Application of artificial intelligence in drilling optimization: A review of current trends and future prospects

The oil and gas industry is undergoing a significant digital transformation, with artificial intelligence (AI) emerging as a key enabler of operational efficiency and cost reduction across the drilling lifecycle. Drilling operations represent one of the most expensive and technically complex phases in hydrocarbon exploration and production, often accounting for 30–40% of total well development costs. Consequently, there is growing interest in leveraging AI-driven technologies to optimize drilling performance, reduce non-productive time (NPT), and enhance wellbore quality.

This paper presents a comprehensive review of the current applications of artificial intelligence - including machine learning (ML), deep learning (DL), and neural networks — in the domain of drilling engineering. The review systematically examines how AI techniques are being applied across key drilling sub-domains, including: (1) Rate of Penetration (ROP) optimization, (2) real-time drill bit wear prediction, (3) automated wellbore stability analysis, (4) stuck pipe prediction and prevention, and (5) drilling parameter optimization through reinforcement learning.

The study draws on published literature from peer-reviewed journals, SPE technical papers, and industry reports over the period 2015 - 2025. Key findings indicate that supervised ML algorithms - particularly Random Forest, Support Vector Machines, and Long Short-Term Memory (LSTM) networks - have demonstrated strong predictive accuracy for ROP forecasting and formation evaluation. Furthermore, integration of real-time sensor data with AI models has shown potential to reduce NPT by up to 20% in controlled field trials.

Despite promising advances, several challenges remain, including data quality and availability, model interpretability, and the integration of AI systems with legacy drilling equipment. This paper critically evaluates these limitations and discusses emerging trends such as physics-informed neural networks (PINNs) and digital twin frameworks as potential pathways for more robust and explainable AI in drilling.
The findings of this review provide a structured foundation for researchers and practitioners seeking to implement AI-based solutions in drilling operations, and highlight priority areas for future research and industry-academia collaboration.