Parameter estimation from spontaneous imbibition into volcanic tuff. Issue 2 (21st February 2022)

• Record Type: Journal Article
• Authors: Kuhlman, Kristopher L.; Mills, Melissa M.; Heath, Jason E.; Paul, Matthew J.; Wilson, Jennifer E.; Bower, John Eric
• Is Part Of: Vadose zone journal. Volume 21:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-21
• Subjects: Soil science -- Periodicals; Zone of aeration -- Periodicals; Groundwater flow -- Periodicals; Groundwater flow; Zone of aeration; Periodicals; Electronic journals; 631.4
• DOI: 10.1002/vzj2.20188
• Languages: English
• ISSNs: 1539-1663

Abstract

Abstract: Two‐phase fluid flow properties underlie quantitative prediction of water and gas movement, but constraining these properties typically requires multiple time‐consuming laboratory methods. The estimation of two‐phase flow properties (van Genuchten parameters, porosity, and intrinsic permeability) is illustrated in cores of vitric nonwelded volcanic tuff using Bayesian parameter estimation that fits numerical models to observations from spontaneous imbibition experiments. The uniqueness and correlation of the estimated parameters is explored using different modeling assumptions and subsets of the observed data. The resulting estimation process is sensitive to both moisture retention and relative permeability functions, thereby offering a comprehensive method for constraining both functions. The data collected during this relatively simple laboratory experiment, used in conjunction with a numerical model and a global optimizer, result in a viable approach for augmenting more traditional capillary pressure data obtained from hanging water column, membrane plate extractor, or mercury intrusion methods. This method may be useful when imbibition rather than drainage parameters are sought, when larger samples (e.g., including heterogeneity or fractures) need to be tested that cannot be accommodated in more traditional methods, or when in educational laboratory settings. Core Ideas: Mass imbibed and wetting front elevation data were collected during spontaneous imbibition. Markov chain Monte Carlo optimizer matches two‐phase flow models to observations. Mass and wetting front data were both required for unique model parameter estimation. An approach sensitive to both moisture retention and relative permeability functional forms was used. Markov chain Monte Carlo approach highlights correlations and uncertainties in results.