Big Data guided Digital Petroleum Ecosystems for Visual Analytics and Knowledge Management
The North West Shelf (NWS) interpreted as a Total Petroleum System (TPS), is Super Westralian Basin with active onshore and offshore basins through which shelf, - slope and deep-oceanic geological events are construed. In addition to their data associativity, TPS emerges with geographic connect...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2021
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| Online Access: | http://hdl.handle.net/20.500.11937/86459 |
| _version_ | 1848764828738387968 |
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| author | Nimmagadda, Shastri Ochan, Andrew Mani, Neel Reiners, Torsten |
| author_facet | Nimmagadda, Shastri Ochan, Andrew Mani, Neel Reiners, Torsten |
| author_sort | Nimmagadda, Shastri |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The North West Shelf (NWS) interpreted as a Total
Petroleum System (TPS), is Super Westralian Basin with
active onshore and offshore basins through which shelf, -
slope and deep-oceanic geological events are construed. In
addition to their data associativity, TPS emerges with
geographic connectivity through phenomena of digital
petroleum ecosystem. The super basin has a multitude of
sub-basins, each basin is associated with several petroleum
systems and each system comprised of multiple oil and gas
fields with either known or unknown areal extents. Such
hierarchical ontologies make connections between
attribute relationships of diverse petroleum systems.
Besides, NWS has a scope of storing volumes of instances
in a data-warehousing environment to analyse and
motivate to create new business opportunities.
Furthermore, the big exploration data, characterized as
heterogeneous and multidimensional, can complicate the
data integration process, precluding interpretation of data
views, drawn from TPS metadata in new knowledge
domains. The research objective is to develop an
integrated framework that can unify the exploration and
other interrelated multidisciplinary data into a holistic TPS
metadata for visualization and valued interpretation.
Petroleum digital ecosystem is prototyped as a digital oil
field solution, with multitude of big data tools. Big data
associated with elements and processes of petroleum
systems are examined using prototype solutions. With
conceptual framework of Digital Petroleum Ecosystems
and Technologies (DPEST), we manage the
interconnectivity between diverse petroleum systems and
their linked basins. The ontology-based data warehousing
and mining articulations ascertain the collaboration
through data artefacts, the coexistence between different
petroleum systems and their linked oil and gas fields that
benefit the explorers. The connectivity between systems
further facilitates us with presentable exploration data
views, improvising visualization and interpretation. The
metadata with meta-knowledge in diverse knowledge
domains of digital petroleum ecosystems ensures the
quality of untapped reservoirs and their associativity
between Westralian basins. |
| first_indexed | 2025-11-14T11:25:34Z |
| format | Conference Paper |
| id | curtin-20.500.11937-86459 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:25:34Z |
| publishDate | 2021 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-864592022-01-20T02:43:53Z Big Data guided Digital Petroleum Ecosystems for Visual Analytics and Knowledge Management Nimmagadda, Shastri Ochan, Andrew Mani, Neel Reiners, Torsten The North West Shelf (NWS) interpreted as a Total Petroleum System (TPS), is Super Westralian Basin with active onshore and offshore basins through which shelf, - slope and deep-oceanic geological events are construed. In addition to their data associativity, TPS emerges with geographic connectivity through phenomena of digital petroleum ecosystem. The super basin has a multitude of sub-basins, each basin is associated with several petroleum systems and each system comprised of multiple oil and gas fields with either known or unknown areal extents. Such hierarchical ontologies make connections between attribute relationships of diverse petroleum systems. Besides, NWS has a scope of storing volumes of instances in a data-warehousing environment to analyse and motivate to create new business opportunities. Furthermore, the big exploration data, characterized as heterogeneous and multidimensional, can complicate the data integration process, precluding interpretation of data views, drawn from TPS metadata in new knowledge domains. The research objective is to develop an integrated framework that can unify the exploration and other interrelated multidisciplinary data into a holistic TPS metadata for visualization and valued interpretation. Petroleum digital ecosystem is prototyped as a digital oil field solution, with multitude of big data tools. Big data associated with elements and processes of petroleum systems are examined using prototype solutions. With conceptual framework of Digital Petroleum Ecosystems and Technologies (DPEST), we manage the interconnectivity between diverse petroleum systems and their linked basins. The ontology-based data warehousing and mining articulations ascertain the collaboration through data artefacts, the coexistence between different petroleum systems and their linked oil and gas fields that benefit the explorers. The connectivity between systems further facilitates us with presentable exploration data views, improvising visualization and interpretation. The metadata with meta-knowledge in diverse knowledge domains of digital petroleum ecosystems ensures the quality of untapped reservoirs and their associativity between Westralian basins. 2021 Conference Paper http://hdl.handle.net/20.500.11937/86459 fulltext |
| spellingShingle | Nimmagadda, Shastri Ochan, Andrew Mani, Neel Reiners, Torsten Big Data guided Digital Petroleum Ecosystems for Visual Analytics and Knowledge Management |
| title | Big Data guided Digital Petroleum Ecosystems for Visual Analytics
and Knowledge Management |
| title_full | Big Data guided Digital Petroleum Ecosystems for Visual Analytics
and Knowledge Management |
| title_fullStr | Big Data guided Digital Petroleum Ecosystems for Visual Analytics
and Knowledge Management |
| title_full_unstemmed | Big Data guided Digital Petroleum Ecosystems for Visual Analytics
and Knowledge Management |
| title_short | Big Data guided Digital Petroleum Ecosystems for Visual Analytics
and Knowledge Management |
| title_sort | big data guided digital petroleum ecosystems for visual analytics
and knowledge management |
| url | http://hdl.handle.net/20.500.11937/86459 |