AI-Driven Real-Time Early Warning System for Sand Blockage Mitigation in Hydraulic Fracturing Operations

Résumé fourni par la source

Abstract Sand blockage, or screen-out, remains one of the most disruptive and costly challenges in hydraulic fracturing, particularly in deep, geologically complex shale formations. Traditional mitigation strategies—relying on human monitoring of surface pressure data—are often reactive and insufficient for preventing treatment interruptions. This paper introduces a novel AI-driven real-time sand blockage early warning system, integrated into the Testing and Fracturing Operation Center (TFOC) platform, which enables proactive intervention during fracturing operations. The system continuously ingests real-time pressure and pumping data, analyzes pressure trends using slope-based algorithms, and forecasts short-term blockage risks up to 120 seconds in advance. Upon detecting potential sand-out scenarios, the system automatically issues alerts and provides operators with actionable recommendations such as reducing slurry rate or halting proppant injection. Field applications across horizontal shale wells in China—including detailed analysis of five critical time points and four additional case studies—demonstrate that the system enhances operational safety, reduces non-productive time (NPT), and improves stimulation effectiveness. Results show that the system not only enables timely adjustments to avoid severe blockages but also supports complex fracture network generation, making it a transformative tool for intelligent fracturing operations in unconventional reservoirs.

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