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 ability to simulate the partial differential equations (PDE's) thatgovern multi-phase flow in porous media is essential for different applicationssuch as geologic sequestration of CO2, groundwater flow monitoring andhydrocarbon recovery from geologic formations [1]. These multi-phase flowproblems can be simulated by solving the governing PDE's numerically, usingvarious discretization schemes such as finite elements, finite volumes,spectral methods, etc. More recently, the application of Machine Learning (ML)to approximate the solutions to PDE's has been a very active research area.However, most researchers have focused on the performance of their modelswithin the time-space domain in which the models were trained. In this work, weapply ML techniques to approximate PDE solutions and focus on the forecastingproblem outside of the training domain. To this end, we use two different MLarchitectures - the feed forward neural (FFN) network and the long short-termmemory (LSTM)-based neural network, to predict the PDE solutions in futuretimes based on the knowledge of the solutions in the past. The results of ourmethodology are presented on two example PDE's - namely a form of PDE thatmodels the underground injection of CO2 and its hyperbolic limit which is acommon benchmark case. In both cases, the LSTM architecture shows a hugepotential to predict the solution behavior at future times based on prior data

LSTM-driven Forecast of CO2 Injection in Porous Media