Expert-guided Machine-learning for Well Location Optimization under Subsurface Uncertainty

Summary This work investigates the application of an expert-guided machine learning technique for identification of connected and high saturated oil volumes for optimal well placement. The technique is designed to work on property maps of a single model as well as ensembles of reservoir models for robust field development optimization under subsurface uncertainty. The methodology is embedded into a structured workflow design for improving a baseline well location design of the Olympus reservoir model ensemble, a public benchmark project for field development optimization under uncertainty. This work suggests an iterative improvement of well location designs using probabilistic well ranking to identify low performing wells, probability maps to understand reservoir performance and analytics-based optimization steps targeting large connected and high saturated oil volumes. The methodology is described, and application results are presented for a full optimization loop. The structured approach highlights the value of novel learning techniques to provide an efficient and manageable solution for optimizing a well location design under subsurface uncertainty.