The ultimate goal of this project was to provide a system that helps technicians to improve maintenance by monitoring the wells close to real-time and see how they are performing. To do this, we developed a solution that detected potentially hazardous scenarios for an oil well and warned the technicians in the control center to act upon these. This approach minimizes the cost of unscheduled maintenance and maximizes the component's lifespan.
Our team of engineers implemented a custom system with the ability to collect data from sensors of oil well pumps remotely. This not only allowed Halliburton to capture more metrics than with the previous third-party solution, but also to customize and structure this information to be used by machine learning models.
The implemented system is able to detect failures that the human eye cannot see given the nuances in deviations from the standard values. The performance results of the machinery, as well as warning signs, are displayed in a user-friendly dashboard for Petroleum engineers. In the case of warning signs, the operators get an automatic notification, which enables them to take real-time actions based on actual data.