Ultra-Deep Carbonate Reservoir Lithofacies Classification Based on Deep Convolutional Neural Network (CNN)- A Case Study in Tarim Basin, China

Abstract

Lithofacies identification is essential in reservoir evaluation, especially in ultradeep carbonate reservoirs. Generally, coring samples are the best sources to identify carbonate lithofacies because they were taken directly from reservoirs. However, core is expensive to obtain, and it is generally greatly limited in its availability. In recent years, deep learning has attracted enormous attention because of its robust nonlinear regression and classification ability. This study applies a deep learning algorithm to identify the lithofacies using geophysical well log data. Six types of well log data, including natural gamma-ray (GR), density (DEN), neutron porosity (CNL), acoustic (AC), and shallow and deep lateral resistivity well logs (RT/RXO), are smoothed by the average sliding method and converted to 2D data. Then, the two-dimensional data are treated as inputs to predict the carbonate lithofacies through the convolutional neural network (CNN). The results indicate that the prediction accuracy rate is 90.2\%. It shows that the convolutional neural network can well identify different carbonate lithofacies.