Systems and methods for completion optimization for waterflood assets

Abstract

Implementations described and claimed herein provide systems and methods for a framework to achieve completion optimization for waterflood field reservoirs. The proposed methodology leverages adequate data collection, preprocessing, subject matter expert knowledge-based feature engineering for geological, reservoir and completion inputs, and state-of-the-art machine-learning technologies, to indicate important production drivers, provide sensitivity analysis to quantify the impacts of the completion features, and ultimately achieve completion optimization. In this analytical framework, model-less feature ranking based on mutual information concept and model-dependent sensitivity analyses, in which a variety of machine-learning models are trained and validated, provides comprehensive multi-variant analyses that empower subject-matter experts to make a smarter decision in a timely manner.

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