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An Imputation Method for Simulating 3D Well Screen Locations from Limited Regional Well Log Data
Georgios Kourakos, Rich Pauloo1, Thomas Harter1
1Department of Land, Air, and Water Resources One Shields Avenue, University of California, Davis, CA, 95616-8628, USA.
Ground Water
|June 27, 2024
Summary
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.
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.
