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The prediction of gas emissions arising from underground coal mining has been the subject of extensive research for several decades, however calculation techniques remain empirically based and are hence limited to the origin of calculation in both application and resolution. Quantification and management of risk associated with sudden gas release d...
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... fundamental physical, chemical, energy and geometric relationships, it is postu- lated that for the purpose of gas emission prediction, dynamic response of the gas reservoir to mining extraction can be reliably predicted using higher resolution input spatial parameters and measured coal property data which is largely available through proximate characterisation parameters such as rank, carbon con- tent, macerals, and moisture content. The range of possible varia- tion in coal properties for one of the study sites is demonstrated in Table 1. ...
Citations
... High temperatures speed up methane desorption in the coal matrix, allowing gas to move quickly through rough fracture networks and raising outburst risks. [1][2][3] Temperature changes also cause thermal expansion, which alters fracture roughness and affects seam permeability and stability. [4][5][6] Considering how fracture roughness evolves in high-temperature coal seams is essential for accurate gas flow and outburst predictions, and it offers important guidance for safe mining and seam stability. ...
Temperature changes in high-temperature mining operations strongly affect gas desorption, migration, and outburst behavior in coal seams. Under these conditions, gas desorbs more quickly from the coal matrix and spreads rapidly through fracture networks, which increases gas emissions. At the same time, changes in fracture roughness, caused by temperature, gas pressure, adsorption–desorption processes, and stress, further influence outburst patterns. To capture these interactions, we propose a thermo-hydro-mechanical model based on porous media theory that treats fracture roughness as a factor linked to permeability. By integrating permeability and gas flow as functions of effective stress and porosity, this model offers a clear way to study how fracture roughness affects gas outbursts under multiple combined factors. Validation and numerical simulations show that the proposed roughness parameter accurately describes changes in fracture structure. These changes then strongly affect permeability, pressure, and desorption intensity. Higher temperatures boost gas activity and promote desorption and migration. However, extremely high temperatures can cause fractures to close, which lowers permeability. These findings provide important support for ventilation design and safety assessments in high-temperature mining.
... Since the shale gas reservoir needs to be put into production after volume fracturing to obtain industrial gas flow, it is very difficult to backflow a large amount of fracturing liquid after it is injected into the reservoir. It is generally believed that a certain amount of injected water will be produced in the initial stage of shale gas well production (Zhang et al., 2016;Booth et al., 2017;Song et al., 2022). In the early stage of shale gas well development, production and wellhead pressure increase rapidly, and most wells are directly put into production with empty casing. ...
With the scale development of shale gas, the importance of selecting appropriate deliquification process has become increasingly evident in maintaining well productivity and improving shale gas recovery rate. At present, the preferred deliquification process are macro-control plate method and field experience method. The existing methods can only qualitatively select the deliquification process by considering limited influencing factors, resulting in poor process implementation. Based on the results of error analysis, the Gray model was optimized to calculate the pressure distribution in the shale gas wellbore and determine the applicable pressure limit. The W.Z.B. empirical model, which fully considers the influence of wellbore inclination, is used to calculate the gas-liquid carrying situation and determine the applicable liquid carrying limit. By analyzing the technical limits of five commonly used deliquification processes in the Changning shale gas field, namely, plunger lift, optimizing pipe string, gas lift, foam drainage, and intermittent production, a quantitative optimization method for deliquification processes was established. This method was then used to obtain the optimization chart for deliquification processes in shale gas wells. This method was applied in Well Ning 209-X, where the corresponding optimization chart for deliquification processes was drawn based on the production characteristics of the gas well. By quantitatively optimizing the deliquification processes and adjusting to suitable techniques, it effectively guided the production of the gas well and improved the gas field recovery rate.
... Wang et al. (2018) improved the shortage of single gray theory to predict gas emission and established the gray system-gas emission geological model. Booth et al. (2017) proposed a spatial dataset method for workface gas emission prediction. Yang and Zhou (2015) created a gas emission time series elliptical orbit to model and predict gas emission from the workface. ...
Coalbed methane (CBM) is a clean energy source, but its utilization is inefficient due to the complexity and low accuracy of its emission prediction model. In this research, we constructed a mathematical model of gas emission from the excavation workface, and combined the experimental results to propose a new model for accurate and concise prediction. The new model was validated in the field workface and compared with the traditional prediction model. Moreover, the sensitivity of gas emission parameters and the participation ratio of gas emission sources were analyzed. The study results show that the new model has higher calculation accuracy than the old model prediction, with an average error reduction of 4.693%. In the excavation workface, the coal fall gas emission conforms to the negative power function equation, and the coal wall gas emission conforms to the negative exponential function equation. In the early stage of excavation, the proportion of coal fall gas emission is higher than that of coal wall gas emission, and the peak proportion reaches 58.5%. In the later stage, the proportion of coal fall gas emission gradually decreases to below 30%. The order of the sensitivity of gas emission parameters is coal wall gas initial velocity > coal fall gas decay coefficient > coal fall gas initial velocity > coal wall gas decay coefficient. The new model is successfully applied in engineering, which helps to improve the efficiency of coal mine gas disaster control and utilization.
... Then, with the advances in computer technology and mathematical sciences, the numerical approach, mainly the computational fluid dynamics (CFD) methodology, has been explored as a potential solution to predict methane emissions [32]. However, precise explosive and flammable forecast methods are still challenging due to the numerous in situ characteristics of each mine operation (e.g., production rate and mining parameters, geological characteristics, topography features) that affect methane gas emissions into the underground mining environment [33]. ...
... In addition, the data collection process is rapid and reliable due to the identification of outliers, patterns, and missing data. Also, it allows data cleaning and validation [33,34]. This section presents the most relevant previous studies that have attempted to develop a methane gas forecasting model for underground coal mine operations based on time series analysis. ...
Methane gas is emitted during both underground and surface coal mining. Underground coal mines need to monitor methane gas emissions to ensure adequate ventilation is provided to guarantee that methane concentrations remain low under different production and environmental conditions. Prediction of methane concentrations in underground mines can also contribute towards the successful management of methane gas emissions. The main objective of this research is to develop a forecasting methodology for methane gas emissions based on time series analysis. Methane time series data were retrieved from atmospheric monitoring systems (AMS) of three underground coal mines in the USA. The AMS data were stored and pre-processed using an Atmospheric Monitoring Analysis and Database Management system. Furthermore, different statistical dependence measures such as cross-correlation, autocorrelation, cross-covariance, and variograms were implemented to investigate the potential autocorrelations of methane gas as well as its association with auxiliary variables (barometric pressure and coal production). The autoregressive integrated moving average (ARIMA) time series model which is based on auto-correlations of the methane gas is investigated. It is established that ARIMA used in the one-step-ahead forecasting mode provides accurate estimates that match the direction (increase/decrease) of the methane gas emission data.
... e source separation prediction method is the most commonly used approach for predicting the amount of gas emitted in production mines. However, due to the inhomogeneity of gas emissions, mines that do not exhibit gas exceeding limits via the source separation prediction method may also present local exceeding limits, instantaneous exceeding limits, and other problems in actual production [4][5][6][7][8]. ...
Coal particle size is an important factor affecting the gas emission law. Taking Wangjialing coal mine as the research object, the particle size distribution of coal mining and caving is analyzed via field tests in order to develop the gas emission theoretical model from granular coal. We also perform the numerical simulation of the coal body and longwall face gas emission characteristics under different particles. Finally, the gas emission rules of coal cutting, caving, longwall face, and goaf in Wangjialing coal mine are analyzed, and the dynamic prediction model, which accounts for the time influence of the coal cutting and coal caving speed based on the particle size distribution characteristics, is derived. Results demonstrate the wide distribution of the coal particle size at Wangjialing coal mine, with a higher proportion of small- and large-sized particles. The smaller the coal particle size, the faster the gas emission and the smaller the desorption ratio of coal at ≥20 mm within 30 min. The comprehensive emission intensity of coal mining and caving can be described by an exponential function. The initial emission intensity of coal mining is observed to exceed that of coal caving, while the attenuation laws of the two are essentially equal, and the majority of the gas emission is completed within 5 min. The error between the results of the multisource dynamic prediction model and the field measurement is small, which is of practical application significance.
... Coal mine carbon emission will be affected by coal output, mining methods, geological conditions, and coal mine deployment (Wang et al. 2018). Therefore, it is difficult to accurately estimate coal mine emissions; the coefficient intensity factor method is mainly adopted in the major coalproducing countries (Booth et al. 2017). Unfortunately, the spontaneous combustion of coal and coal gangue not only causes CO 2 emissions but can also induce gas explosions (Oliveira et al. 2019;Xu et al. 2020). ...
In recent years, concern has been increasing regarding the carbon emissions generated by mining activities. China is an extremely large coal producer (3695 Mt/2015) and consumer (3698 Mt/2015), and Shanxi Province (i.e., a major coal-producing province in China) is a crucial element in China’s energy conservation and emission reduction goals. In this study, the Pingshuo mining area (PMA) in Shanxi Province was chosen as a case to analyze the dynamic changes in carbon emissions based on the Intergovernmental Panel on Climate Change (IPCC) method, and the factors influencing carbon emissions were analyzed via the IPAT equation. Carbon emission sources in opencast mines mainly included fuel and explosive use, coal mine methane escape, coal and gangue spontaneous combustion, and electricity consumption. The carbon emission of the PMA increased from 4 × 10⁴ Mg in 1986 to 1.05 × 10⁶ Mg in 2015, with an average annual increase of 11.64%. In the PMA, 4.71 × 10⁶ Mg of carbon emissions from fuel consumption accounted for 41.79% of carbon emissions, and 5.26 × 10⁶ Mg of carbon emissions from methane emissions accounted for 46.66%. Carbon emissions from explosives and electricity use were 4.1 × 10⁵ Mg and 8.8 × 10⁵ Mg, respectively. In this mining area, the factors influencing carbon emissions included population, GDP, and coal output. The results of this study not only provide a reference for cleaner production in mining areas but also lay a foundation for the study of global opencast coal mining carbon emissions.
... Moreover, study [34] considered the steeply inclined and extremely thick coal seams, a used new method (that employed numerical simulation), which was applied to forecasting methane emission quantity. The issue related to methane emissions in a multifarious geospatial context was analyzed in Reference [35]. ...
Effectively avoiding methane accidents is vital to the security of manufacturing minerals. Coal mine methane accidents are often caused by a methane concentration overrun, and accurately predicting methane emission quantity in a coal mine is key to solving this problem. To maintain the concentration of methane in a secure range, grey theory and neural network model are increasingly used to critically forecasting methane emission quantity in coal mines. A limitation of the grey neural network model is that researchers have merely combined the conventional neural network and grey theory. To enhance the accuracy of prediction, a modified grey GM (1,1) and radial basis function (RBF) neural network model is proposed, which combines the amended grey GM (1,1) model and RBF neural network model. In this article, the proposed model is put into a simulation experiment, which is built based on Matlab software (MathWorks.Inc, Natick, Masezius, U.S). Ultimately, the conclusion of the simulation experiment verified that the modified grey GM (1,1) and RBF neural network model not only boosts the precision of prediction, but also restricts relative error in a minimum range. This shows that the modified grey GM (1,1) and RBF neural network model can make more effective and precise predict the predicts, compared to the grey GM (1,1) model and RBF neural network model.
... Therefore, coalmines across the country have adopted various techniques to prevent gas disasters. The determination of gas content is a common way to facilitate the disaster prevention [2][3][4]. The determination process can be divided into two parts: field measurement of gas desorption, and lab detection of residual gas. ...
Gas content measurement is a common technique in the prevention of coalmine gas disasters. During the measurement, the gas desorption amount of field coal samples needs to be obtained by an instrument working under the principle of gas collection by water displacement (GCWD). The instrument is poorly automated, and susceptible to the influence of subjective factors. To overcome these defects, this paper designs an automatic detector of gas desorption, aiming to realize automated detection. Firstly, the authors analyzed the gas desorption detection process, and clarified the contents and features of the information to be collected. On this basis, the hardware and software systems of the multi-data automatic detector were developed based on digital circuit design and multi-sensor detection. To further improve the measuring accuracy of gas desorption, the multi-range multi-stage mode was introduced to the automatic detector. Application results show that the proposed detector can automatedly collect and store gas desorption amount, ambient pressure, and temperature, greatly improve the degree of automation, and minimize the influence of subjective factors. The popularization of this detector will make gas desorption measurement more efficient and effective, laying a solid basis for the prevention of coalmine gas disasters.
... In study [34], directing at the steeply inclined and extremely thick coal seams, a new method which employed numerical simulation was applied to forecasting gas emission quantity. The issue related gas emission in multifarious geospatial context was analyzed by [35]. ...
Effectively avoiding gas accident is vital to the security of mineral manufacture, and the coal mine gas accident is often caused by gas concentration overrun. The prediction accuracy of gas emission quantity in coal mine is the key to solve this problem. To maintain concentration of gas in a secure range，grey theory and neural network model increasingly diffusely used in forecasting gas emission quantity in coal mine critically. Nevertheless, the limitation of the grey neural network model is that researchers merely bonded the conventional neural network and grey theory. To enhance accuracy of prediction, a modified grey GM(1,1) and RBF neural network model is proposed combined amended grey GM(1,1) model and RBF neural network model. Then the proposed model was put into simulation experiment which is built based on Matlab software. Ultimately, conclusion of the simulation experiment verified that the modified grey GM(1,1) and RBF neural network model not only boosts the precision of prediction, but also restricts relative error in a minimum range. This showed that the modified grey GM(1,1) and RBF neural network model achieves effectiveness in precision of prediction much better than grey GM(1,1) model and RBF neural network model.
... However, the changes in the occurrence characteristics, gas content, gas pressure, geological structure, and other factors of the tunnel-exposed coal seam have a complex nonlinear effect on gas emission, which brings great difficulties to the prediction of abnormal gas emission [5][6][7][8]. In the 1980s, Russian scholars proposed for the first time that gas emission should be predicted during coal mining; Greedya [9], a British scholar, initiated the Airey method to predict the gas emission in coal mines based on time and mining technology; Dong [10] proposed gas emission time series method and used it as the regression function to establish the Gaussian process regression model, the prediction results of which are accurate and reliable; Liang et al. [11] proposed that, under the condition of considering gas emission source and fluid-solid coupling process, it would be more accurate to predict coal mine gas emission by establishing a dynamic gas prediction model; Booth et al. [12] believe that the limitations of existing gas emission prediction can be solved by the prediction results obtained from improved spatial data sets and the technology including basic physics and energy-related principles. The above research methods are often used to predict the gas emission in coal mines, but there are little research on abnormal gas emission in gas tunnels. ...
The risk of gas disaster in the low-gas tunnel is easy to be ignored. By tracking and analyzing the gas monitoring data of the low-gas tunnel, it is found that the cyclic abnormal gas emission occurred many times during the construction period, leading to local gas accumulation, which greatly increases the risk of gas explosion accidents. To scientifically predict the abnormal gas emission in low-gas tunnels, the idea of K-line diagram-based prediction of abnormal gas emission in low-gas tunnels is put forward, and in combination with the field monitoring data of low-gas tunnel (Huangguashan Tunnel), the prediction results with different prediction indexes of K-line diagram are compared and analyzed. The results show that the K-line diagram can reflect the changing trend of gas concentration in real time accurately and show the change law of gas concentration during different construction processes; the moving average (MA) of the K-line diagram can accurately reflect the time of abnormal gas emission, the moving average convergence divergence (MACD) index can reflect the upward or downward power and trend of gas, and the Bollinger Band (BOLL) index can reflect the fluctuation range of gas concentration. The research results can provide a reference for the prediction and prevention of abnormal gas emission in low-gas tunnels.
