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TitleApplications of machine learning to geoscience: nanoporosity and fluid flow in tight formations
LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorChen, ZORCID logo; Stoyanov, S RORCID logo; Liu, X; Mane, JORCID logo; Little, EORCID logo
SourceGeological Survey of Canada, Scientific Presentation 96, 2019, 1 sheet, Open Access logo Open Access
PublisherNatural Resources Canada
Mediaon-line; digital
File formatpdf
Subjectsfossil fuels; sedimentology; petroleum resources; hydrocarbons; hydrocarbon recovery; modelling; reservoir rocks; pore structure; pore size; porosity; fluid flow; scanning electron microscopy; resource estimation; production; economic analyses; Methodology; Artificial intelligence; machine learning; Decision making
Illustrationsphotomicrographs; digital images; schematic diagrams; bar graphs; frequency distribution diagrams; spectra; flow diagrams
ProgramGeoscience for New Energy Supply (GNES) Shale-hosted petroleum resource assessment
Released2019 03 19
Summary(Plain Language Summary, not published)
The Geological Survey of Canada and CanmetENERGY Devon are developing a collaborative engagement to enhance hydrocarbons recovery from tight formations. This engagement couples artificial intelligence (AI)-based characterization with physico-chemical modeling of hydrocarbon distribution and flow through rock nanopores to develop an AI-enabled engineering model. This poster outlines our engagement plan highlighting opportunities for collaboration with industry. It also presents preliminary results from applications of ML in feature extraction from SEM images for nanopore characterization and from physico-chemical modeling of the distribution of hydrocarbons near mineral surfaces. The AI-enabled engineering modeling would be employed by government and industry to help enhance hydrocarbon recovery beyond the current several percent as well as to address environmental challenges by developing additives and processes with lower impact on water pollution.

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