Tomographic Velocity Model Building Using Iterative Eigendecomposition

Osypov, Konstantin; Nichols, David M.; Woodward, Marta; Yarman, Can Evren

doi:10.3997/2214-4609.20147940

Cited by 8 publications

(9 citation statements)

References 12 publications

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“…As described in Osypov et al (2008b), we employ Lanczos iterations to perform eigendecomposition on the Fisher information operator of the data with respect to the model in the preconditioned space,…”

Section: Methodsmentioning

confidence: 99%

“…Here, we judged our model good enough to stop because the final gammas were below 1% and the scale length was short enough. We perform the uncertainty analysis (Osypov et al, 2008b). Two set of a prior distribution were used, one from rock physics and displayed in Figure 1, the other is the relaxed prior used in control experiment in tomography as showed in figure 2 as red ellipse.…”

Section: Case Studymentioning

confidence: 99%

See 1 more Smart Citation

Application of the Rock Physics Model in Anisotropic Seismic Velocity Model Building and Quantitative Reservoir Structural Uncertainty Analysis: Gulf of Mexico Case Study

Yang¹,

Osypov²,

Bachrach³

et al. 2013

All Days

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Accurate anisotropic seismic velocity model building is the key to the success of seismic depth imaging projects in complex geological settings. Tomography has been an industry standard velocity model building tool for decades, but simultaneously solving for P-wave velocity, epsilon, and delta with surface seismic data only is an underdetermined inverse problem and unstable. The ambiguity in seismic migration velocity model leads to structural uncertainty in seismic image and is carried over to uncertainty in reservoir modeling. In this paper, we introduce a new method using rock physics compaction modeling of sandy shales to constrain the anisotropic tomography. An effective-media rock model was calibrated with well data for sedimentary basin and was used to build initial vertical transverse isotropy (VTI) velocity models. By running a stochastic simulation of the rock physics model, covariance functions were extracted from possible combination of to P-wave velocity, epsilon, and delta as a priori information to constrain the following anisotropic tomography updates and uncertainty analysis. The case study area is in the Green Canyon in the Gulf of Mexico. The results show that we can successfully constrain three parameters of tomography with the prior information from rock physics. We also performed seismic uncertainty analysis to assess the non-uniqueness of the tomography solutions. 500 velocity models with equivalent residual move out were generated and used to map migrate the reservoir structures. The gross rock volume P10, P50 and P90 were calculated from these 500 realizations to demonstrate the reduction of uncertainty from the rock physics constraints.

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Section: Methodsmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Application of the Rock Physics Model in Anisotropic Seismic Velocity Model Building and Quantitative Reservoir Structural Uncertainty Analysis: Gulf of Mexico Case Study

Yang¹,

Osypov²,

Bachrach³

et al. 2013

All Days

View full text Add to dashboard Cite

show abstract

“…In this paper, we extend the work (Osypov et al, 2008) to VTI tomography, modify the process of regularization optimization, and propose an updated way for resolution and uncertainty quantification using the apparatus of eigendecomposition.…”

Section: Introductionmentioning

confidence: 99%

“…Along the same lines, Zhang and Thurber (2007) demonstrated that Lanczos iterations with random starting vector can overcome the above mentioned LSQR limitations in LSQR. Osypov et al (2008) applied a similar approach, to tomographic velocity model building as a modification of preconditioned regularized least-squares based on the truncated eigendecomposition of the Fisher information matrix.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

mentioning

confidence: 98%

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Uncertainty and resolution analysis for anisotropic tomography using iterative eigendecomposition

Osypov¹,

Nichols²,

Woodward³

et al. 2008

SEG Technical Program Expanded Abstracts 2008

Self Cite

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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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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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Tomographic Velocity Model Building Using Iterative Eigendecomposition

Cited by 8 publications

References 12 publications

Application of the Rock Physics Model in Anisotropic Seismic Velocity Model Building and Quantitative Reservoir Structural Uncertainty Analysis: Gulf of Mexico Case Study

Application of the Rock Physics Model in Anisotropic Seismic Velocity Model Building and Quantitative Reservoir Structural Uncertainty Analysis: Gulf of Mexico Case Study

Uncertainty and resolution analysis for anisotropic tomography using iterative eigendecomposition

Model‐uncertainty quantification in seismic tomography: method and applications

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