Intelligent Drilling Technology: Modeling and Application

Dear Colleagues,

This Special Issue invites cutting-edge research at the intersection of drilling engineering, hybrid modeling, and advanced AI techniques, aiming to showcase how these technologies are transforming the future of subsurface operations.

We particularly welcome contributions in the following areas:

• Large Language Models (LLMs) for decision support, anomaly interpretation, and automated reporting.
• Knowledge Retrieval Systems built on historical drilling data and operational best practices.
• Hybrid models that combine physics-based simulations with machine learning for enhanced accuracy and interpretability.
• Explainable AI (XAI) approaches that promote trust, transparency, and actionable insights for engineers and operators.
• Digital twin frameworks for real-time monitoring, predictive maintenance, and control optimization.
• Multi-agent systems and AI assistants for coordinated planning, execution, and human-in-the-loop feedback.
• Digital drilling ecosystems that integrate cloud/edge computing, sensor fusion, and autonomous workflows.
• Applications targeting energy efficiency, sustainability, operational safety, and cost reduction.

Machine learning has emerged as a powerful tool with the potential to address complex challenges in drilling technology. From real-time data analytics, performance monitoring, anomaly detection, and predictive maintenance to automation and decision support systems, the integration of machine learning algorithms promises to unlock new horizons in drilling technology.

We invite researchers, engineers, and experts in the field of drilling technology to contribute innovative research, case studies, and reviews that shed light on the modeling and application in various aspects of drilling, including but not limited to, the following:

• Predictive maintenance and equipment lifecycle optimization;
• Formation evaluation using data-driven and hybrid approaches;
• Digital twin technologies for real-time simulation and optimization;
• Explainable AI (XAI) for transparent and trusted decision support;
• Drill bit design and performance optimization;
• Intelligent drilling fluid management systems;
• Wellbore stability prediction and control;
• Real-time monitoring, automation, and process optimization;
• Risk assessment and mitigation using ML models;
• Health, safety, and environmental (HSE) applications in drilling;
• Anomaly detection and event classification;
• AI-assisted decision-making frameworks;
• Autonomous and intelligent drilling control systems;
• Physics-informed machine learning and hybrid modeling;
• Advanced drilling data analytics, storage, and retrieval systems;
• Retrieval-augmented generation (RAG) and knowledge-based LLM systems;
• Digital ecosystems for integrated drilling intelligence.

By disseminating cutting-edge research in this Special Issue, we aim to foster collaboration, share best practices, and advance the adoption of the intelligent drilling industry. Submissions are now open, and we look forward to receiving your contributions.

Prof. Dr. Dan Sui
Guest Editor

Manuscript Submission Information

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• machine learning
• drilling technology
• automation
• real-time monitoring
• decision support
• efficiency
• safety