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A Close-to-Optimal Discretization Strategy for Pumping Test Numerical Simulation.

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Summary

This study optimizes numerical models for pumping test analysis by defining spatiotemporal discretization using dimensionless parameters. An optimal method minimizes runtime while ensuring accuracy, especially when analyzing data from the pumping well itself.

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Area of Science:

  • Hydrogeology
  • Numerical Modeling
  • Geosciences

Background:

  • Pumping test analysis is crucial for aquifer characterization.
  • Analytical solutions have limitations, making numerical modeling a valuable alternative.
  • The impact of discretization on numerical model accuracy and efficiency in pumping tests is not well understood.

Purpose of the Study:

  • To develop a systematic method for spatiotemporal discretization in pumping test numerical models.
  • To investigate the influence of discretization on accuracy and runtime.
  • To determine optimal discretization parameters for efficient and accurate analysis.

Main Methods:

  • Developed a method based on dimensionless parameters to define spatiotemporal discretization.
  • Evaluated two analysis scenarios: observations in the pumping well and in observation wells.
  • Systematically varied discretization parameters to assess their impact on accuracy and runtime.

Main Results:

  • An optimal set of discretization parameters was identified, minimizing runtime while keeping maximum error below 1% for an average aquifer.
  • Significantly lower runtimes were achieved when observations were made in the pumping well.
  • The efficiency in pumping well analysis is linked to the steady-state analytical solution's approximation within the numerical scheme.

Conclusions:

  • A robust method for optimizing pumping test numerical model discretization has been established.
  • The findings provide practical guidance for selecting discretization parameters to balance accuracy and computational efficiency.
  • The study highlights the advantage of focusing analysis on the pumping well for faster, accurate results in specific scenarios.