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All Journal Journal of Geoscience, Engineering, Environment, and Technology JOURNAL OF EARTH ENERGY ENGINEERING Jurnal Mineral, Energi dan Lingkungan Jurnal Mineral, Energi dan Lingkungan CANANG: Jurnal Pengabdian Masyarakat Scientific Contributions Oil and Gas
Novia Rita
Petroleum Engineering Department, Engineering Faculty, Universitas Islam Riau Jl. Kaharuddin Nasution 113 Pekanbaru, Riau, 28284 Indonesia
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Materials Science & Nanotechnology Physics Public Health Social Sciences Other

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Edukasi Metode Stimulasi Sumur untuk Peningkatan Produksi Migas di SMK Perminyakan Dumai Novrianti Novrianti; Doddy Yulianto; Novia Rita; Teguh Sahibullah Fajri; Retno Agustrianingsih
CANANG: Jurnal Pengabdian Masyarakat Vol 5, No 2 (2025)
Publisher : PELANTAR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52364/canang.v5i2.74

Abstract

Well stimulation is one method used to increase oil and gas production rates. Education related to sound stimulation, which involves acidizing and hydraulic fracturing, was conducted during a community service activity by a team of lecturers from the Faculty of Engineering at the University of Riau at the Dumai Petroleum Vocational School. This activity was conducted to enhance students' knowledge and understanding of technological advancements and research developments related to methods for increasing oil and gas production flow rates. The activity consisted of explanations, demonstrations, and discussions related to acidizing and hydraulic fracturing aimed at increasing oil and gas production flow rates. Additionally, the latest research and technology related to sound stimulation were presented to update the students' knowledge on the latest developments in the field of sound stimulation. This activity was well received by the academic community of the Dumai Petroleum Vocational School, as it aimed to enhance the students' knowledge, broaden their horizons, and ultimately prepare them to compete with students from other petroleum vocational schools
METHYL ESTER SULFONATE: AN ANIONIC BIOSURFACTANT FOR ENHANCED OIL RECOVERY IN HARSH CONDITION Muhamad Raihan Al Fikri; Veni Dwi Amelia Putri; Indra Gunawan; Novia Rita; Muslim Abdurrahman
Scientific Contributions Oil and Gas Vol 48 No 1 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i1.1673

Abstract

Chemical enhanced oil recovery (EOR) is a tertiary phase method used to extract significant amounts of residual crude oil that primary and secondary recovery phases cannot recover. Surfactants are crucial in chemical EOR for their impact on rock surfaces and water-oil interfaces. Optimizing these formulations under reservoir conditions is essential before their use in oil recovery. However, screening is challenging due to the variety of surfactants and their sensitivity to reservoir conditions and rock types. This study introduces methyl ester sulfonate (MES), an anionic bio-surfactant, to improve the oil recovery factor (RF). Spontaneous imbibition (SI) experiments measured MES's ability to enhance oil RF in sandstone reservoir rocks under high salinity and temperature. The results showed MES's excellent performance even under high salinity conditions. On day 14, MES samples under 30 kppm salinity and 80°C with concentrations of 0.5 mM, 2 mM, and 3 mM had RF values of 12%, 18%, and 26%, respectively. Under 40 kppm salinity and 80°C, the RF values were 17%, 19%, and 27%, respectively. MES enhances oil recovery efficiency and preserves environmental health due to its biodegradability, making it a safer alternative to traditional surfactants. Its use can significantly improve chemical EOR processes under challenging conditions. As a novelty, this study also explains the mechanism of MES in changing the wettability of sandstone to the intermolecular scale.
Determining The Role of Ion Exchange in Permeability Alteration During Asp Injection: A Laboratory-Scale Study Using Cmg Reactive Transport Modeling Dike Fitriansyah Putra; Mohd Zaidi Jaafar; Tengku Amran Tengku Mohd; Novia Rita; Agus Dahlia; Ichsan Al Sabah Lukman; M. Haidar T. Putra
Scientific Contributions Oil and Gas Vol 48 No 2 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i2.1744

Abstract

Electrokinetic-based Enhanced Oil Recovery (EK-EOR) presents a novel method that applies electric fields to mobilize trapped hydrocarbons in formations with low permeability. This work investigates the impact of ion exchange and mineralogical reactions on permeability behavior during Alkali-Surfactant-Polymer (ASP) flooding, integrating laboratory-scale sand-pack experiments with reactive transport simulation in CMG-GEM. During ASP injection, a marked rise in differential pressure indicated abrupt changes in permeability caused by polymer accumulation, mineral dissolution, and early-stage ion exchange. Two numerical scenarios were assessed: one involving only aqueous-phase chemistry, and another incorporating fluid reactions and solid-surface ion exchange. The latter case required minimal calibration to match experimental data, while the former demanded unrealistic permeability upscaling. The results underscore ion exchange as a vital mechanism influencing fluid transport in EK-EOR. Although wettability alteration is often associated with ASP processes, this study suggests that under short exposure periods, changes in permeability dominate recovery performance. The findings improve reservoir modeling by promoting geochemical integration into simulation workflows.
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.
Co-Authors Adi Novriansyah Agus Dahlia ayyi husbani Azri Agus Rizal Doddy Yulianto Fajril Ambia Fitrianti Fitrianti Ichsan Al Sabah Lukman Indra Gunawan Ira Herawati M. Haidar T. Putra Mohd Zaidi Jaafar Muhamad Raihan Al Fikri Muhammad Ariyon Muhammad Efras Muslim Abdurrahman Novrianti Novrianti Novrianti Novrianti Putra, Dike Fitriansyah Rasyid Gani Hadi1b Retno Agustrianingsih Reyhan Rafsanjani Romal Ramadhan Rosalia M Taufand Syifa Alviola Muhendra Teguh Sahibullah Fajri Tengku Amran Tengku Mohd Toety Marliaty Tomi Erfando Tomi Erfando Veni Dwi Amelia Putri