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dc.contributor.authorMarkovic, Strahinja
dc.contributor.supervisorReza Rezaeeen_US
dc.contributor.supervisorAli Saeedien_US
dc.contributor.supervisorAlexey Cheremisinen_US

This work studies the physicochemical properties of unconventional hydrocarbon resources such as heavy oils and bitumens. The principal methods used in the research consisted of LF-NMR experiments, hypothesis testing, and statistical and data-driven modeling. The research output consists of several machine learning and analytical models capable of predicting heavy oil and bitumen viscosity and core sample water saturation with high accuracy. These results provide a strong case for in-situ LF-NMR applications in well logging.

dc.publisherCurtin Universityen_US
dc.titleApplication of LF-NMR measurements and supervised learning regression methods for improved characterization of heavy oils and bitumensen_US
curtin.departmentWASM: Minerals, Energy and Chemical Engineeringen_US
curtin.accessStatusOpen accessen_US
curtin.facultyScience and Engineeringen_US
curtin.contributor.orcidMarkovic, Strahinja [0000-0002-6143-9370]en_US

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