Abstract

The ultimate goal of collecting data, building models and making predictions is to make an informed decision. In the subsurface realm, such decisions are subject to considerable uncertainty. In this talk, I will present a new framework termed “Bayesian Evidential Learning” (BEL) that streamlines the integration of these four components: data, model, prediction, decision. This idea is published in a new book: “Quantifying Uncertainty in Subsurface Systems” (Wiley-Blackwell, 2018) and applied to five real case studies in oil/gas, groundwater, contaminant remediation and geothermal energy. BEL is not a method, but a protocol based on Bayesianism that lead to the selection of relevant methods to solve real decision problems. In that sense BEL, focuses on decision-focused data collection and model-building. One of the important contributions of BEL is that is a data-scientific approach that circumvents complex inversion modeling relies on machine learning from Monte Carlo with falsified priors. The case studies illustrate how modeling time can be reduced from months to days, making it practical for large scale implementations. In this talk, I will provide an overview of BEL, how it relies on global sensitivity analysis, Monte Carlo, model falsification, prior elicitation and data scientific methods to implement the stated principle of its Bayesian philosophy. The focus will be on the collaboration with Aarhus University on managing the Danish groundwater system

Quantifying uncertainty in subsurface systems, J Caers, C Scheidt, L Li. American Geophysical Union, 2018

You are all welcome to attend the seminar. If you plan to attend, please accept and reply to the invitation. If we have a high number of attendances we might have to find a larger room.