Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Scientific Contributions Oil and Gas
Azri Agus Rizal
Universitas Islam Riau
Chemical Engineering, Chemistry & Bioengineering Energy

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
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.
Co-Authors Fajril Ambia Ira Herawati Novia Rita