摘要：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.

关键字：Prestack seismic data; semisupervised learning (SSL); small-sample problem; tight sandstone reservoir; transfer learning (TL)

ISSN号：1545-598X

卷、期、页：卷: 19

发表日期：2022-01-01

影响因子：3.966100

期刊分区（SCI为中科院分区）：三区

收录情况：SCI（科学引文索引印刷版）,地学领域高质量科技期刊分级目录（国外T1）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

通讯作者：宋朝辉,黎声煌,贺粟梅

第一作者：袁三一,王尚旭

论文类型：期刊论文

论文概要：宋朝辉,黎声煌,贺粟梅,袁三一,王尚旭，Gas-Bearing Prediction of Tight Sandstone Reservoir Using Semi-Supervised Learning and Transfer Learning，IEEE GEOSCIENCE AND REMOTE SENSING LETTERS，2022，卷: 19

论文题目：Gas-Bearing Prediction of Tight Sandstone Reservoir Using Semi-Supervised Learning and Transfer Learning