Carbon dioxide (CO2) capture, utilization, and storage (CCUS) have been proposed as a possible technique to mitigate climate change. In this vein, CO2 storage through enhanced oil recovery (EOR) in depleted hydrocarbon reservoirs is touted as a most effective approach because it synergistically increases oil production and enables permanent sequestration into the reservoirs.However, the construction of a reasonable 3D geological model for this storage reservoir is a major challenge. Thus, this study presents an efficient workflow for constructing an accurate geological model for the evaluation of CO2 storage capacity in a fractured basement reservoir in the Cuu Long Basin, Vietnam. Artificial neural network (ANN) has been used to predict porosity and permeability values through seismic attributes and well log data. The predicted values were selected using high correlation factors with well log data. Subsequently, the Sequential Gaussian Simulation and co-kriging methods were applied to generate a 3D static geological model by using azimuth and dip parameters. Finally, drill stem test matching was performed to validate the accuracy of the porosity and permeability models through dynamic simulation. A validation 3D reservoir model, which integrates geophysical, geological, and engineering data from fractured basement formation in Cuu Long Basin, was further constructed to calculate theoretical CO2 storage capacity. As a result, the calculated storage capacity for the fractured basement reservoir ranged from 7.02 to 99.5 million metric tons. These estimated results demonstrate that fractured basement reservoir has a combined potential for CO2 storage and EOR in the Cuu Long Basin.

Residual Oil Zones (ROZs) become potential formations for Carbon Capture, Utilization, and Storage (CCUS). Although the growing attention in ROZs, there is a lack of studies to propose the fast tool for evaluating the performance of a CO2 injection process. In this paper, we introduce the application of artificial neural network (ANN) for predicting the oil recovery and CO2 storage capacity in ROZs. The uncertainties parameters, including the geological factors and well operations, were used for generating the training database. Then, a total of 351 numerical samples were simulated and created the Cumulative oil production, Cumulative CO2 storage, and Cumulative CO2 retained. The results indicated that the developed ANN model had an excellent prediction performance with a high correlation coefficient (R2) was over 0.98 on comparing with objective values, and the total root mean square error of less than 2%. Also, the accuracy and stability of ANN models were validated for five real ROZs in the Permian Basin. The predictive results were an excellent agreement between ANN predictions and field report data. These results indicated that the ANN model could predict the CO2 storage and oil recovery with high accuracy, and it can be applied as a robust tool to determine the feasibility in the early stage of CCUS in ROZs. Finally, the prospective application of the developed ANN model was assessed by optimization CO2-EOR and storage projects. The developed ANN models reduced the computational time for the optimization process in ROZs.

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