Integrated velocity model estimation for improved positioning with anisotropic PSDM

Bear, Lorie K.; Dickens, Thomas A.; Krebs, Jerry; Liu, Jonathan; Traynin, Peter

doi:10.1190/1.1946219

Cited by 30 publications

(17 citation statements)

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“…The updates in η are around 25%. These positive updates in both velocity and η agree well with the negative travel time misfits in the previous OCS modeling results (Bear et al, 2005). between the depth of 500m and 900m are slightly over compensated to flatten the gathers in the deeper region.…”

Section: Parameterizationsupporting

confidence: 88%

“…This issue is more severe when we consider anisotropy. Several localized tomography around the wells are studied to add the depth dimension into the inversion (Bear et al, 2005;Bakulin et al, 2010b,a). Joint inversion of surface seismic data and borehole data (check-shots and walkaway VSPs) in these studies shows great potential to yield better defined earth models.…”

Section: Introductionmentioning

confidence: 99%

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Wave-equation migration velocity analysis for VTI models

Li¹,

Biondi²,

Clapp³

et al. 2014

GEOPHYSICS

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Wave-equation migration velocity analysis (WEMVA) is a powerful technique for robust velocity model building when the subsurface is complex and the starting model is far from true. However, this traditional isotropic WEMVA technique cannot explain anisotropic wave phenomenon, which has significant effects when the reflectors are steeply dipping and/or the waves are traveling at large angles. Furthermore, errors in the anisotropic parameters cause similar defocusing as errors in isotropic velocity. Any defocusing caused by anisotropy may be translated to false updates in the isotropic velocity, which lead to further mispositioning and misinterpretations. However, simple extension of the isotropic WEMVA to the anisotropic medium cannot provide an unique and reliable solution due to the nonlinear and underdetermined nature of the anisotropic model building problem. Many anisotropic models with vastly different geological interpretations may explain the surface seismic data equally well. This thesis addresses these issues by including anisotropic effects in WEMVA and by integrating other useful information from geology and rock physics to better regularize the inversion. First, I extend the isotropic WEMVA method to the anisotropic medium to evaluate and update the anisotropic Earth models. Instead of the industry standard ray-based tomographic methods, the anisotropic WEMVA technique uses wavefields as information carrier to handle both the complex subsurface and the frequency-dependent behavior of the wave propagation. I include the geological information using steering filters to regularize the gradients. Both synthetic and field 2-D examples show that the anisotropic WEMVA technique can resolve the errors in both velocity and anisotropic parameters. Consequently, the migration images are vi

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Section: Parameterizationsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Wave-equation migration velocity analysis for VTI models

Li¹,

Biondi²,

Clapp³

et al. 2014

GEOPHYSICS

View full text Add to dashboard Cite

show abstract

“…For 1D problems, Malinverno and Parker (2006) proposed using an empirical Bayes approach, in which model priors and residual uncertainties are considered as hyperparameters to be optimized. In general, for 3D seismic tomography, prior information can come from other geophysical and borehole data, or more often as a geoscientist's input (Bear et al 2005). In the latter approach it becomes difficult to rigorously quantify this input in terms of probability functions.…”

Section: Review Of Methods For Tomographic Uncertainty Analysismentioning

confidence: 99%

“…In general, for 3D seismic tomography, prior information can come from other geophysical and borehole data, or more often as a geoscientist's input (Bear et al . ). In the latter approach it becomes difficult to rigorously quantify this input in terms of probability functions.…”

Section: Introductionmentioning

confidence: 97%

Model‐uncertainty quantification in seismic tomography: method and applications

Osypov

Yang

Fournier

et al. 2013

Geophysical Prospecting

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Uncertainty is inherent in every stage of the oil and gas exploration and production (E&P) business and understanding uncertainty enables mitigation of E&P risks. Therefore, quantification of uncertainty is beneficial for decision making and uncertainty should be managed along with other aspects of business. For example, decisions on well positioning should take into account the structural uncertainty related to the non‐uniqueness of a velocity model used to create a seismic depth image. Moreover, recent advances in seismic acquisition technology, such as full‐azimuth, long‐offset techniques, combined with high‐accuracy migration algorithms such as reverse‐time migration, can greatly enhance images even in highly complex structural settings, provided that an Earth velocity model with sufficient resolution is available. Modern practices often use non‐seismic observation to better constrain velocity model building. However, even with additional information, there is still ambiguity in our velocity models caused by the inherent non‐uniqueness of the seismic experiment. Many different Earth velocity models exist that match the observed seismic (and well) data and this ambiguity grows rapidly away from well controls. The result is uncertainty in the seismic velocity model and the true positions of events in our images. Tracking these uncertainties can lead to significant improvement in the quantification of exploration risk (e.g., trap failure when well‐logging data are not representative), drilling risk (e.g., dry wells and abnormal pore pressure) and volumetric uncertainties. Whilst the underlying ambiguity can never be fully eradicated, a quantified measure of these uncertainties provides a valuable tool for understanding and evaluating the risks and for development of better risk‐mitigation plans and decision‐making strategies.

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“…For 1-D problems, Malinverno and Parker (2006) proposed using an empirical Bayes approach, when model priors and data uncertainties are considered as hyperparameters to be optimized. In general, for seismic 3D seismic tomography, prior information can come from other geophysical and borehole data, or more often as a geoscientist's input (Bear et al, 2005). In the latter approach it becomes difficult to rigorously quantify this input in terms of probability functions.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty and resolution analysis for anisotropic tomography using iterative eigendecomposition

Osypov¹,

Nichols²,

Woodward³

et al. 2008

SEG Technical Program Expanded Abstracts 2008

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Tomographic velocity model building has become an industry standard for depth migration. Anisotropy of the Earth challenges tomography because the inverse problem becomes severely ill-posed. Singular value decomposition (SVD) of tomographic operators or, similarly, eigendecomposition of the corresponding normal equations, are well known as a useful framework for analysis of the most significant dependencies between model and data. However, application of this approach in velocity model building has been limited, primarily because of the perception that it is computationally prohibitively expensive, especially for the anisotropic case. In this paper, we extend our prior work (Osypov et al., 2008) to VTI tomography, modify the process of regularization optimization, and propose an updated way for uncertainty and resolution quantification using the apparatus of eigendecomposition. We demonstrate the simultaneous tomographic estimation of VTI parameters on a real dataset. Our approach provides extra capabilities for regularization optimization and uncertainty analysis in anisotropic model parameter space which can be further translated into the structural uncertainty within the image.

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Integrated velocity model estimation for improved positioning with anisotropic PSDM

Cited by 30 publications

References 0 publications

Wave-equation migration velocity analysis for VTI models

Wave-equation migration velocity analysis for VTI models

Model‐uncertainty quantification in seismic tomography: method and applications

Uncertainty and resolution analysis for anisotropic tomography using iterative eigendecomposition

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