Deterministic tools to predict recovery performance of carbonated water injection.

Carbonated water injection (CWI) is an efficient oil recovery method, which provides solution to the drawbacks of existing related recovery techniques such as water flooding and pure CO 2 injection. The recovery factor achieved from CWI is considerably higher than that for related CO 2 - enhanced oil recovery (EOR) methods, due to effective transport phenomena involved in the displacement process. Additionally, the sequestration of anthropogenic CO 2 makes CWI even more attractive for practical implications. Although CWI has been experimentally proven to be an efficient technique, simulation/mathematical models to capture detailed CWI physics have been unreliable because of the complex recovery mechanisms associated with CWI. A majority of models have been developed based on unrealistic assumptions. Thus, existing models become doubtful and the confidence to apply CWI in the larger scales such as pilot plants becomes low. In this research work, smart methods such as artificial neural network (ANN), least squares support vector machine (LSSVM), and gene expression programming (GEP) are suggested to avoid the impractical and inconclusive assumptions. The connectionist techniques (e.g., ANN, LSSVM, and GEP) relate the recovery factor (RF) to the key input parameters such as pressure, temperature, viscosity, permeability, and injection rate based on pattern recognition without detailed knowledge about the process as well as use of the governing equations. The performance of the deterministic models is evaluated through using statistical parameters such as mean squared error (MSE), maximum absolute percentage error (MAAPE), minimum absolute percentage error (MIAPE), and goodness of fit (R2). The results reveal that the ANN model has the lowest MSE (0.35), MIAPE (0.001), and MAAPE (2.47), and the highest R2 (0.99) in the testing phase. Based on the sensitivity analysis, pressure is recognized as the most important parameter, while temperature has the least rank in terms of significance. The findings of this research study can assist engineers and researchers to provide a reasonable estimation of RF achievable from CWI, which can be an asset in better management and planning of CWI processes toward optimal conditions in terms of technical, economic, and environmental prospects. • Effective deterministic tools are introduced to estimate CWI performance. • The pressure and temperature have the highest and lowest importance. • The models are able to accurately simulate CWI behaviors. • The ANN is a more accurate and reliable model among others. • Sensitivity analysis helps researchers and engineers to determine CWI optimal conditions. [ABSTRACT FROM AUTHOR]