PyTheis—A Python Tool for Analyzing Pump Test Data

Chang, Sun Woo; Memari, S. S.; Clement, T. Prabhakar

doi:10.3390/w13162180

Cited by 4 publications

(2 citation statements)

References 19 publications

(44 reference statements)

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“…As it stands, this convolution requires an integration inside of another integration. The computational difficulty has inspired several simplifications, particularly so that iterative optimization can be used to automatically fit parameters

S

T

, and in some cases uncertain

Q

(Singh 2002; von Schroeter et al 2004; Chang et al 2021). For a constant rate pumping test, the derivative of

Q

renders the boundary condition a Dirac delta function and the Theis (1935) solution is recovered.…”

Section: Methodsmentioning

confidence: 99%

Automated Estimation of Aquifer Parameters from Arbitrary‐Rate Pumping Tests in Python and MATLAB

Benson

2023

Groundwater

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Inspired by the analysis by Mishra et al. (2012) of variable pumping rate tests using piecewise‐linear reconstructions of the pumping history, this article contains a derivation of the convolutional form of pumping tests in which the pumping history may take any possible form. The solution is very similar to the classical Theis (1935) equation but uses the Green's function for a pumped aquifer given by taking the time derivative of the well function . This eliminates one integration inside another and renders the convolution including the pumping history about as computationally demanding as calculating the well function alone, so that the convolution can be completed using handy mathematical software. It also allows nonlinear well losses, and because an easily‐computed deterministic model exists for all data points and pumping history, an objective function may include all data, so that errors are reduced in calculating any nonlinear‐well losses. In addition, data from multiple observation wells may be used simultaneously in the inversion. We provide codes in MATLAB and Python to solve for drawdown resulting from an arbitrary pumping history and compute the optimal aquifer parameters to fit the data. We find that the subtleties in parameter dependencies and constructing an appropriate objective function have a substantial effect on the interpreted parameters. Furthermore, the optimization from step‐drawdown tests is typically nonunique and strongly suggests that a Bayesian inversion should be used to fully estimate the joint probability density of the parameter vector.

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S

T

, and in some cases uncertain

Q

(Singh 2002; von Schroeter et al 2004; Chang et al 2021). For a constant rate pumping test, the derivative of

Q

renders the boundary condition a Dirac delta function and the Theis (1935) solution is recovered.…”

Section: Methodsmentioning

confidence: 99%

Automated Estimation of Aquifer Parameters from Arbitrary‐Rate Pumping Tests in Python and MATLAB

Benson

2023

Groundwater

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show abstract

“…• PyTheis-A python tool for analyzing pump test data, doi.org/10.3390/w13162180 (Chang et al, 2021) • Wells-A multi-well, variable-rate, pumping-test analysis tool from Los Alamos National Laboratory in the USA, https://wells.lanl.gov/, (originally developed at the University of Mining and Geology, Sofia, Bulgaria, in 1992 by Velimir V.…”

Section: Using Software To Analyze Hydraulic Test Data With a Pumping...mentioning

confidence: 99%

An Introduction to Hydraulic Testing in Hydrogeology: Basic Pumping, Slug, and Packer Methods

Woessner,

Stringer,

Poeter

2023

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All groundwater investigations (including resource investigations, development of water supplies, and identification and remediation of contaminated sites) require deriving field-based values of basic hydrogeologic properties such as hydraulic conductivity, transmissivity and storativity. In the earlier Groundwater Project book “Hydrogeologic Properties of Earth Materials and Principles of Groundwater Flow” (Woessner and Poeter, 2020) groundwater principles and parameters were defined. Discussion of methods to determine parameters focused on laboratory methods and referenced field hydraulic testing methods but did not provide details on application. This book is a companion to that earlier work, as well as a standalone document that provides foundational methods used to generate field-scale representations of common hydraulic parameters. The authors present a conceptual view of how hydraulic testing methods such as the pumping tests, slug tests, and testing with packers are applied, as well as the advantages and limitations of the underpinning analytical solutions. They focus on methods addressing simplified confined, leaky confined and unconfined groundwater systems. The authors explain how curve-matching of field test data to analytical models is used to interpret test results. Software for analyzing hydraulic test data is briefly discussed, some of which include choices of several additional analytical models and one-button automated analysis, which can lead to misinterpretation of the data, so this book emphasizes basic concepts, principles, and methods. The main body of text along with many illustrations, examples, and exercises with solutions provide the reader with the information needed to correctly apply hydraulic testing and analytical methods.

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Modeling the impacts of various managerial scenarios on groundwater level raising in a coastal aquifer

et al. 2022

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PyTheis—A Python Tool for Analyzing Pump Test Data

Cited by 4 publications

References 19 publications

Automated Estimation of Aquifer Parameters from Arbitrary‐Rate Pumping Tests in Python and MATLAB

Automated Estimation of Aquifer Parameters from Arbitrary‐Rate Pumping Tests in Python and MATLAB

An Introduction to Hydraulic Testing in Hydrogeology: Basic Pumping, Slug, and Packer Methods

Modeling the impacts of various managerial scenarios on groundwater level raising in a coastal aquifer

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