Abstract
Accurately forecasting the daily production of coalbed methane (CBM) is important forformulating associated drainage parameters and evaluating the economic benefit of CBM mining. Daily production of CBM depends on many factors, making it difficult to predict using conventional mathematical models. Because traditional methods do not reflect the long-term time series characteristics of CBM production, this study first used a long short-term memory neural network (LSTM) and transfer learning (TL) method for time series forecasting of CBM daily production. Based on the LSTM model, we introduced the idea of transfer learning and proposed a Transfer-LSTM (T-LSTM) CBM production forecasting model. This approach first uses a large amount of data similar to the target to pretrain the weights of the LSTM network, then uses transfer learning to fine-tune LSTM network parameters a second time, so as to obtain the final T-LSTM model. Experiments were carried out using daily CBM production data for the Panhe Demonstration Zone at southern Qinshui basin in China. Based on the results, the idea of transfer learning can solve the problem of insufficient samples during LSTM training. Prediction results for wells that entered the stable period earlier were more accurate, whereas results for types with unstable production in the early stage require further exploration. Because CBM wells daily production data have symmetrical similarities, which can provide a reference for the prediction of other wells, so our proposed T-LSTM network can achieve good results for the production forecast and can provide guidance for forecasting production of CBM wells.
Highlights
As a high-quality energy source that can replace natural gas, coalbed methane (CBM) is an important energy reserve in China [1]
(T-long short-term memory neural network (LSTM)) CBM production forecasting model. This approach first uses a large amount of data similar to the target to pretrain the weights of the LSTM network, uses transfer learning to fine-tune
Because CBM wells daily production data have symmetrical similarities, which can provide a reference for the prediction of other wells, so our proposed T-LSTM network can achieve good results for the production forecast and can provide guidance for forecasting production of Keywords: Recurrent Neural Network (RNN); Long Short-Term Memory (LSTM); forecasting of CBM daily production; transfer learning
Summary
As a high-quality energy source that can replace natural gas, coalbed methane (CBM) is an important energy reserve in China [1]. The works of Cipolla et al [11] reflect this trend, often employing complex mathematical models to numerically simulate the output of unconventional gas reservoirs. As evidenced by these advancements, material balance equation methods for CBM production forecasting can take into account numerous factors; CBM production is a complex and dynamic process that is not limited to the aforementioned factors, and the difficulty of obtaining these hinders the performance of these methods. Machine learning methods such as BP neural networks and SVMs have a wide range of applications in production forecasting, predicting CBM production is a typical time series problem based on historical production data of gas wells. It innovatively applies transfer learning (TL) to the LSTM network pretraining process and has achieved good results
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