English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The permeability of a pore structure is typically described by stochasticrepresentations of its geometrical attributes. Database-driven numericalsolvers for large model domains can only accurately predict large-scale flowbehaviour when they incorporate upscaled descriptions of that structure. Theupscaling is particularly challenging for rocks with multimodal porositystructures such as carbonates, where several different types of structures areinteracting. It is the connectivity both within and between these differentstructures that controls the porosity-permeability relationship at the largerlength scales. Recent advances in machine learning combined with numericalmodelling and structural analysis have allowed us to probe the relationshipbetween structure and permeability more deeply. We have used this integratedapproach to tackle the challenge of upscaling multimodal and multiscale porousmedia. We present a novel method for upscaling multimodal porosity-permeabilityrelationships using machine learning based multivariate structural regression.A m-CT image of limestone was divided into sub-volumes and permeability wascomputed using the DBS model. The porosity-permeability relationship from Menkeet al. was used to assign permeability values to the microporosity. Structuralattributes of each sub-volume were extracted and then regressed against thesolved permeability using an Extra-Trees regression model to derive an upscaledporosity-permeability relationship. Ten upscaled test cases were then modelledat the Darcy scale using the regression and benchmarked against full DBSsimulations, a numerically upscaled Darcy model, and a K-C fit. We found goodagreement between the full DBS simulations and both the numerical and machinelearning upscaled models while the K-C model was a poor predictor in all cases.

Upscaling the porosity-permeability relationship of a microporous carbonate to the Darcy scale with machine learning