Lessons learned from Chevron Gulf of Mexico data set

TitleLessons learned from {Chevron} {Gulf} of {Mexico} data set
Publication TypeSINBAD Presentation
AuthorsXiang Li, Andrew J. Calvert, Ian Hanlon, Mostafa Javanmehri, Rajiv Kumar, Tristan van Leeuwen, Brendan R. Smithyman, Haneet Wason, Felix J. Herrmann
PublisherSINBAD
Year of Publication2013
Abstract

Chevron Gulf of Mexico data set is very challenging for FWI because of elastic phases, limited offset, lack of low frequencies and salt structure. To overcome these issue, we first use ray-based tomography on the hand-picked first breaks to generate initial model for FWI, and then we apply curvelet-denosing techniques to improve the poor signal-to-noise ratio of the observed data at low frequencies. Finally, Curvelet domain sparsity promoting Gauss-Newton FWI helps to suppress model space artifacts caused by elastic phases. This is joint work with Andrew J. Calvert, Ian Hanlon, Mostafa Javanmehri, Rajiv Kumar, Tristan van Leeuwen, Brendan R. Smithyman, Haneet Wason and Felix J. Herrmann

KeywordsPresentation, private, SINBAD, SINBADFALL2013, SLIM
URLhttps://slim.gatech.edu/Publications/Private/Conferences/SINBAD/2013/Fall/li2013SINBADgom/li2013SINBADgom.pdf
Citation Keyli2013SINBADgom