Gas-Bearing Prediction of Tight Sandstone Reservoir Using Semi-Supervised Learning and Transfer Learning

Predicting gas-bearing reservoirs in tight sandstone is significant but challenging. Although machine learning (ML), especially deep learning (DL), methods provide a potential for solving the issue, the major challenge of their application to gas-bearing prediction is how to generate accurate intelligent models with limited training sets. To relieve the notorious small-sample problem and the overfitting problem caused by limited well-log data, we propose the semi-supervised learning and transfer learning (SSL-TL) method for qualitative gas-bearing prediction. In the SSL-TL method, we first train the k nearest neighbor (kNN) classifier. And we choose the outputs with high confidence as the pseudo-training samples to extend the training sets of the convolutional neural networks (CNNs). Then, we pretrain the CNN models with the pseudo-training samples and subsequently introduce the transfer learning (TL) strategy to fine-tune the pretrained CNN models using the real training samples. Finally, we obtain a strong CNN-based gas-bearing classifier. The TL strategy can make full use of the extended training sets while reducing the negative influence of the pseudo-training samples. We apply the SSL-TL method to a field dataset with limited wells. The test results show that the SSL-TL method has higher lateral continuity in gas prediction and agrees more with the known geological understanding in the studied field compared with the results of the CNN models trained by other strategies.