The oil and gas industry is known for its volatile boom and bust cycles, and this has a particularly strong effect on offshore platforms, which are substantial investments that do not always succeed in generating profit. To generate value and remain competitive in the offshore sector, operators must ensure that their operations are efficient, optimized, unlikely to be interrupted by unexpected failures, and resilient to fluctuations in oil prices. Artificial intelligence, and more specifically, machine learning, applications allow operators to do all of this. This paper presents two specific case studies demonstrating how machine learning has been proven and scaled in a deployed environment, with tangible increase in offshore production leading to significant business value to the operator.

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