for oil well pumps
Halliburton is one of the world's largest providers in the energy industry. With around 60,000 employees in more than 80 countries, it helps its customers maximize value throughout the lifecycle of the reservoir. We partnered with them to implement a monitoring system for their oil pumps scattered across the United States.
Until then, the process was time consuming and the ability to detect faults with accuracy and at scale was very limited. The company was relying on a third-party service to detect anomalies in the performance of oil well pumps, and potential failures prediction. They were gathering very little information from the wells: the data consisted of just 8 data points and was taken every 15 minutes. This didn't enable the use of machine learning.Data was collected from oil wells pumps and then handed to engineering operators at Halliburton. They analyzed the data manually, focusing on any deviations from the standard performance measures.
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.
Headquarters: Houston, TX & Dubai
Market Cap: 18.78B (Dec 2019)
monitored in real time
false positive alerts
Total data control for the client
Absence of required data for a machine learning solution.
By building a custom data collection system, Tryolabs set the foundation for predictive maintenance of machinery, which was leveraged in a second stage of the project.
We implemented a solution using IoT devices attached to the already existing equipment, which allowed us to collect, analyze and later visualize data.