The gas content and permeability of coal reservoirs are the main factors affecting the productivity of coalbed methane. To explore the law of gas content and permeability of coal reservoirs in the Zhijin area of Guizhou, taking No.16, No.27 and No.30 coal seams in Wenjiaba mining area of Guizhou as the engineering background, based on the relevant data of coalbed methane exploration in Wenjiaba block, the geological structure, coal seam thickness, coal quality characteristics,coal seam gas content and permeability of the area were studied utilizing geological exploration, analysis of coal components and methane adsorption test. The results show that the average thickness of coal seams in this area is between 1.32 and 1.85 m; the average buried depth of the coal seam is in the range of 301.3-384.2 m; the gas content of No.16 and No.27 coal seams is higher in the syncline core. The gas content of the No.30 coal seam forms a gas-rich center in the south of the mining area. The buried depth and gas content of coal seams in the study area show a strong positive correlation. Under the same pressure conditions, the adsorption capacity of dry ash-free basis is significantly higher than that of air-dried coal. The permeability decreases exponentially with the horizontal maximum principal stress and the horizontal minimum principal stress. The horizontal maximum primary stress and the flat minimum prominent stress increase with the increase of the buried depth of the coal seam. The permeability and coal seam burial depth decrease exponentially. This work can provide engineering reference and theoretical support for selecting high-yield target areas for CBM enrichment in the block.
Abstract Based on Landsat TM/ETM+ images acquired in 2000, 2005, 2010 and 2015 respectively, land use maps of Jiaodong Peninsular were created to analyze the characteristics of land use change. Then the Logistic-CA-Markov model was selected to simulate the spatial-temporal patterns of land use changes in 2010, 2015 and 2020 at 100 m spatial scale and five-year time interval. The results showed that: (1)During the period 2000-2015, areas of farmland decreased continuously, while urban area, rural settlement and independent industrial-mining increased continuously and rapidly; (2) Ten impact factors were chosen as the independent variables of Logistic regression analysis to establish eight logistic regression equations for eight land use types; (3) As the Logistic-CA-Markov model has a good performance in the simulation of land use maps in 2010 and 2015, it was further used to simulate land use change in 2020. (4)The simulation results showed that farmland, forest would decline during 2015 and 2020; Urban area would still increase.
In statistical parameter estimation problems, how well the parameters are estimated largely depends on the sampling design used. In this article, a modified best linear unbiased estimator of the shape parameter β from log-logistic distribution LLD(α,β) is considered when scale parameter α is known and when α is unknown under simple random sampling (SRS) and ranked set sampling (RSS). In addition, a modified BLUE of β, when α is known using an RSS version based on the order statistic that maximizes the Fisher information for a fixed set size, will be considered. Theoretical properties of the suggested estimators are compared with its counterpart estimators under SRS. It is found that these estimators under RSS can be real competitors against those under SRS.
The prediction of slope stability is considered as one of the critical concerns in geotechnical engineering. Conventional stochastic analysis with spatially variable slopes is time-consuming and highly computation-demanding. To assess the slope stability problems with a more desirable computational effort, many machine learning (ML) algorithms have been proposed. However, most ML-based techniques require that the training data must be in the same feature space and have the same distribution, and the model may need to be rebuilt when the spatial distribution changes. This paper presents a new ML-based algorithm, which combines the principal component analysis (PCA)-based neural network (NN) and transfer learning (TL) techniques (i.e. PCA–NN–TL) to conduct the stability analysis of slopes with different spatial distributions. The Monte Carlo coupled with finite element simulation is first conducted for data acquisition considering the spatial variability of cohesive strength or friction angle of soils from eight slopes with the same geometry. The PCA method is incorporated into the neural network algorithm (i.e. PCA-NN) to increase the computational efficiency by reducing the input variables. It is found that the PCA-NN algorithm performs well in improving the prediction of slope stability for a given slope in terms of the computational accuracy and computational effort when compared with the other two algorithms (i.e. NN and decision trees, DT). Furthermore, the PCA–NN–TL algorithm shows great potential in assessing the stability of slope even with fewer training data.
As a complex two-phase flow in naturally fractured coal formations, the prediction and analysis of CBM production remain challenging. This study presents a discrete fracture approach to modeling coalbed methane (CBM) and water flow in fractured coal reservoirs, particularly the influence of fracture orientation, fracture density, gravity, and fracture skeleton on fluid transport. The discrete fracture model is first verified by two water-flooding cases with multi- and single-fracture configurations. The verified model is then used to simulate CBM production from a discrete fractured reservoir using four different fracture patterns. The results indicate that fluid behavior is significantly affected by orientation, density, and fracture connectivity. Finally, several cases are performed to investigate the influence of gravity and fracture skeleton. The simulation results show that gas migrates upwards to the top reservoir during fluid extraction owing to buoyancy and the connected fracture skeleton plays a dominant role in fluid transport and methane production efficiency. Overall, the developed discrete fracture model provides a powerful tool to study two-phase flow in fractured coal reservoirs.
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