Exebenus and Dragon Oil have signed a Memorandum of Understanding to trial machine learning-based prediction of drilling hazards across Dragon Oil's well construction operations.
Under the MoU, Exebenus will deploy its ML models for predictive analysis of real-time well construction data on Dragon Oil operations. The models monitor standard surface drilling measurements to detect developing hazards — including stuck pipe risk — ahead of incidents, with the objective of reducing non-productive time and improving well delivery.
The agreement was signed at the SPE GOTECH event in Dubai, hosted by Dragon Oil, in the presence of Dragon Oil CEO Ali Al-Jarwan and members of the Dragon Oil board. Matt Regan, VP of Business Development, signed for Exebenus.
Field trials of this kind are how Exebenus validates its models: the methods behind its hazard detection systems are documented in peer-presented technical papers with SPE, IADC and OTC, and their performance is measured on live operations rather than simulations. Results from the Dragon Oil trial will be evaluated against the same standard.
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