Employing Live Subsurface Measurements to Support Supervised and Autonomous Intelligent Decision-Making While Fracturing

Abstract

Historically, real-time changes during fracturing treatments have primarily been limited to avoiding or controlling screenouts using only the guidance of surface treating pressure. This paper introduces a workflow that utilizes live surface-based measurements of perforation friction, perforation efficiency, and Uniformity Index to support intelligent decision-making based on subsurface data during a fracture stage. The study describes how frac fluid and proppant can be reallocated from a poor-performing stage with runaway fractures into a stage with high Uniformity Index that will have a better distributed fracture network and ultimately be a better producer.

Key Findings

• Real-time perforation friction measurement replaces treating pressure guesswork, providing direct subsurface state visibility during live stimulation
• Fluid and proppant can be dynamically reallocated from underperforming stages to high-Uniformity-Index stages with better distributed fracture networks
• Supervised CLF workflow with operator-approved decision logic enables both human-in-the-loop and autonomous execution modes
• Perforation efficiency and Uniformity Index measured continuously provide the subsurface signal that surface treating pressure fundamentally cannot deliver

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