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

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A PROXY MODEL TO PREDICT WATERFLOODING PERFORMANCE IN CHANNELING DELTAIC SAND RESERVOIR Amega Yasutra; Dedy Irawan; Frans Ondihon Sitompul
PETRO: Jurnal Ilmiah Teknik Perminyakan Vol. 9 No. 1 (2020): MARET
Publisher : Jurusan Teknik Perminyakan Fakultas Teknologi Kebumian dan Energi Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1194.198 KB) | DOI: 10.25105/petro.v9i1.5992

Abstract

In recent days, waterflooding activities carried out as a part of secondary recovery. Before performing waterflooding, engineers have to perform reservoir simulation first to predict reservoir performance in order to waterflood. Generally, reservoir simulation is conducted by using numerical simulation method. Numerical simulation gives precise results although it depens on the availiability, quality, and quantity of reservoir characteristic and injection operation data. In addition, numerical simulation also time-consuming and quite complex to use. Proxy model is kind of machine learning. It’s able to predict performance of waterflooding quickly and easier to use. The result isn’t differ too much with numerical simulation method. Proxy model is an equation model that construct form quite many experiment data. This research is trying to predict performance of normal 5 spot waterflooding in reservoir with channeling deltaic sand sedimentation by using proxy model. The proxy model will be tested on a real field case. The results indicate that proxy model is able, faster, reliable and easy to use to predict waterflooding performance in such type of reservoir.
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.
Applying Artificial Neural Network and XGBoost to Improve Data Analytics in Oil and Gas Industry Ricky Simanjuntak; Dedy Irawan
Indonesian Journal of Energy Vol. 4 No. 1 (2021): Indonesian Journal of Energy
Publisher : Purnomo Yusgiantoro Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33116/ije.v4i1.103

Abstract

The application of machine learning and artificial intelligence is popular nowadays to improve data analytics in the oil and gas industry. A huge amount of data can be processed to gain insights about the subsurface conditions, even reducing time for manual review or interpretation. There are three cases to be discussed in this study that starts from porosity estimation of thin core image using Otsu's thresholding, estimation of oil production rate from sucker-rod pumping wells and sonic travel-time log generation. Two supervised learning algorithms are applied, XGBoost and Keras. These algorithms will capture all possible correlations between the input and output data. From data normalization, exploratory data analysis and model building, the workflow is built on Google Colab. The original dataset is split into training and testing. Tuning hyperparameters such as the number of hidden layers, neurons, activation function, optimizers and learning rates are captured to reduce the complexity of the model. The model is evaluated by error values and the coefficient of determination to estimate the model skill on unseen data.
Integrated Static–Dynamic Analysis for Sweet Spot Identification and Reserves Prediction in Low-Permeability Reservoirs Using Production Data and Geospatial Attributes Yuliani Yuniwati; Dedy Irawan; Ivan Kurnia; Alfian Gilang; M. Soleh Ibrahim
Scientific Contributions Oil and Gas Vol 49 No 2 (2026)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v49i2.2085

Abstract

The optimization of infill well placement and the reliable prediction of Estimated Ultimate Recovery (EUR) continue to represent critical challenges in low-permeability reservoir. Conventional permeability evaluation using Pressure Build-Up tests is often impractical due to prolonged shut-in requirements and operational constraints. Although geospatial tools such as Antelope Map effectively identify fracable zones associated with high initial production rates, sustained long-term recovery is not necessarily guaranteed. This study proposes an integrated static–dynamic framework that combines geospatial attributes with production-based analysis to improve reservoirs characterization and well placement decisions. In-situ permeability and flow capacity, expressed as √khXf, are extracted directly from routine production data of 14 hydraulically fractured wells using the Inverted Decline Curve method, thereby bypassing the limitations of pressure transient analysis. The Stretched Exponential Production Decline model is subsequently applied to generate bounded and realistic EUR predictions. Pearson correlation analysis shows a weak relationship between permeability derived from static logs and EUR. In contrast, the production-derived √khXf the parameter shows the strongest positive correlation, reflecting effective flow capacity and the influence of matrix heterogeneities such as trace fossils. By comparing the √kh distribution with the static Antelope Map, this dual-criteria approach helps identify sweet spots that support both favourable fracability and more sustained fluid delivery. In general, this data-driven workflow provides a practical alternative framework to reduce geological uncertainty and optimize well placement in low-quality reservoirs.
Co-Authors Alfian Gilang Amega Yasutra Aziz, Prasandi Abdul Frans Ondihon Sitompul Ivan Kurnia Khreshna Imaduddin Ahmad Syuhada M. Soleh Ibrahim Nabil Visi Samawi Najeela Faza Ramadhani Pahala Dominicus Sinurat Ricky Simanjuntak Sudono Yuliani Yuniwati