Practical AI Lessons Learned

A confluence of groundbreaking technologies bundled with next-generation business models is poised to transform the oil and gas industry. It’s history in the making. This convergence of digital technologies (the Industrial Internet of Things, artificial intelligence [AI], autonomous self-healing assets, drones, etc.) is creating entirely new ways of operating a producing well and massively transforming outcomes like increasing production and decreasing nonproductive time (NPT). It’s interesting to view some real-world examples of transformations that are solving real-world problems, and the takeaways are five lessons learned in the execution process.

Predicting fracture pump failures
Flutura worked with one of the world’s largest original equipment manufacturers (OEMs) of fracture pumps. Fracture pumps are used in harsh conditions, and as a manufacturer the drilling service providers and owner/ operators expect the OEMs to have an intimate understanding of the current health of the fracture pump and the potential ways it could succumb to a fault mode. To make this transition from the electromechanical world to the digital world, the customer created a digital twin of the fracture pump, including its various sub-systems (pumps, engine, transmission, etc.), sensor signals (engine rpm, transmission oil pressure), trips, alarms and fault modes on Cerebra. Once the digital twin was created, a “digital umbilical cord” was created using Cerebra’s algorithmic state assessment module providing remote digital diagnostics for the pump and predicting potential failure modes with associated confidence for the field force to automatically create tickets. This, in addition to reducing downtime of nodal assets in the field, created a new predictable recurrent revenue pool for the customer through its “digital health monitoring as a service” offering.

AI in FLNG carriers
A major global LNG carrier approached Flutura with an operational problem to solve. Floating LNG carriers are used to ship LNG from point to point. This is a complex and delicate process since gas is stored at -162 C (-260 F) for ease of transport, which takes up about 1/600th the volume of natural gas in its gaseous state. There is a lot of cryogenic and leakage risk associated with this process. The global carrier wanted an “edge solution” completely self-contained on the ship to diagnose and predict risky outcomes. It created a digital twin of the LNG carrier using Cerebra modules, and the solution’s advanced deep-learning neural networks detected temperature and leakage anomalies that human eyes could not detect in an unsupervised fashion.

EP Magazine


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