  Oilfield production optimization is challenging due to subsurface model
complexity and associated non-linearity, large number of control parameters,
large number of production scenarios, and subsurface uncertainties.
Optimization involves time-consuming reservoir simulation studies to compare
different production scenarios and settings. This paper presents efficacy of
two hybrid evolutionary optimization approaches for well control optimization
of a waterflooding operation, and demonstrates their application using Olympus
benchmark. A simpler, weighted sum of cumulative fluid (WCF) is used as
objective function first, which is then replaced by net present value (NPV) of
discounted cash-flow for comparison. Two popular evolutionary optimization
algorithms, genetic algorithm (GA) and particle swarm optimization (PSO), are
first used in standalone mode to solve well control optimization problem. Next,
both GA and PSO methods are used with another popular optimization algorithm,
covariance matrix adaptation-evolution strategy (CMA-ES), in hybrid mode.
Hybrid optimization run is made by transferring the resulting population from
one algorithm to the next as its starting population for further improvement.
Approximately four thousand simulation runs are needed for standalone GA and
PSO methods to converge, while six thousand runs are needed in case of two
hybrid optimization modes (GA-CMA-ES and PSO-CMA-ES). To reduce turn-around
time, commercial cloud computing is used and simulation workload is distributed
using parallel programming. GA and PSO algorithms have a good balance between
exploratory and exploitative properties, thus are able identify regions of
interest. CMA-ES algorithm is able to further refine the solution using its
excellent exploitative properties. Thus, GA or PSO with CMA-ES in hybrid mode
yields better optimization result as compared to standalone GA or PSO
algorithms.
