Use Of Large Language Models To Orchestrate Access To Drilling Data And Engineering Services In A Microservice Architecture

From Explainable Ai To Ai That Can Explain Itself

Automated Stuck Pipe Risk Interpretation And Advisory Using LLMs, Operator Drilling Programs And Retrieval-Augmented Generation: Case Studies

From Operational Records To Engineering Decisions: A Grounded Agentic AI Workflow For Drilling Data

From Prediction To Decision: An Agentic Framework For Stuck Pipe Alert Analysis

Operationalizing Real-time Stuck Pipe Risk Prediction Through Explainable Risk Aggregation And Workflow-centered Visualization

Detecting Lateral Vibration From Surface Measurements Alone: A Machine-learning Method For Real-time Severity Estimation And Mitigation In Drilling Optimization Systems

Vision-based Human-Machine Interaction Monitoring During Drill-floor Pipe-handling Operations