Intelligent Drilling Optimization Systems: Using Machine Learning and Automation to Reduce Nonproductive Time and Improve Well Delivery Outcomes
Victor Nnanyelu Onyechi *
Schlumberger (Slb), Nigeria.
Babatunde Ojoawo
Ohio University, United States.
*Author to whom correspondence should be addressed.
Abstract
Aim: This study examines how intelligent drilling optimization systems (IDOS), driven by machine learning (ML) and automation technologies, can reduce nonproductive time (NPT) and enhance well delivery outcomes in the oil and gas industry. It aims to synthesize recent developments in artificial intelligence (AI)-based drilling systems, highlighting their operational benefits, performance improvements, and potential challenges.
Study Design: A comprehensive review of recent advancements in intelligent drilling optimization between 2020 and 2025, focusing on the integration of ML algorithms, automation frameworks, and real-time data analytics in upstream petroleum operations. The review emphasizes the practical impact of these technologies on NPT reduction, drilling efficiency, and sustainable well delivery.
Methodology: A systematic review was conducted, sourcing publications from Google Scholar, Scopus, ScienceDirect and IEEE Xplore. Studies were selected based on relevance to predictive analytics, automation in well control, and data-driven optimization.
Results: Findings reveal that ML and automation technologies significantly improve drilling performance by enabling predictive maintenance, real-time anomaly detection, and autonomous control of drilling parameters. Algorithms such as artificial neural networks (ANNs), support vector machines (SVM), and reinforcement learning (RL), applied in predictive and real-time optimization, achieved 20–35% reductions in NPT. Integration of digital twins, IoT, edge computing, and cloud analytics improved simulation accuracy, minimized operational risks, and facilitated adaptive decision-making, supporting continuous optimization and enhanced well delivery.
Conclusions: Intelligent drilling systems remain limited by challenges such as data heterogeneity, lack of model standardization, and skill gaps in AI implementation. Future research should focus on hybrid modeling approaches that combine physics-based and ML-driven analytics, as well as developing unified frameworks for cross-field data integration to enhance scalability and interpretability.
Keywords: Intelligent drilling, machine learning, nonproductive time, artificial intelligence, reinforcement learning