Predictive maintenance and equipment efficiency in drilling operations and its impact on energy efficiency

Predictive analytics has become an essential pillar of digital transformation in drilling operations, improving reliability and lowering the energy footprint of critical assets. This paper investigates how data-driven maintenance models influence the overall energy performance of drilling systems, focusing on mud pumps, top drives, and power generation equipment. Using a life cycle–based approach, the study evaluates reductions in total energy consumption and carbon intensity achieved through predictive maintenance. The analysis confirms that condition-based monitoring, integrated with digital twins, can reduce operational energy demand by roughly 10–15%, offering a measurable pathway toward low-carbon well construction and sustainable field development.