Anisotropic model building with wells and horizons: Gulf of Mexico case study comparing different approaches

2010 ◽  
Vol 29 (12) ◽  
pp. 1450-1460 ◽  
Author(s):  
Andrey Bakulin ◽  
Yangjun (Kevin) Liu ◽  
Olga Zdraveva ◽  
Kevin Lyons
2011 ◽  
Author(s):  
Olga Zdraveva ◽  
Michael Cogan ◽  
Robert Hubbard ◽  
Michael O'Briain ◽  
David Watts

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. WB21-WB26 ◽  
Author(s):  
Fatmir Hoxha ◽  
Jacqueline O’Connor ◽  
Jeff Codd ◽  
David Kessler ◽  
Alex Bridge ◽  
...  

Performing accurate depth-imaging is an essential part of deep-water Gulf of Mexico exploration and development. Over the years, depth-imaging technology has provided reliable seismic images below complicated salt bodies, and has been implemented in workflows for both prospect generation as well as reservoir development. These workflows include time domain preprocessing using various multiple elimination techniques, anisotropic model building, and depth-imaging using anisotropic reverse time migration (RTM). However, the accuracy of the depth-migrated volumes is basically unknown because they are tested only in the locations where a well is drilled. In order to learn about the accuracy of anisotropic deep water Gulf of Mexico model building, and depth-imaging tools which are used for processing and imaging of field acquired data, we created a 3D vertical transverse isotropic (VTI) anisotropic earth model and a 3D seismic data set representing subsalt Gulf of Mexico geology. The model and data set are referred to as the Tempest data set, the original being created several years ago. The recent model and data set were created incorporating upgraded technology to reflect recent developments in data acquisition, model building and depth-imaging. Our paper presents the new Tempest anisotropic model, data set, and RTM prestack depth-migration (PSDM) results. The Tempest RTM PSDM is being used to learn about the differences between the exact geological model and the RTM PSDM image, helping in the interpretation of real RTM prestack depth-migrated data.


2011 ◽  
Author(s):  
Mike Cogan ◽  
Olga Zdraveva ◽  
Tanya Kairzhanova ◽  
Mike Schoemann

2011 ◽  
Author(s):  
Cristina Reta‐Tang ◽  
Justin Simmons ◽  
Will Whiteside ◽  
Jun Cai ◽  
Roy Camp ◽  
...  

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE303-VE311 ◽  
Author(s):  
Juergen Pruessmann ◽  
Sven Frehers ◽  
Rodolfo Ballesteros ◽  
Alfredo Caballero ◽  
Gerardo Clemente

A seismic depth-imaging project starts from an initial depth model of interval velocities. From time processing of reflection seismic data, a set of stacking parameters or kinematic data attributes usually is available for an initial model building at little effort. Two methods for initial model building from time-processing attributes are compared in this case study, using 3D seismic land data from the coast of the Gulf of Mexico. Conventional normal moveout (NMO)/dip moveout (DMO) time processing performs one-parametric stacking using stacking velocity as the parameter. The stacking velocity field can be converted into a depth model by the well-known vertical Dix inversion, which is very fast and robust but degrades with increasing dip. Common-reflection surface (CRS) time processing, on the contrary, isbased on the multiparametric CRS stacking approach, providing several volumes of CRS-stacking attributes that include the wavefield dip, or horizontal slowness. Inversion of CRS attributes by CRS tomography incorporates this dip information in depth model building. In this case study, CRS or normal-incidence point (NIP) wave tomography is presented as a model-building link between high-resolution CRS time processing and subsequent depth processing. The CRS tomography model shows a better adaptation to the dipping subsurface structures than the Dix model and a good fit to well data. The smooth tomography model is well suited for further use in poststack and prestack depth migrations. It provides a good starting point for iterative model enhancement and salt-body definition in prestack depth migration.


2019 ◽  
Author(s):  
Yuchao Wang ◽  
Wenqing Liu ◽  
Shuhua Hu ◽  
Xiao Wang ◽  
Tao Zhang

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