Toward a Real-Time Decision Support System for Drilling Operations: Conceptual Architecture and Field Feasibility
Keywords:
Real-Time Decision Support, Drilling Optimization, Digital Oilfield, Drilling Data Analytics, Operational Risk Mitigation, Intelligent Well Delivery SystemsAbstract
The increasing complexity of modern drilling operations, driven by deeper wells, tighter financial margins, and elevated subsurface uncertainties, has necessitated the development of real-time decision support systems (DSS) to enhance operational responsiveness and risk mitigation. This paper proposes a structured and modular conceptual framework for a real-time DSS tailored to drilling operations. The model comprises three core architectural layers—data acquisition, analytics, and decision support—designed to enable seamless integration of surface and downhole data, diagnostic analytics, and decision logic. Functional modules support event detection, risk scoring, trajectory correction, and rig performance optimization, while collaborative interfaces foster cross-disciplinary communication and rapid intervention. Implementation considerations address IT infrastructure, cybersecurity, organizational readiness, and governance. The system promises to improve decision speed, reduce non-productive time, and align drilling practices with digital transformation goals. Future pathways include integration with machine learning models, support for autonomous drilling, and advancement toward intelligent, adaptive well delivery systems. The framework establishes a foundation for safer, more efficient, and data-driven drilling operations in an increasingly complex energy landscape.
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