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- Volume 40, Issue 12, 2022
First Break - Volume 40, Issue 12, 2022
Volume 40, Issue 12, 2022
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Looking through the Orange Glass – A Novel Perspective on Key Seismic Features and Their Influence on Hydrocarbon Potential within the Orange Basin, South Africa
Authors Anthony Fielies and Reagan AfricaAbstractThis work evaluates the hydrocarbon potential of the Orange Basin offshore South Africa by considering key seismic features such as basement, Gravity Slide Systems (GSS), Mass Transport Complexes/Deposits (MTCs/MTDs), contourites, mixed depositional systems, and seafloor morphology. Further, by integrating an updated structural domains classification, we revise the chronostratigraphy of the basin. Two gravity slide systems within the northern and southern parts of the basin are mapped in their entirety and named the Orange-Senqu and Bergoli Deepwater Fold and Thrust Belts (DWFTBs), respectively. In addition to uplift, gravitational collapse, and differential sediment loading, sediment failure is likely also triggered by fluid expulsion from the basement and gas hydrate dissociation. MTCs comprise 70–90% of the Cretaceous succession from the slope seaward. We propose the name ‘crestal deformation surface’(creds) for the top of an MTC. Contourite and mixed depositional systems are described for the first time in the basin. The Sandhaai prospect is a new turbidite-contourite fan play type. Venus and Graff-type analogues, and ‘intra MTC’ plays are also recorded within the basin. Finally, we document a segmented continental slope. Segmentation is attributed to sediment mass failure and deposition, and large-scale erosion driven by bottom currents, giving the slope a graded-to-stepped appearance.
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Supervised Learning Approach for Multi-Component Deghosting with a Focus on Shallow Water Data
Authors Rolf H. Baardman, Jewoo Yoo and Rob F. HeggeAbstractIn recent years, the use of machine learning (ML) has shown more and more successful applications, initially mainly in seismic interpretation (classification) but recently also in seismic processing challenges (regression) like denoising, interpolation and demultiple. In this paper, a supervised machine learning algorithm is proposed for receiver deghosting on multi-component data. A neural network is trained to map input pairs of hydrophone and geophone data into the deghosted up-going wavefield. This data-driven approach could offer a user-friendly alternative to existing deghosting methods, like the PZ-summation approach that is commonly used for multi-component streamer and ocean-bottom acquisitions. Although the PZ-summation approach offers an analytical solution to the deghosting problem, it requires some pre-processing steps that make it more time-consuming and user-intensive to run and QC. The advantage of the proposed ML approach is that it can be directly applied to multi-component data without any parameterization. However, the success of the supervised method strongly depends on whether a neural network can be trained easily with minimal user-interaction and can be applied to various types of (field) data without adapting the network for each individual case. To investigate if this is possible for the supervised multi-component receiver deghosting method, the selected neural network is trained on easy to model 1.5D data examples and applied to test data with increasing complexity. Results are very encouraging and evaluated to be at least on par in comparison to the existing PZ-summation deghosting method.
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A Novel Approach to Train Self-Supervised Seismic Denoising Dnn Architectures
Authors Juan Romero, Dimitrios Oikonomou and Olawale IbrahimSummaryRemoving noise present in seismic data is of prime importance for seismic processing workflows and a matter of continuous research in the academic community. The challenging part of seismic noise suppression is the diverse nature of seismic noise: it is found as a combination of random and coloured noise, which can be both structured and unstructured. Algorithms based on signal decomposition, domain transformation, and filtering, among others, have been traditionally applied to denoise seismic data and have been successful for specific imaging targets, hence mostly identifying a specific seismic noise component. Recently, convolutional neural networks-based (CNN) denoisers have greatly outperformed standard denoising techniques mostly in natural and medical imaging applications, and furthermore, self-supervised frameworks have been proposed as a clever alternative to denoising when no ground truth exists. This work leverages four state-of-the-art U-Net type architectures in a novel self-supervised fashion to remove seismic noise. The training seismic data corresponds to a generous number of real seismic surveys. For the labelling, trace-wise corruption is applied to patches of the input data, so the CNN learns to predict the corrupted traces based on the receptive field. Our findings indicate that self-supervised learning using U-Net type architecture trained on real data is able to considerably remove both structured and unstructured seismic noise.
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Reducing Drilling Hazard Risk in a Carbonate Environment Using Seismic Processing, Diffraction Imaging and Interpretation
Authors I. Yakovlev, K. Smirnov, A. Lushkina, O. Mozgovaya, V. Sablina and A. FirsovAbstractBuried structural anomalies within carbonate depositional environments, such as karst sinkholes, impose significant dangers for borehole integrity and drilling equipment due to rapid changes in rock properties (from tight to the porous) potentially resulting in extensive mud loss, drill bit jamming and other complications. This often leads to delays in the well construction planning and sometimes to subsequent sidetrack drilling and a dramatic increase in costs. Thus, Paleo-karst localization, prior to well planning and construction, becomes a critical task for economic optimization. However, this is a non-trivial exercise due to the relatively small scale and size of karst type features and anomalies, which are at the limit of lateral resolution of most standard surface geophysical methods. In this paper, we introduce a methodology of karst prediction based on a combination of targeted 3D seismic processing and integration of the diffracted wavefield anomalies in addition to use of conventional seismic attributes. The presented approach has been proven to be efficient to detect karsts in a real oil field and can be deployed in other regions.
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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)