Case Study: Edge Computing and IIoT Enable Autonomous Artificial Lift and Well Optimization Across Multi-Basin Deployments

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Operators managing large inventories of artificial lift systems and intermittent wells face a persistent challenge: how to detect equipment anomalies, optimize production, and reduce manual intervention across geographically dispersed assets in real time.

Traditional approaches, such as periodic well checks, centralized SCADA polling, and reactive maintenance, leave operators blind to rapidly developing downhole conditions, resulting in avoidable production losses, premature equipment failures, and excessive personnel exposure.

This case study describes how edge computing and industrial internet of things (IIoT) platforms were deployed to automate and optimize production operations across four distinct basins: the Oriente Basin in Ecuador’s Amazon region (IPTC 25145), the Permian Basin in Texas (SPE 216829) the Williston Basin in the Bakken (SPE 222618), and the Haynesville Basin in Louisiana (SPE 229390). Each deployment targeted a different artificial lift method or well type, demonstrating the breadth of the edge‑based approach.

The Challenge

In Ecuador’s Amazon region, a mature brownfield with electric submersible pump (ESP) wells faced compounding constraints. These included no permanently available rig for interventions, a remote jungle location more than 100 km from the nearest city, and a reduced workforce where a single operator was responsible for over 60 wells.

Manual operations exposed personnel to high-pressure, high-temperature, and electrical hazards, while delayed anomaly detection led to frequent ESP failures and deferred production.

In the Permian, an operator managing unconventional horizontal gas-lift wells struggled with gas-lift injection rate optimization. Traditional simulation-based approaches depended on well-calibrated models that could not keep pace with the severe slugging and rapidly changing conditions characteristic of unconventional completions.

In the Bakken, sucker-rod-pump (SRP) wells experienced excessive cycling, with some wells averaging six shutdowns per day, and operators lacked real-time diagnostics to distinguish between gas interference, fluid pound, and tagging events.

In the Haynesville Basin, intermittent gas wells managed with manual or calendar-based shut-in cycling suffered from suboptimal liquid unloading, prolonged downtime, and frequent operator intervention.

The Solution

All four deployments share a common architectural foundation: ruggedized edge computing devices installed at wellsites that ingest high-frequency sensor data and execute analytics locally, enabling closed-loop control with sub-second response times (Fig. 1). Only preprocessed summaries and alerts are transmitted to the cloud, reducing data transmission volumes by 85 to 95% compared with streaming raw sensor data (SPE 202252, SPE 201411).

In the Amazon, the automated well operator (AWO) edge application integrated four workflows:

- Smart production surveillance

- Smart chemical injection

- Smart well test

- Smart surface equipment

These workflows fed into a single digital twin interface enabling remote and autonomous ESP operation, chemical dosing, and automated well testing with artificial intelligence and machine learning (ML)-based stabilization algorithms.

In the Permian, a data-driven gas-lift optimization application ran directly on an IIoT gateway device, iteratively testing injection-rate setpoints and implementing optimized rates via closed-loop actuation without requiring well models or field personnel.