TY - JOUR
T1 - Continuous data assimilation for two-phase flow
T2 - Analysis and simulations
AU - Chow, Yat Tin
AU - Leung, Wing Tat
AU - Pakzad, Ali
PY - 2022/10/1
Y1 - 2022/10/1
N2 - We propose, analyze, and test a novel continuous data assimilation two-phase flow algorithm for reservoir simulation. We show that the solutions of the algorithm, constructed using coarse mesh observations, converge at an exponential rate in time to the corresponding exact reference solution of the two-phase model. More precisely, we obtain a stability estimate which illustrates an exponential decay of the residual error between the reference and approximate solution, until the error hits a threshold depending on the order of data resolution. Numerical computations are included to demonstrate the effectiveness of this approach, as well as variants with data on sub-domains. In particular, we demonstrate numerically that synchronization is achieved for data collected from a small fraction of the domain.
AB - We propose, analyze, and test a novel continuous data assimilation two-phase flow algorithm for reservoir simulation. We show that the solutions of the algorithm, constructed using coarse mesh observations, converge at an exponential rate in time to the corresponding exact reference solution of the two-phase model. More precisely, we obtain a stability estimate which illustrates an exponential decay of the residual error between the reference and approximate solution, until the error hits a threshold depending on the order of data resolution. Numerical computations are included to demonstrate the effectiveness of this approach, as well as variants with data on sub-domains. In particular, we demonstrate numerically that synchronization is achieved for data collected from a small fraction of the domain.
KW - Data assimilation
KW - Multiphase flow
UR - http://www.scopus.com/inward/record.url?scp=85133421691&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85133421691&origin=recordpage
U2 - 10.1016/j.jcp.2022.111395
DO - 10.1016/j.jcp.2022.111395
M3 - RGC 21 - Publication in refereed journal
SN - 0021-9991
VL - 466
JO - Journal of Computational Physics
JF - Journal of Computational Physics
M1 - 111395
ER -