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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
 58   59   60   61   62 
Reduction of Carbon Emission in East Java Power Generation Sector Through The Use of Saline Aquifer as CO2 Storage - A Conceptual Study Bambang Widarsono; Suliantara Suliantara; Herru L Setiawan; Mohamad Romli; Nurkamelia Nurkamelia; Sugihardjo Sugihardjo; Panca W. Sukarno; Junita T Musu; Tri Muji Susantoro; Herizal Herizal; Usman Pasarai; Aziz M Lubad; Sunting Kepies; Diana Dwiyanarti; Rudi S. Wijayako; Muhamad Budisatya; Devitra Saka Rani
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.v48i3.1926

Abstract

East Java region, as one of the most industrialized regions in Indonesia, is a significant contributor to national greenhouse gas (GHG) emissions, and therefore may play a significant role in supporting the national commitment to reach net zero emissions (NZE). This study is made to provide an example of how a conceptual CCS scheme using a saline aquifer is applied in the region. Tanjung Awar-awar coal-fired power plant, which is located in the Tuban city area on the northern coast of East Java, is selected as the GHG source. With its power generation capacity of 2 x 350 MW, it emits 4.5 Mt CO2e annually. The extensively distributed Kujung carbonate formation is chosen as the CO2 saline aquifer storage. Amidst the typical data rarity commonly faced in preliminary studies on saline aquifers, modeling for CO2 storage has been performed using all available primary and secondary data from all available sources. The most likely estimate of storage resource shows 479 Mt CO2e (status: 3U in SRMS classification system), with its A2 block possessing 162.78 MtCO2 storage resource. The CO2 injection scheme is essentially a volumetric balancing between CO2 emissions and injection rates through injection wells. Well injection capacities are estimated, and must be able to cope with CO2 emissions from the power plant. Accordingly, two CCS scenarios of 50% CO2 capture (Scenario A; 4,671 tons/day) and 100% CO2 capture (Scenario B; 9,342 tons/day) are set. To serve the two scenarios, four (4) and eight (8) horizontal wells are needed, respectively. A similar approach has also been made for vertical injection wells. Following the assumptions set in the CCS scheme, a total of 34,098,300 tons and 68,189,300 tons of CO2 can be stored in a 20-year injection permit for Scenario A and Scenario B, respectively. Nonetheless, these figures constitute just fairly small fractions of the Kujung A2 block’s storage resource. This shows the huge potential of the Kujung Formation to act as a saline aquifer storage for CCS schemes in the East Java region. This also presents the potential of the Kujung Formation to sustain multi-CO2 sources and prolonged injection schemes. Despite many challenges faced, especially in relation to data scarcity, the results may serve as a reference for more detailed project-based studies in the future.
The Integration of Hybrid Capacitance Resistance Model and Machine Learning: A Data-Based Workflow for Optimizing Waterflood Performance and Reservoir Management Syifa Alviola Muhendra; Novia Rita; Fajril Ambia; Agus Dahlia
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.v48i4.1928

Abstract

This study aims to minimize uncertainty in waterflood performance by employing a data-driven workflow that combines the Capacitance Resistance Model (CRM) with Machine Learning. Two CRM variants, CRM-P (Producer-based) and CRM-IP (Injector-Producer-based), are utilized to evaluate interwell connectivity and time constants on three reservoir models: homogeneous, heterogeneous, and a real field scenario (Volve Field). The model is evaluated using R² and Mean Absolute Percentage Error (MAPE) and is compared against the Random Forest and eXtreme Gradient Boosting (XGBoost) techniques. The results indicate that CRM-IP provides more realistic estimates than CRM-P, particularly for response time. XGBoost consistently demonstrates superior prediction accuracy, achieving R² values of 0.76–0.98 and MAPE values of 0.5–10%. Three-dimensional (3D) visualizations of interwell connectivity and streamline analysis strengthen the understanding of fluid flow and sweep efficiency. This further demonstrates that integrating CRM and Machine Learning serves as a decision-support tool for Enhanced Oil Recovery optimization, as evidenced by R² and MAPE analyses that characterize sweep efficiency and the reservoir's capacity to accommodate additional injection.
Comparative Study of Capacitance Resistance Model and Machine Learning for Sensitivity Analysis of Polymer Injection Performance Azri Agus Rizal; Fajril Ambia; Novia Rita; Ira Herawati
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.v48i4.1929

Abstract

The objective of this study was to evaluate the performance of polymer injection in the Volve Field by validating full-physics tNavigator simulation results. This process was performed using two independent data-driven approaches: the Capacitance Resistance Model (CRM) and machine-learning algorithms Random Forest and XGBoost. This validation framework addresses uncertainty in flow-parameter and ensures that simulated production responses align with data-driven injection–production behavior. The simulation model was constructed using 20 years of historical field data, consisted of five years of polymer injection at 1000–3000 ppm, followed by 15 years of chase water flooding. The simulation results showed that polymer injection increased the oil recovery factor from 21.12% to 21.30% in the best-case scenario, indicating a modest improvement in sweep efficiency. CRM, applied through CRM-P and CRM-IP configurations, successfully reconstructed production profiles and quantified interwell connectivity (R² = 0.94; MAPE < 10%). Machine-learning validation further confirmed these results, with Random Forest achieving R² = 0.92 (MAPE < 1%) and XGBoost achieving R² = 0.99 (MAPE < 1%). Overall, CRM and machine learning provide effective and independent validation pathways, enhancing confidence in simulation outcomes and allowing for reliable assessment of polymer-injection performance in field applications.
Comparative Analysis of Capacitance-Resistance Models and Machine Learning for Co₂-Eor Production Forecasting: A Case Study of Dynamic Connectivity in Heterogeneous Reservoir Reyhan Rafsanjani; Agus Dahlia; Fajril Ambia; Novia Rita; Ayyi Husbani
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.v48i4.1930

Abstract

This study evaluates an integrated forecasting framework that combines Capacitance-Resistance Models (CRMP and CRMIP) with ensemble machine learning algorithms (Random Forest and XGBoost) to predict CO₂-Enhanced Oil Recovery performance in the heterogeneous Volve Field. Reservoir simulation was performed using tNavigator with CO₂ injection at 941 tons/day (35 MMSCF/day) over 20 years. The results demonstrate the critical influence of CO₂-specific characteristics, with a determined Minimum Miscibility Pressure of 3299.68 psi and a corresponding oil Swelling Factor of 1.19. Machine learning models, particularly XGBoost, significantly outperformed conventional CRM methods, achieving exceptional accuracy (R² = 0.99-1.00, MAPE = 0.44-2.24%) compared to CRMP/CRMIP (R² = 0.55-0.72, MAPE = 16-23%). The CO₂ injection scenario substantially enhanced oil recovery, achieving a cumulative production of 15.73 MMSTB (RF 20.45%) compared to 9.38 MMSTB (RF 12.19%) for waterflooding, representing a 67.7% improvement and incremental recovery of 6.35 MMSTB. Interwell connectivity analysis revealed dynamic reservoir responses with time constants ranging from 916 to 927 days. The integration of physics-based models with non-linear machine learning algorithms significantly improves prediction accuracy while providing comprehensive insights into reservoir dynamics, allowing for optimal CCUS implementation in heterogeneous reservoir systems.
Impact Assessment of Wax Gelation Fluid Pressure and Temperature: Designing Long-Term Preventive Solutions Mohd Wirawan Putra Pamungkas; Meutia Fitri Hasan; Venty Lestari; Hendra Budiman; Aldo Setiawan
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.v48i4.1932

Abstract

Paraffinic precipitation presents a pervasive flow challenge, specifically impacting this light crude oil (API 34.85 °) system, particularly within a pipeline (length 1153 m) operating in cold environments. This study first rigorously assessed the critical impact of flow parameters, confirming the fluid’s thermal profile drops below the pour point (31.67 oC) at a crucial distance of 439.24 m from the wellhead, initiating severe wax gelation. Flow analysis further confirms a detrimental laminar flow regime (NRe = 1262), which, coupled with a significant total pressure drop of 0.155 psia/100 ft along the pipeline, exacerbates the tendency for solidified paraffins to accumulate due to insufficient shear stress. To address this, the research successfully validated a cost-effective, long-term preventative solution: a locally fabricated sand heater with an energy capacity of 175,000 kcal/h. Empirical field testing confirmed the intervention provides a substantial net thermal elevation of 8.5 oC. Subsequent thermal modeling for long-term operational reliability identified the optimal sand-heater placement distance to be within 300 m of the wellhead. This strategic placement ensures the fluid temperature consistently remains safely above the pour point, effectively mitigating the risk of premature wax gelation and guaranteeing uninterrupted system integrity and sustained hydrocarbon production.
The use of the Common Offset of the Common Reflection Surface (CO-CRS) for Velocity Analysis and Data Preconditioning Wahyu Triyoso; Fernando Lawrens Hutapea
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.v48i4.1935

Abstract

This study introduces a Common Offset (CO) extension of the Common Reflection Surface (CRS) method to address seismic imaging challenges in complex geological settings and with noisy data. This CO-CRS approach aims to enhance the signal-to-noise ratio and overcome the limitations of conventional preconditioning techniques that rely on accurate parameterization. Building upon established work on zero-offset CRS (ZO-CRS), the CO method generates regularized prestack data suitable for both time- and depth-domain processing by interpolating missing offsets using a local hyperbolic approximation. Ultimately, this study utilizes CO-CRS for enhanced velocity analysis and data preconditioning prior to performing prestack time migration (PSTM). In this study, the CO-CRS is then used for velocity analysis and prestack time migration. The results show that prestack CO-CRS data yield improved time-migrated seismic images, and we suggest extending the application to the depth domain. To achieve a reliable velocity model for imaging, recursive seismic inversion (RSI) is applied to derive the velocity model using the PSTM stack and a velocity interval time, based on CRS semblance velocity analysis. Furthermore, the prestack depth migration (PSDM) is then tested. The depth-imaging results are reliable, and it can be concluded that combining the benefits of the CRS noise-reduction feature with more accurate velocity analysis and prestack migration can provide enhanced capabilities.
Rock Compressibility Characteristics of Oil and Gas Sandstone Reservoirs in the Western Part of Indonesia Bambang Widarsono; Junita T. Musu; Yohanes D. Wangge Yohanes
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.v48i4.1936

Abstract

Rock compressibility is one of rocks’ properties that is closely related to their response to changes in effective stresses. Various Earth’s subsurface-related processes involve rock compressibility. In petroleum production, for instance, it provides reservoir energy needed for the process. Studies on rock compressibility for Indonesian reservoirs are very limited. Therefore, a study has been carried out to investigate rock compressibility characteristics of Indonesian reservoir rocks, sandstones in particular. A total of 205 sandstone samples of various types have been collected from 34 oil/gas fields in nine productive sedimentary basins in Indonesia. The samples are prepared and laboratory tested for their basic properties and pore volume compressibility following the universally adopted standard methods. Results of this study indicate unclear trends in the rock property of concern in relation to porosity. However, with careful grouping and cluster analyses, clearer trends representing their intrinsic characteristics can be spotted, and appropriate correlations based on a generalized model can be established. The established correlations of maximum effective rock compressibility versus porosity offer opportunities to understand the characteristics of reservoir sandstone compressibility. Special cautions have been discussed, and special suggestions have also been offered for selecting the most appropriate correlations.
Valorization of Tropical Agricultural Waste Into Sustainable Additives for Water-Based Drilling Mud: A Case Study of Orange Peel and Durian Rind Nguyen Hai Nam Le; Trung Hieu Ly; Phuoc Hau Dinh
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.v48i4.1948

Abstract

This study explores the potential of orange peel powder (OPP) and durian rind powder (DRP) as biodegradable additives in water-based drilling mud. These agricultural wastes are processed into fine powders (<45 μm) and are characterized using FTIR, XRD, and SEM to determine their chemical functionality, crystallinity, and morphology. Both additives are incorporated into the base mud at concentrations of 2-8 g per 350 mL, and their rheological and filtration properties are evaluated. Due to its hydrophilic polymer content and microstructure, OPP significantly enhances apparent viscosity, yield point, and gel strength. DRP demonstrates superior performance in filtration control, reducing fluid loss by up to 40.3% and filter cake permeability by 60.2%. Visual and quantitative observations confirm that both additives improve the compactness of the filter cake and sealing efficiency. These results highlight the potential of OPP and DRP as eco-friendly, cost-effective alternatives to conventional chemical additives, supporting waste valorization and sustainable drilling operations.
Utilization of Pineapple Leaf Fiber-Derived CMC to Reduce Filtration Loss and Extend Thickening Time in Oil Well Cementing Fitrianti; Muhammad Alwi Pranata; Owwen Tri Handoko
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.v48i4.1950

Abstract

Cementing operations in oil and gas wells require precise control of cement slurry properties to ensure successful zonal isolation. Two critical parameters—fluid loss control and setting time—significantly influence cement performance. Carboxymethyl cellulose (CMC) has been widely employed to regulate these properties; however, commercial CMC presents cost challenges for large-scale operations. This investigation evaluates the effectiveness of CMC which is synthesized from the waste of pineapple leaf fiber as an alternative additive for Class G drilling cement. The high cellulose content (69.5-71.5%) in pineapple leaf fibers indicates its potential as a cost-effective source of CMC that is compared to conventional agricultural wastes. CMC was extracted from pineapple leaves through alkaline delignification and chemical modification. The slurry of class G cement was formulated with varying CMC concentrations ranging from 0% to 0.4% by weight of cement (BWOC). Fluid loss was measured by using LPLT filter press following API standards, while setting characteristics were evaluated at 40°C and 60°C by using an atmospheric consistometer. Filtrate volumes decreased from 214.69 ml to 153.94 ml as CMC concentration increased from 0.1% to 0.3% BWOC, with all values conforming to API specifications (150-250 ml) for primary cementing. Commercial CMC from literature demonstrates comparable filtrate volumes of 160-180 ml at similar concentrations. Setting time was extended from 329 to 362 minutes at 40°C and from 188 to 266 minutes at 60°C with 0% to 0.4% CMC addition. Temperature significantly influenced hydration kinetics, with elevated temperatures accelerating cement setting regardless of CMC concentration. Pineapple leaf-derived CMC demonstrates comparable performance to commercial additives in controlling fluid loss and extending setting time in the systems of Class G cement. The optimal concentration of 0.3% BWOC provides adequate fluid loss control while maintaining acceptable setting characteristics. Further validation under high-pressure, high-temperature (HPHT) conditions and field-scale implementations are recommended.
Synergy of Nano Silica and Anionic Surfactant Fluid as Chemical Enhanced Oil Recovery Khasan Rowi; Agus Subagio; Ngadiwiyana; Heydar Ruffa Taufiq; Muhammad Mufti Azis; Bayu Dedi Prasetiyo; Victor Sitompul; Sumadi Paryoto; Denie Tirta Winata; Tino Diharja; Michael Arya Yutaka; Abimanyu Putra Syarifudin; Wahyu Firmansyah; Hary Koestono
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.v48i4.1951

Abstract

Silica nanofluids attract significant attention for enhanced oil recovery (EOR) applications due to their ability to alter rock wettability. However, silica nanofluids exhibit limitations in thermal stability. The addition of anionic surfactants aims to overcome these limitations. The synergisticAnionic surfactants are added to address the thermal stability issues of silica nanofluids. The synergy interaction between silica nanoparticles (SNPs) and anionic surfactants enhances  wettability alteration, reduces interfacial tension (IFT), improves thermal stability, and increasing oil recovery. This study investigates the synergistic effects of SNPs, alpha olefin sulfonate (AOS) surfactant, and disodium laureth sulfosuccinate (DLS) co-surfactant in nanofluid formulations applied to sandstone reservoirs. Laboratory experiments employ colloidal nano silica with two particle sizes, 8 nm (SNP-01) and 3 nm (SNP-02), combined with AOS-DLS anionic surfactants at various concentrations . The study showed that the silica nanofluid remains stable for up to 3 months at temperatures below 80°C for both SNP types at a concentration of 0.1% with surfactant concentrations 0.3% AOS and 0.3% DLS in  3% brine solution. The addition of SNPs decreases the contact angle, whereas surfactants do not significantly affect the contact angle; however, surfactant effectively reduce the IFT, while  nano silica shows minimal influence on IFT values. Core flooding analysis showed that the SNP-02 nanofluid produced the highest recovery factor of 12.1% OOIP. Futhermore, SEM analysis showed that silica nanofluid injection removes surfactant impurities and enhances rock porosity.

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