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All Journal Petro : Jurnal Ilmiah Teknik Perminyakan Scientific Contributions Oil and Gas
Dedy Irawan
Bandung Institute of Technology
Chemical Engineering, Chemistry & Bioengineering Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering

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A Techno-Economic Approach to Optimizing CCS Fiscal Parameters in Indonesia: A Case Study of Integrated Oil and Gas Development in CO2-Rich Areas Najeela Faza Ramadhani; Dedy Irawan; Sudono; Prasandi Abdul Aziz
Scientific Contributions Oil and Gas Vol 48 No 3 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i3.1809

Abstract

This study introduces a techno-economic approach to optimizing storage fees for CCS integrated with oil and gas development. The analysis adopts the production sharing contract cost recovery model in accordance with the implementation of Ministerial Regulation of Energy and Mineral Resources No. 16 of 2024, which addresses CCS-related parameters. Technical assessment confirms the studied reservoir’s suitability for long-term CO₂ injection through 5 injection wells, while oil and gas development are supported by 10 oil wells and 8 gas wells. The project’s economic viability under baseline conditions shows an IRR of 10.14% and POT of 15.73 years. Sensitivity analysis across fiscal parameters, such as investment credit, FTP, contractor split, CCS service fee and storage fee, CAPEX, royalty, and tax, identifies the storage fee as the most influential factor for viability. To achieve a commercially viable IRR of 15%, the project requires a minimum CCS service fee of 55 US$/MT and a storage fee of at least 35 US$/MT. The study underscores the need for clear regulations on fiscal incentives, CO₂ pricing, storage fees, and PSC integration to enhance CCS economic viability, while also offering a replicable framework for CO₂ assessments under dynamic fiscal regimes.
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.
Co-Authors Nabil Visi Samawi Najeela Faza Ramadhani Pahala Dominicus Sinurat Prasandi Abdul Aziz Sudono