Ensemble-Based Well Location Optimization Under Subsurface Uncertainty Guided By Deep-Learning Approach To 3D Geological Feature Classification

Abstract

Abstract Ensemble-based reservoir simulation workflows have become popular for estimating prediction uncertainty and optimizing field development objectives under uncertainty. While workflows exist for ensemble generation, ensemble analyses become an increasing challenge for extracting value from ensembles through robust field development guidance. This work presents a self-supervised deep learning modelling approach for classifying 3D-structural features applied to expert-driven field performance optimization under uncertainty. Meaningful representations of subsurface features are essential to efficient machine learning tasks such as classification of categorical outcomes and regression for predicting dependent target variables. Unlabeled data is abundant and used for unsupervised learning which is complex in contrast to the use of labelled data. However, labelling is generally expensive and, in some cases, ill-defined or impractical due to missing modelling approaches. In this work, a self-supervised deep learning modelling approach is applied to learn semantically meaningful representations of subsurface features, e.g.,size and structure of connected volumes. The deep learning model is designed to learn a representation of 3-dimensional subsurface features. Representations are categorized, clustered and ranked for well location decision support. For application demonstration, the self-supervised deep learning modelling approach is embedded in a structured workflow design for guiding well location selection to optimize reservoir delivery performance under subsurface uncertainty. Performance verification and application results are presented for the Olympus semi-synthetic case study. Based on probabilistic success criteria we present an optimized well placement design for which 90% of all producers deliver the economic demand at an 80% probability level or higher. In a second study the workflow is applied to a real field to discuss guidelines of use and to share expected gains in workflow efficiency, result quality and decision support.