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Bayesian evidential learning

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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. I research 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 set of principles derived from 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 contribution of BEL is that is a data-scientific approach that circumvents complex inversion modeling such as history matching and dynamic data integration and instead 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. Components of BEL are global sensitivity analysis, Monte Carlo, model falsification, prior elicitation and data scientific methods that reflect the stated principle of its Bayesian philosophy. Jef Caers
Book: Quantifying Uncertainty in Subsurface Systems