K. Bao, M. Yan, R. Allen, A. Salama, L. Lu, K. Jordan, S. Sun, and D. Keyes
SPE Journal, (2015)
The present work describes a parallel computational framework for CO2
sequestration simulation by coupling reservoir simulation and molecular
dynamics (MD) on massively parallel HPC systems. In this framework, a
parallel reservoir simulator, Reservoir Simulation Toolbox (RST), solves
the flow and transport equations that describe the subsurface flow
behavior, while the molecular dynamics simulations are performed to
provide the required physical parameters. Numerous technologies from
different fields are employed to make this novel coupled system work
efficiently.
One of the major applications of the framework is the modeling of large scale CO2
sequestration for long-term storage in the subsurface geological
formations, such as depleted reservoirs and deep saline aquifers, which
has been proposed as one of the most attractive and practical solutions
to reduce the CO2 emission problem to address the
global-warming threat. To effectively solve such problems, fine grids
and accurate prediction of the properties of fluid mixtures are
essential for accuracy. In this work, the CO2 sequestration
is presented as our first example to couple the reservoir simulation and
molecular dynamics, while the framework can be extended naturally to
the full multiphase multicomponent compositional flow simulation to
handle more complicated physical process in the future.
Accuracy and scalability analysis are performed on an IBM BlueGene/P
and on an IBM BlueGene/Q, the latest IBM supercomputer. Results show
good accuracy of our MD simulations compared with published data, and
good scalability are observed with the massively parallel HPC systems.
The performance and capacity of the proposed framework are well
demonstrated with several experiments with hundreds of millions to a
billion cells.
To our best knowledge, the work represents the first attempt to
couple the reservoir simulation and molecular simulation for large scale
modeling. Due to the complexity of the subsurface systems, fluid
thermodynamic properties over a broad range of temperature, pressure and
composition under different geological conditions are required, for
which the experimental results are limited. Although equations of state
can reproduce the existing experimental data within certain ranges of
conditions, their extrapolation out of the experimental data range is
still limited. The presented framework will definitely provide better
flexibility and predictability compared with conventional methods.