Visual Recognition of Drill Cuttings Lithologies Using Convolutional Neural Networks to Aid Reservoir Characterisation

Kathrada, Muhammad (PETRONAS) | Adillah, Benjamin Jacob (PETRONAS)

OnePetro 

Abstract

There are a vast number of reservoirs with drill cuttings and core images that have classification problems associated with them. This could be due to the images not being classified in the first place, or the images may be available but the interpretation reports could be missing. Another problem is that images from different wells could be interpreted by different wellsite geologists/sedimentologists and hence result in an inconsistent classification scheme. Finally, there could also be the problem of some images being incorrectly classified. Ergo it would be desirable to have an unbiased objective system that could overcome all of these issues. Step in convolutional neural networks. Advances during this decade in using convolutional neural networks for visual recognition of discriminately different objects means that now object recognition can be achieved to a significant extent. Once the network is trained on a representative set of lithological classes, then such a system just needs to be fed the raw drill cuttings or core images that it has not seen before and it will automatically assign a lithological class to each image and an associated probability of the image belonging to that class. In so doing, images below a certain probability threshold can be automatically flagged for further human investigation. The benefit of such a system would be to improve reservoir understanding by having all available images classified in a consistent manner hence keeping the characterization consistent as well. It would further help to reduce the time taken to get human expertise to complete the task, as well as the associated cost.