Non-conventional wells allow to increase considerably hydrocarbon recovery. By considering the high drilling cost and the potential earn in well productivity, optimum implementation of non-conventional wells is an important issue in petroleum industry.

Considering large number of parameters involved in well placement and high reservoir heterogeneities, stochastic methods such as evolutionary algorithms are the most efficient approaches for optimization. In this paper, we present the CMAES (Covariance Matrix Adaptation - Evolution Strategy) method, which is based on evolutionary approach and has been considered as one of the best stochastic optimization method for non-linear problem. The application of CMAES to the problem of well placement optimization is presented. CMAES is an alternative approach for the well placement, and it gives comparable results with respect to the genetic algorithm.

Although the evolutionary methods such as CMAES or genetic algorithm are suitable for the modelling of well placement, their efficiency depends on various parameters involved in the model. In this paper, the impacts of model parameters in the optimization of well placement are also discussed.

Keywords:
evolutionary algorithm, objective function, covariance matrix, Artificial Intelligence, Efficiency, best well position, NPV, well placement, machine learning, Upstream Oil & Gas
Subjects:
Formation Evaluation & Management, Information Management and Systems, Artificial intelligence
Copyright 2008, Society of Petroleum Engineers
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