Leveraging Data Analytics in Multimodal Deep Learning for Predictive Maintenance Aimed at Minimizing Rig Downtime

Abstract

Enhancing uptime and achieving greater operational efficiency in industrial assets and systems is pivotal. The focus of this paper is on a data-led strategy that integrates a scalable data framework, sophisticated analytics, and multimodal deep learning to foster predictive maintenance. This approach significantly reduces expensive downtime in oil and gas drilling activities. A proposed solution involves a cloud-based data lake architecture capable of capturing and storing both structured and unstructured time-series sensor data from drilling equipment like pumps, blowout preventers, and top drives. Massive data pipelines handle billions of sensor readings, and multivariate analysis offers insights into maintenance needs. The introduction of the Deep Maintenance Neural Network (DMNN) maximizes the use of sensor data, past failures, meteorological information, and expert knowledge to foresee a range of system failures in drilling operations. This study underscores the combined power of leveraging industrial-scale data storage solutions, cutting-edge analytics,
and deep learning to pave the way for advanced maintenance strategies. Implementing the discussed approach converts raw sensor data into significant improvements in rig durability and operational downtime. It demonstrates that adopting a data-smart maintenance strategy can substantially elevate productivity at the system level.