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Full-waveform inversion (FWI) has become the centerpiece of velocity model building (VMB) in seismic processing in recent years. It has proven to significantly improve the velocity model, and thus the migration image, for different acquisition types and different geologic settings, including very complex environments such as salt. With the advent of FWI Imaging, which shows remarkable uplifts over conventional imaging approaches because of the inherent least-squares data-fitting process and the utilization of full-wavefield data, FWI further extends its applications from VMB into imaging. However, FWI applications in the industry as of now prevalently employ the acoustic approximation. While the acoustic approximation can sufficiently explain a majority of the recorded seismic data, thus enabling acoustic FWI to derive reasonable velocity models, its limitation manifests itself around strong impedance contrasts where the elastic effect is strong. For example, at the salt-sediment interface, acoustic FWI (A-FWI) almost always leads to apparent salt halos in the resulting velocity models. With synthetic and field data examples, we demonstrate that the salt halo is mainly caused by the large data mismatch between the elastic input data and the acoustic modeled data in acoustic FWI, particularly at middle to long offsets. Therefore, we developed an elastic FWI (E-FWI) algorithm that combines an elastic modeling engine with the time-lag cost function, which we call elastic Time-lag FWI (E-TLFWI). With a more accurate modeling engine, E-TLFWI can significantly reduce the salt halo observed in its acoustic counterpart. Nevertheless, the migration images using the acoustic and elastic FWI velocity models remain similar overall, with some slight improvements around and beneath salt boundaries, particularly near steep salt flanks, as a result of the reduced salt halo. By contrast, FWI Images derived from E-TLFWI show considerable benefits over those from acoustic Time-lag FWI (A-TLFWI), such as improved event focusing, better structural continuity, and higher signal-to-noise ratio (S/N). The sharpened salt boundaries and enhanced quality of the FWI Images justify the value of elastic FWI.

Difficulties in processing land seismic data often arise due to insufficient sampling of the wavefield. Fully unconstrained simultaneous shooting offers a way to substantially increase productivity and hence source densities, leading to improved sampling of the wavefield. In order to achieve this we must be able to separate the signal from the interference noise. Using a de-blending routine based on inverse problems in the curvelet domain (Guillouet et al., 2016) the following case study from The Sultanate of Oman demonstrates the benefits of dense source sampling for broadband, wide-azimuth land data acquired using simultaneous shooting.

‘Risk: The potential for adverse consequences for human or ecological systems, recognising the diversity of values and objectives associated with such systems.’ This widely used definition both clarifies and clouds how environmental scientists can discern and address risk. While it provides a broad basis of understanding, it also raises numerous questions: what is risk composed of; where can it be found; is it stand-alone, systemic, or can it be both; how can we deal with it when we encounter it?

Full waveform inversion (FWI) is a method of velocity estimation which minimizes the misfit between recorded and modeled data, the parameters of the minimization being the velocity model. If the velocity model is smooth, then only the refracted waves are modeled and used, if the velocity model has discon- tinuities, the reflected waves created by the two-way modeling can be used to model the reflected waves. This highly non-linear algorithm can provide impressive results, in particular high resolution velocity from low-frequency data, but is very sensitive to its starting point. Migration velocity analysis (MVA) uses a migration to estimate a reflectivity from the data, then uses a criterion on the reflectivity to find the best velocity model. This method is not as non linear as FWI: it is less sensitive to the starting model, but has less potential to find a very detailed velocity from low- frequency data. In recent years, FWI has made a lot of progress to solve its inherent problems, and MVA techniques have been lagging behind. The main reason is that the gradient of the MVA cost functions exhibits artefacts that perturb the convergence.

Time-lapse seismic processing in carbonate fields having complex geology and in difficult seismic contexts requires highly specialized teams for success. Our field case has a flat structure, a poorly-imaged but highly reflective sea bottom and is covered by towed-streamer data in about 60m of water depth. Multiples contamination is severe and these are coherent with primaries. We tested several 4D seismic processing routes using a base and two monitor surveys and have summed up our experiences in four learning points: A simplified targeted demultiple flow avoiding adaptive methods improves 4D metrics better than a complicated one. A guided co-denoise technique using base and monitor vintages attenuate non-repeated noise from data while preserving 4D timeshifts and 4D amplitude changes. A mute design optimization prior to stack attenuates residual multiples that degrade 4D signal. Finally, seismic acquisition parameters have a strong impact on computed 4D seismic attributes even if this may not be the case in 3D. These learning points coupled with multidisciplinary interactions and an iterative processing QC strategy assure the delivery of data with a more interpretable 4D signal that permit the delineation of depleted zones, flushed zones and by-passed oil for future infill-well drilling and optimal reservoir management.

In this work we propose to revisit some of the main steps of a seismic reservoir characterization workflow, using a MCNV Campos Basin broadband seismic dataset. The objective is to illustrate the differences with conventional seismic data, identify potential pitfalls and suggest best practices in the use of broadband data for reservoir characterization. The resulting study showcases the benefits that broadband data can bring to reservoir uncertainty management – in this case at the exploration stage. (excerpt from the introduction)

Paper written in association with SNH for the March 2018 additions of First Break. Paper focusses on understanding why well success was low for the offshore Douala basin and how the interpretation of the results of the GeoSpec Regrid will enable a better understanding of the prospectivity. The paper pulls together the work of GeoSpec, SRC and Robertsons to show an integrated approach to reviewing the prospectivity.

The northern North Sea is a very active oil and gas exploration region, where several commercial discoveries have been made over recent years. The Late Jurassic sands are one of the major reservoir plays in the Northern Viking Graben. In recent years, the Late Jurassic Intra-Heather Formation sands (Sognefjord, Fensfjord formations and equivalents) have been re-explored over the Horda Platform. Wells targeting these sands have resulted in new discoveries such as Swisher and Blasto. We aim to demonstrate the extent to which new regional seismic data and sedimentary mapping are integral to understanding Late Jurassic reservoir distribution in a mature hydrocarbon basin.

The increasing focus on de-risking near-field exploration in the Northern Viking Graben area demands data with improved imaging. This has led to CGG initiating a new multi-year acquisition programme to acquire east–west triple-source multi-sensor seismic data over a 24,000 km2 area, adding a second azimuth to the existing 44,000 km2 north-south survey. Leveraging the two azimuths in conjunction with the latest imaging and processing technologies produced a significant uplift over the 2018 single-azimuth legacy data. New DAZ data achieved improved structural imaging, illumination, resolution and SNR but also greater confidence in interpretability and seismic inversion studies.