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The optimal transport problem was formulated more than 200 years ago to calculate the optimal way of transporting piles of sand. Due to the interesting properties of its solutions with respect to shifts between the compared distributions, optimal transport has recently been adapted to full-waveform inversion to mitigate the cycle-skipping issue. Various formalisms have been proposed. Here we present an overview of these approaches, emphasizing more specifically the approach based on the bi-dimensional Kantorovich-Rubinstein norm, which has led to numerous successful full-waveform inversion applications. We illustrate these successes with two onshore case studies from the Sultanate of Oman.

In offshore prospective areas, oil slicks at the sea surface are often seen as being the visible expression of a working petroleum system at depth. Synthetic Aperture Radar (SAR) has proven to be an effective tool for identifying these oil slicks by virtue of anomalously low backscatter values they produce. Linking surface oil slicks to features identified in the subsurface, such as vertical breaks in seismic, has been well documented as a successful step forwards in bringing additional value and confidence in both oil slick interpretation and subsurface data. Furthermore, recent progress in broadband seismic data processing and seismic reservoir characterization has opened the door to identifying and understanding subsurface features previously unseen, and has shown to significantly reduce the uncertainty of lithology prediction, especially by virtue of broadband’s enhanced low frequency content. The following case study combines surface oil slicks, broadband 3D seismic, and well data to de-risk the petroleum potential of an area of offshore Malaysia. Our study not only provides a compelling example of oil slicks correlating with features observed in the seismic data, but it also demonstrates the significant value of pushing the workflow further, by linking surface slicks to modelled reservoir through seismic reservoir characterisation.

This paper presents recent advances in the area of seismic interference (SI) attenuation. We show how high amplitude and broadside SI noise can be nearly perfectly attenuated as long as the interfering noise is shot-to-shot incoherent. Furthermore, we present a new algorithm that also allows us to attenuate nearly all forms of shot-to-shot coherent noise. There algorithms, have effectively eliminated the need for re-shooting / time sharing due to SI noise in the North Sea, and have therefore contributed to a significant improvement in acquisition efficiency.

This paper describes a new dense survey acquired in 2020 in the Permian Basin and aims to objectively assess the quality and benefits brought by a richer low end of the spectrum and far offsets. For this purpose, we considered several aspects, from acquisition design and field data to FWI imaging and quantitative interpretation.

Twelve multi-source/multi-receiver land surveys from the Carpathian foothills were reprocessed and merged using the most advanced signal processing and imaging technologies. These included an innovative denoising subtraction using a primary model from the de-migration of a clean reflectivity from PSDM as well as a high-end surface consistent deconvolution taking into account the heterogeneity of signal across the different surveys acquired with dynamite, vibroseis and airguns. A high-end velocity model workflow was followed using MWI, Tomography joint FB/RMO, HD Multi-Layer tomography and TL-FWI and an RTM was performed with enhanced weighted azimuthal illumination to accurately image the deeper subsurface.

In this paper we present a new marine seismic acquisition technique initially developed to meet imaging challenges of the Loppa High in the Norwegian Barents Sea. Two seismic vessels operate in tandem; one streamer vessel towing a spread of deep, densely spaced streamers, and one source vessel towing two or more sources. The source vessel is positioned on top of the seismic spread. The unique configuration facilitates acquisition of zero-offset data. This, together with the fact that both negative and positive offsets are recorded, creates a unique illumination density of the subsurface. The solution is developed and tested in close cooperation between CGG and Lundin through a comprehensive modelling and field trial program involving a series of safety and mechanical tests and also a small 3D test survey. The result is high-resolution seismic images in the shallow and improved S/N in the deeper sections.

While significant progress has been made in CCUS over the last five years, the industrial and financial sectors need to see how projects are being effectively de-risked. By capitalizing on the latest technology advances and the integration of multidisciplinary skills and data, geoscience has a critical role to play in de-risking for CCUS development.

Each year the geoscience industry creates huge volumes of documents containing a wealth of knowledge which cannot be easily queried or extracted. Key to the successful extraction and transformation of data is an understanding of the nature of the data that exists within a corpus of files. For large datasets, it is time-consuming to manually open and review each document in turn. Therefore, in this article, we discuss how machine learning is used at CGG to classify documents in our automated pipeline and reduce project times significantly.

Based on an extensive 3D modelling study utilising full-wavefield Finite-Difference modelling and Full-Waveform Inversion (FWI) we demonstrate that the TopSeis/OBN hybrid acquisition acquired from May to August 2021 in the Nordkapp basin in the Barents Sea has the potential to image salt flanks and sedimentary details, given an accurate initial model in the shallow and a carefully designed deblending and FWI workflow. As a part of this we demonstrate that the large offsets and multi-azimuth recorded by the ocean bottom nodes are crucial to image the complex salt diapirism in the area including the steeply dipping flanks.