Coalbed Methane Production Forecasting Based on LSTM and Transfer Learning Integration

Abstract

Traditional Long Short-term memory (LSTM) method faces a lot of challenges during coalbed methane production prediction, such as poor generalization on the small datasets, difficult hyper parameter turning and poor accuracy in prediction. Due to these challenges this research introduces a new method which is the combination of LSTM and Transfer Learning inorder to encounter to these issues. This approach combines the Person Correlation Coefficient together with the Grey relational degree to identify production factors which is more important by using a “Pre-training Hierarchical Fine-Tuning” transfer learning framework. In order to maintain the ability of extracting temporal features, the LSTM layer uses a low learning rate while fully connected layer uses a high learning rate for faster convergence. When we applied this new strategy to the FSL-34 well, it’s R² value increased from 0.8 to 0.93 and the Mean Absolute Percentage Error (MAPE) decreased from 0.614% to 0.288%, without forgetting in the situation of small datasets around 100, R² increased greatly from -2.3 to 0.94 and the MAPE decreased by 37%. These two examples shows clearly the advantages of the new proposed method over Traditional LSTM models. While using Maximum Mean Discrepancy (MMD) domain arrangement helps bridge the gap between separate domains, utilizing the transfer of knowledge from a source domain reduces the data requirement in the target domain. I general, this strategy provides a very effective coalbed methane production prediction in cases with scanty data samples.