Using AI to Unlock Oil and Gas 'Treasure Trove'

September 4, 2019

© an-kyl-o / Adobe Stock
© an-kyl-o / Adobe Stock

Researchers aiming to help operators maximize recovery are using artificial intelligence (AI) to unlock a vast amount of rw data collected by the oil and gas industry. 

The Intelligent Data Quality Improver (IDQI) project, an initiative involving the University of Aberdeen and co-funders the Oil & Gas Innovation Center (OGIC), software R&D company HyperDAP Ltd, and The Data Lab – Scotland’s innovation center for data and AI, aims to develop algorithms that will bridge the gap between the vast amounts of big data available to the oil and gas industry - predominantly in exploration and production - and its ability to analyze, interpret and manage it effectively.

Professor Wamberto Vasconcelos, from the University’s Department of Computing Science, said, “Data is potentially among the most valuable assets created and owned by a business, but without proper interpretation it has very little value.

“The oil and gas industry alone produces 2.5 quintillion bytes of data each day, but only 1% of this data is analyzed, which is a missed opportunity in terms of asset value maximization and new field discovery.

“The IDQI aims to address this issue, using Distributed Optical Sensing data to develop algorithms capable of performing automated analyses on digital exploration and production datasets.

“This means we can extract and interpret most of the hidden information in a matter of minutes using, among others, a range of AI techniques such as machine learning, fuzzy logic and rule-based reasoning. This has the potential to unlock a vast treasure trove of data that is not currently exploited.”

Nicoletta Compatangelo, Managing Director of HyperDAP Ltd, added, “There is a substantial amount of context-related information hidden inside datasets, which adds value well beyond what data themselves convey on the surface.

“Once extracted and made explicit through data analysis and interpretation, this information can be formally measured against the quality of those datasets – the more information, the higher the corresponding dataset quality.

“At present, very little analysis and interpretation is performed in several specialist areas of oil and gas exploration and production. For example, distributed optical sensing data from wells is constantly acquired but almost invariably stored without in-depth analysis, as it can take weeks for a human expert to analyze one single day of data records.

“IDQI has the potential to revolutionize the way that this raw data is interpreted, providing greater speed, accuracy, and ultimately providing insights that can be of real benefit to operators."



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