Second EAGE/PESGB Workshop on Velocities

The Senja Ridge is a structurally complex high located in the western margin of the Norwegian Barents Sea. A two stage velocity model building approach is implemented, utilising diving wave FWI and high resolution image guided tomography. Shallow gas clouds and shallow channels are resolved with the FWI updates, deeper structures including basement horsts within the Senja Ridge and the flanks of salt diapirs are solved with the tomographic updates.

We present a case study and outline the workflow used to process 5500 km2 of new seismic data in the Porcupine Basin area of the Celtic Sea. Key processing challenges include the imaging of faults in the Jurassic interval, volcanic sills and the high-velocity Cretaceous chalk. In addition to these processing challenges, several shallow-gas pockets and channels with variable-velocity infill require detailed depth-velocity modelling to resolve deflections in the underlying sediments. A depth velocity model building workflow is presented which incorporates FWI along with high-resolution image-guided tomography to produce an accurate model for prestack depth migration. Improved imaging of the complex and potentially prospective structures found within the Porcupine Basin is achieved. Detailed anomalies such as shallow channels and gas clouds are corrected with a combination of high-resolution tomography and FWI, which gives increased confidence in the positioning of events along the underlying sediments.

Full Waveform Inversion (FWI) can create on an inaccurate model as a result of cycle skipping, if the initial model is not close enough to the true one, or there is insufficient low frequencies in the data. Furthermore, FWI model updates can be affected by a reflectivity imprint prior to the resolution of long-wavelength features. Imaging with the resulting incorrect model will create structural uncertainty, and will hamper an evaluation of potential prospects. Cycle skipping can be mitigated by using a robust norm for measuring the data misfit (W2-norm), instead of a traditional L2-norm. Used with a velocity gradient that removes the imprint of the reflectivity, we demonstrate an application to data resolving a high-velocity layer that was not present in the inital model. Corroborated by well data, the resulting earth model accurately reflects the subsurface, which, in turn, reduces uncertainty in the final structural image.

Whether or not we build a parameter field model, or deliver a subsurface image, our industry has been sadly lacking in attempting to assign ‘error bars’ to any of the products created. Given that we can never obtain a “correct” model based on measured data, we need to assess how suitable the derived approximate model or resultant image, is. It transpires that this is an extremely difficult task to undertake in a quantitative manner. There are certain minimum acceptance criteria, which tell us that at least the derived model explains the observed data, namely, flat image gathers following migration with the obtained model, which also match all available well data (at least to within some specified acceptance threshold), but these criteria do not tell us how good the model or image is. Here we adopt a Bayesian analysis of tomographic model error so as to quantify image positioning uncertainty, but more specifically, in this work we consider the effect of the quality of the initial model on the final uncertainty estimation, demonstrating quantitatively how prior model uncertainty affects final posterior image positioning uncertainty estimates.

The seismic inverse problem is considered in a Bayesian framework and uses a sequential filtering approach to invert for elastic parameters. The method employs an iterative ensemble smoother to estimate the subsurface parameters and from the ensemble, an estimation uncertainty can be extracted. The sequential filtering conditions over partitions of the entire data record in order to drive the estimation process in a top-down manner and regularize the inversion process. The method is presented with a synthetic example using seismic shot record for a 1D medium.

Wavefront tomography is an efficient and stable tool for the generation of smooth velocity models based on first and second-order attributes, which describe slope and curvature of the measured wavefronts. While slopes are relatively stable to determine, curvatures can become unreliable in the case of strong lateral heterogeneity. Since wavefront tomography is mainly driven by the misfit of modeled and measured wavefront curvatures, its convergence may be compromised by curvatures of bad quality. A possible solution to overcome this problem are diffractions that have a unique property: all measurements belonging to the same diffraction are connected to the same subsurface region. In recent work, we introduced an event-tagging scheme that automatically assigns a unique tag to each diffraction in the data. We propose to use this information to constrain the inversion by enforcing all diffracted measurements with the same tag to focus in depth, thus overcoming the sole dependency of wavefront tomography on second-order attributes. Results for diffraction-only data with vertical and lateral heterogeneity confirm that it is possible to obtain depth velocity models for zero-offset data without using curvature information and that the suggested approach may help to increase the stability of wavefront tomography in complex settings.

We present an efficient and stable procedure for estimating second- and fourth-order azimuthally-dependent effective parameters from full-azimuth residual moveouts. The residual moveouts are automatically picked at depth image points along full-azimuth angle domain reflection angle gathers. It is assumed that the azimuthally varying residual moveouts are due to fracture systems within compacted sand/shale sediment layers which were not accounted for in the seismic migration. The extracted (up to eight) effective parameters can then be used to obtain local (layer) effective parameters, characterizing the intensity and orientation of the fracture systems at each layer. Finally, the local effective parameters can be inverted to obtain interval anisotropic (e.g., orthorhombic) model parameters to be used in orthorhombic seismic migration.

Constraints can be imposed to a velocity model by rock physics modelling to capture key geological processes that shaped the present-day response of the subsurface. Lithology-dependent compaction trends can provide useful information on the expected range of velocities at different depths. The ability to model those compaction trends and to jointly estimate lithologies and velocities from seismic amplitudes with fully data-driven inversion approaches means that seismic reservoir characterization workflows can be incorporated in the earth model building process to improve imaging velocities. In this North Sea example, we demonstrate how litho-elastic inversion results, which use reflection amplitude- and lithology-driven compaction modelling, are used to update and provide initial low-frequency P- and S-wave velocity models for seismic imaging. The results revealed uplift in the P- and S-wave velocity model, stacked images, and gathers when the low-wavenumber velocities from joint litho-elastic inversion are incorporated into the earth model building workflow. The improvements of the earth model building workflow increase the likelihood of faster and more accurate convergence of subsequent tomographic iterations.

The Central North Sea is a mature basin containing a large number of fields, some of which have been in production for decades. Advances in seismic acquisition and data processing over the life of these fields have brought about improvements in seismic image quality and therefore the understanding of the reservoirs. Here we apply some of the latest imaging techniques such as joint tomography using both reflection and refraction pick data and Q Full-Waveform Inversion (Q-FWI) in a challenging geological setting, to help overcome some prevalent subsurface issues. These include the imaging problems introduced by shallow channels and gas, which induce distortion at reservoir depth.

Desert environments are often characterized by areas with complex geological structures affecting seismic imaging in geophysical exploration. A good velocity model building tool is needed to deal with these difficult systems where geophysical inversion is affected by high non uniqueness or variable sensitivity to the targets. We approach this problem by making use of a recently developed automatic and data-driven surface-consistent refraction method. The developed method is focusing on the analysis of phases (pQC) and amplitudes (aQC) of refracted arrivals. We successfully applied the methodology to many 3D land and marine seismic datasets. As a land example, we show the results for a prominent wadi. Another example is related to marine acquisitions characterized by salt and evaporitic sequences composed of evaporitic and clastic sediments.