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Scientific Contributions Oil and Gas
Published by LEMIGAS
ISSN : 20893361     EISSN : 25410520     DOI : -
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Energy
The Scientific Contributions for Oil and Gas is the official journal of the Testing Center for Oil and Gas LEMIGAS for the dissemination of information on research activities, technology engineering development and laboratory testing in the oil and gas field. Manuscripts in English are accepted from all in any institutions, college and industry oil and gas throughout the country and overseas.
Arjuna Subject : Energi (semua kategori) - Teknologi Bahan Bakar
Articles 30 Documents
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Search results for , issue "Vol 48 No 4 (2025)" : 30 Documents clear
Application of PCA and Machine Learning for Predicting Oil Measurement Discrepancies in Custody Transfer Systems: Understanding from an Indonesian Mature Onshore Facility Wan Fadly; Fiki Hidayat; Noratikah Abu; Muhammad Khairul Afdhol; Dike Putra; Mulyandri
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.v48i4.404

Abstract

Oil measured volume discrepancies in custody transfer systems is becoming a persistent challenge, which is often caused by complex thermal, hydraulic, and compositional interactions. Therefore, this study aimed to introduce a data-driven framework incorporating Principal Component Analysis (PCA) and machine learning (ML) to identify as well as predict discrepancies at a representative onshore gathering station (GS) in Indonesia (Field-X). Major operational parameters, including gross volume, unallocated net oil, pressure, temperature, and Basic Sediment & Water (BS&W), were analyzed to assess the impact on volumetric imbalance. During the analysis, PCA reduced 64 correlated variables to five principal components, explaining 95% of the total variance and showing gross volume, pressure, and temperature as dominant factors. Four ML models, namely XGBoost, Random Forest, Support Vector Regression, and ElasticNet, were trained as well as validated with three-fold time series cross-validation for temporal robustness. Incorporating PCA significantly improved predictive performance, with Support Vector Regression showing the largest R² increase (from –0.0082 to 0.82). Results signified that discrepancies were primarily governed by thermodynamic shrinkage, temperature changes, and BS&W-related metering errors. In addition, the proposed PCA–ML framework offered an interpretable, reliable method for early detection and mitigation of oil volume discrepancies in complex production environments.
Comparative Analysis of The Use of Nanosilica and Potassium Chloride as Shale Inhibitor in Water Based Mud Nur Suhascaryo; Syifa Khasyikirana Ramadhanti; Ketut Rama Wijaya; Miftahul Jannah
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.v48i4.1768

Abstract

Swelling shale is one of the most common problems encountered in oil and gas drilling operations. Potassium chloride (KCl) is widely applied as a shale inhibitor due to its ionic inhibition mechanism; however, excessive KCl concentrations can have detrimental effects on drilling mud performance. This study examines the potential of nanosilica derived from geothermal industrial waste as a substitute for KCl. Five mud samples were tested: base fluid, 1% nanosilica, 3% nanosilica, 1% KCl, and 3% KCl. The samples were evaluated through a series of physical property tests, including density, rheology, filtration loss, pH, methylene blue test (MBT), K⁺ concentration, and Cl⁻ concentration. Swelling-related parameters were also assessed using Linear Swelling Meter (LSM), accretion tests, and erosion tests under both before hot rolling (BHR) and after hot rolling (AHR) conditions at 200°F for 16 hours. The results indicate that nanosilica improved rheological properties and reduced shale swelling compared to the base fluid. Meanwhile, the 1% KCl formulation demonstrated strong performance in LSM and erosion tests. Overall, nanosilica shows potential as a partial substitute for KCl as a shale inhibitor; however, surface modification and field-scale validation are recommended for further confirmation.
Modern Palacio-Blasingame Type Curve Method to Determine Well Production Characteristics and Reserves in Fields in Indonesia Muhammad Zakiy Yusrizal; Edgie Yuda Kaesti; Ratna Widyaningsih; Hari Prapcoyo; Nia Nuraeni
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.v48i4.1784

Abstract

Well production characteristics and reserves are critical parameters in field development planning and production optimization. In general, well production characteristics are obtained through well testing, followed by plotting and extrapolating flow rate against time, commonly referred to as the cumulative production curve. Conventional decline curve analysis models production decline under constant bottom-hole pressure during boundary-dominated flow periods. However, this approach is inadequate for analyzing data obtained during transient flow periods and requires substantial time and cost when applied to large fields with numerous wells. The modern Palacio-Blasingame type-curve method enables the integration of daily production data with reservoir information by accounting for variations in bottom-hole pressure and changes in gas pressure–volume–temperature characteristics as reservoir pressure declines. This approach enhances the accuracy of well performance evaluation and reserve estimation, provides a more comprehensive understanding of reservoir dynamics, improves efficiency by reducing analysis time and associated costs compared with the conventional decline curve methods.
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.
Effects of Palm-Oil-Based Methyl Ester Sulfonate (MES) in Laboratory-Scale Enhanced Oil Recovery Process Onnie Ridaliani; Samsol; Rini Setiati; Muhammad Taufiq Fathaddin; Lilian Anggela; Andry Prima; Nandito Davy; Widia Yanti
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.v48i4.1825

Abstract

Natural Declining oil production is often caused by reduced natural driving forces within reservoirs. To address this limitation, enhanced oil recovery (EOR) technology introduces external energy or chemical agents to mobilize residual oil. This study evaluated the performance of palm-oil-based methyl ester sulfonate (MES) an anionic and biodegradable surfactant synthesized from renewable feedstock for improving recovery efficiency under laboratory-scale conditions. Core-flood experiments were performed using Berea sandstone cores, intermediate 33°API crude oil, low salinity of 10,000 ppm, synthetic brine at 60 °C. The testing sequence included screening test of palm-oil-based MES, brine saturation, oil saturation, waterflooding, and subsequent surfactant flooding with 1.5% MES solution. During waterflooding, the recovery factor reached 62.8 %, leaving 31.29 % residual oil saturation. Injection of 1.5 wt % MES increased the recovery factor to 68.8 % and reduced residual oil saturation to 26.25 %, indicating enhanced displacement and improved microscopic sweep efficiency. The results confirmed that palm-oil-derived MES effectively mobilizes trapped oil and demonstrates strong potential as an environmentally friendly and locally available surfactant for chemical EOR applications in the reservoirs.
Determination of Scale Inhibitor Effective Dose for Well A28 Using A Differential Scale Loop Method Maulana Hardi; Rene Indrawan Pratamora; Oktaviani Kusuma Wardani; Dzulhijah Nur Meisinca
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.v48i4.1826

Abstract

Well A28 in the Rokan Field has been identified with scale deposition on the surface flowline. The Scaling Index (SI) calculated using the Stiff and Davis method was +3.89, showing a high potential for aggressive scale formation. These deposits originate from mineral precipitation in produced water. To address this issue, scale inhibitor injection was applied, and the optimum dosage was determined using the Differential Scale Loop (DSL) method. This method evaluates inhibitor performance based on differential pressure caused by scale formation under field conditions (temperature 127 °C, flow rate 5 mL/min, operating pressure 300 psi). Tests were conducted using inhibitor doses of 25 ppm, 35 ppm, and 50 ppm. The results showed that a dose of 35 ppm produced the highest inhibition efficiency, reaching 100.3%, while also exhibiting minimal pressure drop. This dosage proved more effective than the other concentrations evaluated. Identifying this optimum dose supports reductions in chemical consumption and maintenance frequency, offering practical and cost-efficient benefits for field operations.
CO2 Storage Screening Criteria Based on Seal Capacity in Indonesia Syifa Destiana; Dedy Irawan; Prasandi Abdul Aziz; Ika Merdekawati
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.v48i4.1829

Abstract

CO2 storage screening ensuring the long-term containment of injected CO2 and the integrity of carbon capture and storage. In Indonesia, robust seal evaluation is constrained by the limited availability of caprock core data. This study develops a dimensionless Caprock Quality Index (CQI) as a practical CO2 storage screening tool based on displacement pressure (Pd) and caprock thickness (h). Displacement pressure is estimated using an empirical Pd equation derived from existing caprock core data. The CQI provides a quantitative classification of seal quality within the 0-1 range, where values closer to 1 indicate better caprock quality. Based on the data availability of this study, the results show that the Banggai and Salawati basins currently exhibit the highest CQI, indicating strong suitability for CO₂ storage. This study provides a framework for conducting preliminary CO₂ storage screening, particularly valuable in settings where caprock core data are sparse and contributes to the development of a more efficient, data-driven framework for future CCS planning and implementation.
Optimization of Alternative CMC Sources from Rice Husk, Sawdust, and Caustic Soda, and The Effect of PH Increase on Filtration Loss and Rheology of Drilling Mud Lisa Samura; Cahaya Rosyidan; Mustamina Maulani; Andry Prima; Maman Djumantara; Dina Asmaul Chusniyah; Aqlyna Fattahanisa; Bayu Satiyawira; Mentari Gracia Soekardy; Brilliani
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.v48i4.1849

Abstract

Drilling mud plays a vital role in maintaining wellbore stability, carrying cuttings, and controlling formation pressure during drilling operations. Typically, Carboxy Methyl Cellulose (CMC) is used to enhance mud viscosity and reduce filtration loss, but its synthetic nature makes it relatively expensive. This study investigates rice husk and sawdust as natural, cost-effective alternatives to CMC. Various compositions were evaluated using the Box-Behnken design in Response Surface Methodology (RSM) to optimize the mud formulation. Results indicate that a combination of 6 g rice husk and 6 g sawdust provides the best performance in improving rheological properties such as yield point and gel strength, while significantly reducing filtration loss. Gradual addition of caustic soda (NaOH) effectively increases mud pH to the ideal range (9–11), enhancing chemical stability. RSM successfully modeled the statistical relationship among variables and facilitated identification of the optimal formulation.
Quantitative Assessment of Calcite Scaling of A Vapour-Dominated Well Arya Dwi Candra; Leonardus Farel Putra Agin; Wien Pratama Abi Wicaksono
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.v48i4.1861

Abstract

Geothermal scaling is a prevalent issue that significantly impacts the efficiency of thermal energy production, drawing considerable attention in the field. Scaling formation is attributed to multiple factors, including variations in pressure and temperature. In this field, scaling deposits have been associated with an observed production decline of approximately 3.2%, posing a substantial challenge to maintaining optimal operational efficiency. This study aims to quantitatively assess the potential for calcite scaling in selected production wells and to estimate scaling growth rates as a basis for determining appropriate well-cleaning intervals. Geochemical data from produced fluids were analyzed to evaluate calcite and silica saturation using saturation indices derived from simplified thermodynamic relationships. Calcite scaling potential was assessed using the Calcite Saturation Index (CSI), while silica scaling was evaluated using the Silica Saturation Index (SSI). The growth rate of calcite deposits was estimated using a kinetic-based Calcite Scaling Thickness (CST) approach. The results indicate that one production well exhibits calcite supersaturation, while silica scaling is not expected under the analyzed conditions. Based on the applied assumptions, the estimated calcite scaling growth rate suggests that periodic well-cleaning interventions are required to maintain production performance. However, the calculations rely on simplified geochemical assumptions, including the use of concentration-based approximations and empirical kinetic parameters. Therefore, the results should be interpreted as an operational estimate rather than a definitive prediction, and further validation using activity-based geochemical modeling and direct scale characterization is recommended. This study provides an operationally oriented framework for linking geochemical indicators to well-maintenance planning in vapour-dominated geothermal fields.
Adaptive Neuro Fuzzy Inference System Mathematical Model for Detecting Gasoline Type Using Inter Digital Capacitance Sensor Galang Persada Nurani Hakim; Mohd. Radzi Abu Mansor; Diah Septiyana
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.v48i4.1862

Abstract

In the context of global warming, governments worldwide are striving to control emissions from combustion engines by promoting higher RON gasoline types. However, the higher cost of these fuels has led to a decrease in their usage. Detecting the type of gasoline in a vehicle is a complex and inefficient process. Therefore, this research presents a mathematical model for identifying gasoline type and its components using an Inter Digital Capacitor (IDC) sensor, a small and cost-effective sensor. The model aims to establish a relationship between gasoline type and the components, as well as identify gasoline components in the electrical characteristics. The model has achieved high accuracy, with a small error of 4.03 × 10^-5, demonstrating its effectiveness in building these relations. The conclusion of this study is that mathematical modeling with ANFIS can be used to explain the relationship between the components that make up gasoline and the capacitance value of the IDC sensor used to measure it.

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