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Coupled analytical modeling of water level dynamics and energy use for operational well fields in the Denver Basin aquifers

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dc.contributor.authorDavis, Jennifer Anne, author
dc.contributor.authorRonayne, Michael, advisor
dc.contributor.authorSale, Thomas, advisor
dc.contributor.authorSanford, William, committee member
dc.contributor.authorBau, Domenico, committee member
dc.date.accessioned2007-01-03T05:55:13Z
dc.date.available2007-01-03T05:55:13Z
dc.date.issued2013
dc.description.abstractThe South Metro Denver area in Colorado has been experiencing rapid growth in recent years and many municipalities in this region rely on the groundwater resources available in the Denver Basin as their chief water supply. As the population continues to increase, municipal water demands must be met with a sustainable approach. The Denver Basin aquifer system consists of four major aquifers that are composed of interbedded layers of sandstones, siltstones, and shales. The aquifers receive limited annual recharge and consequently the groundwater within them has the potential to be depleted. Declining water levels associated with groundwater depletion, interference between pumping wells, and fouling of wells is leading to losses in well productivity. Furthermore, declining water levels translates to higher electrical energy costs associated with water production. Regional-scale numerical models developed for the Denver Basin aquifer system do not capture the local-scale drawdown about pumping wells, which is needed to effectively manage existing groundwater well fields. This research project utilizes production well data from the town of Castle Rock, Colorado to test the merits of using a Theis based approach to model water levels about production wells in the Denver and Arapahoe aquifers in Castle Rock. The model applies superposition of the Theis solution throughout both space and time to resolve the combined effects of pumping from multiple wells. This research demonstrated that the analytical method can be successfully applied as a predictor of continuous water levels at pumping wells. In addition, the analytical model provided a novel method for estimating aquifer properties using data from an operational well field, and it contributed a better understanding of the cross-well interferences that increase well drawdown. The model results were used to evaluate alternative pumping scenarios intended to reduce electrical energy costs associated with water production and increase sustainable yields from these aquifers. The alternative pumping scenarios achieved a net reduction in energy consumption ranging from 1.62% to 13.0% and led to a stronger conceptual understanding of how each aquifer responds to varying pumping conditions. This research demonstrates that the analytical solution modeling approach may be beneficial for application to many other projects involving groundwater supply management and optimization.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierDavis_J_colostate_0053N_11888.pdf
dc.identifier.urihttp://hdl.handle.net/10217/80229
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectDenver Basin aquifers
dc.subjectgroundwater modeling
dc.subjectenergy use
dc.titleCoupled analytical modeling of water level dynamics and energy use for operational well fields in the Denver Basin aquifers
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineGeosciences
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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