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All Journal Journal Of Research and Education Chemistry (JREC) Scientific Contributions Oil and Gas
ayyi husbani
Universitas Islam Riau
Chemical Engineering, Chemistry & Bioengineering Chemistry Education Energy

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The ANALISIS PENGARUH HUMIDITY TERHADAP LAJU KOROSI MENGGUNAKAN RIMPANG JAHE MERAH SEBAGAI PENGHAMBAT LAJU KOROSI ayyi husbani; Novrianti Novrianti; Neneng Purnamawati
Journal of Research and Education Chemistry Vol. 4 No. 2 (2022): OKTOBER
Publisher : UIR Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jrec.2022.vol4(2).10712

Abstract

Corrosion is one the problems that occur in the production process in oil and gas industries that can reduce material equipment such as tubing and flowline. corrosion is influenced by humidity and temperature. Corrosion can be control by using organic inhibitors because they are environmentally friendly. This research is a laboratory research that examines the effect of humidity using red ginger on reducing corrosion flow rate. Red ginger is used because it contains phenol antioxidants which can inhibit the corrosion flow rate. The variables used were variations in room humidity, 80%, and 90% and the inhibition time was 72 hours, 144 hours, and 216 hours. The results showed that the sample by adding red ginger inhibitor at room temperature humidity, 80%, 90% was able to maximize decrease in the corrosion rate. The sample with the addition of ginger inhibitor showed the higher the humidity value, the higher the corrosion rate where the highest corrosion rate was 0.1362 mmpy at 90% humidity, while the lowest humidity was obtained at room humidity of 0.0517 mmpy. Keywords : Humidity, Corrosion, Inhibitor, Red ginger, Coating
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 Agus Dahlia Fajril Ambia Neneng Purnamawati Novia Rita Novrianti Novrianti Reyhan Rafsanjani