Premium
Enhanced velocity estimation using gridded tomography in complex chalk
Author(s) -
Sugrue M.,
Jones I.F.,
Evans E.,
Fairhead S.,
Marsden G.
Publication year - 2004
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1111/j.1365-2478.2004.00441.x
Subject(s) - geology , misrepresentation , reflection (computer programming) , economic geology , gemology , regional geology , environmental geology , point (geometry) , heuristic , algorithm , process (computing) , overburden , geologist , geophysical imaging , computer science , geophysics , seismology , engineering geology , paleontology , tectonics , geometry , artificial intelligence , metamorphic petrology , mathematics , volcanism , political science , law , programming language , operating system
The theme of the 2003 EAGE/SEG imaging workshop concerned the contrast between different philosophies of ‘model building’: whether an explicit, user‐determined model should be imposed throughout the processing, with user updates at each step; or alternatively, whether user intervention should be kept to a minimum so as to avoid preconceived bias, and instead to allow the data itself to guide some heuristic process to converge to an optimal solution. Here we consider a North Sea study where our initial approach was to build the subsurface model using interpreted horizons as a guide to the velocity update. This is common practice in the North Sea, where the geology ‘lends itself’ to a layer‐based model representation. In other words, we encourage preconceived bias, as we consider it to be a meaningful geological constraint on the solution. However, in this instance we had a thick chalk sequence, wherein the vertical compaction gradient changed subtly, in a way not readily discernible from the seismic reflection data. As a consequence, imposing the explicit top and bottom chalk horizons, with an intervening vertical compaction gradient (of the form v ( x , y , z ) = v 0 ( x , y ) + k ( x , y ). z ), led to a misrepresentation of the subsurface. To address this issue, a gridded model building approach was also tried. This relied on dense continuous automatic picking of residual moveout in common‐reflection point gathers at each iteration of the model update, followed by gridded tomography, resulting in a smoothly varying velocity field which was able to reveal the underlying local changes within the chalk.