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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 24 Documents
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Search results for , issue "Vol 48 No 3 (2025)" : 24 Documents clear
Reservoir Characterization of Ngrayong Formation, Sandstone with Carbonate Intercalation, Using a Geostatistical Approach Based on Petrophysical Parameters, Northeast Java Basin, Indonesia Handoyo, Handoyo; Ronlei, Bernard Cavin; Wibowo, Andy Setyo; Sigalingging, Asido Saputra; Nathania, Edlyn Yoadan; Fatkhan, Fatkhan; Erdi, Aurio; Avseth, Per; Carbonell, Ramon; Nugroho, Pranowo; Bayu Pandito, Riky Hendrawan; Nasibov, Aladin; Ali Husein, Abdullah Ali
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.v48i3.1828

Abstract

Hydrocarbons have a vital role as a driver of the global economy, which causes demand to continue to increase. To achieve production targets, oil and gas companies try to conduct exploration using efficient and accurate methods to obtain optimal hydrocarbon reserves. One approach in hydrocarbon exploration is to use geostatistical analysis to understand the characteristics of petrophysical parameters of reservoir rocks (e.g. porosity, permeability, water saturation and facies). This study aims to characterize reservoirs in the NE Java Basin using a geostatistical approach that Sequential Gaussian Simulation (SGSIM) to produce random realizations that can be adjusted and validated through geostatistical analysis of data before and after the simulation. The dataset used in this study consist of well data, seismic line, and core data. The results shows the petrophysical properties distribution from the simulation reveals the dominance of carbonate sandstone reservoirs in the central part of the study area with a thinning slope towards the northwest and southeast, while sandstone reservoirs are only dominant in the southeast direction of the study area. This research provides important insights in understanding reservoir characteristics and can be a basis for efficient decision making in the exploration of hydrocarbon resources in this area.
Investigation and Optimization of Enhanced Oil Recovery Mechanism by Sophorolipid Biosurfactant in Carbonate Reservoir Indah Widiyaningsih; Harry Budiharjo Sulistyarso; Ivan Kurnia; Taufan Marhaendrajana; Tutuka Ariadji
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.v48i3.1830

Abstract

The Remaining Oil in Place (ROIP) in carbonate rock reservoirs is often substantial. This is due to the tendency of carbonate rocks to be oil-wet in terms of wettability. The oil's inherent property of wetting the rock causes the residual oil to adhere to the rock's pores, making it challenging to extract to the surface. One method to enhanced oil recovery (EOR) is through biosurfactant injection, i.e., sophorolipid, a fungal biosurfactant that possesses the properties of surfactants in general. This study aims to evaluate the effectiveness of sophorolipid biosurfactant injection in enhancing oil recovery in carbonates, as well as to identify the dominant mechanism at work during the injection process and optimize it through coreflooding simulation. This research was conducted through laboratory testing and validation using a simulator, comprising two phases: coreflooding tests and coreflooding simulations. Coreflooding simulation was conducted to reduce the need for coreflooding experiments, which are time-consuming and costly. The simulator used in this research is CMG-GEM with sensitivity parameter and optimization using CMOST. The Sobol Analysis was conducted to assess the sensitivity parameters and identify the primary mechanism of sophorolipid. Then, optimization is achieved by adjusting the parameters, such as sophorolipid concentration, pore volume (PV) injection, and injection rate. Coreflooding sensitivity results show that the dominant parameter is the nonwetting trapping number (DTRAPN), which is closely related to the mechanism of wettability alteration and mix viscosity. The effectiveness of the Sophorolipid mechanism in modifying wettability, enhancing displacement efficiency, and facilitating emulsion formation, hence improving sweeping efficiency. The recovery factor (RF) increased from the coreflooding simulation optimization results, reaching 19%-33%.
Determination of Hydrotreated Vegetable Oil (HVO) in Blended Diesel Fuel Using Calibration of Isooctane by GC-FID Measurement Novilia Novilia; Sylvia Ayu Bethari; Handajaya Rusli; Muhammad Bachri Amran
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.v48i3.1835

Abstract

Hydrotreated vegetable oil (HVO) is emerging as a promising renewable fuel that is sharing similar chemical characteristics with fossil diesel, making it suitable as a blending component. However, this similarity is presenting challenges in distinguishing and quantifying HVO in diesel blends. The present study is focusing on developing a simple, cost-effective, and reliable method using gas chromatography with flame ionization detection (GC-FID) for determining HVO content in diesel mixtures. Two candidate markers, hexadecane (C₁₆H₃₄) and heptadecane (C₁₇H₃₆), are being evaluated based on linearity, detection limits, and accuracy. Calibration curves are being constructed using HVO–isooctane mixtures from 0 to 50% v/v HVO. The heptadecane peak is demonstrating superior performance with excellent linearity (R² = 0.9994), a low detection limit (1.77% v/v), and quantification limit (5.36% v/v). In contrast, the hexadecane peak is showing similar linearity but lower sensitivity. Accuracy tests are being conducted on diesel samples spiked with 10% HVO, showing recovery rates above 95% for both markers. Overall, heptadecane is proving to be a consistent and reliable marker for quantifying HVO in diesel blends using GC-FID.
Innovation in Inspection Planning Using The Corrosion Assessment Information System (CAIS) Analytical Tool to Prevent Stationary Equipment Failure in Crude Distillation Unit (CDU) Muki Satya Permana; Mirwan Prasetiyo Soeweify; Brammantyo Nugroho; Fauzi Yusupandi; Hary Devianto; Ardian Dwi Prakoso
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.v48i3.1848

Abstract

Equipment failure caused by corrosion remains a critical challenge in ensuring the reliability and refinery asset integrity in the oil and gas industry. This study aims to develop a Corrosion Assessment Information System (CAIS), designed to assess and visualize corrosion severity through a color-coded Process Flow Diagram (PFD). The CAIS methodology consists of four key stages: analysis of design and operational data, process simulation and validation, compilation of contaminant data for each Crude Distillation Unit (CDU), and identification of corrosion mechanisms according to API 581 and API 571 standards. The system produces a corrosion risk map with four color indicators i.e. green (low), blue (moderate), yellow (high), and red (very high) which assists in prioritizing inspection and maintenance activities based on corrosion severity. Implementation results demonstrate that CAIS improves analytical efficiency, data accessibility, and collaboration between engineering and maintenance teams. Furthermore, it supports predictive monitoring and enables faster decision-making to reduce corrosion-related failures. External validation and integration into refinery workflows confirm CAIS as a strategic digital solution that strengthens risk-based inspection and predictive maintenance practices. Overall, CAIS provides a reliable platform for enhancing digital corrosion monitoring and asset integrity management at PT Kilang Pertamina Internasional, Refinery Unit VI Balongan, Indonesia.
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.
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.

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