Shastri Nimmagadda

Shastri Nimmagadda


Curtin University, Australia


SESSION 9:  On Big Data Driven East African Rift System, Managing the Upstream Business Analytics

Shastri L Nimmagadda, Andrew Ochan & Amit Rudra

An upstream business is an ongoing continual activity in many sedimentary basins of the East AfricanRift System (EARS); though the Greenfield exploration, appraisal and field development are challenging, because of large volumes and varieties of data sources. The heterogeneity and
multidimensionality of data sources in these basins exist in different scales, sizes and formats in multiple dimensions and domains (in particular the spatial dimensions from Sudan in the northern part of EARS to Mozambique in the south).

These big data complicate the data organization, integration, and management, affecting the exploration, field development and production entities of upstream business ventures of the national and private oil companies. With the result, the connectivity between data is inherently unknown, at times ambiguously interpreted, risking the exploration ventures. They further pose great challenges related to data sharing and accessibility, as they always happen with the traditional database systems.

Modelling and integrating such high volumes and varieties of data need a new direction, in particular, the data structuring, storage and retrieval including processing of large volume of datasets. New tools and technologies are required to address the data modelling and management challenges.

We propose Big Data concepts, tools and technologies, especially when entities and dimensions in multiple domains of sedimentary basins are needed to bring them together in an integrated upstream business and its project management. We propose a multidimensional warehousing repository system that supported by cloud computing and virtualization features, providing opportunities for delivering quality and just-in-time online customer services.

This approach facilitates data integration, interoperability and adaptability not only in one basin but the data sources from multiple basins of Sudan, Uganda, Kenya, Tanzania, Rwanda, Burundi and Mozambique, associated with western and eastern arms of the EARS. We aim at this holistic system development approach with Digital Petroleum Ecosystem (DPE) and Petroleum Management Information System (PMIS) articulations, built with data modelling, data warehousing and mining, visualization and interpretation artefacts.

Our research objective is to deduce an integrated metadata model, representing and connecting the basins of the entire EARS. Exploring the data connectivity from multiple oil & gas fields of EARS is an additional characteristic feature of the current research. We evaluate the feasibility and applicability of this big metadata model, exploring the connectivity among multiple oil and gas fields and their associated petroleum systems from various basins, providing new insights on data integration and management, minimizing the exploration risks and adding values to the exploration projects and their data analytics. The holistic approach infers that it is more robust productive and sustainable.