Multiphase Simulation Model for Validating the Estimate of Light Non-Aqueous Phase Liquids (LNAPL) Transmissivity Using Bail-Down Test

Ahmed, Waqqar; Palmier, C.; Atteia, Olivier; Class, Holger

doi:10.1007/s13369-018-3663-7

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…Furthermore, the accurate interpretation of the Bail‐Down test data that is used to assess the actual LNAPL thickness is still under discussion (Dippenaar et al 2005; Batu 2012; Jeong and Charbeneau 2014; Palmier et al 2017; Ahmed et al. 2019). On the other hand, some analytical models have been proposed for predicting the LNAPL distribution in the subsurface.…”

Section: Introductionmentioning

confidence: 99%

“…However, this type of in situ test is laborious as it requires considerable time and effort to pump and measure the LNAPL, as well as managing the free LNAPL and impacted groundwater pumped during the test. Furthermore, the accurate interpretation of the Bail-Down test data that is used to assess the actual LNAPL thickness is still under discussion (Dippenaar et al 2005;Batu 2012;Jeong and Charbeneau 2014;Palmier et al 2017;Ahmed et al 2019). On the other hand, some analytical models have been proposed for predicting the LNAPL distribution in the subsurface.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Vertical LNAPL Distribution in the Subsurface under the Fluctuating Water Table Effect

Boumaiza

Chesnaux

Walter

et al. 2022

Groundwater Monitoring Rem

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This column reviews the general features of PHT3D Version 2, a reactive multicomponent transport model that couples the geochemical modeling software PHREEQC-2 (Parkhurst and Appelo 1999) with three-dimensional groundwater flow and transport simulators MODFLOW-2000 and MT3DMS (Zheng and Wang 1999). The original version of PHT3D was developed by Henning Prommer and Version 2 by Henning Prommer and Vincent Post (Prommer and Post 2010). More detailed information about PHT3D is available at the website http://www.pht3d.org.The review was conducted separately by two reviewers. This column is presented in two parts.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Vertical LNAPL Distribution in the Subsurface under the Fluctuating Water Table Effect

Boumaiza

Chesnaux

Walter

et al. 2022

Groundwater Monitoring Rem

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“…Carneiro et al [20] assessed the impact of climate change on a coastal aquifer using density dependent numerical modelling. Ahmed et al [21] used numerical modelling to estimate the transmissivity of light non-aqueous phase liquid during a bail-down test. In addition to helping to understand the aquifer dynamics, numerical models can also be coupled with optimization algorithms to design monitoring networks.…”

Section: Introductionmentioning

confidence: 99%

Modelling Groundwater Hydraulics to Design a Groundwater Level Monitoring Network for Sustainable Management of Fresh Groundwater Lens in Lower Indus Basin, Pakistan

et al. 2020

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The over-extraction of groundwater from thin fresh groundwater lenses is a threat to the livelihood of farmers in the Lower Indus Basin (LIB). It is essential to monitor and regulate this pumping to sustain fresh groundwater lenses. In this study, we applied a modelling approach in combination with geostatistical analysis to identify the critical locations to monitor the groundwater levels for sustaining fresh groundwater in the LIB. Our approach included four steps: (i) simulating temporal heads using a calibrated hydrogeological model; (ii) sampling monitoring locations using a hexagonal pattern of sampling; (iii) applying principal component analysis (PCA) of the temporal head observations, and selecting high scoring locations from the PCA; and (iv) minimizing the observation points to represent the water level contours. The calibrated model was able to replicate the hydro-dynamic behavior of the study area, with a root mean square of 0.95 and an absolute residual mean of 0.74 m. The hexagonal pattern of spatial sampling resulted in a 195 point network, but PCA reduced this network to 135 points and contour classification reduced it even further to 59 points. The 195, 135, and 59 point networks represented the water levels with average standard errors of 0.098, 0.318, and 0.610 m, respectively. Long-term simulations with increased pumping showed that the water levels would best be assessed by 195 monitoring points, although 135 and 59 points would represent the depleting area but would not capture the water logging area.

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“…Assim, a Tn correlaciona-se com a capacidade de recuperação do LNAPL, fornecendo um parâmetro comparável entre locais, sendo uma medida substancialmente melhorada em relação à espessura aparente de NAPL nos poços (HAWTHORNE & KIRKMAN, 2011;EBRAHIMI et al, 2019). A determinação e a interpretação desse parâmetro são complexas, principalmente por não ser uma constante da formação e apresentar imprecisões, dependendo das condições da área, como espessura da franja capilar e viscosidade do produto (HUNTLEY & BECKETT, 2002;ITRC, 2018;AHMED et al, 2019). Ela pode variar de acordo com a metodologia aplicada, condições e duração do teste, mudanças nas condições piezométricas, propriedades dos fluidos e condições em que o LNAPL se encontra em subsuperfície (HAWTHORNE & KIRKMAN, 2011;SIMON, 2012;GATSIOS et al, 2018;ATTEIA et al, 2019).…”

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Avaliação do parâmetro transmissividade de LNAPLs para caracterização de áreas contaminadas no estado de São Paulo

Garcia

Leite

Pede

et al. 2021

Eng. Sanit. Ambient.

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RESUMO Fases líquidas não aquosas leves (LNAPL), como combustíveis líquidos, estão presentes em muitas áreas contaminadas. Para o gerenciamento dessas áreas, é importante conhecer a mobilidade desse produto, a fim de subsidiar decisões sobre sistemas de remediação e avaliação de risco. Uma alternativa para quantificar a mobilidade é o parâmetro transmissividade de LNAPL, ainda pouco aplicado no Brasil. Esse trabalho avaliou a aplicabilidade desse parâmetro em diferentes áreas contaminadas por LNAPL. Foram realizados testes em três áreas com litologias, tipos de contaminantes e históricos de contaminação distintos. Em alguns testes, não foi possível determinar a transmissividade de LNAPL devido à instabilidade do nível de fluidos, por consequência da operação de sistemas de bombeamento e precipitação, além de problemas operacionais durante a realização do teste. Apesar de ser um teste simples, em alguns casos, a determinação de transmissividade de LNAPL pode ser inviável, principalmente para LNAPL de baixa mobilidade por requerer testes mais prolongados. Os principais fatores determinantes no valor de transmissividade de LNAPL foram a litologia, a posição do poço na pluma e a posição do nível d’água em relação a série histórica. Assim, a transmissividade de LNAPL pode ser um parâmetro útil no gerenciamento de áreas contaminadas, cuja interpretação deve ser feita de forma cautelosa e integrada com outros dados de investigação da área.

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Multiphase Simulation Model for Validating the Estimate of Light Non-Aqueous Phase Liquids (LNAPL) Transmissivity Using Bail-Down Test

Cited by 5 publications

References 16 publications

Predicting Vertical LNAPL Distribution in the Subsurface under the Fluctuating Water Table Effect

Predicting Vertical LNAPL Distribution in the Subsurface under the Fluctuating Water Table Effect

Modelling Groundwater Hydraulics to Design a Groundwater Level Monitoring Network for Sustainable Management of Fresh Groundwater Lens in Lower Indus Basin, Pakistan

Avaliação do parâmetro transmissividade de LNAPLs para caracterização de áreas contaminadas no estado de São Paulo

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