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.
Keywords
- Inverse problems
- Geosteering
- Well integrity
- MCMC
- Uncertainty quantification
- MapReduce
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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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