30 May 2022 to 2 June 2022
Asia/Dubai timezone

Estimating permeability of real-rock micro-CT images by physics-informed neural networks

31 May 2022, 17:30
15m
Oral Presentation (MS15) Machine Learning and Big Data in Porous Media MS15

Speaker

Stephan Gärttner (Friedrich-Alexander-Universität Erlangen-Nürnberg)

Description

In recent years, convolutional neural networks (CNNs) have experienced an increasing interest for fast approximations of effective hydrodynamic parameters in porous media research. In this talk, we present a novel approach to improve permeability predictions from micro-CT scans of geological rock samples.

A well-known method to enhance the quality of CNN predictions is the supply of additional information about the input data, leading to the field of physics-informed CNNs. Most commonly for permeability predictions from rock samples, porosity and/or (specific-) surface area are made available to the CNN as auxiliary data. However, these quantities become only loosely correlated to the target permeability for complex 3D geometries posing a poor information basis for the CNN to perform predictions.

Increasing the relevance of the additional physical information provided, we supply a highly correlated graph-network-based quantity to our CNN, namely the maximum flow value. Consequently, detailed information about confined structures dominating overall fluid flow is encoded in this additional input. As a result, high prediction accuracy and robustness for permeability prediction are observed across a variety of sandstone types.

Participation Online
Country Germany
MDPI Energies Student Poster Award No, do not submit my presenation for the student posters award.
Time Block Preference Time Block B (14:00-17:00 CET)
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Primary author

Stephan Gärttner (Friedrich-Alexander-Universität Erlangen-Nürnberg)

Co-authors

Dr Faruk Omer Alpak Mr Andreas Meier Nadja Ray (Friedrich-Alexander Universität Erlangen-Nürnberg) Prof. Florian Frank

Presentation materials