Document Type

Journal Article

Publisher

Wiley

School

School of Engineering

RAS ID

26805

Comments

Originally published as: Lenhard, R. J., Sookhak Lari, K., Rayner, J. L., & Davis, G. B. (2018). Evaluating an analytical model to predict subsurface LNAPL distributions and transmissivity from current and historic fluid levels in groundwater wells: comparing results to numerical simulations. Groundwater Monitoring & Remediation, 38(1), 75-84. Original article available here

Abstract

A recent analytical model predicts free, entrapped, and residual LNAPL saturations and the LNAPL transmissivity in the subsurface from current and historic fluid levels in groundwater wells. As such, the model accounts for effects of fluid level fluctuations in a well. The model was developed to predict LNAPL specific volumes and transmissivities from current fluid level measurements in wells and either recorded historic fluid level fluctuations in wells or estimates. An assumption is made in the model that the predictions are not dependent on whether the historic highest or lowest fluid level elevations in a well occur first. To test the assumption, we conduct two simulations with a modified multiphase flow numerical code TMVOC that incorporates relative permeability-saturation-capillary head relations employed in the model. In one simulation, the initial condition is for fluid levels in a well at the historic highest elevations. In the other simulation, the initial condition is for fluid levels in a well at the historic lowest elevations. We change the boundary conditions so both historical conditions occur followed by generating the current condition. Results from the numerical simulations are compared to model predictions and show the assumption in the analytical model is reasonable. The analytical model can be used to develop/refine conceptual site models and for assessing potential LNAPL recovery endpoints, especially on sites with fluctuating fluid levels in wells.

DOI

10.1111/gwmr.12254

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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