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Managing Uncertainty in Large-Scale Inversions for the Oil and Gas Industry with Big Data

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Part of the book series: Studies in Big Data ((SBD,volume 26))

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Abstract

Inverse problems arise in almost all fields of science when parameters of a postulated model have to be determined from a set of observed data. Due to the increasing volume of data collected by the oil and gas industry, there is an urgent need for addressing large-scale inverse problems. In this article, after examining both deterministic and statistical methods that are scalable for managing large volume of data, we present the MapReduce paradigm as a potential speed up technique for future implementations.

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Authors and Affiliations

  1. University of Houston, Houston, TX, 77004, USA

    Jiefu Chen & Xuqing Wu

  2. Cyentech Consulting LLC, Cypress, TX, 77479, USA

    Yueqin Huang

  3. Weatherford, Houston, TX, 77060, USA

    Tommy L. Binford Jr.

Authors
  1. Jiefu Chen
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  2. Yueqin Huang
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  3. Tommy L. Binford Jr.
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  4. Xuqing Wu
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Corresponding author

Correspondence to Xuqing Wu .

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Editors and Affiliations

  1. Jesse H. Jones School of Business, Texas Southern University , Houston, Texas, USA

    S. Srinivasan

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Chen, J., Huang, Y., Binford, T.L., Wu, X. (2018). Managing Uncertainty in Large-Scale Inversions for the Oil and Gas Industry with Big Data. In: Srinivasan, S. (eds) Guide to Big Data Applications. Studies in Big Data, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-53817-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-53817-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53816-7

  • Online ISBN: 978-3-319-53817-4

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