Spatiotemporal Deep-Learning Model With Graph Convolutional Network for Well Logs Prediction

• Published in: IEEE geoscience and remote sensing letters Vol. 20; pp. 1 - 5
• Main Authors: Feng, Shuo, Li, Xuegui, Zeng, Fang, Hu, Zhongrui, Sun, Yuhang, Wang, Zepeng, Duan, Hanxu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Artificial neural networks; Convolution; Convolutional neural networks; Data models; Datasets; Economic impact; Geological surveys; Graph convolution network; Graph neural networks; Inhomogeneity; Machine learning; Neural networks; Oil exploration; oil–gas energy exploration; Predictions; Predictive models; Regression; regression prediction; Resource development; Spatial dependencies; spatiotemporal dependencies; Spatiotemporal phenomena; temporal convolution network (TCN); Well logs; well logs prediction
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2023.3317349

Well logs play a significant role in geological exploration and oil resource development. Due to the limitation of measurement tools and expensive economic cost, obtaining certain well logs can be challenging. The inhomogeneity of the medium and the complexity of the subsurface conditions also make it difficult for linear regression and traditional neural network methods to provide accurate predictions. Multiple existing methods have difficulty in obtaining and utilizing spatiotemporal dependencies among known well logs to predict unavailable well logs. In this letter, an adaptive spatiotemporal graph neural network (ASTGNN) is designed to capture the spatial dependencies and temporal dependencies among various available well logs. A graph convolution neural network (GCN) is introduced into the field of well logs prediction to extract non-Euclidean spatial characteristics among well logs. In addition, a temporal convolution network (TCN) is employed to capture the temporal correlations. Then, a prediction block (PB) is designed to map the hidden states mined by the GCN and TCN to the target predicted well logs. Experiments on single regression well logs prediction and multiple regression well logs prediction are conducted on the dataset from the Norwegian field and the dataset from Daqing, China, respectively. The results demonstrate the accuracy and superior performance of the designed approach.