Real-Time Detection of Stage Isolation Failure Using Pressure Analysis in Multistage Hydraulic Fracturing

Abstract Achieving effective stage isolation during multistage hydraulic fracturing is essential to ensuring optimal stimulation coverage, efficient proppant placement, and production performance. Traditional diagnostics for stage isolation failure such as plug slip, plug leak, failure to seat the ball, or mechanical degradation have relied heavily on subjective manual interpretations or post-treatment data, offering limited value for real-time decision-making. This study introduces a novel, real-time, automated detection framework that integrates multiple pressure analysis techniques to identify isolation or plug integrity failures with over 95% accuracy during treatment operations. The system evaluates and integrated six key criteria: (a) high frequency water hammer analysis with tubewave velocity calculations, (b) ISIP/falloff signature alignment, (c) sharp intra-stage pressure drops, (d) ball-seat detection and validation, (e) hammer frequency anomalies, and (f) pressure behavior at design rate. These pressure responses are segmented across operational cycles using event detection logic and analyzed independently through algorithmic logic. The individual outputs are combined through a multi-criteria decision analysis using a weighted rubric informed by the Analytical Hierarchy Process (AHP), which adjusts based on completion type (e.g., cased hole plug-and-perf vs. openhole MSF) and treatment mode (proppant vs. acid). This diagnostic engine not only identifies isolation loss but also distinguishes between plug damage and mechanical seating issues, with results validated against SME interpretation. The workflow provides operational teams with actionable insights, reducing non-productive time and enabling preemptive corrections. This is the first known integration of real-time multi-model pressure analytics with digital logic automation for isolation diagnostics in field treatments, offering an approach with near-100% accuracy for plug performance monitoring and frac design optimization, with a potential to save 10s to 100s operational hours.