1. 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. Accurate prediction of CBM production can not only forecast the economic benefits of CBM, but also provide advice for the establishment of mining plans, which play an important role in the production process of CBM. However, 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 as an unconventional gas resource [
2]. Therefore, accurate production forecasts for CBM present certain challenges. At present, 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] among others.
The type curve and decline curve methods have been widely used for production analysis, achieving important results. Fetkovich [
8] proposed the first-generation production type curve, which subsequent type curves have improved upon. Aminian et al. [
2] proposed a series of curves that could predict CBM and water production, studying the effects of different parameters on these curves and discussing their applications and limitations, the most notable of which being the difficulty in representing a uniform model at the production cycle’s different stages. Jang et al. [
9] used the method of decline curve analysis and combined material balance and fluid state analysis to predict the production dynamics of CBM, subsequently establishing a comprehensive production data model. Decline curve analysis is currently the most commonly used and effective yield forecasting method, but it also has its limitations [
10].
The numerical simulation method uses a complicated mathematical model. When using this method, it is necessary to obtain enough production data and measure geological parameters as accurately as possible [
6]. These factors will have a very large impact on the production simulation. Therefore, if these influencing factors cannot be accurately obtained, the numerical simulation method will not be used. A detailed description of numerical simulation techniques can be found in [
11]. Numerical simulation technology is increasingly used in unconventional gas reservoirs, and more and more geological factors are taken into consideration. Li et al. [
12] used geological survey and experimental data to study the formation history of coalbed methane reservoirs and analyzed the role of various stages in the process of coalbed methane production. Zhou et al. [
13] used numerical simulation technology to predict production and believed that the skin factor and coal shrinkage rate have important impacts on CBM production. Thararoop et al. [
14] proposed a numerical simulation model that took into account the water in the coal matrix and the swelling and shrinkage of coal. In addition, numerical simulation software for coalbed methane reservoirs, whose development was based on the C++ programming language, was proposed in [
15]. Numerical simulation technology requires sufficient geological data to provide support, but it is difficult to obtain these factors in actual production. Complex mathematical models also limit the application of this method.
Material balance is also an important method for estimating the reserves of coalbed methane, since this method can comprehensively consider the influence of many factors. In [
5], two material balance methods were proposed, which were used to predict unconventional gas reservoirs and estimate the original gas, respectively. The difference between this method and the traditional material balance method is that the influence of adsorbed gas is taken into account. Shi et al. [
16] established a material balance equation to estimate coalbed methane reserves, taking into account factors such as dissolved gas and free gas. Sun et al. [
17] used a flow material balance equation, combined with the relationship between pressure and saturation, to analyze the production of low-permeability CBM wells and achieved good results. More and more material balance models that consider multiple factors have been developed. However, the actual production of coalbed methane is a dynamic process. The influencing factors are complex and diverse, and it is impossible to take all of them into consideration. Additionally, similar to the numerical simulation method, it is very difficult to obtain many factors, so the material balance equation is also restricted in the prediction of coalbed methane reserves.
The development of machine learning provides a new method for forecasting the production of CBM. Compared with the previous method, the advantage of machine learning is that it does not need to obtain the geological conditions of the coalbed methane reservoir and can make predictions only from the production data. For example, the back propagation (BP) neural network method can efficiently predict production without having to understand the conditions of coalbed methane reservoirs or deal with insufficient production data [
6]. Xia et al. [
18] achieved favorable results through their proposal of a hybrid method to forecast CBM production capacity. This method takes both the rough set (RS) and least-square support vector machine models into consideration. Huang and Wang [
7] optimized their own SVM using a genetic algorithm (GA), and their results showed that their GA-SVM model could also achieve high accuracy in CBM production capacity predictions. The machine learning method has the characteristics of being simple and convenient to implement, and it does not need to consider the complex factors in the actual process, so it is widely used in production forecasting. However, traditional machine learning methods, such as SVM, the Bayes method [
19], multiple regression analysis [
20,
21], and neural networks do not consider time dependence when processing time series data. Therefore, the use of such methods to predict CBM production also has limitations.
The actual production process of CBM is very complicated, involving the interaction of geological factors and human factors. However, the production data of CBM has certain rules, which reflects the production process of CBM to a certain extent. As such, it is possible to predict future production by focusing on inherent laws and tendencies based on the available historical data rather than complex process research. This can be accomplished through long short-term memory neural networks (LSTM NNs). LSTM NNs are deep learning recurrent neural network structures with the ability to process long-term sequence data [
22]. These networks can learn the inherent laws present in historical data through a time series without having to consider complex coal seam environments. LSTMs can also be applied to other fields such as environmental science, in which some scholars have applied LSTM models to predict PM
2.5 concentrations in air pollution, achieving valuable results [
22,
23,
24,
25]. In terms of public transportation, Chen et al., Tian et al., and Li and Cao [
26,
27,
28] used the LSTM model to study traffic flow. In [
29], LSTMs were used to forecast traffic speed in the Beijing area. Petersen et al. [
30] used CNNs (Convolutional Neural Networks) and LSTMs to predict bus travel time. In the financial sector, Fischer and Krauss [
31] and Kim and Won [
32] applied the LSTM model to market forecasts and achieved superior results to those of random forests methods and deep neural networks (DNNs). Vochozka et al. [
33] used the LSTM model to establish a method for predicting company bankruptcy, which provided a reference for the company’s future development. In the industrial and energy sectors, Wu et al. [
34] used the LSTM network to estimate the remaining service life of an engineering system, while Peng et al. [
35] used LSTMs and differential evolution to predict the price of electricity, and the prediction accuracy was superior to current models. Sagheer and Kotb [
36] proposed a genetic algorithm-optimized deep LSTM method to predict oil production, which has proven to be more accurate than statistical and software calculation methods. Although the LSTM model has been widely used in research related to production and price prediction, it is rarely applied in the research of unconventional gas reservoirs. Xu et al. [
37] used LSTM networks to predict the production of coalbed methane and achieved good results. However, this study did not consider the influence of multiple factors and only used coalbed methane production data.
In view of the complexity and limitations of the current methods, the objective of this article is to propose an artificial intelligence-based method for CBM production forecasting. This paper proposes the use of multivariate LSTM NNs as a prediction method for CBM production. Auxiliary data such as casing pressure, water production, and bottom hole temperatures are also inputted into the LSTM NN to improve prediction performance. This combined model was validated using production data from a CBM well; its results were compared with those obtained using a traditional LSTM NN model. The results demonstrated that auxiliary data can improve the prediction outcome. In addition, this paper proposes a multi-step prediction method more in line with the time process of CBM production, as forecasting performances will deteriorate as time lag increases.
4. Conclusions
A multivariate long short-term memory neural network (M-LSTM NN) model for coalbed methane production forecasting was proposed in this paper. Due to the optimization of the parameters presented in the model (including the number of LSTM NN hidden layer nodes, learning rate, and others), 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 CBM production. The results show that the use of deep neural networks to predict CBM production can achieve good results without considering complex geological factors and by only using historical production data. The M-LSTM network provides a fast artificial intelligence prediction method for CBM production.
(2) Thirty independent and repeated experiments were conducted, comparing the results of LSTM NN models without additional auxiliary inputs and our own M-LSTM NN model, with MAE, MAPE, and RMSE values indicating that the M-LSTM NN model achieved better results than LSTM NN model. In addition, we analyzed the impact of each variable on the results and found that water production and casing pressure can improve the accuracy of the prediction, while inputting the temperature into the M-LSTM network did not improve the results. This shows that the bottom hole temperature has almost no effect on the production of CBM in the actual production process of CBM, and inputting it into the M-LSTM network cannot improve prediction accuracy. Since we have only obtained very little auxiliary production information, we cannot analyze the factors that have the highest impact on CBM production forecasts. Therefore, in future research, it is necessary to select those variables that are highly correlated with CBM production.
(3) A multi-step predictions model was also developed that was more consistent with the actual production processes of CBM, utilizing historical as well as current data to predict future CBM production. During our experimenting process, it was found that prediction accuracy was inversely related to the increase in time lag, regardless of whether the CBM production in question was at daily or monthly intervals. This finding suggests that to successfully predict term CBM production, more historical data may be needed to calibrate and train future models.