Marta Woodward

A decade of tomography

Over the past 10 years, ray-based postmigration grid tomography has become the standard model-building tool for seismic depth imaging. While the basics of the method have remained unchanged since the late 1990s, the problems it solves have changed dramatically. This evolution has been driven by exploration demands and enabled by computer power. There are three main areas of change. First, standard model resolution has increased from a few thousand meters to a few hundred meters. This order of magnitude improvement may be attributed to both high-quality, complex residual-moveout data picked as densely as 25 m to 50 m vertically and horizontally, and to a strategy of working down from long-wavelength to short-wavelength solutions. Second, more and more seismic data sets are being acquired along multiple azimuths, for improved illumination and multiple suppression. High-resolution velocity tomography must solve for all azimuths simultaneously, to prevent short-wavelength velocity heterogeneity from being mistaken for azimuthal anisotropy. Third, there has been a shift from predominantly isotropic to predominantly anisotropic models, both VTI and TTI. With four-component data, anisotropic grid tomography can be used to build models that tie PZ and PS images in depth.

Automated 3D tomographic velocity analysis of residual moveout in prestack depth migrated common image point gathers

Residual moveout analysis of prestack depth migrated common image point gathers is used for velocity model building in areas of complex geology. Where the velocity varies smoothly, the model is commonly built from stacking velocity analysis of the residual curvature on the gathers after conversion back to time. Where the velocity varies more rapidly, the model must be built instead by projecting the residual depth errors back over individually traced raypaths in a full tomographic inversion in depth. In this paper we show results of the latter method applied to a Gulf Coast data set. The 3D field example was run in a highly automated fashion. The depth errors (residual moveout) were first picked in batch mode throughout a sparsely sampled volume of common image point gathers and then minimized through a global inversion of all picks at all depths simultaneously. The inverse problem was constrained with preconditioning to solve for the smoothest part of the velocity field first. This automated approach improves turnaround by minimizing human intervention.

Building tilted transversely isotropic depth models using localized anisotropic tomography with well information

Tilted transverse isotropyTTI is increasingly recognized as a more geologically plausible description of anisotropy in sedimentary formations than vertical transverse isotropy VTI .A lthough model-building approaches for VTI media are well understood, similar approaches for TTI media are in their infancy, even when the symmetry-axis direction is assumed known. We describe a tomographic approach that builds localized anisotropic models by jointly inverting surface-seismic and well data. We present a synthetic data example of anisotropic tomography applied to a layered TTI model with a symmetry-axis tilt of 45 degrees. We demonstrate three scenarios for constraining the solution. In the first scenario, velocity along the symmetry axis is known and tomography inverts for Thomsen’s and parameters. In the second scenario, tomography inverts for, , and velocity, using surface-seismic data and vertical check-shot traveltimes. In contrast to the VTI case, both these inversions are nonunique. To combat nonuniqueness, in the third scenario, we supplement check-shot and seismic data with the profile from an offset well. This allows recovery of the correct profiles for velocity along the symmetry axis and. We conclude that TTI is more ambiguous than VTI for model building. Additional well data or rock-physics assumptions may be required to constrain the tomography and arrive at geologically plausible TTI models. Furthermore, we demonstrate that VTI models with atypical Thomsen parameters can also fit the same joint seismic and check-shot data set. In this case, although imaging with VTI models can focus the TTI data and match vertical event depths, it leads to substantial lateral mispositioning of the reflections.

Uncertainty And Resolution Analysis For Anisotropic Tomography Using Iterative Eigendecomposition

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.

Seismic pore-pressure prediction using reflection tomography and 4-C seismic data

Pore pressure is important for both exploration and drilling. During the exploration phase, a prediction of pore pressure can be used to develop fluid migration models, to study the effectiveness of seals, and to rank prospects. In drilling, a predrill pore pressure prediction allows the appropriate mud weight to be selected and allows the casing program to be optimized, thus enabling safe and economic drilling. A predrill estimate of pore pressure can be obtained from seismic velocities given a suitable velocity to pore pressure transform. This paper describes the use of seismic reflection tomography and 4-C seismic data for pore pressure prediction. Reflection tomography gives higher spatial resolution than conventional methods based on the Dix equation, while the additional information provided by 4-C data may help to reduce the ambiguity between variations in pore pressure and variations in lithology and fluid content.

Application of Steering Filters to Localized Anisotropic Tomography With Well Data

Andrey Bakulin, Marta Woodward*, Yangjun (Kevin) Liu, Olga Zdraveva, Dave Nichols, Konstantin Osypov WesternGeco Summary Estimation of anisotropic parameters for depth models requires some type of joint inversion of seismic and borehole data. We demonstrate that conventional grid reflection tomography can be adapted to simultaneously invert for all parameters of a local 3D anisotropic model. Success requires three key ingredients: jointly invert seismic and well data, localize tomography to a small volume around the borehole, and steer the updates along seismic horizons with steering filters. We describe steering filters and demonstrate 3D anisotropic tomography regularized with steering-filter preconditioners on a synthetic data set.

Model‐uncertainty quantification in seismic tomography: method and applications

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

Anisotropic model building with uncertainty analysis

Velocity estimation is usually an ill-posed problem even for isotropic media. Widespread use of anisotropic imaging has been shown to aid better focusing and positioning. However, it greatly escalates the complexity of the model building and makes the velocity estimation much more illposed. Conventional techniques continue to rely on gradient-based methods that deliver a single solution (or realization) of the model to the user. Here we demonstrate an alternative approach that acknowledges the nonuniqueness of the problem. It delivers an entire suite of models that fit the data equally well, allowing the user to select the most geologically plausible solution.

Pore pressure prediction using well-conditioned seismic velocities

Abnormal pore pressures are encountered worldwide, often resulting in drilling problems such as borehole instability, stuck pipe, lost circulation, kicks, and blow-outs (Dutta, 1997). To optimize the choice of casing and mud weight while drilling abnormally pressured formations, a pre-drill prediction of pore pressure is required. A pre-drill estimate of pore pressure can be obtained from seismic velocities using a velocity to pore pressure transform calibrated from offset well data. However, velocities obtained from processing seismic reflection data often lack the spatial resolution needed for accurate pore pressure prediction, due to assumptions such as layered media and hyperbolic moveout. In addition, the uncertainty in velocity is often not quantified. In this example from the Gulf of Mexico, seismic velocities obtained using reflection tomography are combined with well data to produce a refined velocity field that honours the available well information. The refined velocity field is then used to predict pore pressure.

Depth imaging examples and methodology in the Gulf of Mexico

Advances in seismic imaging over the past several years have revolutionized interpretation of geologic structures in complex areas. Nowhere has the impact of this technology been greater than the Gulf of Mexico, where the ability to interpret structures in subsalt areas has led to very large oil discoveries. Interestingly, although many techniques used in imaging these complex areas are new, the basis for many may be found in the classic methods of time imaging, many of which are still used in large-scale production depth imaging. Inherent in these techniques has been the assumption of a locally flat-layered earth consistent with hyperbolic moveout, which although incorrect for complex areas such as the subsalt imaging in the Gulf of Mexico, carries with it a robustness and determinism. Gradually, depth-imaging techniques are evolving to overcome the limitations of a local flat-layer earth assumption but at the cost of this determinism. For a true depth model, velocities, densities, and other parameters must be determined in a spatially varying way, so there is an explosion in the number of parameters to be determined. A consistent earth model from the depth-imaging perspective relies on measurement redundancy—i.e., reflections from the subsurface appear on traces from numerous source-receiver pairs in the acquisition geometry. The imaging process is then done in a way that produces not one but many images, each created using energy that has traveled through different parts of the earth. Consistency requires that these images agree. Hence, difficulties have shifted from the imaging algorithms themselves, as with the local flat-layer assumption in time imaging, to those associated with the data, their redundancy, and the uncertainty in determining a consistent model. In this article we examine a number of examples of depth imaging in the Gulf of Mexico. In the process we chronicle depth-imaging methods as they …