Supplementary data to the article: Upscaling reactive transport and clogging in shale microcracks by deep learning

doi: 10.4121/13138502.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/13138502
Datacite citation style:
Wang, Ziyan; Battiato, Ilenia (2020): Supplementary data to the article: Upscaling reactive transport and clogging in shale microcracks by deep learning. Version 1. 4TU.ResearchData. dataset.
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
usage stats
cc-0.png logo CC0
Fracture networks in shales exhibit multiscale features. A rock system may contain a few main fractures and thousands of microcracks, whose length and aperture are orders of magnitude smaller than the former. It is computationally prohibitive to resolve all the fractures explicitly for such multiscale fracture networks. One traditional approach is to model the small-scale features (e.g. microcracks in shales) as an effective medium. Although this fracture-matrix conceptualization significantly reduces the problem complexity, there are classes of physical processes that cannot be accurately upscaled by effective medium approximations, e.g. microcrack clogging during mineral reactions. In this work, we employ deep learning in place of effective medium theory to upscale physical processes in small-scale features. Specifically, we consider reactive transport in a fracture-microcrack network where microcracks can be clogged by precipitation. A deep learning multiscale algorithm is developed, in which main fractures are explicitly resolved while reactive transport and clogging in microcracks are upscaled as a wall boundary condition of the main fractures. The wall boundary condition is constructed by recurrent neural networks, which take concentration histories as input and predict the solute transport from main fractures to microcracks. The deep learning multiscale algorithm is firstly employed in specific scenarios, then a general model is developed which can work under various conditions. The new approach is validated against fully resolved simulations and an analytical solution, providing a reliable and efficient solution for problems that cannot be upscaled by effective medium models.
  • 2020-10-30 first online, published, posted
  • Center for Mechanistic Control of Unconventional Formations (CMC-UF) (grant code DE-SC0019165) [more info...] Office of Basic Energy Sciences
Stanford University, Department of Energy Resources Engineering


files (2)