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Learning how to best mimic seismic processing algorithms or workflows with deep learning (DL) has become a very active field of research. However, seismic processing own particularities may necessitate adaptations of current DL methods. In this paper, we explain and illustrate how the different DL components can affect the outcome of a given seismic processing task. Among others, we show that the Unet neural network architecture ( Ronneberger et al., 2015 ) is naturally suited to learn how to “separate” the events into kinematics and their amplitudes, and how to use both information efficiently to perform the common image gathers preconditioning, skeletonization (or picks probability computation) and muting task. We also show how the convolution kernel shapes, the number of layers, the training cost function and the batch size can be adapted to specific data and seismic processing tasks.

Depth imaging of converted-wave (P-to-S) ocean-bottom seismic (OBS) data requires a depth model for both the P- and S-wave velocities. Building the S-wave velocity model is very challenging: conventional techniques include PP and PS image registration, or joint PP and PS tomography. These approaches are often impeded by the lack of a reliable PS image in the shallow part of the model due to the sparse-receiver acquisition of typical OBS surveys, and have limited resolution to deal with complex lateral velocity variations. We introduce a new full-waveform inversion technique to update the S-wave velocity using converted-wave reflection data recorded in the radial component of OBS surveys. Key aspects of the method include the use of acoustic Born-modeling, a robust objective function to handle kinematic and dynamic differences, and a layer-stripping strategy to simplify the non-linearity of the inversion problem. The proposed approach is validated on different synthetics, and demonstrated on a field data example, giving an improved S-wave velocity and better reflector continuity for PS imaging.

Carbonate velocity model building is challenging due to the complex geometry and sharp velocity contrasts associated with carbonates. Full-waveform inversion (FWI), together with long offsets, wide azimuth and good low frequency data, is known to be a powerful tool to address these challenges. Unfortunately, many vintage streamer datasets are handicapped by limited offsets and azimuth coverage, and a noisy low-frequency component. We used vintage streamer datasets acquired in the South China Sea to demonstrate that Time-lag FWI (TLFWI), together with other tools like dipconstrained tomography and well calibration, can overcome those shortcomings and produce a highresolution velocity field, leading to improved images. TLFWI uses a crosscorrelation cost function to mitigate amplitude mismatch and low signal-to-noise ratio problems. However, the carbonates being out of reach of diving waves can still be challenging to update with FWI, if the starting background velocity is far from the true model. In this case, an iterative FWI flow with well-constrained velocity updates inbetween offers a more reliable solution. The carbonate fracture system poses another challenge for estimating anisotropic parameters inside the carbonate layer. Here we use diffraction imaging to guide the fracture system identification, which helps to estimate an HTI system.

Screen opportunities using the latest North Sea seepage data results, which comprise more than 5,000 interpreted satellite-borne radar scenes to provide unrivalled insight into the nature and locations of hydrocarbon seepage across the entire North Sea.

For long in seismic imaging, velocity model building and depth migration/inversion have produced information on the subsurface velocity model with no overlap in terms of resolved vertical wavelengths. The not covered wavelengths, among which the famous mid frequency gap, had then to be recovered in stratigraphic inversion by external information such as borehole data. The progresses in terms of acquisitions (long offset and low frequency) and imaging tools put us now in the situation of an overlap between all these processing/imaging/inversion approaches. FWI provides for example a velocity model building tool that covers potentially the full range of vertical frequencies in the area investigated by recorded diving waves. High resolution tomography from its side reaches vertical resolutions up to 6 Hz overlapping the resolution that can be obtained from depth migration and then stratigraphic inversion with low frequency data (down to 2.5 Hz). This new status has motivated investigations about improved ways of integrating these sources of information. We review here several of these attempts that allow taking advantage of the various approaches for the benefits of reliability and interpretability of the results. The estimation of uncertainties in ray based tomography is for example a precious add on for assessing the reliability of the final result of the imaging/inversion workflow.

Survey design comparison regarding seismic reservoir characterization objectives: a case study from South Tunisia L. Michou, L. Michel, P. Herrmann, T. Coleou, P.Feugere, J.L. Formento The objective of this onshore survey designs’ comparison case study is to highlight the impact of the acquisition trace density on seismic reservoir characterization in order to optimize acquisition geometries. Structural, AVO and AVAz seismic reservoir attributes and QCs from 4 seismic surveys over the same area, the Jebel Grouz South Tunisian field, are compared. Results highlight the added value of the acquisition trace density in comparison to the source strength or the source and receiver proportion, especially for elastic and anisotropic seismic reservoir characterizations.

Executives from CGG, PGS and TGS shared how their companies are helping businesses minimize their carbon footprints by implementing CCS subsurface technologies and services.

By August 2014 a full Permanent Reservoir Monitoring system was installed at the Grane field; since going live, five PRM surveys have been acquired. This paper describes how a robust sequence has been designed and optimized, thanks to the successful collaboration between processing (CGG) and interpretation (Statoil) teams. The 4D products are available to the interpreters 8 to 10 days after the last shot. An example is presented, illustrating the value of fast delivery of fresh 4D PRM seismic data; we also discuss a 4D processing designed to address the different acquisition geometries between vintage OBC surveys and PRM surveys.

Sophie Zurquiyah joined CGG in 2013 and was appointed CEO in 2018. The collapse in oil price,Covid-19 pandemic and energy transition have created a tough business environment during her near four-year tenure in this role, but also created opportunities. In this interview, Sophie talks about CGG’s path into the future.