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Data as an asset: What the oil and gas sector can learn from other industries about “Big Data”

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  • Perrons, Robert K.
  • Jensen, Jesse W.

Abstract

The upstream oil and gas industry has been contending with massive data sets and monolithic files for many years, but “Big Data” is a relatively new concept that has the potential to significantly re-shape the industry. Despite the impressive amount of value that is being realized by Big Data technologies in other parts of the marketplace, however, much of the data collected within the oil and gas sector tends to be discarded, ignored, or analyzed in a very cursory way. This viewpoint examines existing data management practices in the upstream oil and gas industry, and compares them to practices and philosophies that have emerged in organizations that are leading the way in Big Data. The comparison shows that, in companies that are widely considered to be leaders in Big Data analytics, data is regarded as a valuable asset—but this is usually not true within the oil and gas industry insofar as data is frequently regarded there as descriptive information about a physical asset rather than something that is valuable in and of itself. The paper then discusses how the industry could potentially extract more value from data, and concludes with a series of policy-related questions to this end.

Suggested Citation

  • Perrons, Robert K. & Jensen, Jesse W., 2015. "Data as an asset: What the oil and gas sector can learn from other industries about “Big Data”," Energy Policy, Elsevier, vol. 81(C), pages 117-121.
    • Handle: RePEc:eee:enepol:v:81:y:2015:i:c:p:117-121
    DOI: 10.1016/j.enpol.2015.02.020
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    References listed on IDEAS

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    1. Philip Ball, 2000. "Chemistry meets computing," Nature, Nature, vol. 406(6792), pages 118-120, July.
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    Cited by:

    1. Maroufkhani, Parisa & Desouza, Kevin C. & Perrons, Robert K. & Iranmanesh, Mohammad, 2022. "Digital transformation in the resource and energy sectors: A systematic review," Resources Policy, Elsevier, vol. 76(C).
    2. Shrestha, Yash Raj & Krishna, Vaibhav & von Krogh, Georg, 2021. "Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges," Journal of Business Research, Elsevier, vol. 123(C), pages 588-603.
    3. Amankwah-Amoah, Joseph, 2016. "Competing technologies, competing forces: The rise and fall of the floppy disk, 1971–2010," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 121-129.
    4. Rachel E. Brackenridge & Vasily Demyanov & Oleg Vashutin & Ruslan Nigmatullin, 2022. "Improving Subsurface Characterisation with ‘Big Data’ Mining and Machine Learning," Energies, MDPI, vol. 15(3), pages 1-23, January.
    5. Mohamed Saeudy & Ali Meftah Gerged & Khaldoon Albitar, 2022. "Accounting Perspectives on The Business Value of Big Data During and Beyond The COVID-19 Pandemic," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 21(2), pages 174-199, June.
    6. Schuelke-Leech, Beth-Anne & Barry, Betsy & Muratori, Matteo & Yurkovich, B.J., 2015. "Big Data issues and opportunities for electric utilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 937-947.
    7. Di Salvo, André L.A. & Agostinho, Feni & Almeida, Cecília M.V.B. & Giannetti, Biagio F., 2017. "Can cloud computing be labeled as “green”? Insights under an environmental accounting perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 514-526.
    8. Amankwah-Amoah, Joseph, 2019. "Big data analytics and business failures in data-Rich environments: An organizing framework," MPRA Paper 91264, University Library of Munich, Germany.
    9. Perrons, Robert K. & Cosby, Tonya, 2020. "Applying blockchain in the geoenergy domain: The road to interoperability and standards," Applied Energy, Elsevier, vol. 262(C).
    10. Tengku Adil Tengku Izhar & Mohd Shamsul Mohd Shoid, 2016. "A Research Framework on Big Data awareness and Success Factors toward the Implication of Knowledge Management: Critical Review and Theoretical Extension," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 6(4), pages 325-338, April.
    11. Samuel B. Condic & Roger Morefield, 2021. "Hayek on the essential dispersion of market knowledge," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 34(4), pages 449-463, December.
    12. Perrons, Robert K. & McAuley, Derek, 2015. "The case for “n«all”: Why the Big Data revolution will probably happen differently in the mining sector," Resources Policy, Elsevier, vol. 46(P2), pages 234-238.

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