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An Imputation Method for Simulating 3D Well Screen Locations from Limited Regional Well Log Data.

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Accurate groundwater modeling requires precise well data. This study introduces a new imputation framework to reconstruct missing well information, ensuring data integrity for aquifer assessments.

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Area of Science:

  • Hydrogeology
  • Environmental Science
  • Data Science

Background:

  • Accurate spatial and intensity identification of groundwater sources and sinks is crucial for effective modeling.
  • Obtaining precise well construction data, including location and pumping rates, is challenging due to data gaps and imprecisions in historical records.
  • Detailed well screen distribution is essential for accurate groundwater quality assessments and contaminant transport modeling.

Purpose of the Study:

  • To propose and validate an imputation framework for reconstructing missing well data in groundwater modeling.
  • To address the challenges of incomplete and imprecise well data, particularly in large-scale aquifer systems.
  • To ensure the statistical integrity and spatial consistency of reconstructed well data.

Main Methods:

  • Development of an imputation framework that leverages available information while accommodating data gaps and inaccuracies.
  • Application of the framework to a subregion of the Central Valley aquifer in California, USA.
  • Validation of the imputation method by assessing its ability to preserve statistical properties and spatial distribution of well data.

Main Results:

  • The proposed framework successfully imputes missing well data, maintaining statistical properties of the available information.
  • Reconstructed data remained consistent with the known three-dimensional spatial distribution of well screens and pumping rates.
  • Demonstrated the framework's utility in improving the accuracy of groundwater modeling inputs.

Conclusions:

  • The imputation framework provides a robust solution for reconstructing essential, yet often missing, well data in groundwater studies.
  • This method enhances the reliability of groundwater models, particularly for assessments involving complex aquifer systems and nonpoint source pollution.
  • Improved well data reconstruction contributes to more accurate predictions of groundwater behavior and contaminant transport.