Visualization as a Big Data Artefact for Knowledge Interpretation of Digital Petroleum Ecosystems

  • Shastri L. Nimmagadda Curtin University
  • Amit Rudra Curtin University


In the current upstream business environment, we examine the risk involved in the petroleum exploration and field development. Many sedimentary basins worldwide possess hundreds of petroleum systems with thousands of oil and gas fields, geographically scattered. A significant amount of unstructured heterogeneous and multidimensional data are locked up in many industrial applications and knowledge domains. Our objective is to bring the relevant data together, integrate and visualize for adding values to the existing interpretation. We simulate a Big Data guided digital petroleum ecosystem (DPE) approach, a digital oil field solution, a new direction in the analysis of a total petroleum system (TPS), in which multiple sedimentary basins may have been grouped, inheriting an interconnectivity between the systems. The DPE is articulated in a framework, organizing variety of data associated with the elements and processes of complex petroleum systems and integrating their data dimensions and attributes. We develop an ontology based data warehousing and mining artefacts. We present warehoused metadata, with slicing and dicing of data views for visualization of new prospects in the investigating area. We further investigate the risk of exploratory drilling campaigns and how the integrated framework, with visualization and interpretation artefacts can holistically support the delivery of high-quality products and services.


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[1] Castañeda G. O. J., Nimmagadda, S.L, Cardona Mora, A. P, Lobo, A, and Darke, K. (2012), On Integrated Quantitative Interpretative Workflows for interpreting structural and combinational traps for risk minimizing the exploratory and field development plans, Bolivarian Geophysical Symposium proceedings, held in Cartagena, Colombia.

[2] Erdman, M. and Studer, R. (2001), Heterogeneous Information Resources Need Semantic Access, Volume 36, Issue 3, Data and Knowledge Engineering, p. 317335.

[3] Gilbert, R. Liu, Y. and Abriel, W. (2004), Reservoir modeling: integrating various data at appropriate scales, The Leading Edge, Vol. 23(8) (pp. 784-788), EAGE, The Netherlands.

[4] Han, J. and Cercone, N. (2000), Aviz: A visualization system for discovering numeric association rules. In: Terano. T, Liu. H, Chen A.L.P.
(eds.) PAKDD 2000, LNCS (LNAI) vol. 1805, pp. 269280, Springer, Heidelberg.

[5] King, E. (2000), “Data Warehousing and Data Mining: Implementing Strategic Knowledge Management”, 1st Ed, CTR Corporation, ISBN 1566070782, SC, USA.

[6] Longley, I.M. Bradshaw, M.T. & Hebberger, J. (2001), Australian petroleum provinces of the 21st century, in Downey, M.W. Threet, J.C. & Morgan, W.A (2001) Petroleum provinces of the 21st century, AAPG Memoir, 74, 287-317.

[7] Lugmayr, A. Stockleben, B. Scheib, C. M. Mailaparampil, M. Mesia, N. and Ðanta, H. (2016), “A Comprehensive Survey on Big Data Research and It’s Implications - What is really ’new’ in Big Data? It’s Cognitive Big Data,” Proceedings of the 20th PacificAsian Conference on Information Systems (PACIS 2016), S.-I.C. Shin-Yuan Hung Patrick Y.K. Chau TingPeng Liang, ed.

[8] Marakas, M. G. (2003), “Modern Data Warehousing, Mining, and Visualization Core Concepts”, Prentice Hall Pub.

[9] Nimmagadda, S.L, Dreher, H, Noventianto. A, Mustofa. A and Fiume. G. (2012), Enhancing the process of knowledge discovery from integrated
geophysical databases using geo-ontologies, a paper presented and published in the proceedings of Indonesian Petroleum Association (IPA) conference, held in Jakarta, Indonesia.

[10] Nimmagadda, S. L. and Dreher, H. (2012), “On new emerging concepts of Petroleum Digital Ecosystem (PDE)”, Journal WIREs Data Mining Knowledge Discovery, 2012, 2: 457–475 doi: 10.1002/widm.1070.

[11] Nimmagadda, S.L. (2015), Data Warehousing for Mining of Heterogeneous and Multidimensional Data Sources, Verlag Publisher, Scholar Press, OmniScriptum GMBH & CO. KG, p. 1-657, Germany.

[12] Nimmagadda, S.L. and Rudra, A. (2016), Big Data Information Systems for Managing Embedded Digital Ecosystems (EDE), in a book entitled “Big Data and Learning Analytics in Higher Education: Current Theory and Practice”, Springer International, DOI: 10.1007/978-3-319-06520-5, ISBN: 978-3-319-065199, The Netherlands.

[13] Post, H.F., Gregory, M. N. and Bonneu, G. (2002), Data Visualization: The State of the Art, Research Paper TU Delft, EUROGRAPHICS 2002, The Netherlands.
How to Cite
NIMMAGADDA, Shastri L.; RUDRA, Amit. Visualization as a Big Data Artefact for Knowledge Interpretation of Digital Petroleum Ecosystems. International SERIES on Information Systems and Management in Creative eMedia (CreMedia), [S.l.], n. 2016/2, p. 34-43, june 2017. ISSN 2341-5576. Available at: <>. Date accessed: 03 dec. 2023.


Digital Petroleum Ecosystem; Big Data; Data Visualization; Interpretation; Knowledge Discovery.
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