Browsing by Author "Xu, Sheng"
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- ItemOpen AccessInvestigation of mechanisms involved in generation of foamy oil flow(2007) Xu, Sheng; Maini, Brij B.Some heavy oil reservoirs in Western Canada and Venezuela under solution-gasdrive show anomalous primary performance: high oil recovery and low production GOR. This anomalous oil production behaviour under solution gas drive has been observed since the 1980's and one of the factors responsible for such behaviour is thought to be foamy oil flow, i.e. flow of gas in the form of dispersed gas bubbles. However, the mechanisms behind the formation of gas dispersion under foamy oil flow conditions remain unclear. There are two contradictory theories to explain the formation of a dispersion: explosive nucleation theory and dispersion due to dynamic equilibrium between the processes of break-up and coalescence. The objective of this work was to further examine the basic mechanisms behind the formation of dispersed gas bubbles and infer which one of the theories of foamy oil flow is consistent with the experiments. The study included a series of sand pack and fluid property measurements, and three series of depletion experiments. Based on the results from these depletion tests, it was concluded that the hypothesis of "explosive nucleation" may not be correct. The mechanism involved in the formation of gas dispersion under solution gas drive appears to be that of the break-up of mobilized gas ganglia. The bubble size distribution is maintained by a dynamic equilibrium between the processes of break-up and coalescence. Some other notable observations were that the capillary number fluctuation corresponded with the fluctuation of simultaneous gas production rate and that the apparent critical gas saturation was 1 % to 16%, increasing with increasing depletion rate.
- ItemOpen AccessSegmentation of Mobile LiDAR Point Clouds in Urban Environment(2018-09-19) Xu, Sheng; Wang, Ruisheng; El-Sheimy, Naser; Gao, YangMobile LiDAR System (MLS) is a popular tool which collects 3D information while vehicles drive along urban streets. Nowadays, MLS becomes more and more important in obtaining 3D point clouds because of its efficiency and cost-effectiveness. One demanding issue is how to segment objects from MLS point clouds accurately and efficiently. Due to the fact that LiDAR point clouds are noisy, uneven and the lack of topology, the object segmentation has become a challenging task. This thesis aims to provide promising solutions for segmentation of MLS point clouds. In the beginning, this thesis proposes an elevation-based method to split MLS point clouds into ground points and off-ground points. Then, it presents an accurate method to extract road curbs from ground points. Contributions of the proposed curb extraction method include that, it formulates an energy function to extract candidate curb points, and it completes curb paths by a new least cost path model. As this curb extraction method works on the 3D points directly rather than on the 2D projected data, there is no loss of 3D geometry information, which improves the extraction performance. For the off-ground object segmentation, two new methods are introduced in this thesis. The first method is an optimal hierarchical clustering (OHC) approach. The cluster combination in the hierarchical clustering is formulated as a problem of the bipartite graph matching, which is optimally solved by the minimum-cost perfect matching (McPM) of a point-based graph. The second method is a component-level segmentation approach for dealing with the separation of overlapping objects. Contributions of the second method include that, it formulates an optimal-vector-field to provide the component consistency information for object segmentation, and it presents a strategy for multi-object segmentation using binary labels only. Due to the fact that power lines are divided into pieces by the previous object segmentation methods, the final work is to extract power lines from input data. Contributions include that, it proposes an estimator to enhance the segmentation robustness against Gaussian noise, and it groups segmented components into individual power line spans based on linear structure information.