Hybrid Evolutionary Optimization Approach for Oilfield Well Control Optimization

TL;DR

This paper evaluates hybrid evolutionary algorithms combining GA, PSO, and CMA-ES for optimizing oilfield well control, demonstrating improved results with distributed cloud computing and hybridization strategies.

Contribution

It introduces hybrid optimization approaches combining GA, PSO, and CMA-ES for oilfield well control, showing enhanced efficiency and solution quality over standalone methods.

Findings

Hybrid algorithms outperform standalone GA and PSO in convergence and solution quality.

Hybrid optimization requires fewer simulation runs than standalone methods.

Cloud computing effectively reduces optimization turnaround time.

Abstract

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.