Application of machine-learning to construct equivalent continuum models from high-resolution discrete-fracture models

Xupeng He, Ryan Santoso, Hussein Hoteit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

43 Scopus citations

Abstract

Modeling fluid flow in fractured media is of importance in many disciplines, including subsurface water management and petroleum reservoir engineering. Detailed geological characterization of a fractured reservoir is commonly described by a discrete-fracture model (DFM), in which the fractures and rock-matrix are explicitly represented by unstructured grid elements. Traditional static-based and flow-based upscaling methods used to generate equivalent- continuum models from DFM suffer from low accuracy and high computational cost, respectively. This work introduces a new deep-learning technique based on neural networks to accelerate upscaling of discrete-fracture models. The objective of this work is to automate the process of permeability upscaling from detailed discrete-fracture characterizations. We build an
Original languageEnglish (US)
Title of host publicationInternational Petroleum Technology Conference
PublisherInternational Petroleum Technology Conference
ISBN (Print)9781613996751
DOIs
StatePublished - Jan 11 2020

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