
%0 Journal Article
%A Nimmagadda, Shastri L 
%A Rudra, Amit 
%A Reiners, Torsten 
%T The North West Shelf (NWS), a Digital Petroleum Ecosystem (PDE) in a Big Data Scale
%J ASEG Extended Abstracts
%D 2018/02/26
%V 2018
%N 1
%P 1-5
%K 
%R https://doi.org/10.1071/ASEG2018ABP008
%U https://www.publish.csiro.au/paper/ASEG2018ABP008 
%X The North West Shelf (NWS) and its associated petroleum systems have varied geographies, geomorphologies and complex geological environments. In spite of the ongoing exploration activities in many sedimentary basins, the appraisal and field development campaigns are challenging. Besides, interpreting the connectivity between petroleum systems is challenging. The heterogeneity and multidimensionality of multi-stacked reservoirs associated with multiple oil and gas fields complicate the data integration process. Volumes and varieties of data existing in these basins are in different scales, sizes and formats, demanding new storage and retrieval methods, emphasizing both data integration and data structuring. Since the data are in terabyte size; the multiple dimensions and domains need to be brought in a single repository, we take advantage of Big Data tools and technologies. In this context, we aim at articulating the digital petroleum ecosystems and petroleum database management systems, with new data modelling, data warehousing and mining, visualization and interpretation artefacts. This approach facilitates the data management not only for individual basins but groups of basins of the NWS. Warehoused cuboid metadata can explore the connections providing new insights in the data interpretation and knowledge of new prospective areas. The multidimensional warehousing repository that supported by cloud computing, data analytics and virtualization features, provide new opportunities for delivering quality and just-in-time online ecosystem services. Other goals are deducing an integrated unified metadata model and characterizing the connectivity among the basins of the NWS and associated oil &amp; gas fields. The study supports the features of PDE and its knowledge management. 
