Neuralix brings AI to produced water operations in the Permian Basin

Neuralix is applying industrial AI to produced water gathering, recycling, treatment and disposal in the Permian Basin, where water volumes are rising faster than infrastructure. The company says one midstream deployment cut operating cost per barrel about 14% in phase one and improved energy performance about 12% in phase two.

Why it matters: - Produced water has become a major operating burden in the Permian Basin, with volumes now far beyond simple disposal logistics. - Operators need better ways to cut energy use, manage infrastructure performance and reduce costs across gathering, recycling, treatment and disposal networks. - Faster growth in water volumes makes small inefficiencies more expensive across the basin’s pipeline, pump, storage and disposal systems.

What happened: - Neuralix said it is applying industrial AI and operational intelligence to produced water operations in the Permian Basin. - The company has deployed its approach in a water midstream project in the basin. - In that deployment, the first phase reduced operating cost per barrel by about 14%. - A subsequent phase improved energy performance by about 12%.

The details: - The Permian Basin averaged about 6.6 million barrels of crude per day in 2025, nearly half of U.S. output. - The basin also generated roughly 22 million barrels of produced water per day, or about three to four barrels of water for every barrel of oil. - Produced water volumes have more than tripled since 2017 and are expected to rise about 39% more by 2035. - Neuralix said its platform combines operational data, engineering principles and machine learning. - The applications include equipment health monitoring, anomaly detection, leak detection, water movement forecasting, pump optimization and energy optimization. - The technology is designed to work with existing SCADA systems, historians, sensors, meters and equipment controls. - The project used time-series operational data plus equipment and energy information. - Inputs included electricity consumption, equipment specifications, operating histories, pump curves, failure events and electricity price variability. - Earlier detection of abnormal conditions can help operators respond before problems escalate. - Forecasting can give teams more time to manage changing water volumes and infrastructure constraints.

Between the lines: - Produced water is moving from a disposal problem to a system optimization problem. - Neuralix is pitching AI that stays tied to physical equipment behavior, not just dashboards of raw data. - That matters because water networks in the Permian are interconnected, and a change in one asset can affect costs and performance elsewhere. - The push toward recycling, treatment and beneficial reuse adds more operating variables and makes visibility across the full water lifecycle more valuable. - Texas regulators have also added requirements for saltwater disposal in parts of the Permian and created pathways to evaluate beneficial reuse of treated produced water. - Neuralix is also working with pressure pumping service operators to analyze hydraulic fracturing data. - Those models can assess stage execution, equipment behavior and variation between stages and wells. - Better completion visibility can help explain downstream flowback and produced water volumes.

What's next: - Neuralix plans to keep extending its industrial AI approach across the full water lifecycle, from sourcing and completions to flowback, gathering, recycling, disposal, treatment and desalination. - Continued growth in produced water, recycling and treatment is likely to keep pressure on operators to improve efficiency and coordination. - The company’s next opportunity is proving that AI can consistently improve operating decisions inside existing workflows, not just identify problems after the fact.

The bottom line: - The Permian’s water challenge is no longer just about moving more barrels. - It is about operating a complex network more efficiently, and Neuralix is positioning AI as a tool to do that.