Petrophysical guided preconditioning applied to electrofacies classification by machine learning
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Enhancing formation resistivity factor estimation in carbonate reservoirs using electrical zone indicator and multi-resolution graph-based clustering methods. [PDF]
Mohammadi M +4 more
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A Comparative Study Based on Petrophysical and Cluster Analysis Approach for Identification of Rock Types in Heterogeneous Sandstone Reservoirs. [PDF]
Munir MN +6 more
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Study on displacement units for water flooded low-permeability oil reservoirs. [PDF]
Zhu W, Zhu Y, Bu W, Wang Y, Liu Y.
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Multi-scale reservoir characterization of pre-rift reservoirs in the October field, Gulf of Suez using integrated seismic, well logs, and core data. [PDF]
Ghaly M +3 more
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Hydraulic flow unit and rock types of the Asmari Formation, an application of flow zone index and fuzzy C-means clustering methods. [PDF]
Eftekhari SH +6 more
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Rapid identification of an effective bauxite gas reservoir by principal component analysis. [PDF]
Wang Y, Guo J, Ma Z, Zhou L.
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Machine learning with hyperparameter optimization applied in facies-supported permeability modeling in carbonate oil reservoirs. [PDF]
Al-Mudhafar WJ +3 more
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Estratigrafía y disposición estructural del Terciario Inferior en el subsuelo del Sector Central de la Cuenca del Ebro [PDF]
Klimowitz, J.
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Electrofacies analysis of the Asmari reservoir, Marun oil field, SW Iran
Geosciences Journal, 2020This article integrates core and well log data to determine reservoir electrofacies of the Oligo-Miocene Asmari Formation in the western Dezfol Embayment, SW Iran. At the start, an unsupervised neural network was employed based on the selforganizing map (SOM) technique to identify and extract electrofacies groups of Asmari Formation in the Marun ...
Bahman Soleimani, Iman Zahmatkesh
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