This paper focuses on valuation of shale based opportunities, arguing that the valuation process should be decision driven and model based, grounded in Bayesian principles. Risk should be controlled in order to maximize value. Using a decision tree to structure the valuation, we demonstrate that a method based on hypothesis testing can be utilized to determine the probabilities associated with chance nodes. It is shown that the probabilities depend on the scope and quality of the appraisal program. The risk level posterior to a chance node is discussed and calculated by Bayesian inversion. It is shown that the model underpinning the valuation must be updated posterior to chance nodes according to Bayes theorem. Failure to do so may lead to underestimating the value of the opportunity. Finally we discuss the use of simple truncation as an approximate method for updating the model after a pilot production phase.

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