Residual oil zones (ROZ) arise under the oil–water contact of main pay zones due to diverse geological conditions. Historically, these zones were considered economically unviable for development with conventional recovery methods because of the immobile nature of the oil. However, they represent a substantial subsurface volume with strong potential for CO2 sequestration and storage. Despite this potential, effective techniques for assessing CO2-EOR performance coupled with CCUS in ROZs remain limited. To address this gap, this study introduces a machine learning framework that employs artificial neural network (ANN) models trained on data generated from a large number of reservoir simulations (300 cases produced using Latin Hypercube Sampling across nine geological and operational parameters). The dataset was divided into training and testing subsets to ensure generalization, with key input variables including reservoir properties (thickness, permeability, porosity, Sorg, salinity) and operational parameters (producer BHP and CO2 injection rate). The objective was to forecast CO2 storage capacity and oil recovery potential, thereby reducing reliance on time-consuming and costly reservoir simulations. The developed ANN models achieved high predictive accuracy, with R2 values ranging from 0.90 to 0.98 and mean absolute percentage error (MAPRE) consistently below 10%. Validation against real ROZ field data demonstrated strong agreement, confirming model reliability. Beyond prediction, the workflow also provided insights for reservoir management: optimization results indicated that maintaining a producer BHP of approximately 1250 psi and a CO2 injection rate of 14–16 MMSCF/D offered the best balance between enhanced oil recovery and stable storage efficiency. In summary, the integrated combination of reservoir simulation and machine learning provides a fast, technically robust, and cost-effective tool for evaluating CO2-EOR and CCUS performance in ROZs. The demonstrated accuracy, scalability, and optimization capability make the proposed ANN workflow well-suited for both rapid screening and field-scale applications.
Abdulwarith et al. (Sat,) studied this question.