An Upstream Business Data Science in a Big Data Perspective

An Upstream Business Data Science in a Big Data Perspective

Petroleum industry

Big data

Gas industry

Petroleum prospecting

Geology

Information management

Ecosystems

Data integration

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 The Author(s).

1881-1890

112

Nimmagadda, Shastri L

Reiners, Torsten

Rudra, Amit

21st International Conference on Knowledge - Based and Intelligent Information and Engineering Systems, KES 2017, September 6, 2017 - September 8, 2017

2017

conferencePaper

18770509

10.1016/j.procs.2017.08.236

http://dx.doi.org/10.1016/j.procs.2017.08.236