Coalbed methane concentration prediction and early-warning in fully mechanized mining face based on deep learning

Wen, Hu; Li, Yan; Jin, Yongfei; Wang, Zhipeng; Guo, Jun; Deng, Jun

doi:10.1016/j.energy.2022.126208

Cited by 17 publications

(10 citation statements)

References 40 publications

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“…The calculation of permeability follows Darcy's law. 58 The permeability can be expressed as K Q L S P (10) where K the absolute permeability (μm 2 ), Q the total flow rate through the porous medium (cm 3 /s), μ the dynamic viscosity of the fluid (MPa s), L the total length of the sample in the flow direction (cm), S the cross-sectional area of the sample (cm 2 ), and ΔP the pressure difference between the inlet and outlet (MPa). Due to the poor connectivity of some coal samples on the Z-axis and the good connectivity of all coal samples on the Y-axis, the unidirectional water flow direction chosen was to be in the Y-axis direction.…”

Section: Permeability Simulation Under MImentioning

confidence: 99%

“…7 However, due to the characteristics of low porosity and low permeability of CBM reservoirs and the gradual increase in mining depth, 8 the mining difficulty of CBM is increasing. 9,10 At present, in order to increase the mining efficiency of CBM, common methods for increasing fractures and permeability include hydraulic fracturing, 11,12 thermal stimulation (heat injection), 13−15 gas injection displacement, 16 solvent extraction, 17 liquid nitrogen freeze−thaw, 18 etc. Each of these methods has its own advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Study on Differential Thermal Response and Pore-Fracture Structure Evolution Characteristics of Coals under Microwave Irradiation: A Case Study of Five Different Rank Coals

Li,

Zhang,

Xiao

et al. 2024

Energy Fuels

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Microwave irradiation (MI) is a leading-edge technology for increasing the permeability and production of coalbed methane (CBM) reservoirs. To study the characteristics of thermal response, pore-fracture structure and seepage behavior of coals under MI, a microwave heating device was operated to irradiate five different rank coal samples for 2 and 4 min. The variation of chemical structure has been explored using Fourier transform infrared spectroscopy (FTIR). The variation of pore fracture has been studied via low-field nuclear magnetic resonance (LF-NMR) and micro-computed tomography (μ-CT). Additionally, the pore distribution heterogeneity has been analyzed by multifractal dimension, an equivalent pore-fracture network model has been established by μ-CT, and the modification benefit of coal pore-fracture structure under MI has been revealed by simulation of absolute permeability. The results showed that the temperature of the coal samples reached 220−440 °C under MI for 4 min. The chemical structure of the coal samples did not change significantly. The P total , P free , connectivity, and D f increased, while the proportion of adsorption pores, P bound , and T 2c decreased under MI. The permeability significantly increased under MI, based on the single-phase water fluid flow simulation. The modification of coal pore fracture under MI was controlled by the physical and chemical structures of coal. The more developed the pore fracture, the better the modification effect under MI. The chemical structure of low-rank coal was more disordered, compared to high-rank coal, and the higher the thermal stress value generated under MI, the stronger the degree of pore-fracture damage. This study might provide some details for increasing fractures and permeability in coal reservoirs and increasing CBM production.

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Section: Permeability Simulation Under MImentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental Study on Differential Thermal Response and Pore-Fracture Structure Evolution Characteristics of Coals under Microwave Irradiation: A Case Study of Five Different Rank Coals

Li,

Zhang,

Xiao

et al. 2024

Energy Fuels

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“…19,20 The input parameters determine whether the neural network can converge, while the values of weights and thresholds restrict the model quality and efficiency. 21,22 To improve the adaptability of BPNN in CBM content prediction, we conducted a series of optimizations to address the aforementioned BPNN weakness and proposed a fast and accurate model. The grey relational analysis (GRA) method was used to extract feature factors to improve the model's convergence.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, RF is resistant to interference and generalizes well in the face of noisy or missing data, whereas it is unable to make predictions beyond the range of training set data. Compared with the SVM and RF approaches, BPNN is more suitable for regression analysis owing to its strong nonlinear mapping capacity, good learning ability, simple structure, and easy modeling. − However, single-handed application of the BPNN exposes two inevitable issues, slow convergence speed and easy to fall into local optimal, which strongly associate with the selection of input parameters in the network and the change of network structure parameters during iteration. , The input parameters determine whether the neural network can converge, while the values of weights and thresholds restrict the model quality and efficiency. , …”

Section: Introductionmentioning

confidence: 99%

New Data-Driven Method for In Situ Coalbed Methane Content Evolution: A BP Neural Network Prediction Model Optimized by Grey Relation Theory and Particle Swarm

et al. 2023

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In situ coalbed methane (CBM) content accurate evolution is critical to target area optimization and long-term CBM production. In this study, we first proposed a new data-driven method, an improved BP neural network model optimized by grey relational analysis (GRA) and particle swarm optimization (PSO) algorithm for in situ CBM content prediction. The results show that the GRA method is useful to determine the feature input parameters for the BP neural network model which speeds up operation and reduces the influence of redundant parameters simultaneously. Meanwhile, the PSO algorithm with asynchronous learning factors is applied successfully to optimize the weights and thresholds of the BP neural network to increase modeling accuracy. To prove the prediction accuracy, the proposed model was trained and validated using field measured data from 36 CBM wells in Zhengzhuang block in the southern Qinshui Basin. The proposed modeling method yielded reliable results, outperforming traditional prediction models in terms of prediction accuracy (3.71% relative error only). Moreover, the proposed model is thought to be useful for high accuracy prediction of in situ CBM content in heterogeneous reservoirs under complicated geological structure conditions since it has higher robustness and stronger generalization.

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“…Coalbed methane (CBM) is a high-heat, clean energy source, with a heat generation of about (3.35–3.77) × 10 5 J/m 3 of CBM, − equivalent to the heat of 1 kg of standard coal, and the pollution it produces is only 1/40 of that of oil and 1/800 of that of coal. − It can be seen that methane has a very large potential as a common fuel and chemical raw material. − For the mine types of CBM outburst mines and high CBM mines, the simultaneous extraction of coal and CBM is the best measure for the utilization of coal mine CBM resources and the control of CBM disasters. − The seepage law of CBM in coalbeds is one of the basic problems in the research field of coal mine CBM disaster prevention and control, which has important guiding significance for the basic theoretical research on CBM outburst, CBM extraction, and coal and CBM outburst prevention and control. − …”

Section: Introductionmentioning

confidence: 99%

Fluid–Solid Coupling Characteristics of Methane-Containing Coal during Borehole Extraction of Coalbed: Numerical Modeling and Experimental Research

Yan,

Wen,

Jin

et al. 2023

ACS Omega

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The control and utilization of coalbed methane (CBM) are crucial for ensuring the safety of coal mining operations and mitigating greenhouse gas emissions. Predrainage of CBM from boreholes plays a pivotal role in preventing CBM accidents, harnessing CBM energy resources, and reducing greenhouse gas emissions. To better understand the evolution of key parameters during the predrainage process of CBM boreholes, this study, based on fundamental assumptions of coupling models, integrates the theories of elasticity, seepage mechanics, and fluid mechanics. It establishes a comprehensive mathematical model that reveals the interrelationships among the stress field, deformation field, and seepage field within methane-containing coal systems. By comparing numerical solutions with analytical solutions and conducting physical similarity simulation experiments, the study demonstrates the correctness of the methane-containing coal fluid–solid coupling model. The model developed in this study represents an improvement over traditional methane-containing coal seepage theories and fluid–solid coupling model theories and can be widely applied in the prevention of coal and CBM outbursts as well as CBM extraction.

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Coalbed methane concentration prediction and early-warning in fully mechanized mining face based on deep learning

Cited by 17 publications

References 40 publications

Experimental Study on Differential Thermal Response and Pore-Fracture Structure Evolution Characteristics of Coals under Microwave Irradiation: A Case Study of Five Different Rank Coals

Experimental Study on Differential Thermal Response and Pore-Fracture Structure Evolution Characteristics of Coals under Microwave Irradiation: A Case Study of Five Different Rank Coals

New Data-Driven Method for In Situ Coalbed Methane Content Evolution: A BP Neural Network Prediction Model Optimized by Grey Relation Theory and Particle Swarm

Fluid–Solid Coupling Characteristics of Methane-Containing Coal during Borehole Extraction of Coalbed: Numerical Modeling and Experimental Research

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