An experiment in automatic stratigraphic correlation using convolutional neural networks
XU Zhaohui1, LIU Yuming1, ZHOU Xinmao2, HE Hui2, ZHANG Bo3, WU Hao3, GAO Jian2
1 College of Geosciences, China University of Petroleum-Beijing, Beijing 102249 2 Research Institute of Petroleum Exploration and Development, CNPC, Beijing 10083 3 Department of Geoscience, University of Alabama, Tuscaloosa, USA 35487
Deep learning is good at extracting the inherent abstract features from input data. It has achieved great success in speech recognition, semantic analysis, image analysis and other fields in the past ten years, which has greatly promoted the development of artificial intelligence. Based on the convolutional neural networks algorithm widely used in deep learning, this paper carries out well auto-correlation experiments which take a block of Daqing Oilfield as the object. In the experiments, some wells were randomly selected as training samples and the other wells were used as tested samples to predict the welltops. The predicted welltops were compared with the original welltops for error analysis. The experiments were divided into 4 groups according to the proportion of training well data, which was 65%, 40%, 20%, and 10% respectively. Each group of experiments consisted of three independent experiments, including oil layer group, sand group, and single layers. The 12 experiment results show that the more training data and the higher stratigraphic unit (or the larger thickness) can get, the better the well auto-correlation result,and the 20% training data can reliably perform the well auto-correlation of sand group and above stratigraphic units (thickness is no less than 10m). It also indicates that the convolutional neural networks algorithm can be effectively applied to reservoir-scale well auto-correlation based on well logs and has a promising future.
Key words：
automatic stratigraphic correlation; deep learning; convolutional neural networks; training and testing
