Abstract: In this paper we discuss the development of an Augmented Reality (AR) - based scientific visualization system prototype that supports identification, localisation, and 3D visualisation of oil leakages sensors datasets. Sensors generates significant amount of multivariate datasets during normal and leak situations. Therefore we have developed a data model to effectively manage such data and enhance the computational support needed for the effective data explorations. A challenge of this approach is to reduce the data inefficiency powered by the disparate, repeated, inconsistent and missing attributes of most available sensors datasets. To handle this challenge, this paper aim to develop an AR-based scientific visualization interface which automatically identifies, localise and visualizes all necessary data relevant to a particularly selected region of interest (ROI) along the virtual pipeline network. Necessary system architectural supports needed as well as the interface requirements for such visualizations are also discussed in this paper.
Abstract: Octree compression techniques have been used
for several years for compressing large three dimensional data
sets into homogeneous regions. This compression technique
is ideally suited to datasets which have similar values in
clusters. Oil engineers represent reservoirs as a three dimensional
grid where hydrocarbons occur naturally in clusters. This
research looks at the efficiency of storing these grids using
octree compression techniques where grid cells are broken
into active and inactive regions. Initial experiments yielded
high compression ratios as only active leaf nodes and their
ancestor, header nodes are stored as a bitstream to file on
disk. Savings in computational time and memory were possible
at decompression, as only active leaf nodes are sent to the
graphics card eliminating the need of reconstructing the original
matrix. This results in a more compact vertex table, which can
be loaded into the graphics card quicker and generating shorter
refresh delay times.