Three digital earth models were designed and constructed during SEAM Phase II to study exploration challenges at the scale of modern land seismic surveys. Although built as generic models, each was based on one or more related geologic type areas. The Barrett model represents the seismic anisotropy of complex laminated and fractured shale reservoirs, based on the Woodford and Eagle Ford formations and set below a stratigraphic overburden and near surface of a North American midcontinent basin. The Arid model features the extreme property contrasts of desert terrains in a 500 m thick near surface that juxtaposes hard carbonate bedrock and soft sediments filling karsts, typical of the Saudi Arabian Peninsula. The Foothills model contains sharp surface topography and alluvial fan-like sediments above complex fold-and-thrust structures based on the compressive tectonics of the Llanos Foothills of South America. All three models were built in workflows that combined automated steps with a large measure of manual model building, which represents the current state of the art in geologic modeling for large-scale geophysical simulations. The Barrett and Arid models each contain about 1.5 billion grid cells representing regions 10 × 10 × 3.75 km in physical size. The Foothills model has about 2 billion cells representing a region about 14.5 × 12.5 × 11 km. Full elastic-wave simulations with these models were run for a combined total of about 170,000 shots, usually with millions of recorded channels per shot, generating several petabytes of seismic data in standard and novel shot-receiver geometries. Selected shots from these simulations show that large, detailed earth models can reproduce features of land seismic surveys that continue to challenge the best modern seismic data processing and imaging techniques.
Geologic model building in SEAM Phase II — Land seismic challenges
Carl Regone, Joseph Stefani, Peter Wang, Constantin Gerea, Gladys Gonzalez, Michael Oristaglio; Geologic model building in SEAM Phase II — Land seismic challenges. The Leading Edge ; 36 (9): 738–749. doi: https://doi.org/10.1190/tle36090738.1
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