During the drilling of a well, a huge quantity of data is acquired in real-time. In order too mitigate risks due to geological uncertainties, to increase operational efficiency, to optimize processes and create new business models, Eni has developed its own cross-functional integrated data platform, which ensures data availability to all subsurface technical functions sharing a common data model. In this paper we describe an innovative approach, born from the collaboration between expert geologists and data scientists. The integrated team has developed a tool based on Artificial Intelligence (AI) supporting operations geologist during drilling phases. Two different tools have been created: litho-fluid interpretations, a set of AI algorithms used to identify in real-time the lithology and to interpret the formation fluids; well-to-well log correlation and look ahead, models used to find analogies between intervals of the well being drilled and the reference well, allowing to estimate the distance and time of arrival to a given geological event.

The results obtained have been remarkable in terms of accuracy. The positive feedbacks from the operations geologists give the assurance of the usefulness of the tools and their expected benefits: the tools allow to better control geological uncertainties and speed up some repetitive and time-consuming tasks. The results presented in this paper are focused on two UAE applications of litho-fluid and well-to-well log correlations.

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