Towards Future-Fit Asset Management: AI-Enabled Optimization of Event-Based Maintenance Strategies for High-Reliability Drilling Operations

Abstract

Achieving cost-efficient and high-reliability operations is increasingly critical as the energy industry advances toward greater digitalization and sustainability. Drilling services fleets alone account for substantial annual maintenance expenditures, highlighting the need for data-driven approaches that improve asset performance while reducing operational costs. This paper presents an integrated, AI-enabled framework that enhances Event-Based Maintenance (EBM) by applying advanced analytics, Natural Language Processing (NLP), and mathematical optimization to transform maintenance planning, failure classification, and reliability decision-making. The proposed workflow combines large volumes of operational data, historical maintenance logs, and unstructured incident reports into a unified decision-support architecture. A data engineering system constructs missions from tool operational histories, while a custom NLP model automatically extracts and classifies failure modes from unstructured text, substantially improving data quality for reliability modeling. Multiple probabilistic reliability models are developed to characterize failure trends and quantify uncertainty, enabling a more accurate representation of equipment risk profiles. A maintenance cost model is integrated with reliability outputs to quantify the financial impact of various maintenance scenarios. The combined dataset feeds into a mathematical optimization engine that determines optimal maintenance intervals by balancing cost, risk, and reliability performance. This automated, AI-driven optimization method provides a scalable foundation for proactive maintenance management across diverse drilling tool families. The methodology is validated through field applications on formation-evaluation, directional drilling, and power telemetry tools. Results demonstrate significant reductions in maintenance expenditure, improved failure mode classification accuracy, and measurable improvements in fleet reliability. Beyond optimizing EBM intervals, this framework modernizes the maintenance ecosystem by enabling predictive insights, reducing reliance on reactive maintenance, and minimizing costly root cause analysis investigations. By integrating digital technologies with reliability engineering principles, this AI-driven EBM optimization framework provides operators with a robust, future-fit asset management approach that strengthens operational resilience and supports the transition toward fully digitalized, high-reliability drilling operations.