Automatic facies detection based on oilfield core images

This paper proposes a machine learning method for automatically detecting and identifying facies from digital images of the oil well core. The method is based on artificial neural networks, specifically, pre-trained deep convolutional neural networks, improved using histogram of oriented gradients and local binary pattern methods. AlexNet, Inception, MobileNet, Xception, and DenseNet extract features from images. An algorithm based on the behavior of fireflies selects the most informative features. The k-nearest neighbors, support vector machines, and random forest methods are considered as classifiers. The proposed approach uses a model that has been trained and tested on a core rock samples dataset of > 23,000 images related to four facies as coal, sandstone, siltstone, and shale. The experimental results show that the proposed HOG + LBP + DenseNet model combined with a random forest is better than support vector machines and k-nearest neighbors methods regarding facies recognition from core photographs and the precision (95.5%), recall (96.46%), and F-measure (95.98%) metrics.