Using Evolution Strategy with Meta-models for Well Placement Optimization

TL;DR

This paper introduces a novel optimization approach combining CMA-ES with meta-models and adaptive techniques to improve well placement decisions, reducing computational costs and increasing net present value in reservoir management.

Contribution

The paper develops a new CMA-ES based method with adaptive penalization and meta-model integration for efficient well placement optimization in complex reservoirs.

Findings

Outperforms genetic algorithms in net present value.

Reduces the number of reservoir simulations needed.

Improves well placement decisions in heterogeneous reservoirs.

Abstract

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 an approximate ranking procedure. Therefore, we can reduce the number of reservoir simulations, which are computationally expensive. Several examples are presented. Our new approach is compared with a Genetic Algorithm incorporating the Genocop III technique. It is shown that our approach outperforms the genetic algorithm: it leads in general to both a higher NPV and a significant reduction of the number of reservoir simulations.