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Recently developed machine learning techniques, in association with theInternet of Things (IoT) allow for the implementation of a method of increasingoil production from heavy-oil wells. Steam flood injection, a widely usedenhanced oil recovery technique, uses thermal and gravitational potential tomobilize and dilute heavy oil in situ to increase oil production. In contrastto traditional steam flood simulations based on principles of classic physics,we introduce here an approach using cutting-edge machine learning techniquesthat have the potential to provide a better way to describe the performance ofsteam flood. We propose a workflow to address a category of time-series datathat can be analyzed with supervised machine learning algorithms and IoT. Wedemonstrate the effectiveness of the technique for forecasting oil productionin steam flood scenarios. Moreover, we build an optimization system thatrecommends an optimal steam allocation plan, and show that it leads to a 3%improvement in oil production. We develop a minimum viable product on a cloudplatform that can implement real-time data collection, transfer, and storage,as well as the training and implementation of a cloud-based machine learningmodel. This workflow also offers an applicable solution to other problems withsimilar time-series data structures, like predictive maintenance.

Machine Learning and the Internet of Things Enable Steam Flood Optimization for Improved Oil Production