Optimizing Multi-Stage Hydraulic Fracturing Treatments for Economical Production in Permian Basin Using Machine Learning

Due to the heavy computational costs of pure simulation-based reservoir models when used for hydraulic fracturing design, machine learning (ML) approaches become promising in petroleum industry to provide effective and efficient well completion optimization solutions. In this work, we propose a framework of using ML approaches to optimize multi-stage hydraulic fracturing design for economical development of unconventional wells in Permian basin, West Texas. Since the goals of maximizing oil production and minimizing completion cost are generally conflicting, combining both as a bi-objective optimization for maximal profit is the main objective in this study. Two ML regression models are selected to predict the $1^{\mathrm{s}\mathrm{t}}$-year oil production and completion cost, respectively. The bi-objective optimization is shown to help gain more profit than the single-objective optimizations of oil production or completion cost only. From the case study of an exam well, we predict the profit as ${\$}$12.5 millions when optimizing oil production only, ${\$}$7.9 millions when optimizing completion cost only, and ${\$}$13.4 millions when optimizing both. This bi-objective optimization approach in this study can help to increase profit up to ${\$}$1 million. Moreover, the framework can help the decision makers to select solutions of optimal completion design parameters based on their preferences.