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- Volume 39, Issue 12, 2021
First Break - Volume 39, Issue 12, 2021
Volume 39, Issue 12, 2021
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Seismic characterization of the Middle Jurassic Hugin sandstone reservoir in the southern Norwegian North Sea with unsupervised machine learning applications for facies classification
Authors Satinder Chopra, Thang Ha, J. Marfurt Kurt and Ritesh Kumar SharmaSummaryBecause they allow us to integrate the information content contained in multiple seismic attribute volumes, machine learning techniques hold significant promise in the identification and delineation of heterogeneous 3D seismic facies. However, considerable care must be taken in choosing not only the appropriate, but also in their scaling. Sometimes such exercises are carried out mechanically, resulting in compromised interpretations and discouraging results. We examine some of the more well-established unsupervised machine learning techniques such as principal component analysis (PCA) and kmeans clustering, as well as some less common clustering techniques like independent component analysis (ICA), self-organizing mapping (SOM), and generative topographic mapping (GTM) as applied to a seismic data volume from the southern Norwegian North Sea. We find that the machine learning methods can provide increased vertical and spatial resolution. However, machine learning is also good at enhancing noise and artifacts. For this reason, the interpreter needs to ensure the data are adequately conditioned, the assumptions on which some of the techniques being applied are based are met, and finally, the most appropriate technique among those discussed in this paper is utilized.
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Optimal transport full-waveform inversion: from theory to industrial applications with examples from the Sultanate of Oman
Authors Jérémie Messud, Diego Carotti, Olivier Hermant, Anna Sedova and Gilles LambaréSummaryThe optimal transport problem was formulated more than 200 years ago to calculate the optimal way of transporting piles of sand. Due to 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.
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Simultaneous inversion of velocity and reflectivity
Authors Yang Yang, Jaime Ramos-Martinez, Dan Whitmore, Guanghui Huang and Nizar CheminguiAbstractWe describe a new seismic inversion workflow to simultaneously invert for velocity and reflectivity. With a single modelling engine, parameterized in terms of velocity and vector reflectivity, the two earth properties are iteratively updated using their appropriate sensitivity kernels based on inverse scattering theory. The vector reflectivity is estimated as a data domain least-squares migration and is the key for updating the velocity model beyond the maximum penetration depth of refracted and diving wave energy. With field examples, we demonstrate how the new solution for vector-reflectivity modelling combined with proper inversion kernels enable us to address the long-standing challenge of building full-bandwidth earth models.
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An automated pipeline for first break picking and identifying geometry errors
Authors Kalashnikov Nikita, Podvyaznikov Dmitry, Kuvaev Alexander and Semin DaniilAbstractThis paper describes a unified automated pipeline for first break picking and acquisition geometry control with deep learning models. It consists of three global stages, which require minimal human intervention and can be fine-tuned for new surveys to further increase the quality of the result. Evaluation of this pipeline on the ongoing projects showed that it significantly sped up both tasks, while the quality of the result was on par with the traditional procedures, performed mostly manually.
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Recent seismic reprocessing to revitalize research along the Atlantic Margin, offshore Ireland
Authors R.J.J. Hardy, K. Hernon, C. Morgan, S. Roy, G. Chrustek, R. Hunter, L. Lee, C. Abu, A. Anantan and N. O’NeillAbstractIn this study, we reveal what can be achieved from modern reprocessing of legacy regional 2D data using modern broadband and depth imaging techniques. We achieve a higher signal-to-noise ratio, improved event continuity and more reliable deeper Cretaceous and Jurassic images. Use of a single 3D velocity model-building method incorporating several 2D seismic lines simultaneously ensures line ties even for 2D regional data. The largest problems encountered were related to the 100 m to 4800 m range of water depths which required a variety of demultiple methods needed to ensure regionally consistent results in the presence of strong 3D effects. We illustrate regional quality control tools used and the removal of some spurious events associated with the original processing flow. We draw attention to some nuances involved in the interpretation of standard difference displays, show the improvements arising from the use of water column function velocities and quantify the additional value of Full Waveform Inversion (FWI) to improved imaging for the project.
Improved imaging results will expand understanding of the basins’ geometry and sediment fill and should provide an excellent tool for future industry and academic research in the Irish offshore.
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Inversion-based imaging: from LSRTM to FWI imaging
More LessSummaryLeast Squares RTM (LSRTM) is a powerful inversion-based imaging algorithm which minimizes the data misfit between observed seismic recordings and forward-modelled synthetic data. The algorithm, which can be implemented in either data or image domains, carries a fundamental limitation because it is based on a linear inversion theory which cannot accommodate velocity refinement as part of its model update process. Successful application of LSRTM therefore requires highly accurate velocity information, and if the velocity model is in significant error, modelled events will not be aligned kinematically with the observed data, and the algorithm will tend to produce unsatisfactory results.
FWI is another inversion-based algorithm that enjoys widespread industry use. Unlike LSRTM, FWI poses its inverse problem within a non-linear framework whereby it updates the velocity model and associated wave paths throughout its iterative process, gradually aligning modelled events with observed events. With the recent convergence of FWI and LSRTM methodologies, FWI is not only being used as a velocity update tool, but also as a direct imaging tool, thereby achieving two key imaging goals, namely refining the velocity model and deriving a better-quality seismic image. The latter process, which is known as ‘FWI imaging’, has recently been gaining a lot of industry attention as it offers the possibility of high-quality imaging along with workflow simplification.
In this article we will compare and contrast LSRTM and FWI. We conclude that the process of generating the FWI-imaging essentially amounts to nonlinear, data-domain inversion. This recognition facilitates a ready comparison against the data-domain form of LSRTM, the latter being a linear, data-domain inversion.
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Volumes & issues
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Volume 42 (2024)
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Volume 41 (2023)
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Volume 40 (2022)
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Volume 39 (2021)
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Volume 38 (2020)
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Volume 37 (2019)
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Volume 36 (2018)
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Volume 35 (2017)
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Volume 34 (2016)
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Volume 33 (2015)
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Volume 32 (2014)
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Volume 31 (2013)
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Volume 30 (2012)
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Volume 29 (2011)
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Volume 28 (2010)
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Volume 27 (2009)
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Volume 26 (2008)
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Volume 25 (2007)
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Volume 24 (2006)
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Volume 23 (2005)
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Volume 22 (2004)
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Volume 21 (2003)
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Volume 20 (2002)
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Volume 19 (2001)
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Volume 18 (2000)
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Volume 17 (1999)
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Volume 16 (1998)
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Volume 15 (1997)
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Volume 14 (1996)
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Volume 13 (1995)
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Volume 12 (1994)
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Volume 11 (1993)
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Volume 10 (1992)
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Volume 9 (1991)
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Volume 8 (1990)
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Volume 7 (1989)
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Volume 6 (1988)
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Volume 5 (1987)
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Volume 4 (1986)
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Volume 3 (1985)
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Volume 2 (1984)
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Volume 1 (1983)