This paper explores the potential for hyperscale public cloud high-performance compute (HPC) to enable efficient deployment of a semi-global approach to multi-parameter full-waveform inversion (FWI) over large areas. We introduce several novel aspects to semi-global FWI that improve convergence and suppress crosstalk, while establishing that the algorithm’s embarrassingly parallel nature is well suited for public cloud implementation. We describe how various public cloud services can be taken advantage of to reduce the cost of the inversion and provide a reference architecture for the deployment of semi-global FWI to the Amazon Web Services (AWS) platform. Finally, we apply semi-global FWI to raw data from a large-scale legacy surface seismic dataset acquired offshore Australia as part of a re-processing sequence undertaken recently. Our results demonstrate that semi-global FWI can be effectively parallelized across more than one million logical central processing units (CPUs) and is able to recover an anisotropic velocity model in a few hours and in an automated fashion.

Keywords:
upstream oil & gas, cloud computing, machine learning, seismic model, semi-global fwi, fwi, reservoir characterization, multi-parameter fwi, information, inversion
Subjects:
Reservoir Characterization, Information Management and Systems, Seismic processing and interpretation, Seismic modeling
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