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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 619 Documents
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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.
Estimation of Well Flowing Bottomhole Pressure (FBHP) Using Machine Learning Sugiyanto; Ditdit Nugeraha Utama
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.v48i3.1851

Abstract

Flowing Bottomhole Pressure (FBHP) is an essential factor for oil well performance evaluation, but conventional measurement methods can be costly and lack real-time capability. This study presents a machine learning approach to estimate FBHP using simulated data from established vertical flow correlations. The proposed framework includes four main steps: collecting input parameters, simulating pressure drops calculation, developing an artificial neural network (ANN) model, and designing the FBHP calculation algorithm. The ANN was developed using key input variables, including inlet pressure, system temperature, tubing size, inclination, segment length, gas-oil ratio (GOR), water cut, oil API gravity, gas gravity, fluid rate, and vertical flow correlation type. A dataset of 790,409 points from several multiphase flow simulations was used, covering various well conditions for naturally flowing oil wells without artificial lift. The optimal ANN architecture featured six hidden layers and was trained with transformed, encoded, and normalized inputs, achieving a testing mean absolute error (MAE) of 7.8259 psia and R² of 0.9993. Segment-level predictions are then conducted iteratively to estimate FBHP for the whole well trajectory. Compared to earlier studies, the novelty of this work lies in its large and diverse set of well-flowing conditions, combined with comprehensive tubing geometry using segmentation. This approach enables the modelling of a wider range of flow scenarios and complex well trajectories.
A Fully Implicit Reservoir Simulation Using Physics Informed Neural Network Agus Wahyudi; Tutuka Ariadji; Taufan Marhaendrajana; Kuntjoro Adji Sidarto; Zuher Syihab
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.v48i3.1858

Abstract

The accuracy of simulation of multiphase flow in porous media is critical for reservoir management but is hindered by the nonlinear, coupled nature of governing equations and truncation errors in mesh-based numerical solvers. This study introduces a mesh-free, fully implicit Physics-Informed Neural Networks (PINN) framework for two-phase immiscible oil–water flow, where feedforward neural networks simultaneously approximate continuous pressure and saturation fields, embedding the governing PDEs, boundary, and initial conditions directly into the loss function. Three network topologies of single-row (N1), dual-row (N2), and branched-layer (NY) were tested across nine configurations which include variants of the networks. The novelty lies in the fully implicit PINN formulation of branched networks architectures with capability to reduce interference between pressure and saturation predictions. Benchmarking against the commercial simulator (Eclipse©) showed the NY achieved the best performance, with a mean squared error of less then 1.0×10-10. The N1 showed the ability to maintain stability at successive timesteps, while N2 models converged more slowly. The deep and narrow networks yielded higher accuracy but required almost double computation per iteration. Results demonstrate that even though with higher computational cost, the proposed PINN-based approach delivers high-fidelity solutions for complex reservoir problems without spatial meshing, offering a promising alternative to common numerical methods for both regular and irregular geometries.
Impact of Rhamnolipid Biosurfactants on Chemical Composition, Rheology, and Imbibition Performance of Crude Oils Harry Budiharjo Sulistyarso; Indah Widiyaningsih; Yulius Deddy Hermawan; Joko Pamungkas; Sayoga Heru Prayitno
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.v48i3.1859

Abstract

The application of biosurfactants in enhanced oil recovery (EOR) has generated significant interest owing to their biodegradability, low toxicity, and effectiveness in modifying oil–rock–brine interactions. Rhamnolipids—glycolipid biosurfactants synthesized by bacterial species—exhibit a distinctive amphiphilic structure that can alter the characteristics of crude oil at both molecular and macroscopic levels. This study offers a novel integrative evaluation of rhamnolipid-induced alterations in chemical composition, rheological properties, and imbibition efficacy of medium and light crude oils. The study utilizes gas chromatography–mass spectrometry (GC–MS) to clarify compositional changes in hydrocarbon fractions, viscosity assessments to measure rheological alterations in oil-biosurfactant mixtures, IFT measurements, and spontaneous imbibition experiments to analyze wettability changes and recovery efficacy. This study simultaneously examines compositional, viscosity, IFT, and capillarity-driven displacement mechanisms across two distinct crude oil categories, contrasting with prior research that focused solely on either compositional or interfacial properties within a single crude oil type, thereby offering comparative insights into biosurfactant–hydrocarbon interactions. The results are anticipated to enhance comprehension of biosurfactant-mediated enhanced oil recovery mechanisms, refine rhamnolipid application methodologies, and connect molecular-level alterations with core-scale oil recovery efficacy. This integrated method provides a novel framework for customizing biosurfactant formulations to particular crude oil varieties, thus improving recovery while preserving environmental sustainability.
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.
Two Decades of Smart Field Evolution (2005–2025): Global Insights and Indonesian Perspectives Amega Yasutra
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.v48i3.1870

Abstract

Between 2005 and 2025, smart field technologies evolved from sensor-based pilots into enterprise-wide digital operations and, more recently, AI-enabled workflows. This review of 36 technical papers from SPE, OTC, IPTC, URTeC, JPT, and SCOG maps advances, outcomes, barriers, and mitigation strategies across four eras: pilots (2005–2010), integration (2011–2015), enterprise adoption (2016–2020), and AI-driven operations (2021–2025). Findings show that while innovations such as real-time surveillance, digital twins, and predictive analytics expanded steadily, measurable success depended equally on leadership, governance, and workforce readiness. Representative cases—including Chevron San Ardo, Saudi Aramco Haradh-III, Equinor’s cloud-enabled intervention, Petrobras’ Mero field, Pertamina Hulu Rokan’s SSDP dashboard, and Pertamina EP’s machine learning application for idle well reactivation in the Cepu mature field—demonstrate both global and Indonesian perspectives. Lessons indicate that Indonesia is not only adopting but also actively contributing to digital oilfield practices. Coordinated actions from regulators, operators, and academia are required to accelerate adoption, sustain mature field productivity, and strengthen national energy security.
Introducing Cork As An Alternative Insulator to Polyurethane in Field X Production Pipelines: A Simulation Study Astra Agus Pramana; Adhikara Paramayoga; Utami Farahdibah; M. Kurniawan
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.v48i3.1871

Abstract

Wax deposition is a major flow assurance challenge in hydrocarbon production systems, as paraffinic components tend to precipitate when the temperature of the flowing fluid drops below the Wax Appearance Temperature (WAT). One of the most practical mitigation strategies is to maintain the fluid temperature above WAT by using thermal insulation along the production pipeline. This study investigates the effectiveness of insulation in reducing wax deposition and compares the performance of two insulation materials—cork and polyurethane—when applied to production pipelines. Fluid characterization was performed using Multiflash PVT Modeling & Flow Assurance software, while dynamic multiphase flow simulations were conducted to evaluate temperature distribution, wax layer growth, and heat retention within the pipeline system. The results show that both materials effectively reduce heat loss and delay wax formation; however, cork insulation provides comparable thermal performance to polyurethane while offering environmental and economic advantages due to its natural composition and sustainability. Overall, this study highlights cork as a promising alternative insulation material for wax deposition control, combining efficient thermal retention with eco-friendly characteristics.
Development of a New Empirical Formula Using Machine Learning for Pore Pressure Prediction in the South Sumatera Basin Aly Rasyid; Hendarmawan; Agus Didit Haryanto; Cipta Endyana
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.v48i3.1885

Abstract

Accurate pore pressure prediction is crucial for maintaining wellbore stability and preventing drilling hazards. Therefore, this research aimed to present a new empirical method derived from machine learning models, applied to two wells in South Sumatra Basin (S-3 and S-4) comprising 214 depth intervals. The method integrated geomechanics principles, statistical correlation analysis, and neural network optimization to generate an interpretable and transferable equation. The internal parameters of the trained model were extracted and reformulated into a transparent empirical expression that engineers could apply directly in practice. This was distinct from the conventional black-box artificial neural network (ANN). Model performance was rigorously validated against analytical pore pressure measurements. Additionally, the method achieved strong predictive accuracy, with coefficients of determination (R²) of 0.94 and 0.91 for S-3 and for S-4, and root mean square error (RMSE) of 115 psi and 142 psi, respectively. These values represented a significant improvement compared to traditional methods. For example ANN-derived formula reduced RMSE by 28% and 22% in contrast to Eaton’s equation and the Bowers velocity–effective stress relationship. It also outperformed Normal Compaction Trendline (NCT) method in intervals with abrupt lithological changes. The clear identification of significant predictors, namelytemperature, gamma ray, porosity, and water saturation, helped bridges the gap between machine learning accuracy and engineering usability. The results showed that converting advanced computational models into interpretable tools significantly enhanced operational safety, reduced non-productive time, and improved drilling efficiency in Indonesian most prolific hydrocarbon provinces.
Porosity Estimation in A Natural CO2-Water Reservoir Using Integrated Density-Resistivity Log Approach Pahala Dominicus Sinurat; Hari Sasongko; Nabil Samawi
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.v48i4.1887

Abstract

Natural CO₂ reservoirs represent important analogues for Carbon Capture and Storage (CCS) and Carbon Capture, Utilization, and Storage (CCUS), as they provide direct evidence of long-term CO₂ retention and trapping mechanisms. This study assesses porosity in a natural carbonate CO₂ reservoir using an integrated density–resistivity log approach. Conventional porosity logs, such as density, neutron, and sonic, often overestimate porosity in carbonate systems due to their limited sensitivity to pore connectivity. To overcome this limitation, density log-derived total porosity was integrated with resistivity-derived effective porosity, allowing for the differentiation between connected and isolated pore systems. Fluid density estimations, including supercritical CO₂ and brine, were computed and validated against standard references to ensure accuracy. The results show that density-only porosity overestimates values by up to 10% in dolomitic intervals, whereas the integrated method provides estimates that are more consistent with core measurements. Isolated porosity, averaging 2% in the upper dolomite and 1.5% in the lower dolomite, was identified as a non-contributing pore volume for injectivity, although it remains relevant for storage capacity. These findings underscore the importance of integrated log interpretation for precise reservoir characterization and offer new insights into evaluating natural CO₂ reservoirs for long-term geological storage.

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