Uncertainty Quantification of Medium‐Term Heat Storage From Short‐Term Geophysical Experiments Using Bayesian Evidential Learning

Abstract

In theory, aquifer thermal energy storage (ATES) systems can recover in winter the heat stored in the aquifer during summer to increase the energy efficiency of the system. In practice, the energy efficiency is often lower than expected from simulations due to spatial heterogeneity of hydraulic properties or non‐favorable hydrogeological conditions. A proper design of ATES systems should therefore consider the uncertainty of the prediction related to those parameters. We use a novel framework called Bayesian Evidential Learning (BEL) to estimate the heat storage capacity of an alluvial aquifer using a heat tracing experiment. BEL is based on two main stages: pre‐ and postfield data acquisition. Before data acquisition, Monte Carlo simulations and global sensitivity analysis are used to assess the information content of the data to reduce the uncertainty of the prediction. After data acquisition, prior falsification and machine learning based on the same Monte Carlo are used to directly assess uncertainty on key prediction variables from observations. The result is a full quantification of the posterior distribution of the prediction conditioned to observed data, without any explicit full model inversion. We demonstrate the methodology in field conditions and validate the framework using independent measurements.

Efficient Bayesian Inverse Modeling of Water Infiltration in Layered Soils

“…For a high‐dimensional and complex system (e.g., with more than 100 input parameters), a large number of training data are required to train a GP surrogate, which would be extremely time consuming. In this situation, some sampling‐free methods, e.g., Bayesian evidential learning (Hermans et al, 2018) or more advanced dimension reduction techniques (Cunningham and Ghahramani, 2015; Ju et al, 2018), should be used.…”

Hydrogeophysical modelling of Hisarcik (Kütahya) geothermal field, western Turkey

“…McKenna and Blackwell (2004) highlighted the effects of varying permeability and basal heat flow condition on temperature and fluid flow distribution. The role of heterogeneous permeability fields and boundary conditions impacting the fluid flow and temperature pattern are discussed in detail in a recent study by Hermans et al (2018). As pointed out by previous studies, sensitivity analysis is an essential requirement to model complex subsurface systems.…”

Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)

“…We establish the geological uncertainty quantification framework based on BEL, which is briefly reviewed in this section. BEL is not a method, but a prescriptive and normative data-scientific protocol for designing uncertainty quantification within the context of decision making (Athens and Caers, 2019;Hermans et al, 2018;Scheidt et al, 2018). It integrates four constituents in UQ -data, model, prediction, and decision under the scientific methods and philosophy of Bayesianism.…”

“…Recently, an uncertainty quantification protocol termed Bayesian evidential learning has been proposed to address decision making under uncertainty, and it has been applied to cases in oil or gas, groundwater contaminant remediation and geothermal energy (Athens and Caers, 2019;Hermans et al, 2018Hermans et al, , 2019Scheidt et al, 2018). It provides explicit standards that need to be reached at each stage of its UQ design with the purpose of decision making, including model falsification, global sensitivity analysis, prior elicitation, and data-science-driven uncertainty reduction under the principle of Bayesianism.…”