Using Evolution Strategy with Meta-models for Well Placement Optimization

Optimum implementation of non-conventional wells allows us to increase considerably hydrocarbon recovery. By considering the high drilling cost and the potential improvement in well productivity, well placement decision is an important issue in field development. Considering complex reservoir geology and high reservoir heterogeneities, stochastic optimization methods are the most suitable approaches for optimum well placement. This paper proposes an optimization methodology to determine optimal well location and trajectory based upon the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) which is a variant of Evolution Strategies recognized as one of the most powerful derivative-free optimizers for continuous optimization. To improve the optimization procedure, two new techniques are investigated: (1). Adaptive penalization with rejection is developed to handle well placement constraints. (2). A meta-model, based on locally weighted regression, is incorporated into CMA-ES using ...

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Zyed Bouzarkouna, Didier Yu Ding, Anne Auger
• Keywords: Complex Reservoir Geology, CORR 2010, Education, Genetic Algorithm, Reservoir Simulations