Abstract

Classification of different lithofacies is crucial in seismic interpretation because different rocks have different permeability and fluid saturation for a given porosity. The ideal sources for lithofacies classification are core samples of rocks extracted from wells. Nevertheless, core samples cannot always be obtained due to associated costs. The conventional classification method is based on manually assigning lithofacies by human interpreters and is a very tedious and time-consuming process. We aimed at automating this classification process through the use of machine learning and deep learning methods. We had selected wells from the region which have the mud log and wireline logs present with them. Machine learning algorithm, Support vector machines (SVM) was employed to build an automatic lithofacies classifier and accuracy of the model was validated on a well with unlabelled lithofacies and accuracy of 0.63 was achieved. In the next phase, a Deep learning (DL) model based on Convolutional Neural Network was developed and it achieved a classification accuracy of 0.71. Due to the skewed nature of the dataset, the validation accuracy of the model showed a stark drop when compared with training accuracy. This major drop in accuracy occurs while classifying those facies which have limited number of training example. The accuracy could be further improved by incorporating adjacent lithofacies in classification task, which was the limitation of the target dataset.