Well Placement Optimization under Uncertainty with CMA-ES Using the Neighborhood

TL;DR

This paper introduces a neighborhood-based method combined with CMA-ES to optimize well placement under geological uncertainty, significantly reducing the number of reservoir simulations needed.

Contribution

The paper proposes a novel neighborhood approach that estimates objective functions using fewer reservoir simulations in well placement optimization under uncertainty.

Findings

Reduces reservoir simulations by around 80%.

Effectively captures geological uncertainty with fewer simulations.

Outperforms traditional methods using all realizations.

Abstract

In the well placement problem, as well as in other field development optimization problems, geological uncertainty is a key source of risk affecting the viability of field development projects. Well placement problems under geological uncertainty are formulated as optimization problems in which the objective function is evaluated using a reservoir simulator on a number of possible geological realizations. In this paper, we present a new approach to handle geological uncertainty for the well placement problem with a reduced number of reservoir simulations. The proposed approach uses already simulated well configurations in the neighborhood of each well configuration for the objective function evaluation. We use thus only one single reservoir simulation performed on a randomly chosen realization together with the neighborhood to estimate the objective function instead of using multiple simulations on multiple realizations. This approach is combined with the stochastic optimizer CMA-ES. The proposed approach is shown on the benchmark reservoir case PUNQ-S3 to be able to capture the geological uncertainty using a smaller number of reservoir simulations. This approach is compared to the reference approach using all the possible realizations for each well configuration, and shown to be able to reduce significantly the number of reservoir simulations (around 80%).