Abstract
This chapter mainly covers two well-known evolutionary algorithms: genetic and particle swarm. Initially, genetic algorithm's brief history and its applications in the oil and gas industry are illustrated. Next, genetic algorithm workflow is explained including population, fitness function evaluation, parent selection, crossover, and mutation. All the mentioned definitions are explained for a maximization example using Python. Then “genetic algorithm” library in Python is used to maximize well estimated ultimate recovery by optimizing some well design parameters. In the second part of this chapter, particle swarm algorithm is introduced with a brief background, its applications in the oil and gas industry, and the theory. All the steps of particle swarm optimization are illustrated in Python for a problem with four local and one global minimum. Finally, “pyswarm” package is used to maximize the net present value for a Barnett shale gas section, modeled by a reservoir simulator.
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