Modeling and prediction of ventilation methane emissions of U.S. longwall mines using supervised artificial neural networks

Methane emissions from a longwall ventilation system are an important indicator of how much methane a particular mine is producing and how much air should be provided to keep the methane levels under statutory limits. Knowing the amount of ventilation methane emission is also important for environmental considerations and for identifying opportunities to capture and utilize the methane for energy production.Prediction of methane emissions before mining is difficult since it depends on a number of geological, geographical, and operational factors. This study proposes a principle component analysis (PCA) and artificial neural network (ANN)-based approach to predict the ventilation methane emission rates of U.S. longwall mines.Ventilation emission data obtained from 63 longwall mines in 10 states for the years between 1985 and 2005 were combined with corresponding coalbed properties, geographical information, and longwall operation parameters. The compiled database resulted in 17 parameters that potentially impacted emissions. PCA was used to determine those variables that most influenced ventilation emissions and were considered for further predictive modeling using ANN. Different combinations of variables in the data set and network structures were used for network training and testing to achieve minimum mean square errors and high correlations between measurements and predictions. The resultant ANN model using nine main input variables was superior to multilinear and second-order non-linear models for predicting the new data. The ANN model predicted methane emissions with high accuracy. It is concluded that the model can be used as a predictive tool since it includes those factors that influence longwall ventilation emission rates.     

采动区远程卸压煤层气抽采地面井产能影响因素

以淮南矿区远程卸压煤层气地面井抽采工程实践为依托,通过工程试验和系统分析,探讨了远程卸压煤层气地面井的产能特点及其影响因素。研究结果表明,远程卸压煤层气地面井的产能曲线可分为两个阶段,在较短时间内顺利完成第Ⅰ阶段的井才能有较高产能。研究还显示,煤层气地面井产能受地层结构和采动影响较大。在研究区,当地层结构为松散层厚度<406 m,基岩与松散层厚度比值>0.74,下保护层与被保护层间距为6670 m,且平均采高≤2.2 m,平均产煤低于3 898 t/d时,利于远程卸压煤层气地面井抽采;当松散层厚度>430 m,11-2煤和13-1煤层间距>74 m,基岩与松散层厚度比值<0.7时,卸压煤层气地面直井成功率较低,此时,可通过改变井位和优化井身结构来适应地层结构的变化,提高地面井抽采成功率。      

Development and application of reservoir models and artificial neural networks for optimizing ventilation air requirements in development mining of coal seams

In longwall development mining of coal seams, planning, optimizing and providing adequate ventilation are very important steps to eliminate the accumulation of explosive methane–air mixtures in the working environment. Mine operators usually try to supply maximum ventilation air based on the capacity of the system and the predicted need underground. This approach is neither economical nor safer as ventilation capacity may decrease in time depending on various mining and coalbed parameters. Thus, it is important to develop better engineered approaches to optimize mine ventilation effectiveness and, therefore, to ensure a safer work environment.This study presents an approach using coalbed methane reservoir modeling and an artificial neural network (ANN) design for prediction and optimization of methane inflows and ventilation air requirements to maintain methane concentrations below statutory limits. A coalbed reservoir model of a three-entry development section, which is typical of Pittsburgh Coalbed mines in the Southwestern Pennsylvania section of Northern Appalachian Basin, was developed taking into account the presence and absence of shielding boreholes around the entries against methane inflow. In the model, grids were dynamically controlled to simulate the advance of mining for parametric simulations.Development and application of artificial neural networks as an optimization tool for ventilation requirements are introduced. Model predictions are used to develop, train, and test artificial neural networks to optimize ventilation requirements. The sensitivity and applications of proposed networks for predicting simulator data are presented and discussed. Results show that reservoir simulations and integrated ANN models can be practical and powerful tools for predicting methane emissions and optimization of ventilation air requirements.     

波兰煤矿瓦斯高效抽放技术特点

目前,在波兰41对主要生产矿井中,有23对高瓦斯矿井进行了瓦斯抽放。2004年瓦斯平均抽放率为30%,平均利用率为39%。根据瓦斯地质条件、瓦斯涌出特点和采区通风方式,着重介绍了波兰煤矿强化煤层、围岩和采空区瓦斯抽放,提高矿井瓦斯抽放效率的工艺技术特点。在工作面的瓦斯排放中,注重开采、通风与瓦斯抽放一体化,通过优化抽放钻孔布置,取得钻孔瓦斯抽放的最佳效果,是波兰煤矿瓦斯治理的一项成功经验,成为煤矿持续安全高效生产的重要技术保障。     

Improving stress environment in development entries through an alternate longwall mining layout

Most coal mines in China use the longwall mining system. High stresses are frequently encountered around development entries at deep mines. This paper presents an alternate longwall mining layout for thick coal seams to minimize ground control problems. In a conventional longwall panel layout, development entries on both ends of the panel are located along the floor, and a coal pillar (chain pillar) is left between adjacent panels to ensure stability. Gateroads on either end of a longwall panel using the layout proposed in this paper are located at different vertical levels within a thick coal seam or in a geologically split coal seam for improved stability. The headgate entry/ies are driven along the floor while the tailgate entry/ies are driven along the roof. Therefore, a longwall face has a gradually elevated or curved section on one end of the panel. For the adjacent panel, the development entry may be located directly below the development entry of the previous panel or may be offset horizontally with respect to it. Based on physical and numerical modeling approaches, it is demonstrated that the stress environment for development entries employing the longwall layout is significantly improved; ground control problems are therefore minimized.     

Gas emission prediction and recovery in underground coal mines

Strata gas can be released and captured from non-active and active gas resources either from virgin or relaxed strata, both prior to and when mining activities take place. The high and irregular gas emissions associated with high production longwall mining have provided a need to optimise the methods used to predict these gas levels and the ventilation requirements for gas dilution. A forecast of gas emissions during development drivage and longwall mining indicated possible gas and ventilation problems requiring the introduction of various gas drainage techniques and in maintaining the necessary air quantities in ventilation systems to satisfy the statutory gas limitations for various coal production rates. Although there are sound principles used in world-recognised methods of gas emission prediction, a new approach developed from long-term practical experience in underground gassy coal mine practices and gas-rock mechanics studies appear most suitable for local conditions and mining systems in use. The Lunagas ‘Floorgas' and ‘Roofgas' geomechanical and gas emission models offer an effective solution to these problems. Both programs are the most advanced engineering, numerical tools available to calculate gas source contributions to total gassiness and improve the accuracy and quality of gas control, gas capture technologies and ventilation system design.     

A GIS-based Weights-of-Evidence model for mapping cliff instabilities associated with mine subsidence

The Weights-of-Evidence (W-of-E) technique was applied, within a geographic information system (GIS), to derive a model of rockfall potential associated with mining-induced subsidence. The purpose of the model was to describe the potential for rockfalls from up to 60 m high steep sandstone gorges and slopes associated with proposed underground longwall operations within the immediate vicinity of a previously mined area. Ten known rock falls associated with the previous mining operation were used as training points. Six evidential themes were considered-slope, cliff height, planform curvature, profile curvature, the distance of the cliff areas from the longwall panels, and the distance of the cliff areas from the river. Two models were created, one based on a mine layout in which longwall panels extend beneath the steep areas of a nearby river, and a second in which the mine layout is modified so that mining does not occur directly beneath or within 50 m of the steep slopes. This is to allow for the comparison of rockfall potential based on different mining configurations. The results demonstrate that the W-of-E method is a suitable tool for mine subsidence impact assessment, and suggest that not mining directly under the Nepean river may decrease rockfall potential, on average, by approximately ten times. Numerous limitations with the results, relating to the availability of appropriate evidential theme data and the accuracy of training points, are discussed.     

沁南地区煤层气生产潜力研究

本文以沁水盆地南部地区煤层气生产试验井的徘采资料为基础，结合煤田地质勘探和中联公司煤层气勘探所获得的地质、生产资料，阐述了煤层气井的排采机理。并以TL—003井排采试验为例进行详细剖析，运用储层模拟技术，研究、分析影响煤层气产出的主要参数，初步预测了该区煤层气井的生产潜力。     

Shrinkage and swelling of coal induced by desorption and sorption of fluids: Theoretical model and interpretation of a field project

Geologic sequestration in deep unmineable coal seams and enhanced coalbed methane production is a promising choice, economically and environmentally, to reduce anthropogenic gases such as carbon dioxide in the atmosphere. Unmineable coal seams are typically known to adsorb large amounts of carbon dioxide in comparison to the sizeable amounts of sorbed methane, which raises the potential for large scale sequestration projects. During the process of sequestration, carbon dioxide is injected into the coalbed and desorbed methane is produced. The coal matrix is believed to shrink when a gas is desorbed and swell when a gas is sorbed, sometimes causing profound changes in the cleat porosity and permeability of the coal seam. These changes may have significant impact on the reservoir performance. Therefore, it is necessary to understand the combined influence of swelling and shrinkage, and geomechanical properties including elastic modulus, cleat porosity, and permeability of the reservoir.The present paper deals with the influence of swelling and shrinkage on the reservoir performance, and the geomechanical response of the reservoir system during the process of geologic sequestration of carbon dioxide and enhanced coalbed methane production in an actual field project located in northern New Mexico. A three-dimensional swelling and shrinkage model was developed and implemented into an existing reservoir model to understand the influence of geomechanical parameters, as well as swelling and shrinkage properties, on the reservoir performance. Numerical results obtained from the modified simulator were compared to available measured values from that site and previous studies. Results show that swelling and shrinkage, and the combination of geomechanical and operational parameters, have a significant influence on the performance of the reservoir system.     

Application of a Generalised Influence Function Method for Subsidence Prediction in Multi-seam Longwall Extraction

The purpose of mining subsidence prediction is to produce a reliable assessment of ground movement arising from underground mineral extraction. The results of the prediction are used to assess the likelihood of the associated effects on surface structures. In most countries, the assessment of mining subsidence has become an essential part of mining plans, which must be approved by relevant government bodies and mining regulators. It is therefore important to develop a subsidence prediction method that is suitable for a particular country or mine field. Further to the recent development of a Generalised Influence Function Method (GIFM) for subsidence prediction at RMIT University, a case study in Hunter coalfield of in New South Wales, Australia is presented to illustrate the applicability of the GIFM approach for subsidence prediction in multi-seam longwall mining. A computer program is used to calculate subsidence, horizontal displacement and principle strains arising from the extraction of longwall panels. The observed subsidence across the longwall panels and the corresponding ground movements are compared to the model’s output and the results analysed. A discussion of the discrepancies between the GIFM models and the behaviour of complex geological strata is presented. The GIFM method is found to be a powerful tool when applied to complex extraction configurations and can produce useful output for mining subsidence assessments. Of particular importance is its ability to provide both tensile and compressive strain information over the whole affected areas which would otherwise not have been available for the assessment of damage potential to surface structures.     

基于AHP-熵权法的采动区煤层气开发潜力评价

采动区煤层气抽采利于高效开发采煤工作面卸压煤层气,降低井下瓦斯安全风险,但地质条件、煤炭采掘条件的差异使得地面井开发效果差异较大,因此,对预备采区进行选区评价具有指导意义。为了探索煤矿采动区煤层气高效开发的选区方式,通过建立一票否决制度及评价指标体系、结合层次分析法及熵权法确定指标权重、最后量化计算综合得分的方法,建立一套完整的采动区煤层气开发潜力评价体系,并应用于铁法矿区晓南矿。结果显示,研究区16个规划采区中有4个得分大于0.6,建议进行采动区煤层气开发;同时,评价结果排序靠前的单元净收益也较高,且处于矿区内煤层气赋存有利构造区域,与评价结果相吻合,验证了评价结果的合理性。综合认为,采用层次分析–熵权法组合权重的评价结果更为合理,有效避免了采动区煤层气选区开发的盲目性,评价结果具备一定实际工程开发部署及经济效益参考价值。      

物质平衡法对定容煤层气藏生产动态的预测

由于煤层气藏特殊的赋存及渗流方式,使其有别于常规气藏,而煤层气藏开采初期的多相渗流问题使其产能预测更为复杂。在定容煤层气藏物质平衡法基础上,利用历史生产数据完成线性拟合,并用拟合得到的参数对未来生产动态进行预测。通过将模型预测的平均地层压力、平均含水饱和度及产气、产水量随时间变化与CMG数值模拟结果进行对比分析,结果显示,本文方法预测结果较为准确可靠,尤其是在煤层气峰值产量之后。      

Determination of Suitable Pillar Size for Protecting Gas Well Drilled Through a Longwall Mining Abutment Pillar Using Numerical Modelling Approach: A Case Study

Gas well drilled through longwall mining abutment pillar could potentially face instability issue due to the strata deformation following longwall panel extraction. Therefore, it is imperative to adequately design the pillar size of a longwall mining in order to ensure the stability of the gas well penetrated longwall mining abutment pillar. In this paper, the determination of suitable pillar size for protecting gas well subjected to longwall mining operation was investigated. Two scenarios of longwall gateroad system including the three and four entry system with varying pillar sizes were assessed using numerical modelling approach. The results of this study indicate that the pillar geometry plays an important role on the vertical gas well stability. In addressing the suitable pillar size for the given case study considering three entry system, the suitable chain pillar and abutment pillar size were found to be 80 ft (24.4 m) wide by 120 ft (36.6 m) length and 104 ft (31.7 m) wide by 120 ft (36.6 m) length rib to rib, respectively. Whereas, if four entry system is used, the suitable chain pillar size is 48 ft (14.6) wide by 120 ft (36.6 m) length and the abutment pillar size is 104 ft (31.7 m) wide by 120 ft (36.6 m) length rib to rib. The proposed numerical modelling procedure presented in this paper can be a viable alternative and applied to other similar projects in order to determine an optimal pillar size for protecting gas well in longwall mining area.

     

Assessment of Chock Capacity and Strata Caving for a Longwall Mine

A mine scale numerical analysis of modern day longwall using a 3D Cosserat continuum method has been presented. The effect of mine specific geological conditions on viability of introducing a modern day longwall is comprehensively investigated and analysed in this paper. The various longwall parameters like chock (face support) convergence and strata caving mechanism are evaluated. The varying thickness of the sandstone present in the roof can be seen to have a strong impact on the magnitude and pattern of chock convergence. The paper also discusses the performance of chocks with different capacities under identical conditions. The effect of overlaying sandstone properties and width of the longwall panels have also been investigated. The analyses carried out in this study is expected to provide valuable process guidance during the mine design in relation to selecting the optimal mine geometry and support capacity so that the potential mining hazards could be minimized.     

注热联合井群开采煤层气运移采出规律数值模拟

为总结注热联合井群开采低渗透储层煤层气运移采出规律,基于传热学、弹性力学、渗流力学、岩石力学理论,建立了注蒸汽开采低渗透储层煤层气藏过程的热固流耦合数学模型。结合潞安矿区山西组3#煤层地质参数,利用有限元软件进行了注热联合井群开采煤层气藏运移规律的数值模拟,得到了不同布井方式下注热10 d、开采100 d过程中煤层温度场、应力场及煤层气渗流场变化规律。结果显示,煤层平均传热速度为1.57 m/d,注热10 d后,中心井35 m范围内为有效注热区;随井筒数量的增加和井间距的减小,井间干扰作用增强,煤储层压力下降加快,煤层气供气及解吸区域增加,累积产量显著增加。七井模型20 m井间距注热开采累积产气量是五井模型30 m井间距未注热开采累积产气量的2.01倍。模拟结果显示了注热和井间干扰开采优势,为低渗透储层煤层气井群注热联合工业开采提供理论依据。     

基于BP神经网络方法的矿井涌水量预测

鉴于矿井涌水威胁煤矿安全生产及其影响因素的复杂性,提出基于BP神经网络的矿井涌水量预测方法.在充分分析新安煤矿＋25m开采水平的涌水影响因素的基础上,选取大气降水、采空区面积和底板构造断裂和采动裂隙三个影响因子,建立了非线性人工神经网络预测模型,对＋25m开采水平的正常涌水量进行了预计.其结果和实际观测数据能够较好地相吻合,表明采用人工神经网络预计矿井涌水量是可行的.     

柿庄南区块3号煤层含气量三维建模

煤层含气量对煤层气开发有直接影响。柿庄南区块煤层含气量相对较高,但开发过程中存在较多低效井,开展含气量三维地质建模有助于厘定含气性对煤层气井产量的影响。以沁水盆地柿庄南区块3号煤层为研究对象,运用多元回归分析方法依次建立基于埋深、灰分、挥发分及固定碳含量等参数的含气量预测公式及基于测井数据的煤岩工业分析各组分含量预测公式,最终得出柿庄南区块基于测井数据的含气量预测模型并应用于全区,与实测值对比表明预测结果较好。运用Petrel软件基于预测结果构建含气量模型,探讨3号煤层含气量三维分布特征。研究表明,区内3号煤层含气量介于11~20 m3/t,其主控因素为煤层埋深和构造部位。该模型对研究区煤层气井低产因素厘定和煤层气开发生产具有指导意义。移动阅读      

Burden prediction in blasting operation using rock geomechanical properties

Burden prediction is a vital task in the production blasting. Both the excessive and insufficient burden can significantly affect the result of blasting operation. The burden which is determined by empirical models is often inaccurate and needs to be adjusted experimentally. In this paper, an attempt was made to develop an artificial neural network (ANN) in order to predict burden in the blasting operation of the Mouteh gold mine, using considering geomechanical properties of rocks as input parameters. As such here, network inputs consist of blastability index (BI), rock quality designation (RQD), unconfined compressive strength (UCS), density, and cohesive strength. To make a database (including 95 datasets), rock samples are used from Iran’s Mouteh goldmine. Trying various types of the networks, a neural network, with architecture 5-15-10-1, was found to be optimum. Superiority of ANN over regression model is proved by calculating. To compare the performance of the ANN modeling with that of multivariable regression analysis (MVRA), mean absolute error (E _a), mean relative error (E _r), and determination coefficient (R ²) between predicted and real values were calculated for both the models. It was observed that the ANN prediction capability is better than that of MVRA. The absolute and relative errors for the ANN model were calculated 0.05 m and 3.85%, respectively, whereas for the regression analysis, these errors were computed 0.11 m and 5.63%, respectively. Moreover, determination coefficient of the ANN model and MVRA were determined 0.987 and 0.924, respectively. Further, a sensitivity analysis shows that while BI and RQD were recognized as the most sensitive and effective parameters, cohesive strength is considered as the least sensitive input parameters on the ANN model output effective on the proposed (burden).     

Application of neural networks to predict net present value in mining projects

Net present value (NPV) is the most popular economic indicator in evaluation of the investment projects. For the mining projects, this criterion is calculated under uncertainty associated with the relevant parameters of say commodity price, discount rate, etc. Accurate prediction of the NPV is a quite difficult process. This paper mainly deals with the development of a new model to predict NPV using artificial neural network (ANN) in the Zarshuran gold mine, Iran. Gold price (as the main product), silver price (as the byproduct), and discount rate were considered as input parameters for the ANN model. To reach an optimum architecture, different types of networks were examined on the basis of a trial and error mechanism. A neural network with architecture 3-15-10-1 and root mean square error of 0.092 is found to be optimum. Prediction capability of the proposed model was examined through computing determination coefficient (R ²?=?0.987) between predicted and real NPVs. Absolute error of US$0.1 million and relative error of 1.4 % also confirmed powerfulness of the developed ANN model. According to sensitivity analysis, it was observed that the gold price is the most effective and discount rate is the least effective parameter on the NPV.