InterPore2022

A multi-dimensional parametric study of variability in multi-phase flow dynamics during geologic CO2 sequestration accelerated with machine learning

31 May 2022, 09:20

1h 10m

Poster Presentation (MS15) Machine Learning and Big Data in Porous Media Poster

Speaker

Hao Wu (Los Alamos National Laboratory)

Description

Successful geologic CO2 storage projects depend on numerical simulations to predict reservoir performance
during site selection, injection verification, and post-injection monitoring phases of the project. These numerical
simulations solve non-linear sets of coupled partial differential equations, while accounting for multi-phase fluid
dynamics on the basis of constitutive equations that are embedded into the solution scheme. As a consequence,
individual simulations often require tens to hundreds of hours to complete on high-performance computing
clusters. Moreover, laboratory experiments reveal that parametric functions for capillary pressure and relative
permeability exhibit substantial variability, even within the same rock type. This combination of computational
expense and wide-ranging parametric variability means that there remains substantial uncertainty in the
behavior of multi-phase CO2-water systems, particularly in the context of feedbacks between relative permeability
and capillary pressure. To bridge this knowledge gap, we develop a novel workflow that utilizes physicsbased
numerical simulation to train an artificial neural network (ANN) emulator for interrogating the multivariate
parameter space that governs both capillary pressure and relative permeability. With this approach, the
ANN is trained to emulate both fluid pressure distribution and CO2 saturation, which are then interrogated
quantitatively to generate parametric response surface mappings with high-fidelity resolution. Results from this
study initially show that capillary entry pressure is the dominant control on both CO2 plume geometry and fluid
pressure propagation when considering the combined effects of capillary pressure and relative permeability,
particularly when phase interference is low and residual CO2 saturation is high. Moreover, the ANN emulator
provides tremendous computational speed-up by computing 2691 individual simulations in several minutes;
whereas, the same simulation ensemble would have required ~3 years of simulation time using only physicsbased
simulation methods (25,000 times speed up).

Presentation materials

MS15_ID97.pdf