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The Haugaland High, in the Norwegian North Sea, consists of a layered overburden of sub-horizontal sediments of almost 2km thick which sits on the Cretaceous Chalk. The background velocity regime of these top layers has a low vertical gradient down to the Chalk interface. This velocity behavior is particularly poorly suited for diving wave FWI and the strong multiple content present in the data does not allow an efficient tomographic update. Using all reflection and diving waves recorded in the Time-Lag FWI has allowed to provide a high-resolution velocity field, well explaining the complex velocity variation present in the overburden and easing the reservoir imaging. With the use of narrow azimuth towed streamer covering 2000km2, the velocity was updated up to 40Hz both helping structural imaging and bringing additional information to better understand the rock property of the basement over the entire region.

This study aimed to provide greater insight into the question of whether a near-field Paleozoic interval could retain sufficient reservoir quality to be attractive for future exploration. To meet this challenge, an integrated multidisciplinary approach was adopted that brought together detailed geological analysis with seismic inversion. Both of which were complimented by synchronized concurrent seismic reprocessing. The project workflow began with a stratigraphic review before investigating the detailed sedimentology and diagenesis for reservoir characterisation, with the latter incorporating burial history modelling to constrain the role of compaction. The results of seismic attribute analysis and seismic inversion performed during data reprocessing were then integrated with the geological story to generate a truly holistic reservoir quality evaluation.

To improve the imaging of the Barents Sea’s Nordkapp basin, either in terms of resolution or geological structure, a new seismic acquisition design was proposed, using a widespread hexa-source sitting on top of 18 non-flat streamers. Imaging this new recorded data required specific and complex processing steps using advanced algorithms to maintain high spatial resolution. This were not easy to handle onboard or through a fast-processing flow, so we implemented an Ultra-Fast Track processing by leveraging the capability of Deep Neural Networks to perform the pre-processing stages, ensuring quality in a limited timeframe. Pseudo-synthetic training sets were built from the first batch of received data and data augmentation was applied, with different setting for each processing steps. This result showed an improved resolution with respect to the legacy volume and was used to start interpreting potential hydrocarbon prospects and initiate the velocity model building. The Ultra-Fast Track also served as a helpful tool to assess the remaining challenges to be faced during the full processing.

The 3700km2 Nordkapp Basin area, Barents Sea, was recently acquired with a wide-spread source-over-spread design. With its 6 sources sitting on top of 18 multi-sensor streamers, one sail-line can record a dense carpet of 108 sublines separated by only 6.25m. By thinly sampling the near offset over the full azimuth, this new source-over-spread setup is particularly well suited to image the steeply dipping salt flanks that extend up to the water-bottom. After application of a dedicated processing sequence carefully designed to honour the full resolution of the recorded data, the obtained high-resolution image is able to distinguish even small-scale geological features. This large 3D volume was also compared with an NFH image. Using the full benefits of the hexa-source, a high-end processing sequence was applied to the NFH data to overcome the usual weak signal-to-noise ratio of such records. The comparison between the two final images confirms the high-resolution quality of the source-overspread volume, which includes enhanced lateral resolution, especially along the crossline direction, and access to AVO and RMO information. On the other hand, the very thin vertical sampling of the NFH data extends the recorded bandwidth by two octaves, making it a possible complement to the source-over-spread image.

Make a technical workflow including rockphysics, post-stack gestatistical inversion, seismic forward research and coal constrained pre-stack geostatistical inversion for the fine reservoir predictiong under coal interbedded. This work flow achieved huge success in the new development wells drilling.

We discuss the processing and imaging solutions designed to assess 4D signal in a monitor Vertical Seismic Profiling (VSP) survey acquired with Distributed Acoustic Sensing (DAS) over the Culzean field, Central North Sea. With a baseline DAS VSP survey acquired during a pilot programme in 2019, a monitor survey acquired in 2021 aimed to provide the means to assess reservoir and overburden time shifts in the Culzean field ahead of full field 4D seismic acquisition. We show how a 4D compliant processing workflow effectively tackled non-repeatability aspects of the monitor survey including significantly increased background noise levels compared to the baseline and variation in the source signatures and the recorded data. An improved de-multiple workflow utilizing Wave Equation Deconvolution Imaging and final imaging with Reverse Time Migration, achieved a high quality image in the overburden and at the key reservoir section for analysis of 4D time shift signal.

Multi-component data recording from ocean-bottom seismic (OBS) surveys captures both PP and PS (converted wave) events. Processing such data can produce superior images compared to those obtained from conventional streamer acquisitions. In addition, PP and PS images can provide valuable insights into reservoir properties. However, PS imaging needs high-quality and high-resolution P- and S-wave velocity models in depth. While full-waveform inversion (FWI) for P-wave velocity model building is well established, an equivalent tool for updating the S-wave velocity (Vs) is still a challenge. A recent FWI methodology based on PS reflection data (PS-RFWI) has been proposed for the Vs model building. Updates from this technique are typically low wavenumber in nature. In this abstract, we show an application of PS-RFWI to OBS data from the Central North Sea and demonstrate an approach to update the high-wavenumber Vs components. Our real data application produces a high-quality 30 Hz Vs model that reduces the image undulations at the reservoir level and allows to generate a subsequent high-resolution Vs FWI Image.

The Dugong area in the Norwegian North Sea was surveyed by North-South (N–S) orientated, variable depth streamer data, and recently, East-West (E–W) orientated triple source multi-sensor data. By reprocessing the original N-S data in combination with the E–W, we found that a combined dual-azimuth (DAZ) volume can provide significant imaging improvements supporting the de-risking of nearfield exploration targets. The uplift came in the form of enhanced structural imaging, resolution, signal-to-noise ratio and amplitude reliability. These were due to the complimentary illumination, sampling, and cable-varying characteristics of the two surveys, combined with advanced DAZ velocity model building and reprocessing methods. The benefits were found to directly aid in development decisions. Firstly, an inversion study utilizing both azimuths in a joint manner yielded more reliable probabilistic estimates of reservoir-level oil sands when compared to a single azimuth inversion due to the richer illumination and hence amplitude fidelity. Secondly, DAZ full-waveform inversion (FWI) imaging facilitated a substantial improvement to near-surface resolution with the potential for shallow hazard identification.

Convolutional-based neural network (CNN-based) architectures have shown promise in performing denoising tasks. However, it can be demonstrated that their predictions are of limited use for some tasks because they produce signal leakage. For these tasks, a possible improvement is to incorporate CNN-based architectures as one component of, rather than replacement for, the conventional denoising algorithms. In this paper, we formally define a class of denoising problems usually solved iteratively for which using CNN-based predictions as an initial solution can improve efficiency. We illustrate our points using a land data deblending example, for which the CNN-based prediction quality was higher than that of the conventional first iteration but lower than that of the final product. The CNN-complemented conventional deblending leads to satisfactory and efficient results.