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Recently, land full-waveform inversion (FWI) has shown great potential in resolving near-surface complexity in the Delaware Basin, providing significant imaging uplift and useful information for shallow hazard identification. However, deep section updates beyond diving wave penetration remain challenging. We present an application of land FWI in the Central Basin Platform (CBP) for both shallow and deep updates. Results show that, with a time-lag cost function, a fine spatially sampled data set with proper preconditioning, and a good starting model for regions beyond diving wave penetration, land FWI was able to produce a high-resolution velocity model to resolve small-scale anomalies in the deep sections as well as detailed velocities in the near surface, leading to improved seismic images at reservoir levels. Furthermore, the impact of the FWI input data spatial sampling and the starting model in inversion are studied respectively.

Machine learning (ML) has garnered great attention within the field of seismic processing due to its vast achievements for quality and efficiency in the area of computer vision. Recent academic papers have demonstrated some potential for the use of machine learning in processing seismic signal, such as random and coherent noise removal, deblending, and interpolation. In this paper, we illustrate some uses of ML on real 3D seismic data and discuss the common challenges that need to be addressed in order to fulfill the promises of the deep neural network (DNN) for seismic processing. We also point out that, in some cases, the result of ML could be good enough for some fit-for-purpose applications. Finally, we summarize a few learnings based on our research and experiences in both the seismic processing and ML worlds.

A new method is outlined that allows you to use isotropic seismic modeling and inversion technology in an anisotropic setting. The method is based on the mapping of the isotropic elastic parameters into effective elastic parameters, appropriate for the expected type of anisotropy. This paper outlines the transforms for VTI and HTI, but the method is extensible to TTI and other types of anisotropy. Examples show that the isotropic modeling with the effective elastic parameters produces the same results as full anisotropic modeling. Results from isotropic pre-stack seismic inversion can be predicted and analyzed using this technology.

CGG has always been at the forefront of industrial High Perfor­mance Computing (HPC) architectures: we were operating vector supercomputers (Convex, Cray and NEC) in the early 1990s, and large parallel supercomputers (Convex SPP, IBM SP, Sgi Origin) by the end of that decade. At the turn of the millennium, we were pioneering the use of commodity clusters, and started to add accelerators a couple of years later, even before GPGPU programming languages formally emerged.

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.