Machine Learning Makes Inroads into Oil and Gas
Grant Gerke | February 9, 2018
The recent 2017 corporate tax cut is providing oil supermajors a nice chunk of change, as reported in late January, ExxonMobil received a $5.9 billion tax break in 2017. So, the question is where will oil and gas related companies invest? Conventional wisdom points to oil and gas supermajors investing in offshore exploration, but a new twist in the operational efficiency initiatives within the industry could see more companies diving into machine learning applications.
Exxon said it plans to invest “billions of dollars to increase oil production in the Permian Basin” of West Texas and New Mexico, expand existing operations, enhance infrastructure and build new manufacturing sites, according to CNN.
To meet this five-year trend towards operational efficiencies, more partnerships and acquisitions are sprouting up throughout the industry. Just announced this week is the memorandum of understanding between Falkonry Inc, an operational analytics company for industrial companies, and Invasystems. Invasystems sells into the consumer product goods, pharmaceutical, chemical and manufacturing industries.
Digital oilfield applications seem to be on the radar with this announcement and, specifically, machine learning for better predictive maintenance approaches. 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. These features “will help oil and gas operators reduce downtime and increase production by detecting patterns and conditions in existing operations data, according to the press release.”
“Falkonry LRS provides predictive insights from the time series data that already exists within production operations,” says Dr. Nikunj Mehta, Founder and CEO of Falkonry. “Our relationship with Invasystems will help their clients derive value from predictive analytics sooner, with minimal risk and little upfront cost.