CT scan images provide valuable three-dimensional information on the mineralogical composition and overall internal structure of cores. X-ray computerized tomography (CT) imaging of whole cores has therefore become a routine step in core analysis workflows. This new data type gives new possibilities in reservoir characterization. Lithological classification of reservoir rocks, in its turn, is an essential step to better understand the depositional environment and for subsequent effective reservoir characterization: the chemical composition of the minerals, combined with their grain size, sorting and pore size distribution is known to highly affect the transport properties of reservoir rocks. Lithological classification on the extracted whole core material is thus consequential; however, it also requires significant investments, being traditionally conducted through visual inspection performed by expert geologists. This manual process is time consuming, and prone to subjective interpretations and human errors. Therefore, a current research and development trend is to find automated methods for computer-assisting the assessment of this type of data, eventually reducing time and costs of core analysis, and improving the overall business decisions.

In this study we explore the application of Convolutional Neural Networks (CNN) to automatically classify lithofacies. We propose a workflow for high resolution lithofacies classification using whole core three-dimensional CT images, and we assess the validity of our approach on a field-example from the Norwegian continental shelf. The novelty of our approach is thus learning, through a CNN, the relationship between convolution-derived three-dimensional features and expert-derived lithofacies classes. We thus extend approaches working on two-dimensional images into a workflow that uses high-resolution three-dimensional CT images as direct input. In our work the training data set includes information obtained from manual core description. Prior to training, the three-dimensional CT images are pre-processed so that undesired artefacts are automatically flagged and removed before being fed into the network. The approach is validated using the trained CNN classifier to predict lithofacies in a set of unseen three-dimensional CT data. The trained model can predict lithofacies classes with high accuracy, with a misclassification rate of about 3%. We found that these misclassifications are mainly associated with the presence of high-density material such as pyrite nodules and drilling mud invasions. Dipping fractures and missing values, not completely removed by image pre-processing, are additional reasons for model deficiency in some of the incorrectly classified images. Overall, the trained classifier exhibits higher pixel-wise precision and captures the high-resolution heterogeneities more accurately compared to the manual core descriptions.

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