrowati, Sekar Ayu; Dedy Irawan; Pahala Dominicus Sinurat
Journal of Earth Energy Science, Engineering, and Technology Vol. 7 No. 3 (2024): JEESET VOL. 7 NO. 3 2024
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/6qfar389

Abstract

The Indonesian oil and gas industry faces significant challenges in exploring low-resistivity reservoirs, such as the Talang Akar Formation in South Sumatra, the Tanjung Formation in East Kalimantan, and the Gumai Formation in South Sumatra and West Java. These reservoirs often contain clay, clayey sand, and conductive minerals, which complicate geophysical log interpretation, leading to missed hydrocarbon potential. Common methods such as Archie’s Law are often used to calculate water saturation but tend to be inaccurate in formations with high conductivity due to clay content. The Simandoux method attempts to address this limitation by considering the conductivity of clay, but the assumption of homogeneous clay distribution often does not match actual conditions. This study proposes a modification to the Simandoux method by accounting for the non-linear behavior of clay conductivity and formation-specific parameters derived from core analysis. This approach integrates multi-parameter log data and advanced petrophysical models to address mineralogical heterogeneity and clay distribution. The results show that the modified Simandoux method provides more accurate water saturation estimates in low-resistivity zones. Validation with core and production data demonstrates the improved reliability of this model, supporting optimal field development and hydrocarbon exploration in Indonesia.
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.
Trap Prevention in Machine Learning in Prediction of Petrophysical Parameters: A Case Study in The Field X Adam Putra Pratama Zainuri; Pahala Dominicus Sinurat; Dedy Irawan; Hari Sasongko
Scientific Contributions Oil and Gas Vol 46 No 3 (2023)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.46.3.1586

Abstract

Petrophysical parameters such as porosity and water saturation are vital in the petroleum industry for reservoir characterization. These aspects are typically assessed through laboratorium measurements of core samples or intricate petrophysical calculations. Machine Learning (ML) offers a cost-effective and efficient approach as an alternative to conventional methods of predicting those parameters. However, developing ML models can be prone to the invisible traps such as overfitting, underfitting, feature selection, and feature importance. This study is intended to share how to identify the traps and its mitigation by establishing a synergistic workflow between ML and petrophysical theory. A model was developed based on data from several wells in X field, where they are randomized and split into test and train data. Well-log normalization preceded data splitting, and input features were normalized with outlier removal. A feature selection function was then employed to choose a specific amount of log data. Finally, the model selection function identified the highest-scoring model. Without a proper workflow, overfitting, irrelevant feature selection, and imprecise ranking issues emerged. However, with the proper workflow, these invisible traps were mitigated, even with a relatively small dataset. The final model could accurately predict porosity and water saturation
OPTIMIZATION OF LOG SHAPE CLUSTERING USING VARIOUS FEATURE EXTRACTION METHODS AND MACHINE LEARNING-BASED CLUSTERING ALGORITHMS IN THE NVS FIELD Nabil Visi Samawi; Dedy Irawan; Pahala Dominicus Sinurat
Petro : Jurnal Ilmiah Teknik Perminyakan Vol. 15 No. 1 (2026): Maret 2026
Publisher : Jurusan Teknik Perminyakan Fakultas Teknologi Kebumian dan Energi Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/petro.v15i1.25619

Abstract

Electrofacies clustering is fundamental to reservoir characterization but is often hindered by the subjectivity and inefficiency of conventional manual interpretation, particularly in heterogeneous fields. This study presents a robust, data-driven workflow for automating electrofacies identification using unsupervised machine learning, applied to Gamma Ray (GR) logs from 66 wells across 16 reservoir intervals in the NVS Field, Central Sumatra Basin. The methodology systematically evaluates the impact of feature representation by comparing Statistical, Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) extraction techniques coupled with K-Means, BIRCH, and Gaussian Mixture Model (GMM) clustering algorithms. Performance assessment using Silhouette scores and the Davies-Bouldin Index demonstrates that LSTM-based features consistently yield superior clustering results by capturing critical sequential log-shape dependencies essential for resolving vertical heterogeneity. While algorithmic efficacy was found to be context-dependent—with GMM favoring transitional facies and K-Means excelling in high-contrast zones—the integration of the optimal models successfully reconstructed geological patterns without prior labeling. External validation against reference facies maps confirmed that the unsupervised clusters exhibit strong spatial coherence, accurately delineating the Northwest-Southeast (NW-SE) depositional trend of Tidal Bar Axis and Margin zones. Furthermore, the model demonstrated high geological sensitivity by successfully identifying localized features such as Isolated Sand Bars. These findings verify the geological plausibility of the proposed workflow and underscore the necessity of sequence-aware feature extraction, offering a reproducible and objective framework for reservoir modeling in data-limited environments.
Porosity Estimation in A Natural CO2-Water Reservoir Using Integrated Density-Resistivity Log Approach Dominicus Sinurat, Pahala; Sasongko, Hari; Samawi, Nabil
Scientific Contributions Oil and Gas Vol 48 No 4 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i4.1887

Abstract

Natural CO₂ reservoirs represent important analogues for Carbon Capture and Storage (CCS) and Carbon Capture, Utilization, and Storage (CCUS), as they provide direct evidence of long-term CO₂ retention and trapping mechanisms. This study assesses porosity in a natural carbonate CO₂ reservoir using an integrated density–resistivity log approach. Conventional porosity logs, such as density, neutron, and sonic, often overestimate porosity in carbonate systems due to their limited sensitivity to pore connectivity. To overcome this limitation, density log-derived total porosity was integrated with resistivity-derived effective porosity, allowing for the differentiation between connected and isolated pore systems. Fluid density estimations, including supercritical CO₂ and brine, were computed and validated against standard references to ensure accuracy. The results show that density-only porosity overestimates values by up to 10% in dolomitic intervals, whereas the integrated method provides estimates that are more consistent with core measurements. Isolated porosity, averaging 2% in the upper dolomite and 1.5% in the lower dolomite, was identified as a non-contributing pore volume for injectivity, although it remains relevant for storage capacity. These findings underscore the importance of integrated log interpretation for precise reservoir characterization and offer new insights into evaluating natural CO₂ reservoirs for long-term geological storage.
Co-Authors Abda Abda Adam Putra Pratama Zainuri Adam Putra Pratama Zainuri Citrowati, Sekar Ayu Dedy Irawan Dedy Irawan Dedy Irawan Efrata, Marojahan Benedict Hari Sasongko Hari Sasongko Nabil Visi Samawi Samawi, Nabil Sasongko, Hari