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

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8 Author(s)
Commer, M. ; Lawrence Berkeley National Laboratories, California 94720, USA ; Newman, G.A. ; Carazzone, J.J. ; Dickens, T.A.
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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 (and processors) on the IBM Blue Gene/L™ (BG/L) supercomputer at the IBM T. J. Watson Research Center. Over a 24-hour period, we were able to image a large-scale marine CSEM field dataset that previously required more than 4 months of computing time on distributed clusters utilizing 1,024 tasks on an InfiniBand® fabric. The total initial data-fitting errors (i.e., “misfits”) could be decreased by 67% within 72 completed inversion iterations, indicating the existence of an electrically resistive region in the southern survey area below a depth of 1,500 m underneath 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.

Note: The Institute of Electrical and Electronics Engineers, Incorporated is distributing this Article with permission of the International Business Machines Corporation (IBM) who is the exclusive owner. The recipient of this Article may not assign, sublicense, lease, rent or otherwise transfer, reproduce, prepare derivative works, publicly display or perform, or distribute the Article.  

Published in:

IBM Journal of Research and Development  (Volume:52 ,  Issue: 1.2 )