An Ordered Flow-State Identification Method for Unconventional Gas Wells Based on a Five-Region Analytical Model and RTA Window Features

Unconventional gas-well production is jointly controlled by fracture conductivity, stimulated-region supply, matrix replenishment, boundary propagation, and low-pressure fluid-property changes. In practice, RTA diagnostic curves are often affected by variable operating schedules, pressure-measurement errors, and production disturbances, making flow-stage boundaries difficult to define consistently. To reduce the subjectivity of manual interpretation and to capture stage evolution rather than whole-well classes, an ordered flow-state identification method based on a five-region analytical model and RTA sliding-window features is developed. A fully random, large-sample production-response library is generated with the five-region model. Each well production curve is divided into local time windows, from which dynamic features, including RNP, material-balance time, local slopes, pseudopressure derivatives, and normalized cumulative gas production are extracted. K-means clustering is then used to identify local states, which are reordered by material-balance time to form an ordered S1–S5 sequence. Results from 10,000 synthetic wells yielded 689,394 RTA windows, an inter-cluster separation of 1.8924, a stage-regression rate of 0.0238, and an average of 4.24 states per well. S1–S5 represent early fracture–stimulated-region response, stimulated-region supply development, matrix composite supply transition, enhanced boundary/control-volume effects, and late low-pressure property response, respectively. Application to Well M1 shows that S4 contributes the most gas (37.83%), followed by S5 (23.47%), indicating dominant mid-to-late effective supply and low-pressure long-tail production. The method converts empirical flow-regime division into reproducible and comparable window-state identification results, supporting stage diagnosis and production-strategy adjustment for unconventional gas wells.