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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.

Over two million files, containing geochemical information, have been collected from tens of thousands of wells drilled during decades of exploration in the Gulf of Mexico (GOM) and are available to geoscientists in the public domain. While these files represent a vast knowledgebase covering subsurface geology and petroleum systems, data extraction, systematic compilation and quality control was previously too cumbersome to harness the full power of the data to make basin wide correlations, uncover new trends and ultimately opportunities. A novel machine learning approach was employed to automate data classification and extraction across three protraction areas for all public domain geochemistry and PVT documents to provide a single consistent database from un-tagged, legacy formats stored in entirely different subfolders. The resulting database provides the ability to rapidly screen and integrate data from multiple disciplines over a large scale, in terms of data volume as well as geospatial coverage. This in turn opens up petroleum systems analysis work to a wider user base by acting as a bridge between disciplines, such as reservoir engineering and geochemistry. Removing disciplines from silos is critical to enhancing collaboration between teams, improving efficiencies around specific workflows such as fluid property prediction and therefore reducing uncertainty.

A subsurface volume that can be reliably interpreted in terms of geologically-relevant attributes is a reasonable objective for products from depth inversion workflows. Commonly the field geophysics data available are inherently non-unique and deficient (noise, aliasing, etc.), so an implementation of some type of constraint is required to encourage reasonable inversion outputs. We illustrate an implementation of cross-gradient inversion where surface geological information is included in the input data set. The basic application covers the usual structural similarity objective – comparing the gradient fields of distinct property volumes derived from different geophysical domains – but a particular advantage comes when including gradients derived from surface or subsurface geology, or any ancillary property set, providing reference gradient control during single or joint domain inversions of geophysical data.

The development of time-lag FWI (TLFWI) in recent years has enabled the use of the full wavefield (primary reflection, multiple, ghost, and diving waves) in inversion. With this advance it is now possible to include ever more detail in the velocity model, ultimately reaching the point of deriving from the velocity a migration-like reflectivity image, called the FWI Image. When the FWI maximum inverted frequency is increased, velocity model details can reveal superior reservoir information than present in recent conventional imaging results. Two case studies will be discussed, the first in the Greater Castberg area where the 150 Hz FWI Imaging greatly surpassed the Q Kirchhoff pre-stack depth migration imaging from the water bottom level down to the reservoir, located at a depth of around 1.5 km. For the second, over the Nordkapp basin, use of the full wavefield for shallow ultra-high resolution (UHR) imaging run at 200 Hz revealed reverse faulting and pockmark details that were invisible with either KPSDM or RTM. By using additional information present in multiple, ghost and diving waves, a spatial resolution of 2 m was achieved, making it possible to image very thin features without the need for a dedicated high-resolution acquisition design. The current UHR FWI Image obtained in the near-surface can then be used to de-risk and plan well placement as well as the foundations for wind turbines, providing important velocity information in addition to the reflectivity image.