Integrated Optimization of Hybrid Steam-Solvent Injection in Post-CHOPS Reservoirs with Consideration of Wormhole Networks and Foamy Oil Behavior

For this paper, integrated techniques have been developed to optimize the performance of the hybrid steam-solvent injection processes in a depleted post-cold heavy oil production with sand (CHOPS) reservoir with consideration of wormhole networks and foamy oil behavior. After a reservoir geological model has been built and calibrated with the measured production profiles, its wormhole network is inversely determined using the newly developed pressure-gradient-based (PGB) sand failure criterion. Such a calibrated reservoir geological model is then used to maximize the net present value (NPV) of a hybrid steam-solvent injection process by selecting injection time, soaking time, production time, injection rate, steam temperature, and steam quality as the controlling variables. The genetic algorithm (GA) has been integrated with orthogonal array (OA) and Tabu search to maximize the NPV by delaying the displacement front as well as extending the reservoir life under various strategies. Considering the wormhole network and foamy oil behavior and using the NPV as the objective function, such a modified algorithm can be used to allocate and optimize the production-injection strategies of each huff ‘n’ puff (HnP) cycle in a post-CHOPS reservoir with altered porosity and increased permeability within a unified, consistent, and efficient framework.