On Big Data guided Unconventional Digital Ecosystems and their Knowledge Management
© Proceedings of the 23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019. Establishing the reservoir connections is paramount in exploration and exploitation of unconventional petroleum systems and their reservoirs. In Big Data scale...
| Main Authors: | , , , |
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| Format: | Conference Paper |
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2019
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| Online Access: | https://aisel.aisnet.org/pacis2019/7 http://hdl.handle.net/20.500.11937/81146 |
| _version_ | 1848764325687197696 |
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| author | Nimmagadda, Shastri Reiners, Torsten Wood, Lincoln Zhu, Dengya |
| author_facet | Nimmagadda, Shastri Reiners, Torsten Wood, Lincoln Zhu, Dengya |
| author_sort | Nimmagadda, Shastri |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © Proceedings of the 23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019. Establishing the reservoir connections is paramount in exploration and exploitation of unconventional petroleum systems and their reservoirs. In Big Data scale, multiple petroleum systems hold volumes and varieties of data sources. The connectivity between petroleum reservoirs and their existence in a single petroleum ecosystem is often ambiguously interpreted. They are heterogeneous and unstructured in multiple domains. They need better data integration methods to interpret the interplay between elements and processes of petroleum systems. Large-scale infrastructure is needed to build data relationships between different petroleum systems. The purpose of the research is to establish the connectivity between petroleum systems through resource data management and visual analytics. We articulate a Design Science Information System (DSIS) approach, bringing various artefacts together from multiple domains of petroleum provinces. The DSIS emerges as a knowledge-based digital ecosystem innovation, justifying its need, connecting geographically controlled petroleum systems and building knowledge of oil and gas prospects. |
| first_indexed | 2025-11-14T11:17:34Z |
| format | Conference Paper |
| id | curtin-20.500.11937-81146 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:17:34Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-811462021-01-19T07:14:28Z On Big Data guided Unconventional Digital Ecosystems and their Knowledge Management Nimmagadda, Shastri Reiners, Torsten Wood, Lincoln Zhu, Dengya © Proceedings of the 23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019. Establishing the reservoir connections is paramount in exploration and exploitation of unconventional petroleum systems and their reservoirs. In Big Data scale, multiple petroleum systems hold volumes and varieties of data sources. The connectivity between petroleum reservoirs and their existence in a single petroleum ecosystem is often ambiguously interpreted. They are heterogeneous and unstructured in multiple domains. They need better data integration methods to interpret the interplay between elements and processes of petroleum systems. Large-scale infrastructure is needed to build data relationships between different petroleum systems. The purpose of the research is to establish the connectivity between petroleum systems through resource data management and visual analytics. We articulate a Design Science Information System (DSIS) approach, bringing various artefacts together from multiple domains of petroleum provinces. The DSIS emerges as a knowledge-based digital ecosystem innovation, justifying its need, connecting geographically controlled petroleum systems and building knowledge of oil and gas prospects. 2019 Conference Paper http://hdl.handle.net/20.500.11937/81146 https://aisel.aisnet.org/pacis2019/7 unknown |
| spellingShingle | Nimmagadda, Shastri Reiners, Torsten Wood, Lincoln Zhu, Dengya On Big Data guided Unconventional Digital Ecosystems and their Knowledge Management |
| title | On Big Data guided Unconventional Digital Ecosystems and their Knowledge Management |
| title_full | On Big Data guided Unconventional Digital Ecosystems and their Knowledge Management |
| title_fullStr | On Big Data guided Unconventional Digital Ecosystems and their Knowledge Management |
| title_full_unstemmed | On Big Data guided Unconventional Digital Ecosystems and their Knowledge Management |
| title_short | On Big Data guided Unconventional Digital Ecosystems and their Knowledge Management |
| title_sort | on big data guided unconventional digital ecosystems and their knowledge management |
| url | https://aisel.aisnet.org/pacis2019/7 http://hdl.handle.net/20.500.11937/81146 |