In this study, a systematical technique has been developed to experimentally and numerically evaluate enzyme-assisted hot waterflooding performance in a heavy oil reservoir for the first time. Experimentally, an enzyme solution (i.e., a protein-based liquid catalyst) is prepared and used to displace heavy oil in sandpacked experiments at elevated temperatures, during which pressures and fluid productions are continuously monitored and measured. Numerically, reservoir simulation is performed to reproduce the experimental measurements and then extended to evaluate the performance in a targeted heavy oil reservoir. Once history matching on the experimental measurements is completed, such a calibrated model is then employed to optimize enzyme concentration, temperature, and aging time, respectively. It is found from the displacement experiments that temperature imposes a significant impact on heavy oil recovery with its appropriate range of 45°C-55°C, and enzyme positively contributes to heavy oil recovery for most scenarios. Compared to the traditional waterflooding mechanisms, the enzyme-assisted hot waterflooding process shows its considerable potential in heavy oil recovery by means of reducing oil viscosity, altering wettability, and reducing interfacial tension.

In this paper, integrated techniques have been developed to optimize performance of the hybrid steam-solvent injection processes in a depleted post-CHOPS reservoir with consideration of wormhole networks and foamy oil flow. With the experimentally determined properties of injected gases and reservoir fluids by performing PVT tests, history matching of the reservoir geological model is completed through the relationship between fluid and sand production profiles and reservoir pressure. Meanwhile, the wormhole network has been inversely determined with the newly developed pressure-gradient-based (PGB) sand failure criterion. Once the history matching is completed, the calibrated reservoir geological model is used to optimize the solvent(s) and CO2 concentrations, provided that thermal energy, injection rates, and flowing bottomhole pressures are chosen as the controlling variables. The genetic algorithm has been modified and used to maximize the objective function of net present value (NPV) while delaying the displacement front as well as extending the reservoir life with optimal oil recovery under various strategies. Depending on the formation pressure and temperature, soaking time is optimized as a function of solvent concentration and fluid properties. Subsequently, considering the wormhole network and foamy oil flow, such a modified algorithm can be used to allocate and optimize the production-injection strategies with the NPV as the objective function.