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  1. Resolution Enhancement with Relative Amplitude Preservation for Unconventional Targets.Anna Kwietniak, Kamil Cichostępski & Kaja Pietsch - 2018 - Interpretation 6 (3):SH59-SH71.
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  • Sweet Spot Identification Through Seismic Inversion and Multiattribute Transform: A Case Study of the Niobrara and Codell Unconventionals.Sheila Harryandi & Ali Tura - 2019 - Interpretation 7 (4):T887-T898.
    The Niobrara and Codell unconventional tight reservoir play at the Wattenberg field, Colorado, has potentially two billion barrels of oil equivalent, requiring hundreds of wells to access this resource. Due to the formations’ high heterogeneity and variable thicknesses, we model the facies at the well-bore scale and upscale it to the seismic scale to guide the development strategy and evaluate future exploration targets. A facies classification from well data supervises the prestack seismic inversion and multiattribute transformation workflows to build a (...)
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  • Relationships Between Lineal Fracture Intensity and Chemical Composition in the Marcellus Shale, Appalachian Basin.Keithan G. Martin, Liaosha Song, Payam Kavousi & Timothy R. Carr - 2019 - Interpretation 7 (4):SJ33-SJ43.
    Within mudrock reservoirs, brittle zones undergo failure during hydraulic stimulation, creating numerous artificial fractures which enable hydrocarbons to be liberated from the reservoir. Natural fractures in mudrock reduce the tensile strength of the host rock, creating planes of weaknesses that are hypothesized to be reactivated during hydraulic stimulation. Combined, brittleness and natural fractures contribute to creating more abundant and complex fracture networks during hydraulic stimulation. Research efforts toward quantifying rock brittleness have resulted in numerous mineral-/compositional-based indices, which are used during (...)
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  • Attribute Selection in Seismic Facies Classification: Application to a Gulf of Mexico 3D Seismic Survey and the Barnett Shale.Yuji Kim, Robert Hardisty & Kurt J. Marfurt - 2019 - Interpretation 7 (3):SE281-SE297.
    Automated seismic facies classification using machine-learning algorithms is becoming more common in the geophysics industry. Seismic attributes are frequently used as input because they may express geologic patterns or depositional environments better than the original seismic amplitude. Selecting appropriate attributes becomes a crucial part of the seismic facies classification analysis. For unsupervised learning, principal component analysis can reduce the dimensions of the data while maintaining the highest variance possible. For supervised learning, the best attribute subset can be built by selecting (...)
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  • Resolution Enhancement with Relative Amplitude Preservation for Unconventional Targets.Anna Kwietniak, Kamil Cichostępski & Kaja Pietsch - 2018 - Interpretation: SEG 6 (3):SH59-SH71.
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  • 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.
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