Intermittent wells, particularly in gas-producing basins such as Haynesville, traditionally are managed with manual or partially automated controls, requiring frequent operator intervention and relying on static supervisory-control and data-acquisition (SCADA) infrastructure. This approach limits production efficiency and responsiveness to changing well conditions, especially in the presence of liquid loading. To address these limitations, this paper presents an autonomous, data-driven solution deployed at the edge, designed specifically for intermittent well optimization.
Introduction
This paper introduces a field-proven Industrial Internet of Things application that manages the liquid-unloading cycle autonomously using a data-driven, edge-based control system. Running directly on a local gateway device at the well site, the application ingests real-time pressure, flow rate, and temperature data continuously.
