Early- and Late-Time Prediction of Counter-Current Spontaneous Imbibition, Scaling Analysis and Estimation of the Capillary Diffusion Coefficient
Peer reviewed, Journal article
Published version
Permanent lenke
https://hdl.handle.net/11250/3073186Utgivelsesdato
2023-03Metadata
Vis full innførselSamlinger
Originalversjon
Andersen, P.Ø. (2023) Early- and Late-Time Prediction of Counter-Current Spontaneous Imbibition, Scaling Analysis and Estimation of the Capillary Diffusion Coefficient. Transport in Porous Media. 147, 573–604 (2023) 10.1007/s11242-023-01924-6Sammendrag
Solutions are investigated for 1D linear counter-current spontaneous imbibition (COUSI). It is shown theoretically that all COUSI scaled solutions depend only on a normalized coefficient Λn (Sn)
with mean 1 and no other parameters (regardless of wettability, saturation functions, viscosities, etc.). 5500 realistic functions Λn were generated using (mixed-wet and strongly water-wet) relative permeabilities, capillary pressure and mobility ratios. The variation in Λn appears limited, and the generated functions span most/all relevant cases. The scaled diffusion equation was solved for each case, and recovery vs time RF was analyzed. RF could be characterized by two (case specific) parameters RFtr
and lr (the correlation overlapped the 5500 curves with mean R2=0.9989): Recovery follows exactly RF=T0.5n before water meets the no-flow boundary (early time) but continues (late time) with marginal error until RFtr (highest recovery reached as T0.5n) in an extended early-time regime. Recovery then approaches 1, with lr quantifying the decline in imbibition rate. RFtr was 0.05 to 0.2 higher than recovery when water reached the no-flow boundary (critical time). A new scaled time formulation Tn=t/τTch
accounts for system length L and magnitude D¯¯¯¯ of the unscaled diffusion coefficient via τ=L2/D¯¯¯¯
, and Tch separately accounts for shape via Λn. Parameters describing Λn and recovery were correlated which permitted (1) predicting recovery (without solving the diffusion equation); (2) predicting diffusion coefficients explaining experimental recovery data; (3) explaining the challenging interaction between inputs such as wettability, saturation functions and viscosities with time scales, early- and late-time recovery behavior.