An integrated procedure for migration velocity analysis in complex structures of thrust belts

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper IPTC 18293, “Near-Surface Velocity Model of Dukhan Field From Multiphysics Survey To Enhance PSDM Seismic Imaging,” by K. Setiyono, Qatar Petroleum; S. Gallo, C. Boulanger, F. Bruere, F. Moreau, and B. Rondeleux, CGG; and J. Snow, Qatar Petroleum, prepared for the 2015 International Petroleum Technology Conference, Doha, Qatar, 7-9 December. The paper has not been peer reviewed. Dukhan field affords an opportunity to assess alternative near-surface velocity-modeling work flows that specifically integrate microgravity (MGR) and vertical electrical sounding (VES). A pilot study of the Dukhan seismic data demonstrates that integration of MGR/VES into prestack depth migration (PSDM) improves the fidelity of seismic images and results in an improved depth match. The resultant MGR/VES PSDM pilot model improved the velocity model between the surface and top Simsima formation on the basis of visible impact on the deeper structure obtained from imaging and better fit between the seismic depth horizons and well measured depths. Introduction The Dukhan field (Fig. 1) along the southwest coast of Qatar is an elongated anticline that has significant hydrocarbon reservoirs in the Jurassic Arab and deeper formations. In 2007, Qatar Petroleum invested in a new full-field, land and shallow-water 3D-seismic survey of the area. Despite considerable and continuous progress in seismic technology, obtaining an accurate, quantitative, well-focused seismic depth image at reservoir level remains challenging. In land seismic, a poor characterization of the near-surface geology is detrimental to the depth imaging of underlying structures. The proper knowledge of the near surface to correct for its perturbations onto the seismic wavefield remains one of the main challenges to be addressed. The near surface influences the kinematics as well as the dynamics of the source-generated seismic waves. Knowledge of the near surface to correct for its induced distortions is critical for a reliable use of seismic images, structures, and amplitude at reservoir level. The near-surface lateral and vertical velocity variations often are not accessible with seismic methods, even to the seismic refraction survey, because their depth of investigation is limited to the top of the shallowest hard layer. In addition, rapid lateral changes and large velocity contrasts are unfavorable to seismic refraction.

ABSTRACTLeast‐squares inversion of seismic reflection waveform data can reconstruct remarkably detailed models of subsurface structure and take into account essentially any physics of seismic wave propagation that can be modelled. However, the waveform inversion objective has many spurious local minima, hence convergence of descent methods (mandatory because of problem size) to useful Earth models requires accurate initial estimates of long‐scale velocity structure. Migration velocity analysis, on the other hand, is capable of correcting substantially erroneous initial estimates of velocity at long scales. Migration velocity analysis is based on prestack depth migration, which is in turn based on linearized acoustic modelling (Born or single‐scattering approximation). Two major variants of prestack depth migration, using binning of surface data and Claerbout's survey‐sinking concept respectively, are in widespread use. Each type of prestack migration produces an image volume depending on redundant parameters and supplies a condition on the image volume, which expresses consistency between data and velocity model and is hence a basis for velocity analysis. The survey‐sinking (depth‐oriented) approach to prestack migration is less subject to kinematic artefacts than is the binning‐based (surface‐oriented) approach. Because kinematic artefacts strongly violate the consistency or semblance conditions, this observation suggests that velocity analysis based on depth‐oriented prestack migration may be more appropriate in kinematically complex areas. Appropriate choice of objective (differential semblance) turns either form of migration velocity analysis into an optimization problem, for which Newton‐like methods exhibit little tendency to stagnate at nonglobal minima. The extended modelling concept links migration velocity analysis to the apparently unrelated waveform inversion approach to estimation of Earth structure: from this point of view, migration velocity analysis is a solution method for the linearized waveform inversion problem. Extended modelling also provides a basis for a nonlinear generalization of migration velocity analysis. Preliminary numerical evidence suggests a new approach to nonlinear waveform inversion, which may combine the global convergence of velocity analysis with the physical fidelity of model‐based data fitting.

This study introduces a Common Offset (CO) extension of the Common Reflection Surface (CRS) method to address seismic imaging challenges in complex geological settings and with noisy data. This CO-CRS approach aims to enhance the signal-to-noise ratio and overcome the limitations of conventional preconditioning techniques that rely on accurate parameterization. Building upon established work on zero-offset CRS (ZO-CRS), the CO method generates regularized prestack data suitable for both time- and depth-domain processing by interpolating missing offsets using a local hyperbolic approximation. Ultimately, this study utilizes CO-CRS for enhanced velocity analysis and data preconditioning prior to performing prestack time migration (PSTM). In this study, the CO-CRS is then used for velocity analysis and prestack time migration. The results show that prestack CO-CRS data yield improved time-migrated seismic images, and we suggest extending the application to the depth domain. To achieve a reliable velocity model for imaging, recursive seismic inversion (RSI) is applied to derive the velocity model using the PSTM stack and a velocity interval time, based on CRS semblance velocity analysis. Furthermore, the prestack depth migration (PSDM) is then tested. The depth-imaging results are reliable, and it can be concluded that combining the benefits of the CRS noise-reduction feature with more accurate velocity analysis and prestack migration can provide enhanced capabilities.

Overburden (shallow) anomalies such as channels, sink holes, or karst features pose challenges for seismic time imaging, resulting in an obscured image below the anomalies i.e. pull-ups or push-downs. These anomalies can propagate down to the reservoir masking the image and create structural uncertainties. These relatively small scale (< 1 – 2 km) overburden anomalies cannot be resolved with conventional depth imaging usually, based on migration velocity analysis and residual move out (RMO) minimization only. This paper proposes the application of dip-constrained tomography, in combination with RMO tomography to help resolve these shallow anomalies. Although the method has been widely used elsewhere e.g. in offshore Brazil and North Sea (Chen et al., 2012, Guillaume et al., 2013, Carotti et al., 2015 and Hollingworth et al., 2015), this is the first time it has been applied in a carbonate oilfield in offshore Abu Dhabi. An accurate velocity model is required for seismic depth imaging. The velocity model is optimized using a tomographic technique which is a non-linear optimization process with relevant constraints imposed. The data are migrated using an initial velocity model and common image gathers are obtained. RMO is defined in a cost function and non-linear tomography finds a velocity model minimizing the cost function. An additional structural constraint in the form of an offset-dependent dip constraint is introduced in the cost function for minimizing the misfit between the offset-dependent dip of the events and the expected dip. Dip-constrained tomography was able to obtain a high resolution velocity model in the overburden and provided a robust seismic image essentially free of pull-up and push-down effects in the reservoir. The structural uncertainty in the reservoir was subsequently reduced. Inverting the dip term together with RMO term can potentially correct image distortions e.g. pull-ups and push-downs and focus the image simultaneously. The refined subsurface image can help optimize the reservoir model with less structure uncertainty and can enhance the production profile by providing more flexibility in well design and planning. The methodology was applied on a pilot area which gave quite encouraging results and leads to extend the pre-stack depth imaging to a full-field application.

In order to compare prestack depth migration algorithms for imaging complex structures, we constructed typical Foothills geological model that included thrust faults and duplex structures and computed the model response (synthetic seismograms) by ray tracing. We investigated the performance of the Kirchhoff and the explicit finitedifference (also called the f-x) algorithms on these synthetic data. For cases where the correct velocity model was used, comparisons showed the f-x method to be more effective and robust in performing prestack depth migration from topography than the Kirchhoff method. In cases, where the velocity model was not assumed to be known, we performed migration/velocity analysis by examining “smiles/frowns” in common image gathers and determined the macro-velocity model by using “focusing analysis”. We proposed a quantitative migration velocity analysis technique based on the curvature of “smiles/frowns”. The characteristics of coincident time of “smiles/frowns” of this new approach could be used as a diagnostic tool for migration/velocity analysis in the future.

Velocity analysis with local event slopes related probability density function

SUMMARY The accurate and fast estimation of velocity models is crucial in seismic imaging. Conventional methods, such as tomography, stereotomography, migration velocity analysis and full-waveform inversion (FWI), obtain appropriate velocity models; however, they require intense and specialized human supervision and consume much time and computational resources. In recent years, some works investigated deep learning (DL) algorithms to obtain the velocity model directly from shots or migrated angle panels, obtaining encouraging predictions of synthetic models. This paper proposes a new flow to recover structurally complex velocity models with DL. Inspired by the conventional geophysical velocity model building methods, instead of predicting the entire model in one step, we predict the velocity model iteratively. We implement the iterative nature of the process when, at each iteration, we train the DL algorithm to determine the velocity model with a certain level of precision/resolution for the next iteration; we name this process as ‘Deep-Tomography’. Starting from an initial model, that is an ultrasmooth version of the true model, Deep-Tomography is able to predict an appropriate final model, even in complete unseen during the training data, like the Marmousi model. When used as the initial model for FWI, the models estimated by Deep-Tomography can also improve substantially the final results obtained with FWI.

Through the use of iterative turning‐ray tomography followed by wave‐equation datuming (or tomo‐datuming) and prestack depth migration, we generate accurate prestack images of seismic data in overthrust areas containing both highly variable near‐surface velocities and rough topography. In tomo‐datuming, we downward continue shot records from the topography to a horizontal datum using velocities estimated from tomography. Turning‐ray tomography often provides a more accurate near‐surface velocity model than that from refraction statics. The main advantage of tomo‐datuming over tomo‐statics (tomography plus static corrections) or refraction statics is that instead of applying a vertical time‐shift to the data, tomo‐datuming propagates the recorded wavefield to the new datum. We find that tomo‐datuming better reconstructs diffractions and reflections, subsequently providing better images after migration. In the datuming process, we use a recursive finite‐difference (FD) scheme to extrapolate wavefield without applying the imaging condition, such that lateral velocity variations can be handled properly and approximations in traveltime calculations associated with the raypath distortions near the surface for migration are avoided. We follow the downward continuation step with a conventional Kirchhoff prestack depth migration. This results in better images than those migrated from the topography using the conventional Kirchhoff method with traveltime calculation in the complicated near surface. Since FD datuming is only applied to the shallow part of the section, its cost is much less than the whole volume FD migration. This is attractive because (1) prestack depth migration usually is used iteratively to build a velocity model, so both efficiency and accuracy are important factors to be considered; and (2) tomo‐datuming can improve the signal‐to‐noise (S/N) ratio of prestack gathers, leading to more accurate migration velocity analysis and better images after depth migration. Case studies with synthetic and field data examples show that tomo‐datuming is especially helpful when strong lateral velocity variations are present below the topography.

The idea of path-integral imaging is to sum over the migrated images obtained for a set of migration velocity models. Velocities where common-image gathers align horizontally are stationary, thus favoring these images in the overall stack. The overall image forms with no knowledge of the true velocity model. However, the velocity information associated with the final image can be determined in the process. By executing the path-integral imaging twice and weighting one of the stacks with the velocity value, the stationary velocities that produce the final image can then be extracted by a division of the two images. The velocity extraction, interpolation, and smoothing can be done fully automatically, without the need for human interpretation or other intervention. A numerical example demonstrated that quantitative information about the migration velocity model can be determined by double path-integral migration. The so-obtained velocity model can then be used as a starting model for subsequent velocity analysis tools like migration velocity analysis or tomographic methods.

Summary Sharp velocity contrasts in the overburden may cause problems for seismic imaging of deeper targets. Pre-stack depth migration in combination with traveltime tomography to build the velocity model is generally considered as a solution to the problem. However, building the velocity model with sufficient lateral and vertical resolution near discontinuities is difficult and imperfect velocity models will result in imperfect seismic images. Forward modelling seismic data followed by PSDM is a way to investigate and quantify sensitivity to imperfections in the velocity model. Kirchhoff modelling offers an efficient alternative to full-wavefield modelling; it is based on advanced ray tracing and can model diffractions, but is still computationally quick. In this study Kirchhoff pre-stack modelling provides a means for testing the impact of velocity errors on the final seismic image: Kirchoff modelling is followed by Kirchhoff target migration with the model velocity field and with degraded velocity fields. Results reveal the scale of potential imaging errors under a shallow velocity discontinuity at the Golden Eagle field in the North Sea. Results show that the danger of creating false structure due to image distortion is low, but that vertical positioning due to inaccurate velocity models carries a higher risk.

One of the most serious problems in anisotropic velocity analysis is the trade-off between anisotropy and lateral heterogeneity, especially if velocity varies on a scale smaller than the maximum offset. We have developed a P-wave MVA (migration velocity analysis) algorithm for transversely isotropic (TI) models that include layers with small-scale lateral heterogeneity. Each layer is described by constant Thomsen parameters e and δ and the symmetry-direction velocity V0 that varies as a quadratic function of the distance along the layer boundaries. For tilted TI media (TTI), the symmetry axis is taken orthogonal to the reflectors. We analyzed the influence of lateral heterogeneity on image gathers obtained after prestack depth migration and found that quadratic lateral velocity variation in the overburden can significantly distort the moveout of the target reflection. Consequently, medium parameters beneath the heterogeneous layer(s) are estimated with substantial error, even when borehole information (e.g., check shots or sonic logs) is available. Because residual moveout in the image gathers is highly sensitive to lateral heterogeneity in the overburden, our algorithm simultaneously inverts for the interval parameters of all layers. Synthetic tests for models with a gently dipping overburden demonstrate that if the vertical profile of the symmetry-direction velocity V0 is known at one location, the algorithm can reconstruct the other relevant parameters of TI models. The proposed approach helps increase the robustness of anisotropic velocity model-building and enhance image quality in the presence of small-scale lateral heterogeneity in the overburden.

Seismic imaging is a significant technology to provide the image of the subsurface in several fields such as hydrocarbon exploration/production and civil engineering. A fundamental problem in seismic imaging is that both the depth reflectivity and velocity distribution of the subsurface have to be predicted by only seismic events observed at the surface, and it still remains a challenging research topic. Joint migration inversion (JMI) is one of the seismic waveform imaging algorithms that were recently proposed. JMI is capable of simultaneously estimating velocity and reflectivity models of the subsurface by exploiting reflected waves including internal multiples. The seismic modeling algorithm in the JMI process is a method termed full wavefield modeling (FWMod), which is a one-way propagator-based reflection modeling algorithm, including higher-order scattering and transmission effects. In this thesis, two directions to improve the accuracy of seismic imaging based on JMI are discussed. On one hand, an extension of FWMod is proposed to correctly deal with not only reflected waves but refracted/diving waves via one-way propagators in the horizontal direction, and this method is extended to a new JMI algorithm. On the other hand, we assume that only reflected waves including internal multiples are utilized in the imaging based on JMI and present two novel methods for the inversion and pre-processing: 1) iterative reflectivity-constrained velocity estimation, 2) surface amplitude correction via learning from synthetic models for land seismic data. The reflectivity-constrained velocity estimation is employed to improve the accuracy of the estimated velocity by exploiting the estimated reflectivity in the JMI process. The surface amplitude correction process is introduced to mitigate the influence of the amplitude variations caused by source/receiver response sensitivities and the difference of the features between observed land seismic data and the simulated data by the used imaging scheme. The numerical and field data examples for both land and marine cases demonstrate that the proposed approach is capable of effectively estimating reflectivity and velocity model, even though the low frequency components of the observed data are absent.

A generalized inversion technique was used to determine interval shear wave velocities by inversion of vertical seismic profiling (VSP) data collected at four sites in the Mississippi Embayment. The inversion is based on an iterative process that minimizes arrival time residuals. A layer stripping method was developed to solve the forward problem and was used to compute synthetic VSP arrival times for a given velocity model. The inversion software was tested with synthetic data first and then was applied to actual VSP data. The velocities thus obtained were compared with the velocities obtained previously using a waveform matching approach (Ge et.al., 2007). In general, the inverted velocities determined using the two methods match well for three of the sites. For the fourth site the match is not good probably because the VSP data are noisy and the first arrival picks are of poor quality.

Migration velocity analysis is a labor-intensive part of the iterative prestack time migration (PSTM) process. We have developed a velocity estimation scheme to improve the efficiency of the velocity analysis process using an automatic approach. Our scheme is the numerical implementation of the conventional velocity analysis process based on residual moveout analysis. The key aspect of this scheme is the automatic event picking in the common-reflection point (CRP) gathers, which is implemented by semblance scanning trace by trace. With the picked traveltime curves, we estimate the velocities at discrete grids in the velocity model using the least-squares method, and build the final root-mean-square (rms) velocity model by spatial interpolation. The main advantage of our method is that it can generate an appropriate rms velocity model for PSTM in just a few iterations without manual manipulations. In addition, using the fitting curves of the picked events in a range of offsets to estimate the velocity model, which is fitting to a normal moveout correction, can prevent our scheme from the local minima issue. The Sigsbee2B model and a field data set are used to verify the feasibility of our scheme. High-quality velocity model and imaging results are obtained. Compared with the computational cost to generate the CRP gathers, the cost of our scheme can be neglected, and the quality of the initial velocity is not critical.

An important research topic for prospecting seismology is to provide a fast accurate velocity model from pre-stack depth migration. Aiming at such a problem, we propose a quadratic precision generalized nonlinear global optimization migration velocity inversion. First we discard the assumption that there is a linear relationship between residual depth and residual velocity and propose a velocity model correction equation with quadratic precision which enables the velocity model from each iteration to approach the real model as quickly as possible. Second, we use a generalized nonlinear inversion to get the global optimal velocity perturbation model to all traces. This method can expedite the convergence speed and also can decrease the probability of falling into a local minimum during inversion. The synthetic data and Marmousi data examples show that our method has a higher precision and needs only a few iterations and consequently enhances the practicability and accuracy of migration velocity analysis (MVA) in complex areas.