Digital Health Analyzer Enables Proactive Maintenance and Reliable Operations for Downhole Tools in Coiled Tubing Drilling

Abstract This paper presents the development and implementation results of a digitally enabled downhole tool’s Health Analyzer, designed to address reliability challenges in coiled tubing drilling (CTD) caused by high temperatures, shock, and abnormal wear. It aims to reduce non-productive time (NPT) through data-driven models for potential tools’ function anomaly detection, optimizing maintenance decisions, and enhancing operational reliability and efficiency. The proposed approach integrates advanced data analysis and artificial intelligence (AI)-based classifiers to detect anomalies in the tool’s function and predict failures in CTD tools. The solution employs standard deviation thresholds for anomaly detection, temperature-dependent behavior analysis, and machine learning classifiers to establish maintenance triggers. The AI thresholds align maintenance schedules with actual tool wear and tear, reducing unnecessary maintenance procedures thereby shortening turnaround time. Such features proactively detect and prevent failures during key operational stages, such as wellsite rerun decisions. Field data from 2022 to 2024 demonstrates that the CTD health analyzer would have prevented 44% of NPT and proactively detected 67% of failures, optimizing tool reliability. AI-based thresholds have optimized maintenance workflows by reducing the tool’s electronics heat test requirements and aligning service level decisions with actual component wear and aging. In offline mode, the system enables maintenance technicians to efficiently diagnose root causes, while in online mode, it enhances wellsite crews’ ability to make correct, real-time rerun decisions. This approach has resulted in significant operational improvements, reducing resource needs and extending tool life. The findings demonstrate the potential for this predictive framework to enhance tool reliability and streamline maintenance workflows. This paper introduces an innovative application of data-driven predictive maintenance to CTD operations, leveraging large datasets, advanced statistical models, and AI to prevent NPT. By integrating standard deviation-based anomaly detection, temperature thresholds, and AI classifiers tests into maintenance workflows, this approach delivers a novel method for aligning maintenance decisions with real-time tool wear, contributing to the CTD reliability and efficiency.