Determination of optimal well locations plays an important role in the efficient recovery of hydrocarbon resources. However, it is a challenging and complex task because it relies on reservoir, and fluid and economic variables that are often nonlinearly correlated. Traditionally, well placement optimization (WPO) has been done through experience and use of quality maps. However, reservoir management teams are beginning to appreciate the use of automatic optimization tools for well placement that will yield the largest financial returns or highest net present value (NPV). In addition, the performance of a reservoir is time and process dependent, therefore well placement decisions cannot be based on static properties alone. On the other hand, well placement optimization requires a large number of simulator runs in an iterative process, and thus several runs to reach the maximum achievable NPV. Therefore, there is a real need for automatic well placement approach that uses highly efficient optimization method, which can improve the result quality, speed of the convergence process to optimal result and thus decrease the time required for computation.

The objective of this work is to determine the optimal well locations in a heavy oil reservoir under production using a novel recovery process, in which steam is generated, in-situ, using thermochemical reactions. Self-adaptive differential evolution (SaDE) and particle swarm optimization (PSO) methods are used as the global optimizer to find the optimal configuration of wells that will yield the highest NPV. Comparison analysis between the two proposed optimization techniques is introduced. The CMG STARS Simulator is utilized in this research to simulate reservoir models with different well configurations.

Comparison of results is made between the NPV achieved by the well configuration proposed by the SaDE and PSO methods. The results show that SaDE performed better than PSO in terms of higher NPV after ten years of production while under in-situ steam injection process using thermochemical reactions. This is the first known application where SaDE and PSO methods are used to optimize well locations in a heavy oil reservoir that is recovered by injecting steam generated in-situ using thermo-chemical reactions. This research shows the importance of well placement optimization in a highly promising and novel heavy oil recovery process. This also is a step forward in the direction to eliminate the CO2 emissions related to thermal recovery processes.

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