Accurate well placement plays an essential role in increasing field recovery and storage while reducing operational costs. This task is complex, requiring robust solutions that can handle optimization problems efficiently. Despite numerous existing solutions, a need remains for a fast, highly accurate, computer-aided optimization tool. In this paper, an auto-adaptive workflow is described that leverages a complex interplay between machine learning (ML), physics of fluid flow, and a gradient-free algorithm to enhance solving well-placement problems.
Introduction
The study’s main objective is to develop a novel surrogate-based hybrid intelligent system to handle real well-placement problems in realistic field conditions while overcoming the drawbacks of conventional approaches.
