Massively-parallel electrical-conductivity imaging of hydrocarbons using the Blue Gene/L supercomputer
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Massively-parallel electrical-conductivity imaging of hydrocarbons using the Blue Gene/L supercomputer

  • Author(s): Commer, M.
  • Newman, G.A.
  • Carazzone, J.J.
  • Dickens, T.A.
  • Green, K.E.
  • Wahrmund, L.A.
  • Willen, D.E.
  • Shiu, J.
  • et al.
Abstract

Large-scale controlled source electromagnetic (CSEM) three-dimensional (3D) geophysical imaging is now receiving considerable attention for electrical conductivity mapping of potential offshore oil and gas reservoirs. To cope with the typically large computational requirements of the 3D CSEM imaging problem, our strategies exploit computational parallelism and optimized finite-difference meshing. We report on an imaging experiment, utilizing 32,768 tasks/processors on the IBM Watson Research Blue Gene/L (BG/L) supercomputer. Over a 24-hour period, we were able to image a large scale marine CSEM field data set that previously required over four months of computing time on distributed clusters utilizing 1024 tasks on an Infiniband fabric. The total initial data misfit could be decreased by 67 percent within 72 completed inversion iterations, indicating an electrically resistive region in the southern survey area below a depth of 1500 m below the seafloor. The major part of the residual misfit stems from transmitter parallel receiver components that have an offset from the transmitter sail line (broadside configuration). Modeling confirms that improved broadside data fits can be achieved by considering anisotropic electrical conductivities. While delivering a satisfactory gross scale image for the depths of interest, the experiment provides important evidence for the necessity of discriminating between horizontal and vertical conductivities for maximally consistent 3D CSEM inversions.

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