Automated Real-Time Frac Stage Detection and Assignment Using AI/ML

Abstract The hydraulic fracturing industry faces a significant data-management challenge: when operational telemetry streams arrive from multi-well pads, they frequently lack labels identifying which well and which stage is active. Without metadata linking records to specific wells and stages, real-time analytics and automated decision support are difficult to operationalize. Manual "staging" of data after the job introduces latency, inconsistency, and errors that propagate into reporting and operational performance. This paper presents a production-grade AI/ML framework that automatically assigns incoming data streams to the correct well and stage in real time and detects stage boundaries during hydraulic fracturing. The system employs an XGBoost classifier over features generated by MiniRocketMultivariate transforms, applied to multichannel telemetry comprising wellhead pressures, slurry flow rates, proppant concentrations, and offset-well pressures. The training corpus exceeds 100 fracturing jobs (~20,000 labeled stages, ~45,000 hours of data). By eliminating the reliance on post-stage reporting, the system enables immediate, actionable insights within stringent latency budgets. The contributions include an auto-staging algorithm that maps pad-level streams to the active well; a boundary-detection mechanism that recognizes pressure transitions and proppant taper indicative of frac start and frac end; integration of offset-well pressures to reduce cross-well confusion; and rigorous data-validation and quality-control checks. Deployed via API-driven ingestion and KServe-based serving, the system delivers real-time recommendations that fit existing operational workflows. Field deployments across zipper, simul-frac, hot-swap, and continuous-pumping campaigns demonstrate substantial improvements in timeliness and accuracy, with ~92% staging accuracy versus manual benchmarks. The framework converts retrospective practices into live, data-driven operations and establishes a platform for broader AI in completions.