机械比能理论及其在钻井工程中的应用

论述了机械比能理论的进展概况,对现有的机械比能模型进行了系统的分类与评价,分别指出了机械比能、钻进比能及水力机械比能不同模型的适应性及其各自的优缺点。从钻前预测机械钻速、随钻评价钻井效率、随钻诊断井底工况并优化钻井参数、随钻监测钻头磨损、钻后优选与评价钻头与提高机械钻速六个方面分析了机械比能理论在钻井工程中应用的发展状况。其应用效果良好,具有良好的推广价值与应用前景,但还有很多问题没有解决。结合现代钻井技术,对机械比能研究的未来发展趋势作了初步探讨。基础模型研究、钻头力学动态特性测试、应用范围的控潜及拓展将成为机械比能研究的主要发展方向。     

Evaluating landslide susceptibility based on cluster analysis,probabilistic methods,and artificial neural networks

In this study, the cluster analysis (CA), probabilistic methods, and artificial neural networks (ANNs) are used to predict landslide susceptibility. The Geographic Information System (GIS) is used as the basic tool for spatial data management. CA is applied to select non-landslide dataset for later analysis. A probabilistic method is suggested to calculate the rating of the relative importance of each class belonging to each conditional factor. ANN is applied to calculate the weight (i.e., relative importance) of each factor. Using the ratings and the weights, it is proposed to calculate the landslide susceptibility index (LSI) for each pixel in the study area. The obtained LSI values can then be used to construct the landslide susceptibility map. The aforementioned proposed method was applied to the Longfeng town, a landslide-prone area in Hubei province, China. The following eight conditional factors were selected: lithology, slope angle, distance to stream/reservoir, distance to road, stream power index (SPI), altitude, curvature, and slope aspect. To assess the conditional factor effects, the weights were calculated for four cases, using 8 factors, 6 factors, 5 factors, and 4 factors, respectively. Then, the results of the landslide susceptibility analysis for these four cases, with and without weighting, were obtained. To validate the process, the receiver operating characteristics (ROC) curve and the area under the curve (AUC) were applied. In addition, the results were compared with the existing landslide locations. The validation results showed good agreement between the existing landslides and the computed susceptibility maps. The results with weighting were found to be better than that without weighting. The best accuracy was obtained for the case with 5 conditional factors with weighting.