Machine learning based prediction of channelisation during dissolution
of carbonate rocks
Abstract
Evolving preferential dissolution channels are common features formed
during reactive fluid flow in carbonate rocks. Understanding these is of
particular importance in applications involving subsurface engineered
reservoirs but predicting their progression is currently challenging and
poorly understood. Here, we propose a new approach to predict both the
spatial distribution and extent of dissolution using a combination of
experimental work, X-ray microtomography (μCT) and machine learning. We
have conducted experiments, under reservoir conditions of temperature
and pressure, involving pre- and post-flooding μCT characterisations,
and coupled the outputs with a neural network to predict locations where
carbonate was most likely to be dissolved. Our simulations demonstrate
that our new solution can identify the key geometrical features that are
important during dissolution, and can accurately predict the location
and spread of dissolution. An important benefit of this approach is that
it can accurately predict dissolution channels through forward
prediction, while it does not require further chemical parameters, using
instead common and accessible variables.