  Recently developed machine learning techniques, in association with the
Internet of Things (IoT) allow for the implementation of a method of increasing
oil production from heavy-oil wells. Steam flood injection, a widely used
enhanced oil recovery technique, uses thermal and gravitational potential to
mobilize and dilute heavy oil in situ to increase oil production. In contrast
to traditional steam flood simulations based on principles of classic physics,
we introduce here an approach using cutting-edge machine learning techniques
that have the potential to provide a better way to describe the performance of
steam flood. We propose a workflow to address a category of time-series data
that can be analyzed with supervised machine learning algorithms and IoT. We
demonstrate the effectiveness of the technique for forecasting oil production
in steam flood scenarios. Moreover, we build an optimization system that
recommends 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 cloud
platform that can implement real-time data collection, transfer, and storage,
as well as the training and implementation of a cloud-based machine learning
model. This workflow also offers an applicable solution to other problems with
similar time-series data structures, like predictive maintenance.
