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All Journal Petro : Jurnal Ilmiah Teknik Perminyakan Jurnal Migasian Scientific Contributions Oil and Gas
Ajeng Purna Putri Oktaviani
Universitas Pertamina
Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science

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OPTIMIZATION OF SURFACTANT FLOODING ON BJG FIELD USING DYNAMIC PATTERN Ajeng Purna Putri Oktaviani; Leksono Mucharam
PETRO: Jurnal Ilmiah Teknik Perminyakan Vol. 9 No. 2 (2020): JUNI
Publisher : Jurusan Teknik Perminyakan Fakultas Teknologi Kebumian dan Energi Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2051.541 KB) | DOI: 10.25105/petro.v9i2.6984

Abstract

Mature fields, also known as brownfields, are fields that are in a state of declining production or reaching the end of their production lives.  Development of mature oil fields has been, and will increasingly be, an exciting subject (Babadagli, 2007). New studies already discovered innovative ways of finding, developing, and producing hydrocarbons that are efficient and cost-effective and minimize harm to the environment. BJG Field is one of the mature fields which is produced in 1927, one of the efforts for enhancing the production is using waterflood at the beginning of 2001. To increase production further, then we need to conducted studies as an application of the second recovery from BJG Field. The oil recovery factor BJG field can be increased using a surfactant flooding scenario. This research aimed to conduct a study of dynamic pattern surfactant flooding using simulations as applicable for the mature field. The research is expected to obtain an optimum surfactant injection scenario using IMEX and STARS simulator. Simulation is done with real data from the BJG field, and the result has shown the scenario which has the most significant oil production. The highest recovery factor is the chosen scenario. From the results of studies and simulation shown that dynamic pattern inverted five-spot pattern can be used. The increment of oil recovery factor is 32.29% from the waterflood case.
Studi Simulasi Reservoir pada Lapangan Shale Gas Prima Adhi Surya; Ajeng Purna Putri Oktaviani
Jurnal Migasian Vol 6 No 1 (2022): Jurnal Migasian
Publisher : LPPM Institut Teknologi Petroleum Balongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36601/jurnal-migasian.v6i1.193

Abstract

Indonesia has potential shale gas reserves as much as 303 TCF. To maximize shale gas production, reservoir stimulation by hydraulic fracturing and injection may be needed. Furthermore, because shale gas reservoir is very complex, reservoir simulation needs to be performed to help produce gas trapped in the shale formation. This study provides process that is done to model shale gas field and observe desorption phenomenon in the reservoir during production phase. This study also performs N2 gas injection to analyze the effect of injecting the gas for reservoir performance. The number of simulations done in this study is four simulation scenarios. From all four shale gas reservoir simulations, cumulative production of methane gas, reservoir pressure, recovery factor, and cumulative of N2 gas that are produced back to the surface will be obtained. From the study it is shown that hydraulic fracturing, desorption phenomenon, and apparent permeability due to non-darcy fluid behavior becomes important in shale gas reservoir. Each simulation scenario has differences and shows that desorption phenomenon is more impactful to the reservoir performance in shale gas field. Simulation results also shows that N2 injection doesn’t significantly affect shale gas reservoir production performance.
An Lstm-Based Anomaly Detection on Subsea Oil-Producing Well Dara Ayuda Maharsi; Syaloom Zefanya Tampi; Ajeng Purna Putri Oktaviani
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 Dara Ayuda Maharsi Leksono Mucharam Prima Adhi Surya Syaloom Zefanya Tampi