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With the development of technology in recent years, the amount of data that geoscientists have to work with is increasing in size and quantity. Conventional analytics methods are gradually supplemented by big-data analytics methods and tools, such as artificial intelligence applications, to improve work efficiency and productivity. To collect a large enough set of labels with high confidence to train and predict geological features (such as lithofacies) is time and economically consuming.
In this study, we propose an active learning workflow as a solution for the challenge mentioned above by showing an example of how we improve the quality, quantity and diversity of the lithofacies interpretation. An extensive dataset was used in this study, including: i) 700 000s cuttings samples, ii) 77 000 meters of core, and iii) 70 000s kilometers of logs from 1744 exploration wells in the Norwegian Continental Shelf.