An Upstream Business Data Science in a Big Data Perspective
The rugged geographies, geomorphologies and complex geological environments make the explorers more challenging exploration and production (E & P). Despite challenges, many sedimentary basins, associated oil & gas fields and E & P Ventures are productive and commercially viable. Th...
| Main Authors: | , , |
|---|---|
| Format: | Conference Paper |
| Published: |
2017
|
| Online Access: | http://hdl.handle.net/20.500.11937/58013 |
| _version_ | 1848760155312750592 |
|---|---|
| author | Nimmagadda, Shastri Reiners, Torsten Rudra, Amit |
| author_facet | Nimmagadda, Shastri Reiners, Torsten Rudra, Amit |
| author_sort | Nimmagadda, Shastri |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The rugged geographies, geomorphologies and complex geological environments make the explorers more challenging exploration and production (E & P). Despite challenges, many sedimentary basins, associated oil & gas fields and E & P Ventures are productive and commercially viable. The difficulty in understanding the connectivity among multiple reservoirs is due to lack of knowledge of multidisciplinary data of petroleum systems, complicating the data integration and interpretation process. The geological and geophysical data of an upstream business are vital assets of any oil & gas industry, in particular in E & P perspective. The data are often unstructured with a variety of anomalous attributes, mingling with volumes of spatial-temporal dimension attributes and instances. In recent years, the concepts of Big Data have taken different hype in petroleum industries, because of involvement of big sized data in the data integration process. Because of the unstructured data sources, a new direction in the database organization is needed. Investigating the science behind the Big Data and their integrated interpretation of the upstream project is a principal objective of the research. In this context, various constructs and models are articulated with different artefacts. Opportunities of Big Data are explored with exploration data and business analytics, supporting sustainable E & P systems. Petroleum management information systems (PMIS) and digital petroleum ecosystems (PDE) are developed to establish a connectivity among various data sources in multiple domains and systems. The implementation of robust methodologies ascertains the significance of the integrated upstream business in the oil and gas industries that comply with the characteristics of the Big Data. |
| first_indexed | 2025-11-14T10:11:17Z |
| format | Conference Paper |
| id | curtin-20.500.11937-58013 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:11:17Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-580132018-01-10T08:08:12Z An Upstream Business Data Science in a Big Data Perspective Nimmagadda, Shastri Reiners, Torsten Rudra, Amit The rugged geographies, geomorphologies and complex geological environments make the explorers more challenging exploration and production (E & P). Despite challenges, many sedimentary basins, associated oil & gas fields and E & P Ventures are productive and commercially viable. The difficulty in understanding the connectivity among multiple reservoirs is due to lack of knowledge of multidisciplinary data of petroleum systems, complicating the data integration and interpretation process. The geological and geophysical data of an upstream business are vital assets of any oil & gas industry, in particular in E & P perspective. The data are often unstructured with a variety of anomalous attributes, mingling with volumes of spatial-temporal dimension attributes and instances. In recent years, the concepts of Big Data have taken different hype in petroleum industries, because of involvement of big sized data in the data integration process. Because of the unstructured data sources, a new direction in the database organization is needed. Investigating the science behind the Big Data and their integrated interpretation of the upstream project is a principal objective of the research. In this context, various constructs and models are articulated with different artefacts. Opportunities of Big Data are explored with exploration data and business analytics, supporting sustainable E & P systems. Petroleum management information systems (PMIS) and digital petroleum ecosystems (PDE) are developed to establish a connectivity among various data sources in multiple domains and systems. The implementation of robust methodologies ascertains the significance of the integrated upstream business in the oil and gas industries that comply with the characteristics of the Big Data. 2017 Conference Paper http://hdl.handle.net/20.500.11937/58013 10.1016/j.procs.2017.08.236 http://creativecommons.org/licenses/by-nc-nd/4.0/ fulltext |
| spellingShingle | Nimmagadda, Shastri Reiners, Torsten Rudra, Amit An Upstream Business Data Science in a Big Data Perspective |
| title | An Upstream Business Data Science in a Big Data Perspective |
| title_full | An Upstream Business Data Science in a Big Data Perspective |
| title_fullStr | An Upstream Business Data Science in a Big Data Perspective |
| title_full_unstemmed | An Upstream Business Data Science in a Big Data Perspective |
| title_short | An Upstream Business Data Science in a Big Data Perspective |
| title_sort | upstream business data science in a big data perspective |
| url | http://hdl.handle.net/20.500.11937/58013 |