Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Model building for tilted transversely isotropic media has commonly been performed by a single parameter tomography that updates the velocity in the symmetry direction, while the orientation of the symmetry axis and Thomsen parameters [Formula: see text] and [Formula: see text] are typically estimated from the migration stack and well data. Unfortunately, well data are often not available. In addition, when they are available, their lateral sampling is typically very sparse and their vertical sampling usually spans only a limited range of depths. In order to obtain spatially varying anisotropic models, with or without well data, we developed a multiparameter joint tomographic approach that simultaneously inverts for the velocity in the symmetry axis direction, [Formula: see text] and [Formula: see text]. We derived a set of reflection tomography equations for slowness in the symmetry axis direction and Thomsen parameters [Formula: see text] and [Formula: see text]. In order to address the nonuniqueness of the tomography, we developed a regularization strategy that uses an independent regularization operator and regularization factor for each individual anisotropy parameter. Synthetic tests found that ambiguity exists between the anisotropy parameters and that velocity has a better resolution than [Formula: see text] and [Formula: see text]. They also confirmed that joint tomography provides a better data fit than single parameter tomography. The field example was used to test a way to incorporate the sonic data in the model building process and limit the tomographic updates on certain anisotropy parameters by adjusting the regularization.
Model building for tilted transversely isotropic media has commonly been performed by a single parameter tomography that updates the velocity in the symmetry direction, while the orientation of the symmetry axis and Thomsen parameters [Formula: see text] and [Formula: see text] are typically estimated from the migration stack and well data. Unfortunately, well data are often not available. In addition, when they are available, their lateral sampling is typically very sparse and their vertical sampling usually spans only a limited range of depths. In order to obtain spatially varying anisotropic models, with or without well data, we developed a multiparameter joint tomographic approach that simultaneously inverts for the velocity in the symmetry axis direction, [Formula: see text] and [Formula: see text]. We derived a set of reflection tomography equations for slowness in the symmetry axis direction and Thomsen parameters [Formula: see text] and [Formula: see text]. In order to address the nonuniqueness of the tomography, we developed a regularization strategy that uses an independent regularization operator and regularization factor for each individual anisotropy parameter. Synthetic tests found that ambiguity exists between the anisotropy parameters and that velocity has a better resolution than [Formula: see text] and [Formula: see text]. They also confirmed that joint tomography provides a better data fit than single parameter tomography. The field example was used to test a way to incorporate the sonic data in the model building process and limit the tomographic updates on certain anisotropy parameters by adjusting the regularization.
Subsalt imaging has been a long-term challenge for the oil and gas industry. The substantial progress made in data acquisition and imaging since the late 1990s has made some subsalt imaging problems tractable, but building earth models that enable imaging under complex salt remains a challenge. Labor-intensive workflows remain industry standard practice. Not only are these costly and time consuming, they have also performed poorly in many areas of economic interest. Various automatic model-building tools have been proposed to overcome these disadvantages. One such tool, full-waveform inversion (FWI), has already revolutionized velocity-model building in areas with shallow gas. Prior to 2006, imaging in these areas had been considered challenging and labor intensive, just as imaging under complex salt remains today. Modeling indicates that low frequencies and wide offsets may be the key to success when building velocity models using FWI. Just how low and how wide that may be required for FWI success depends on the particular problem. At the Atlantis Field in the deepwater Gulf of Mexico we recently acquired wide-offset ocean-bottom-node data with conventional airguns. By taking care during the acquisition, we recorded usable signal down to a lower frequency than previously achieved. We then applied FWI to the resulting data set and used the resulting velocity model, unmodified, to reverse time migrate the seismic data. It produced some of the best subsalt images of the Atlantis reservoir structure ever seen. Furthermore, the FWI velocity model revealed several major interpretation errors in the legacy salt model; thus the FWI result also offered an excellent basis for updating the salt model with the conventional workflow. These results demonstrate that with appropriate seismic data to support it, and with due care taken during processing and inversion, FWI truly offers a paradigm shift in model building and imaging in areas of complex salt.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.