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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.