Classification of rock facies using deep convolutional neural network

Investigating rock facies is very important in the study of hydrocarbon reservoirs. The logs used in this research are related to facies logs from nine wells from the gas reservoir located in Kansas, USA. The rock facies identified in this study are based on a visual examination of a 4,149-foot core from 9 wells. The physical and chemical properties of stones were determined using special tools. The well-log includes five wireline logging curves (GR, resistivity log (RL), photoelectric effect (PE), difference neutron density porosity (DPHI) and average neutron density porosity (PHIA)) and two geological limiting variables (non-marine index - Nautical (NM_M) and relative position (RP)). Our goal in this paper is to develop an effective model based on deep learning for geological facies classification in wells. The classification of facies is done by studying the lithological characteristics of rocks, which are the characteristics of modern sediments accumulated in specific physical and geographical conditions. This study presents a new 1D-CNN model trained on different optimization algorithms. Using normal well-log data is one of the main advantages of the proposed model. The proposed model is compared with the support vector machine model and the nearest neighbor model and shows more accurate results compared to them. This model shows successful results in the study of well log data and therefore it can be suggested as a suitable and effective approach for processing well log data required for lithology.