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All Journal Journal of Engineering and Technological Sciences Journal of Geoscience, Engineering, Environment, and Technology Scientific Contributions Oil and Gas
Dara Ayuda Maharsi, Dara Ayuda
Petroleum Engineering Study Program, Institut Teknologi Bandung, Jalan Ganesha No. 10, Bandung 40132,
Chemical Engineering, Chemistry & Bioengineering Earth & Planetary Sciences Energy Engineering Environmental Science Physics

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Surfactant-Polymer Coreflood Simulation and Uncertainty Analysis Derived from Laboratory Study Hakiki, Farizal; Maharsi, Dara Ayuda; Marhaendrajana, Taufan
Journal of Engineering and Technological Sciences Vol 47, No 6 (2015)
Publisher : ITB Journal Publisher, LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (767.754 KB) | DOI: 10.5614/j.eng.technol.sci.2015.47.6.9

Abstract

This paper presents a numerical simulation study on coreflood scale derived from a laboratory study conducted on light oil and water-wet sandstone samples from fields at Tempino and Kenali Asam, Sumatra, Indonesia. A rigorous laboratory study prompted a specified surfactant type among dozens of screened samples, i.e. AN3NS and AN2NS-M for Kenali Asam and Tempino, respectively. The coreflood scale numerical simulation study was performed using a commercial simulator, on the basis of the results from the laboratory study, at a constant temperature of 68°C, 0.3 cc/min injection rate and under 120 psia confining pressure. To get better recovery, the cores were tested using surfactant and polymer in a blended mode, containing 0.03% w/w polymer diluted in each field brine, which accommodated around 8000 ppm salinity. The most significant variable in the multiphase flow is the relative permeability curve, which is affected by interfacial tension (IFT) during waterflooding and surfactant-polymer (SP) flooding. This study shows that relative permeability will be shifted at ultra-low IFT (10-3 to 10-4 mN/m). This shifting phenomenon is governed by the interpolation parameter set, which implicitly represents the capillary number. Further work in matching the numerical results to the coreflood was conducted by changing the interpolation parameters.
Performance Comparison of Isolation Forest and COPOD Algorithms for Anomaly Detection in Electrical Submersible Pumps Asmara, Hygiano Paksi Widya; Maharsi, Dara Ayuda; Rachmanto, Rian
Journal of Geoscience, Engineering, Environment, and Technology Special Issue from The 2nd International Conference on Upstream Energy Technology and Digitalization
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2025.10.1.1.23849

Abstract

The Electrical Submersible Pump (ESP) is a crucial technology in enhancing oil production, yet its performance can be compromised by anomalies that lead to operational disruptions and financial losses. Early detection of these anomalies is vital for minimizing risks and optimizing ESP lifespan. This study compares the performance of two machine learning algorithms—Isolation Forest and Copula-Based Outlier Detection (COPOD)—in identifying anomalies in ESP operational data. The study uses both long-term historical data and short-term period data from a well in Field X, focusing on key operational parameters such as amperes, frequency, voltage, discharge pressure, motor temperature, vibration, and gross rate. The results indicate that Isolation Forest outperforms COPOD in detecting anomalies, particularly in the presence of missing data. Short-term data detection yields clearer correlations between anomalies in different features, highlighting its advantage over long-term historical data. The findings underscore the importance of utilizing short-term operational data and demonstrate how anomaly detection algorithms can enhance ESP monitoring for improved performance and cost-efficiency.
An Lstm-Based Anomaly Detection on Subsea Oil-Producing Well Maharsi, Dara Ayuda; Tampi, Syaloom Zefanya; Putri Oktaviani , Ajeng Purna
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.v48i4.1819

Abstract

The oil and gas industry faces substantial operational risks from anomalous events, necessitating effective Abnormal Event Management (AEM) to mitigate production losses and safety hazards. This study presents a supervised anomaly classification approach using Long Short-Term Memory (LSTM) networks on the 3W Dataset—comprising over 2,000 real, simulated, and expert-drawn events from offshore wells. Focusing on real instances with sufficient normal-state duration, the dataset was refined and segmented using observation windows of 60, 120, and 180 seconds. The models were trained on four selected pressure and temperature features and evaluated using precision, recall, and F1-score. Comparative analysis with Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) models shows that the LSTM model consistently performs best, achieving a peak F1-score of 92% at a 120-second window. Furthermore, event-level performance analysis highlights the LSTM model’s strengths and limitations across different anomaly types. Compared to existing supervised and unsupervised methods on the 3W Dataset, the LSTM-based approach demonstrates competitive accuracy and robustness for real-time anomaly detection in offshore oil production systems.
Co-Authors Asmara, Hygiano Paksi Widya Farizal Hakiki, Farizal Putri Oktaviani , Ajeng Purna Rachmanto, Rian Tampi, Syaloom Zefanya Taufan Marhaendrajana, Taufan