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Least-squares depth migration approximates the inverse of the forward modeling. We show two real data applications of a single iteration (non-iterative) Kirchhoff least-squares depth migration process, generically referred to as migration deconvolution, to highlight the benefits of this process. Our first example demonstrates improved amplitude behavior of least-squares migration deconvolution images in an offshore Gabon data set. In the second example we propose an efficient way to include attenuation in the least-squares migration process and, using a Central North Sea data set, highlight a stable uplift in both resolution and illumination of the final image.

Offshore Cameroon is a proven petroleum province with commercial production from the Douala Kribi-Campo Basin (DKC) and the prolific Rio del Rey Basin (RDR). The recent joint cooperation agreement between Cameroon and Equatorial Guinea will lead to the development of the Yoyo and Yolanda discoveries and open up the underexplored deep-water DKC Basin. CGG, together with Société Nationale des Hydrocarbures (SNH), has completed a basin-wide PSDM reprocessing project, which, coupled with favourable government terms, provides the opportunity to accelerate exploration in Cameroon.

A Rayleigh surface wave tomography with optimal coverage approach based on the creation of virtual raypaths by interferometry is proposed. The array based conventional surface wave picking methods often provides inhomogeneous or sparse coverage for high-resolution tomography. The delivered inversion result can suffer from acquisition pattern imprints or poor lateral resolution. We propose to create new optimally chosen virtual raypaths that better conditions the information. Rayleigh wave Green’s functions kinematics is then analyzed by a direct inversion of the phase interference pattern. Proof of concept on synthetics then illustrated on 3D real data are shown.

Ocean Bottom Node acquisition is known to improve seismic velocity model building and imaging, especially for deep and complex targets. Due to cost considerations, OBN surveys are typically only acquired around the production field, using a relatively dense receiver grid. At the exploration phase, the industry often has to rely on Narrow-Azimuth Towed Streamer data sets that are not always adequate to properly image deeper prospects, resulting in increased uncertainty and risk. Recently, hybrid blended acquisition combining towed-streamer and sparse node has emerged as a potential cost-effective solution to reduce exploration risk for larger areas. From the Norwegian North Sea to the challenging Nordkapp basin in the Barents Sea, we will illustrate how, by leveraging sparse node data combining Interferometry, Acoustic and Elastic full waveform inversion, we can enhance the streamer seismic image and deliver a high-resolution velocity field, reducing exploration risk and pushing the boundaries of imaging technologies one step further.

For subsurface commercial ventures or environmental projects that rely on maps of faulted horizons, accurate maps are fundamental. Fault topology provides an ideal tool for analysis of connectivity of fault systems. The data required to undertake the analysis is straightforward to extract from fault maps and can readily be compared to analogue data. In this paper we introduce the concept of fault topology and present existing and new analogue data. To get the most from applying topology the analysis must be coupled with knowledge of the structural history. This includes, the magnitude of faulting, the number of phases of activity and the angle of intersection of successive faulting events. We present a series of case studies that firstly illustrate how topology can capture and define variations in connectivity of fault systems and, secondly, demonstrate how fault topology can be used to identify potential anomalies.

We introduce a method for separating residual statics and NMO velocities by maximising sparsity in tau-v and tau-p domains. Surface Consistent statics are computed without a-priori knowledge about velocities needed for NMO corrections. In such way static time shifts will not be applied to compensate velocity errors. The new approach provides accurate focusing and positioning of the reflectors without needs to iterate updates of residual statics and velocities.

We present the application to a 3D real dataset of full waveform inversion (FWI) with optimal transport (OT) using the Kantorovich-Rubinstein (KR) distance as proposed by Métivier et al. (2016). This approach involves an efficient numerical implementation for OT in time and space directions, allowing taking into account lateral coherency of the traces; this has an important impact on the quality of the results. The approach also exhibits a slightly reduced sensitivity to local minima compared to least square (LSQ) misfit. Moreover the iterative method used for the computation of the KR distance allows producing a set of intermediary solutions that span progressively from LSQ to OT. We recall the main components of the approach and present its numerical implementation in 3D. We show the improvement of the results compared to conventional FWI on 2D synthetic and 3D real datasets for the same number of velocity update iterations.

Guided wave inversion (GWI) estimates accurate P-wave velocity in the near surface by analyzing the dispersion curves of guided waves. In this paper, we propose a robust inversion scheme to reduce the non-unique solutions and discuss the usage of GWI for full waveform inversion. This method is applied to a North Sea towed steamer survey. The FWI converge faster with GWI and the updates are more geologically plausible. GWI improves the seismic images and gather flatness at both shallow and deep targets.

Full-waveform inversion (FWI), proposed by Lailly and Tarantola in the 1980s, is considered the most promising data-driven tool to automatically build velocity models. Many successful examples have been reported using FWI to update shallow sediments, gas pockets, and mud volcanoes. However, successful applications of FWI to update salt structures had almost only been seen on synthetic data until recent progress at the Atlantis field, Gulf of Mexico (GoM). We revisited some aspects of FWI algorithms to minimize cycle-skipping and amplitude discrepancy issues and derived an FWI algorithm that is able to build complex salt velocity models. We applied this algorithm to a variety of data sets including WAZ (wide-azimuth) and FAZ (full-azimuth) streamer data as well as OBN (ocean bottom node) data with different geologic settings in order to: 1) demonstrate the effectiveness of the method for salt velocity updates, and 2) examine some fundamentals of the salt problem. We observe that in multiple cases, salt velocity models from this FWI produce subsalt images of superior quality. We demonstrate with one FAZ streamer data example in Keathley Canyon that we probably do not need very high frequency in FWI for subsalt imaging purposes. Based on this observation, we envision that sparse node for velocity (NFV) acquisition may provide appropriate data to handle large and complex salt bodies with FWI. We believe that the combination of advanced FWI algorithms and appropriate data acquisition will bring a step-change to subsalt imaging.