Comparing Well and Geophysical Data for Temperature Monitoring within a Bayesian Experimental Design Framework

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1029/2022WR033045. This is version 1 of this Preprint.

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Authors

Robin Thibaut , Nicolas Compaire, Nolwenn Lesparre, Maximilian Ramgraber, Eric Laloy, Thomas Hermans

Abstract

Temperature logs are an important tool in the geothermal industry. Temperature measurements from boreholes are used for exploration, system design, and monitoring. The number of observations, however, is not always sufficient to fully determine the temperature field or explore the entire parameter space of interest. Drilling in the best locations is still difficult and expensive. It is therefore critical to optimize the number and location of boreholes. Due to its higher spatial resolution and lower cost, four-dimensional (4D) temperature field monitoring via time-lapse Electrical Resistivity Tomography (ERT) has been investigated as a potential alternative. We use Bayesian Evidential Learning (BEL), a Monte Carlo-based training approach, to optimize the design of a 4D temperature field monitoring experiment. We demonstrate how BEL can take into account various data source combinations (temperature logs combined with geophysical data) in the experimental design (ED). To optimize the ED and determine the best data source combination, we use the Root Mean Squared Error (RMSE) of the predicted target in the low dimensional latent space where BEL is solving the prediction problem. The generated models agree well with the true models and are accurate enough to be used in optimal ED.
Furthermore, the method is not limited to monitoring temperature fields and can be applied to other similar experimental problems.
The method is computationally efficient and requires little training data. A training set of only 200 is sufficient for the considered optimal design problem.

DOI

https://doi.org/10.31223/X5ZD20

Subjects

Earth Sciences, Hydrology, Physical Sciences and Mathematics, Water Resource Management

Keywords

ATES, machine learning, Bayesian, Data Fusion, ERT, PCA, cca, dimensionality reduction, experimental design, optimal monitoring design

Dates

Published: 2023-01-25 00:06

Last Updated: 2023-01-25 05:06

License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
https://www.kaggle.com/datasets/robustus/4d-ert-monitoring