A Multivariate Long Short-Term Memory Neural Network for Coalbed Methane Production Forecasting

Xijie Xu,Yiwen Ju,Tian Yu,Xiaoping Rui,Yonglei Fan

doi:10.3390/sym12122045

Xijie Xu, Yiwen Ju + Show 3 more

Open Access

https://doi.org/10.3390/sym12122045

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Abstract

Owing to the importance of coalbed methane (CBM) as a source of energy, it is necessary to predict its future production. However, the production process of CBM is the result of the interaction of many factors, making it difficult to perform accurate simulations through mathematical models. We must therefore rely on the historical data of CBM production to understand its inherent features and predict its future performance. The objective of this paper is to establish a deep learning prediction method for coalbed methane production without considering complex geological factors. In this paper, we propose a multivariate long short-term memory neural network (M-LSTM NN) model to predict CBM production. We tested the performance of this model using the production data of CBM wells in the Panhe Demonstration Area in the Qinshui Basin of China. The production of different CBM wells has similar characteristics in time. We can use the symmetric similarity of the data to transfer the model to the production forecasting of different CBM wells. Our results demonstrate that the M-LSTM NN model, utilizing the historical yield data of CBM as well as other auxiliary information such as casing pressures, water production levels, and bottom hole temperatures (including the highest and lowest temperatures), can predict CBM production successfully while obtaining a mean absolute percentage error (MAPE) of 0.91%. This is an improvement when compared with the traditional LSTM NN model, which has an MAPE of 1.14%. In addition to this, we conducted multi-step predictions at a daily and monthly scale and obtained similar results. It should be noted that with an increase in time lag, the prediction performance became less accurate. At the daily level, the MAPE value increased from 0.24% to 2.09% over 10 successive days. The predictions on the monthly scale also saw an increase in the MAPE value from 2.68% to 5.95% over three months. This tendency suggests that long-term forecasts are more difficult than short-term ones, and more historical data are required to produce more accurate results.

Highlights

Coalbed methane (CBM) is an important unconventional clean energy source that has the potential to eventually replace natural gas
A multivariate long short-term memory neural network (M-long short-term memory neural networks (LSTM NN)) model for coalbed methane production forecasting was proposed in this paper
Due to the optimization of the parameters presented in the model, as well as the subsequent results, we were able to reach the following conclusions from our study: (1) The M-LSTM NN model we developed was able to achieve favorable results in predicting

Summary

Introduction

Coalbed methane (CBM) is an important unconventional clean energy source that has the potential to eventually replace natural gas. As such, it is attracting increasing attention from around the world. CBM is subject to a range of complex interactions relevant to the many factors involved in its creation [1]. These unique features have led to its classification. Accurate production forecasts for CBM present certain challenges. The prediction methods for CBM production mainly include the type curve and decline curve methods [3], numerical simulation methods [4], material balance methods [5], and machine learning methods, including neural networks [6] and support vector machines (SVMs) [7]

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Results

Conclusion