18 found
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  1.  18
    SaltSeg: Automatic 3D Salt Segmentation Using a Deep Convolutional Neural Network.Yunzhi Shi, Xinming Wu & Sergey Fomel - 2019 - Interpretation 7 (3):SE113-SE122.
    Salt boundary interpretation is important for the understanding of salt tectonics and velocity model building for seismic migration. Conventional methods consist of computing salt attributes and extracting salt boundaries. We have formulated the problem as 3D image segmentation and evaluated an efficient approach based on deep convolutional neural networks with an encoder-decoder architecture. To train the model, we design a data generator that extracts randomly positioned subvolumes from large-scale 3D training data set followed by data augmentation, then feed a large (...)
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  2.  28
    Incremental Correlation of Multiple Well Logs Following Geologically Optimal Neighbors.Xinming Wu, Yunzhi Shi, Sergey Fomel & Fangyu Li - 2018 - Interpretation 6 (3):T713-T722.
    Well-log correlation is a crucial step to construct cross sections in estimating structures between wells and building subsurface models. Manually correlating multiple logs can be highly subjective and labor intensive. We have developed a weighted incremental correlation method to efficiently correlate multiple well logs following a geologically optimal path. In this method, we first automatically compute an optimal path that starts with longer logs and follows geologically continuous structures. Then, we use the dynamic warping technique to sequentially correlate the logs (...)
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  3.  15
    Incremental Correlation of Multiple Well Logs Following Geologically Optimal Neighbors.Xinming Wu, Yunzhi Shi, Sergey Fomel & Fangyu Li - 2018 - Interpretation: SEG 6 (3):T713-T722.
    Well-log correlation is a crucial step to construct cross sections in estimating structures between wells and building subsurface models. Manually correlating multiple logs can be highly subjective and labor intensive. We have developed a weighted incremental correlation method to efficiently correlate multiple well logs following a geologically optimal path. In this method, we first automatically compute an optimal path that starts with longer logs and follows geologically continuous structures. Then, we use the dynamic warping technique to sequentially correlate the logs (...)
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  4.  17
    Predictive Coherence.Parvaneh Karimi, Sergey Fomel, Lesli Wood & Dallas Dunlap - 2015 - Interpretation: SEG 3 (4):SAE1-SAE7.
    Detection and interpretation of fault systems and stratigraphic features and the relationship between them are crucial for seismic interpretation and reservoir characterization. To provide better interpretation insight and to be able to extract overlooked features out of seismic data volumes, we have developed a new attribute that detects faults and other discontinuities while handling local nonstationary variations across them. First, we used predictive painting to form a structural prediction of seismic events from neighboring traces according to the local structural slopes. (...)
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  5.  20
    Automated Spectral Recomposition with Application in Stratigraphic Interpretation.Yihua Cai, Sergey Fomel & Hongliu Zeng - 2013 - Interpretation: SEG 1 (1):SA109-SA116.
    Analyzing seismic attributes in the frequency domain is helpful for reservoir characterization. To analyze the reservoir interval of interest in detail, it is important to capture the seismic response at each frequency subset. Spectral recomposition can be used to extract significant components from the seismic spectrum. We propose a separable nonlinear least-squares algorithm for spectral recomposition, which estimates linear and nonlinear parts automatically in separate steps. Our approach is applied to estimate fundamental signal parameters, peak frequencies, and amplitudes, with which (...)
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  6.  10
    Seismic Diffraction Interpretation at Piceance Creek.William A. Burnett, Alexander Klokov, Sergey Fomel, Rishidev Bansal, Enru Liu & Tim Jenkinson - 2015 - Interpretation: SEG 3 (1):SF1-SF14.
    We applied time-domain seismic diffraction imaging to a 3D data set from the Piceance Creek Field, Piceance Basin, northwest Colorado. The work was motivated by the need for insight into natural fracture distribution, thought to influence production. We used a novel chain of two previously developed processing steps to separate diffractions from the recorded wavefield — One step is applied to the conventional stack volume, and the other was applied to migrated dip-angle gathers. The diffractions were then imaged independently for (...)
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  7.  6
    Predictive Painting Across Faults.Zhiguang Xue, Xinming Wu & Sergey Fomel - 2018 - Interpretation: SEG 6 (2):T449-T455.
    Predictive painting can effectively spread information in 3D volumes following the local structures of seismic events. However, it has trouble spreading information across faults with significant displacement. To address this problem, we incorporate fault-slip information into predictive painting to correctly spread information across faults. The fault slip is obtained using a local similarity scan to measure local shifts of the different sides of a fault. We have developed three methods to use the fault-slip information: the area partition method, which uses (...)
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  8.  15
    Automatic Approaches for Seismic to Well Tying.Roberto H. Herrera, Sergey Fomel & Mirko van der Baan - 2014 - Interpretation: SEG 2 (2):SD9-SD17.
    Tying the synthetic trace to the actual seismic trace at the well location is a labor-intensive task that relies on the interpreter’s experience and the similarity metric used. The traditional seismic to well tie suffers from subjectivity by visually matching major events and using global crosscorrelation to measure the quality of that tying. We compared two automatic techniques that will decrease the subjectivity in the entire process. First, we evaluated the dynamic time warping method, and then, we used the local (...)
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  9.  5
    Predictive Painting Across Faults.Zhiguang Xue, Xinming Wu & Sergey Fomel - 2018 - Interpretation 6 (2):T449-T455.
    Predictive painting can effectively spread information in 3D volumes following the local structures of seismic events. However, it has trouble spreading information across faults with significant displacement. To address this problem, we incorporate fault-slip information into predictive painting to correctly spread information across faults. The fault slip is obtained using a local similarity scan to measure local shifts of the different sides of a fault. We have developed three methods to use the fault-slip information: the area partition method, which uses (...)
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  10.  12
    Creating Detailed Subsurface Models Using Predictive Image-Guided Well-Log Interpolation.Parvaneh Karimi, Sergey Fomel & Rui Zhang - 2017 - Interpretation: SEG 5 (3):T279-T285.
    Integration of well-log data and seismic data to predict rock properties is an essential but challenging task in reservoir characterization. The standard methods commonly used to create subsurface model do not fully honor the importance of seismic reflectors and detailed structural information in guiding the spatial distribution of rock properties in the presence of complex structures, which can make these methods inaccurate. To overcome initial model accuracy limitations in structurally complex regimes, we have developed a method that uses the seismic (...)
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  11.  3
    Carbonate Reservoir Characterization Using Seismic Diffraction Imaging.Luke Decker, Xavier Janson & Sergey Fomel - 2015 - Interpretation: SEG 3 (1):SF21-SF30.
    Although extremely prolific worldwide, carbonate reservoirs are challenging to characterize using traditional seismic reflection imaging techniques. We used computational experiments with synthetic models to demonstrate the possibility that seismic diffraction imaging has of overcoming common obstacles associated with seismic reflection imaging and aiding interpreters of carbonate systems. Diffraction imaging improved the horizontal resolution of individual voids in a karst reservoir model and identification of heterogeneous regions below the resolution of reflections in a reservoir-scale model.
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  12.  9
    Local Skewness Attribute as a Seismic Phase Detector.Sergey Fomel & Mirko van der Baan - 2014 - Interpretation: SEG 2 (1):SA49-SA56.
    We propose a novel seismic attribute, local skewness, as an indicator of localized phase of seismic signals. The proposed attribute appears to have a higher dynamical range and a better stability than the previously used local kurtosis. Synthetic and real data examples demonstrate the effectiveness of local skewness in detecting and correcting time-varying, locally observed phase of seismic signals.
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  13.  6
    Missing Log Data Interpolation and Semiautomatic Seismic Well Ties Using Data Matching Techniques.Sean Bader, Xinming Wu & Sergey Fomel - 2019 - Interpretation 7 (2):T347-T361.
    Relating well-log data, measured in depth, to seismic data, measured in time, typically requires estimating well-log impedance and a time-to-depth relationship using available sonic and density logs. When sonic and density logs are not available, it is challenging to incorporate wells into integrated reservoir studies because the wells cannot be tied to seismic. We have developed a workflow to estimate missing well-log information, automatically tie wells to seismic data, and generate a global well-log property volume using data matching techniques. We (...)
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  14.  9
    Introduction to Special Section: Computer-Assisted Seismic Interpretation Methods.David H. Johnston, Geoffrey Dorn, Sergey Fomel, Jesse Lomask, Murray Roth & Tracy Stark - 2017 - Interpretation: SEG 5 (3):SJi-SJii.
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  15.  19
    Automatic Channel Detection Using Deep Learning.Nam Pham, Sergey Fomel & Dallas Dunlap - 2019 - Interpretation 7 (3):SE43-SE50.
    We have developed a method based on an encoder-decoder convolutional neural network for automatic channel detection in 3D seismic volumes. We use two architectures borrowed from computer vision: SegNet for image segmentation together with Bayesian SegNet for uncertainty measurement. We train the network on 3D synthetic volumes and then apply it to field data. We test the proposed approach on a 3D field data set from the Browse Basin, offshore Australia, and a 3D Parihaka seismic data in New Zealand. Applying (...)
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  16.  6
    Introduction to Special Section: Diffraction Imaging.Marianne Rauch-Davies, John O’Brien, William A. Burnett & Sergey Fomel - 2015 - Interpretation: SEG 3 (1):SFi-SFii.
  17.  5
    Introduction to Special Section: Thin Beds.Hongliu Zeng, Kurt Marfurt, Sergey Fomel, Satinder Chopra, Gregory Partyka, Bradley Wallet, Michael Smith, Marcilio Matos, Huailai Zhou, Yihua Cai & Osareni Ogiesoba - 2015 - Interpretation: SEG 3 (3):SSi-SSii.
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  18.  9
    Time-Variant Wavelet Extraction with a Local-Attribute-Based Time-Frequency Decomposition for Seismic Inversion.Rui Zhang & Sergey Fomel - 2017 - Interpretation: SEG 5 (1):SC9-SC16.
    Seismic impedance inversion has been widely used to estimate subsurface properties. Conventional inversion assumes that seismic data are the convolution result of seismic wavelet and reflectivity, implying that seismic data are stationary when a constant wavelet is considered. However, seismic data are nonstationary because of noise contamination and attenuation during wave propagation, which means that the frequency spectrum of the seismic signal changes from shallow to deep formations. We have developed a time-variant wavelet extraction method by using a local-attribute-based spectral (...)
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