Research Article
Analysis of Enhance Oil Recovery Methods, A Comparative Approach to Determine the Suitable Eor Method for an Experimental Reservoir
Joseph Sekyi Ansah*
,
Stephen Eduku,
Nkansah Derrick,
Lord Kwame Segbeawu
Issue:
Volume 14, Issue 3, June 2026
Pages:
37-60
Received:
17 January 2026
Accepted:
3 February 2026
Published:
4 June 2026
Abstract: This study presents a comparison of three Enhanced Oil Recovery (EOR) methods—Surfactant/Polymer (SP) EOR, Hot Water Injection EOR, and CO2 Gas Injection EOR—to identify the most suitable technique for application in an experimental reservoir using simulated data of the reservoir. The methods were evaluated based on key performance indicators, including cumulative oil and water production, oil recovery factor, and water cut. The results demonstrate that Surfactant/Polymer EOR achieves the highest cumulative oil production (0.047815 MMSTB) and oil recovery factor (92.2909%), making it the most effective method for maximizing oil extraction. However, this method also produces a higher volume of water, necessitating robust water management strategies. Hot Water Injection EOR, while effective in heavy oil reservoirs, yielded the lowest oil production (0.0269035 MMSTB) but minimized water production. CO2 Gas Injection EOR provided a balanced performance with moderate oil recovery and higher water production. Based on the comparative analysis of the simulation results, Surfactant/Polymer EOR is recommended as the most suitable recovery method for the experimental reservoir due to its outstanding oil recovery potential and overall production performance. Nevertheless, successful implementation of this method would require proper planning and management of the increased water production to optimize operational efficiency and economic viability
Abstract: This study presents a comparison of three Enhanced Oil Recovery (EOR) methods—Surfactant/Polymer (SP) EOR, Hot Water Injection EOR, and CO2 Gas Injection EOR—to identify the most suitable technique for application in an experimental reservoir using simulated data of the reservoir. The methods were evaluated based on key performance indicators, incl...
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Research Article
Machine Learning Prediction of Dead-end Pores from VTK Based Velocity Fields in Porous Media
Issue:
Volume 14, Issue 3, June 2026
Pages:
61-69
Received:
2 June 2026
Accepted:
15 June 2026
Published:
30 June 2026
DOI:
10.11648/j.ogce.20261403.12
Downloads:
Views:
Abstract: Dead-end pores are important microstructural features in porous media because they strongly influence flow efficiency, transport behaviour, and solute retention. However, their identification remains challenging due to geometric complexity and the limitations of conventional post-processing methods. In this study, we developed a machine-learning framework for the automated prediction of dead-end pores from pore-scale velocity-field data obtained through numerical fluid-flow simulations. Two-dimensional and three-dimensional porous geometries were constructed using CAD tools and simulated under Stokes and Navier-Stokes flow regimes using the CSMP platform. The resulting pressure and velocity fields were exported in Visualization Toolkit (VTK) format, and spatial as well as hydrodynamic features were extracted for analysis. A supervised classification approach based on a Random Forest model was trained on 222,810 pore-scale data points, using spatial coordinates and pressure as predictor variables. Dead-end pores were defined as stagnant regions weakly connected to the main flow pathways and labelled accordingly. Model performance was evaluated using accuracy, precision, recall, and ROC-AUC, along with three-dimensional visualizations for physical validation. The proposed model achieved an accuracy of 98.20%, a recall of 96.19% for dead-end pore identification, and a ROC-AUC score of 0.9985, indicating excellent predictive performance and robustness. These results show that machine learning can reliably distinguish active flow channels from stagnant dead-end regions directly from velocity-field data without explicit connectivity analysis. The approach offers a scalable and objective tool for pore-scale characterization with potential applications in enhanced oil recovery, contaminant transport, and digital rock physics.
Abstract: Dead-end pores are important microstructural features in porous media because they strongly influence flow efficiency, transport behaviour, and solute retention. However, their identification remains challenging due to geometric complexity and the limitations of conventional post-processing methods. In this study, we developed a machine-learning fr...
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