Challenges are reviewed for multiple-attenuation workflows for shallow-water surveys, including the 3D surface-related multiple elimination (3D SRME) workflow as well as workflows that combine wavefield extrapolation and 3D SRME. A proposed workflow improves on 3D SRME results for shallow-water surveys while aiming to remove all surface-related multiples rather than just a subset from those multiples. The key step in this workflow is a 3D SRME prediction of free-surface multiples using two input data sets — the recorded data and another data set preprocessed to remove a subset of water-layer-related multiples. This approach reduces some of the amplitude distortions in the SRME model and leads to overall improvement in results. Properties of the proposed workflow are illustrated with data from two shallow-water surveys acquired in the North Sea with multimeasurement steamers. Processing of the densely sampled 3D shot gathers obtained by joint interpolation and deghosting using the multimeasurement...

Multiple attenuation for shallow-water surveys: Notes on old challenges and new opportunities

Seismic multiples in marine seismic data can affect the identification of oil and gas reservoirs. The efficiency of traditional multiple suppression methods, such as the Radon transform, depends on the accuracy of the velocity model for primaries and multiples, and the assumption of random background noise. To attenuate multiples with background noise, a method of primary reconstruction using a deep neural network based on data augmentation training is proposed. The designed deep neural network (DNN) includes convolutional encoding and decoding processes. The convolutional encoding process uses convolutional layers and maximum pooling layers to learn the features of the primaries, multiples, and background noise in a seismic data set. The convolutional decoding process uses these features to reconstruct the primaries and suppress the multiples and background noise. Using the data augmentation training method in the training phase, the full-wavefield data and the predicted multiples are added to background noise and then rotated to constitute the augmented data sets. This allows the DNN to have a better multiple suppression effect and better robustness. A well-trained DNN can be used for primary reconstruction in the same work area directly or in other work areas with a transfer learning method. Three examples of synthetic data with two simple models and a Pluto model verify the effectiveness, efficiency, stability, and good generalization of the proposed method for primary reconstruction and multiple suppression. Another example from field data finds that the proposed method can efficiently suppress seismic multiples under complex conditions. 

Seismic multiple suppression based on a deep neural network method for marine data

The Triassic Yanchang Formation is currently a hot spot for lacustrine shale-gas exploration in the Ordos Basin, China. A seismic-sedimentological study using a newly acquired 3D survey was conducted to advance the interpretation and prediction of the shale-gas reservoirs. The seismic volume was converted into a log-lithology volume at the thin-bed level. The inverted acoustic impedance volume was stratal sliced to extract seismic geomorphologic information. Deltaic facies and depositional history were interpreted using lithologic and planform morphology of seismic attributes on stratal slices. Detailed core analysis data including total organic content (TOC), mineral composition, and mechanical properties of sandstones and shales were correlated with seismic-derived lithology and facies. Microseismic data were evaluated for reservoir performance in production wells. Finally, shale-gas sweet spots, or optimal drilling targets, were predicted by mapping relatively high-brittleness lithofacies in 3D...

Seismic expression of delta to deep-lake transition and its control on lithology, total organic content, brittleness, and shale-gas sweet spots in Triassic Yanchang Formation, southern Ordos Basin, China

Data is recorded by sensors while an underground formation is explored, e.g., using a seismic acquisition system that emits and receives waves. A model, which is indicative of primary waves contained in the received data, is derived using a multi-order Green's function. Using the model, an image of the underground formation is generated.

Method and apparatus for modeling and separation of primaries and multiples using multi-order green's function

Model-based water-layer demultiple (MWD) is an effective method for attenuating water-layer-related multiples (WLRMs), especially in shallow water environments. Regular 3D MWD uses regularized common-offset cubes for both shot- and receiver-side multiples. We propose a selective-input adaptation of regular 3D MWD workflow that uses both regularized shot gathers and regularized common-offset cubes as input and prioritizes the data selected for model prediction; data are selected first from regularized shot gathers and then from regularized cubes. Using field data, we demonstrate that this selective-input MWD removes more multiples than regular MWD, especially for high-order multiple reverberations.

Selective-input Adaptation of Model-based Water-layer Demultiple

This paper focuses on the attenuation of Water-Layer-Related Multiples (WLRMs or peg-leg multiples) which reflect at least once between the water bottom and the water surface. WLRMs are often the most dominant multiples in shallow-water seismic data. We propose a Model-based Water-layer Demultiple (MWD) algorithm to calculate the Green's functions of the Water-Layer Primary Reflections (WLPRs: Green's functions convolved with source signature) based on the known seabed and water-layer velocity model and then convolve them with the recorded data to predict the WLRMs. Combined with adaptive subtraction, MWD can effectively attenuate WLRMs. We apply MWD to field data from the Hibernia oilfield area which has a water depth of 70-90 m. The results show that while Surface-Related Multiple Elimination (SRME) by itself has limited success, MWD is effective in attacking WLRMs. Once the WLRMs have been removed by MWD, successive SRME can then be applied to predict and eliminate other types of surface-related multiples (SRMs).�The combination of MWD and SRME is demonstrated as an effective multiple attenuation package for shallow-water data and results in fewer residual multiples and better preserved pri- maries over tau-p gapped deconvolution. This, in turn, contributes to a more realistic velocity model and higher-quality images. from the auto-correlation, to predict WLRMs. DWD pre- dicts WLRMs with correct amplitudes. However, when the water-bottom is complex (and thus multi-arrivals of WLPRs present) the water-layer model derived from the time-domain

A model-based water-layer demultiple algorithm

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyImproving subsalt images using FWI and FWI imaging: A streamer case study at Big FootAuthors: Weihong FeiJingru ChenMin YangLeah GrassiErik EversonPaul BovetMarin LaomanaSheree WoldAlex Yanjun HaoChi ChenShubin ZengZhiyuan WeiWeihong FeiChevronSearch for more papers by this author, Jingru ChenChevronSearch for more papers by this author, Min YangChevronSearch for more papers by this author, Leah GrassiChevronSearch for more papers by this author, Erik EversonChevronSearch for more papers by this author, Paul BovetChevronSearch for more papers by this author, Marin LaomanaEquinorSearch for more papers by this author, Sheree WoldMarubeniSearch for more papers by this author, Alex Yanjun HaoCGGSearch for more papers by this author, Chi ChenCGGSearch for more papers by this author, Shubin ZengCGGSearch for more papers by this author, and Zhiyuan WeiCGGSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3746207.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractBig Foot is a Chevron operated oil field with three-way Miocene pay sand truncated to Big Foot salt. The field is in an active development stage with several wells remaining in the drill queue. However, the subsalt images below the Big Foot salt and nearby Sigsbee salt remain challenging after several rounds of velocity model building and imaging efforts in the last few years using conventional top-down approach. The main imaging challenges for Big Foot include: the complexity of overburden salts which create large velocity uncertainty; the strong swing noise originating from Big Foot salt which impacts fault resolution; and the high dip structure against Big Foot salt which creates poor illumination. To help reduce uncertainty in the field development plan, we applied the latest pre-processing workflow and FWI interleaving interpretation-guided scenario tests using full azimuth (FAZ) and wide azimuth (WAZ) streamer data over the Big Foot area, which significantly improved the velocity model resolution, seismic image quality, fault delineation, and image frequency bandwidth. Additionally, we applied FWI Imaging, a superior method for subsurface imaging, and improved the image of highly dipping target reflectors with better continuity and higher S/N. Compared with the legacy image, the latest drilling result has showed the seismic misties have been significantly reduced using interpretations based on the re-processed final image.Keywords: FWI, full-waveform inversion, post migration optimization, velocity model buildingPermalink: https://doi.org/10.1190/image2022-3746207.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Weihong Fei, Jingru Chen, Min Yang, Leah Grassi, Erik Everson, Paul Bovet, Marin Laomana, Sheree Wold, Alex Yanjun Hao, Chi Chen, Shubin Zeng, and Zhiyuan Wei, (2022), "Improving subsalt images using FWI and FWI imaging: A streamer case study at Big Foot," SEG Technical Program Expanded Abstracts : 902-906. https://doi.org/10.1190/image2022-3746207.1 Plain-Language Summary KeywordsFWIfull-waveform inversionpost migration optimizationvelocity model buildingPDF DownloadLoading ... 

Improving subsalt images using FWI and FWI imaging: A streamer case study at Big Foot

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A model-based water-layer demultiple algorithm

Improving subsalt images using FWI and FWI imaging: A streamer case study at Big Foot