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

7 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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