Speaker
Omar Al-Farisi
(Khalifa University of Science and Technology)
Description
Towards the quest for accuracy and efficient characterization of heterogeneous cretaceous carbonate, the path for geology and data scientists is full of challenges. The main challenge for achieving both accuracy and efficiency simultaneously is the ability to have the machine understand the heterogeneity effect on the rock's physical properties. From rock micro-Computed Tomography (uCT) images to rock types, we propose a fully artificial intelligence-based workflow. We enable the machine to identify the pore throat heterogeneity types, determine the rock's physical static properties; porosity, lithology, permeability, and capillary pressure, then finally classify the rock types using the novel Carbonate Morphology Chart, CAMO Chart.
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Primary authors
Omar Al-Farisi
(Khalifa University of Science and Technology)
Prof.
Mohamed Sassi
(Khalifa University)
Dr
Djamel Ouzzane
(ADNOC)
