Invasystems to adopt Falkonry’s ‘ready to use’ machine learning system for digital oilfield applications

Falkonry, Inc. has signed a deal with Invasystems, a Texas-based industry solution provider that brings together data analytics and first principle (physics) based software solutions to better manage the value chain for clients, to help oil and gas operators reduce downtime, increase production.

Digital oilfield applications leverage the capital investments in field data collection that oil and gas companies have already made. These investments include deploying more sensors, installing SCADA systems and storing the data in a historian.

While traditional approaches, such as exception-based surveillance, can proactively manage oil and gas assets, they are not as effective at predicting failures. Machine learning can help identify many issues which are not easy to predict using conventional steady-state or transient models, especially for thousands of wells.

As part of their agreement, Invasystems will adopt the Falkonry LRS machine learning system and empower well operation engineers to predict equipment and system failures. This will help oil and gas operators reduce downtime and increase production by detecting patterns and conditions in existing operations data.

“We look forward to using Falkonry as part of our comprehensive suite of software that drives productivity gains right from the reservoir to the refinery stage for our oil and gas clients,” Ashok Dixit, SVP of Oil & Gas at Invasystems, said in a statement. “Falkonry’s machine learning system enables field engineers to determine the corrective action to take, without the need for data scientists.”

“Falkonry LRS provides predictive insights from the time series data that already exists within production operations,” added Nikunj Mehta, founder and CEO of Sunnyvale, California-based Falkonry. “Our relationship with Invasystems will help their clients derive value from predictive analytics sooner, with minimal risk and little upfront cost. Resource constrained teams can build upon the rapid learnings and equip their operations to pursue digital oilfield, smart plant and smart factory initiatives.”

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