Although the hydraulic fracturing treatment can improve the conductivity of shale reservoirs, the low recovery rate of the fracturing fluid may cause potential environmental and production issues. For an accurate investigation of these issues, an appropriate model of the water imbibition in shales is required. However, the hydraulic parameters related to water imbibition in shales are hard to be measured due to their tiny pores. In this study, an effective method is proposed to estimate the water imbibition volume. The nuclear magnetic resonance method is applied to obtain the related parameters including the capillary curve, the intrinsic and relative permeability of the shale, which can significantly cut down the time and cost needed to get these data. This model is validated by water imbibition experiments. In addition, we compare two empirical equations used to calculate intrinsic permeability in the NMR method and calibrate the corresponding parameter a for shale, which is poorly investigated in literature. Finally, we suggest that the capillary force dominates the early stage of water imbibition process in unsaturated shales, and the late period may be influenced more by other mechanisms such as the osmosis and the surface hydration.

Abstract. The trajectory data generated by various position-aware devices is widely used in various fields of society, but its conventional vector representation and various analysis algorithms based on it have high computational complexity. This makes it difficult to meet the application requirements of real-time or near real-time management and analysis of large-scale trajectory data. In view of the above challenges, this paper proposes a trajectory data management and analysis technology framework based on the Spatiotemporal Grid Model (STGM). First, the trajectory data is represented by spatiotemporal grid encoding instead of vector coordinates, and it can achieve dimensionality reduction and integrated management of high-dimensional heterogeneous trajectory data. Second, the trajectory computing and analysis methods based on STGM are introduced, which reduce the computing complexity of algorithms. Furthermore, various types of trajectory mining and applications are realized on the basis of high-performance computing technologies. Finally, a trajectory data management and analysis prototype system based on the STGM is developed, and experimental results verify the reliability and effectiveness of the proposed technology framework.

With the continuous increase in the mining depth of underground mineral resources, the geological conditions encountered in mining have become more complex. The complex geological conditions have led to varying degrees of roof damage, especially the frequent occurrence of roof collapse accidents in metal mines, causing huge losses to mining enterprises. How to evaluate the risk of roof subsidence, falling, and even collapse under different geological conditions has become the primary issue. This article first selects the main evaluation indicators in the domestic and foreign roadway roof failure research literature for statistical analysis. Then, according to the statistical results, a classification approach of roof damage degree using the fuzzy comprehensive clustering method is established with roof rock strength, broken degree, roadway section size, buried depth, and roof sinking amount as evaluation indicators. The damage of the top plate is divided into five grades: minor damage, obvious damage, serious damage, extremely serious damage, and devastating damage. Finally, the established evaluation method was applied to the project site-supporting work of the 760 m main transport roadway in Yunnan Maoping Lead-Zinc Mine. The evaluation result is consistent with the actual situation on-site. The research results can provide a reference for the roof stability analysis under the background of this project and similar projects and at the same time help the next step in the classification control of different levels of roof stability and the design of the overall roadway support.

Roof collapse is the most frequent production accident in the mine production process, which seriously threatens the efficient and safe production of the mine. Therefore, it is urgent to carry out practical research on the roof collapse tendency of the roadway. After searching and analyzing the relevant documents, the primary influencing factors of roof collapse risk based on AHP are determined, namely engineering geology, rock mass support, construction management and natural environment. After refining the main influencing factors, the evaluation factor set is obtained, the fuzzy comprehensive evaluation relationship matrix is established, and the fuzzy comprehensive evaluation model of roof collapse risk is obtained. Finally, the quantitative evaluation of no collapse risk, weak collapse risk, medium collapse risk and high collapse risk is carried out. Taking a metal mine as an example, the risk of roof collapse of its C11 haulage roadway is selected for fuzzy evaluation. The evaluation result is high collapse risk, which is consistent with the evaluation result of the current specification, indicating that the model can be used for mine roof collapse risk evaluation. This method of estimating roof collapse has been applied on-site, which is consistent with the actual situation and has achieved good results. It has guiding significance for predicting the stability of tunnels and supporting operations.