Background: In shale basins, petroleum systems are complex; they hold data sources in Big Data scales. The motivation of research lies with the facts of exploring effective inherent connectivity between unconventional petroleum systems. The connectivity between energy reservoir systems is ambiguous within a distinctive petroleum ecosystem. Heterogeneity and multidimensionality of unstructured data sources are additional challenges, precluding systematic modelling of diverse petroleum systems and their data integration process, including growing demand for storage systems. The research aims to establish the knowledge-based connectivity between petroleum systems through Information System (IS) articulations, visual analytics and data management.
Method: We investigate the knowledge-based IS guided exploration and production systems to explore the connectivity between diverse unconventional petroleum systems and forecast the reservoir energy. We articulate Design Science Information System (DSIS), bring various IS artefacts, unify multiple domains of petroleum provinces and analyze the associativity between petroleum systems. In addition, use, reuse, effectiveness and interoperability are utility properties of IS artefacts that we evaluate. We implement IS solutions in the oil and gas industries to facilitate database management and reservoir energy exploration.
Results: We simulate DSIS as an Unconventional Digital Petroleum Ecosystem (UDPE) as it allows us to investigate and ascertain the interplay between petroleum systems’ elements and processes. Metadata cubes are computed for data views to visualize, interpret, and implement IS articulations in energy systems. We compute the structure and reservoir attribute views for interpreting energy-driven petroleum systems, prospect evaluation and business-knowledge management with a viable DSIS solution.
Conclusions: The DSIS emerges as a knowledge-based digital ecosystem innovation, demonstrating how it can effectively interconnect geographically controlled petroleum systems. Its development, in the exploration of unconventional shale basins, is a knowledge-based reservoir-energy management solution. This research is beneficial to IS practitioners who wish to pursue energy research in reservoir ecosystem contexts.
Nimmagadda, Shastri; Mani, Neel; Reiners, Torsten; and Wood, Lincoln C.
"Big Data Guided Unconventional Digital Reservoir Energy Ecosystem and its Knowledge Management,"
Pacific Asia Journal of the Association for Information Systems: Vol. 13:
1, Article 1.
Available at: https://aisel.aisnet.org/pajais/vol13/iss1/1