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  1.  37
    Brittleness Evaluation of Resource Plays by Integrating Petrophysical and Seismic Data Analysis.Bo Zhang, Tao Zhao, Xiaochun Jin & Kurt J. Marfurt - 2015 - Interpretation: SEG 3 (2):T81-T92.
    The main considerations for well planning and hydraulic fracturing in unconventional resources plays include the amount of total organic carbon and how much hydrocarbon can be extracted. Brittleness is the direct measurement of a formation about the ability to create avenues for hydrocarbons when applying hydraulic fracturing. Brittleness can be directly estimated from laboratory stress-strain measurements, rock-elastic properties, and mineral content analysis using petrophysical analysis on well logs. However, the estimated brittleness using these methods only provides “cylinder” estimates near the (...)
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  2.  31
    A Comparison of Classification Techniques for Seismic Facies Recognition.Tao Zhao, Vikram Jayaram, Atish Roy & Kurt J. Marfurt - 2015 - Interpretation: SEG 3 (4):SAE29-SAE58.
    During the past decade, the size of 3D seismic data volumes and the number of seismic attributes have increased to the extent that it is difficult, if not impossible, for interpreters to examine every seismic line and time slice. To address this problem, several seismic facies classification algorithms including [Formula: see text]-means, self-organizing maps, generative topographic mapping, support vector machines, Gaussian mixture models, and artificial neural networks have been successfully used to extract features of geologic interest from multiple volumes. Although (...)
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  3.  28
    Estimation of Total Organic Carbon and Brittleness Volume.Sumit Verma, Tao Zhao, Kurt J. Marfurt & Deepak Devegowda - 2016 - Interpretation: SEG 4 (3):T373-T385.
    The Barnett Shale in the Fort Worth Basin is one of the most important resource plays in the USA. The total organic carbon and brittleness can help to characterize a resource play to assist in the search for sweet spots. Higher TOC or organic content are generally associated with hydrocarbon storage and with rocks that are ductile in nature. However, brittle rocks are more amenable to fracturing with the fractures faces more resistant to proppant embedment. Productive intervals within a resource (...)
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  4.  21
    Characterizing a Turbidite System in Canterbury Basin, New Zealand, Using Seismic Attributes and Distance-Preserving Self-Organizing Maps.Tao Zhao, Jing Zhang, Fangyu Li & Kurt J. Marfurt - 2016 - Interpretation: SEG 4 (1):SB79-SB89.
    Recent developments in seismic attributes and seismic facies classification techniques have greatly enhanced the capability of interpreters to delineate and characterize features that are not prominent in conventional 3D seismic amplitude volumes. The use of appropriate seismic attributes that quantify the characteristics of different geologic facies can accelerate and partially automate the interpretation process. Self-organizing maps are a popular seismic facies classification tool that extract similar patterns embedded with multiple seismic attribute volumes. By preserving the distance in the input data (...)
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  5.  55
    Semisupervised Multiattribute Seismic Facies Analysis.Jie Qi, Tengfei Lin, Tao Zhao, Fangyu Li & Kurt Marfurt - 2016 - Interpretation: SEG 4 (1):SB91-SB106.
    One of the key components of traditional seismic interpretation is to associate or “label” a specific seismic amplitude package of reflectors with an appropriate seismic or geologic facies. The object of seismic clustering algorithms is to use a computer to accelerate this process, allowing one to generate interpreted facies for large 3D volumes. Determining which attributes best quantify a specific amplitude or morphology component seen by the human interpreter is critical to successful clustering. Unfortunately, many patterns, such as coherence images (...)
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  6.  39
    Seismic Attenuation Attributes with Applications on Conventional and Unconventional Reservoirs.Fangyu Li, Sumit Verma, Huailai Zhou, Tao Zhao & Kurt J. Marfurt - 2016 - Interpretation: SEG 4 (1):SB63-SB77.
    Seismic attenuation, generally related to the presence of hydrocarbon accumulation, fluid-saturated fractures, and rugosity, is extremely useful for reservoir characterization. The classic constant attenuation estimation model, focusing on intrinsic attenuation, detects the seismic energy loss because of the presence of hydrocarbons, but it works poorly when spectral anomalies exist, due to rugosity, fractures, thin layers, and so on. Instead of trying to adjust the constant attenuation model to such phenomena, we have evaluated a suite of seismic spectral attenuation attributes to (...)
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  7.  12
    Multispectral Coherence: Which Decomposition Should We Use?Bin Lyu, Jie Qi, Fangyu Li, Ying Hu, Tao Zhao, Sumit Verma & Kurt J. Marfurt - 2020 - Interpretation 8 (1):T115-T129.
    Seismic coherence is commonly used to delineate structural and stratigraphic discontinuities. We generally use full-bandwidth seismic data to calculate coherence. However, some seismic stratigraphic features may be buried in this full-bandwidth data but can be highlighted by certain spectral components. Due to thin-bed tuning phenomena, discontinuities in a thicker stratigraphic feature may be tuned and thus better delineated at a lower frequency, whereas discontinuities in the thinner units may be tuned and thus better delineated at a higher frequency. Additionally, whether (...)
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  8.  20
    Constraining Self-Organizing Map Facies Analysis with Stratigraphy: An Approach to Increase the Credibility in Automatic Seismic Facies Classification.Tao Zhao, Fangyu Li & Kurt J. Marfurt - 2017 - Interpretation: SEG 5 (2):T163-T171.
    Pattern recognition-based seismic facies analysis techniques are commonly used in modern quantitative seismic interpretation. However, interpreters often treat techniques such as artificial neural networks and self-organizing maps as a “black box” that somehow correlates a suite of attributes to a desired geomorphological or geomechanical facies. Even when the statistical correlations are good, the inability to explain such correlations through principles of geology or physics results in suspicion of the results. The most common multiattribute facies analysis begins by correlating a suite (...)
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  9.  25
    Facies Analysis by Integrating 3D Seismic Attributes and Well Logs for Prospect Identification and Evaluation — A Case Study From Northwest China.Xingjian Wang, Bo Zhang, Tao Zhao, Junbo Hang, Hao Wu & Ziquan Yong - 2017 - Interpretation: SEG 5 (2):SE61-SE74.
    Characterization of facies within a hydrocarbon reservoir is essential for potential prospect identification and evaluation. We have developed a practical workflow that integrates poststack seismic attributes and well-log facies analysis to understand the development and depositional setting of the Triassic-age Akekule Formation in Tahe field, Northwest China. The workflow begins with sequence and sedimentary cycle analysis on selected benchmark wells. We then identify sand bodies within each sedimentary cycle using well logs. The analysis from well logs and drilling cuttings together (...)
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  10.  4
    Introduction to Special Section: Machine Learning in Seismic Data Analysis.Haibin Di, Tao Zhao, Vikram Jayaram, Xinming Wu, Lei Huang, Ghassan AlRegib, Jun Cao, Mauricio Araya-Polo, Satinder Chopra, Saleh Al-Dossary, Fangyu Li, Erwan Gloaguen, Youzuo Lin, Anne Solberg & Hongliu Zeng - 2019 - Interpretation 7 (3):SEi-SEii.
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  11. Introduction to Special Section: Permian Basin Challenges and Opportunities.Sumit Verma, Olga Nedorub, Fangyu Li, Tao Zhao, Mohamed Zobaa, Robert Trentham, Ron Bianco & Vikram Jayaram - 2019 - Interpretation 7 (4):SKi-SKi.
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