Using a Digital Twin in Predictive Maintenance

Abstract

The oil and gas industry is facing unprecedented and brutal market conditions. While the industry was already in the midst of digitalization, the oil price crash has instilled a fresh impetus on its adoption to cut costs through innovation and new technologies. One such technology is predictive maintenance. When equipment on a rig breaks down, the resulting problem often is not that of replacement but the forced downtime in production or drilling. Therefore, predicting when equipment or a system is going to fail and determining the root cause of failure unlocks significant value. Predictive maintenance has rapidly gained in popularity, spurred by well publicized advances in high-performing computing and Internet of Things (IoT) technologies. Some companies are experiencing the benefits of predictive maintenance firsthand. For example, engineers at Baker Hughes implemented predictive maintenance on the company’s fleet of fracturing trucks. They collected nearly a terabyte of data from pumps on these trucks and then used signal-processing techniques to identify the relevant sensors. Finally, they applied machine-learning techniques to distinguish a healthy pump from an unhealthy one and reduced overall costs by $10 million (MathWorks 2019). This success story and others like it have made pursuing predictive maintenance projects a priority among both oilfield operators and services firms.