Physics-Constrained AI-Assisted Flowing Material Balance for Productivity Evaluation During High-Volume, Long-Duration Flowback in Ultra-Deep Wells

Abstract

High-volume, long-duration flowback in ultra-deep fractured wells couples pressure, rate, water production, fracture conductivity, and stress-sensitive reservoir properties, making it difficult for pressure transient analysis (PTA), flowing material balance (FMB), and rate transient analysis (RTA) to maintain parameter continuity across flowback stages. This study proposes a physics-constrained artificial-intelligence (AI)-assisted workflow centered on FMB. PTA provides permeability, fracture half-length, and fracture-conductivity priors; RTA, Blasingame, and Agarwal–Gardnerdiagnostics provide production-dynamic constraints; and machine-learning models perform anomaly screening, stage recognition, time-series correction, type-curve discrimination, and multi-method fusion. The workflow was applied to Well Baitan 1, an ultra-deep fractured gas well with eight-stage fracturing and multi-regime flowback data. Isolation forest preprocessing removed 32 abnormal records, random forest (RF) drainage-type classification reached 93% accuracy, and long short-term memory (LSTM) correction improved production-forecast fitting from 87% to 95%. Neural-network fusion yielded matrix permeability of 0.49 mD and dynamic reserves of 1.93 × 104 m3, reducing static geological-volume validation error to 2.8%. The results show that AI improves productivity evaluation when constrained by diagnostic flow models and geological validation, providing a traceable basis for optimizing flowback intensity, monitoring frequency, and stabilized deliverability estimation.