A Monte Carlo Approach to Value of Information Evaluation

The value of information (VOI) methodology can be used for determining whether further information should be collected before making a decision. Typically, a VOI is calculated on an expected monetary value (EMV) basis by means of a decision tree, and the cost of the information is compared to the VOI to determine whether to undertake further data collection. A majority of VOI studies employ the discrete decision tree approach to VOI evaluation, thus simplifying the problem by reducing the range of the outcomes and the number of uncertainties addressed at the same time. In order to overcome and address the simplifications introduced when performing a discrete VOI evaluation, a Monte Carlo approach founded on Bayesian decision theory can be applied. This increases computational complexity, but allows for a full uncertainty description of range variables such as oil in place (OIP) and can be integrated with quantitative prospect evaluation methods. The Monte Carlo VOI (MCVOI) approach is presented and compared to the discrete decision tree approach by means of an appraisal well decision. In addition, a complete MCVOI workflow is proposed. The paper aims at familiarizing VOI practitioners with the MCVOI approach by explaining how it works and by illuminating its benefits, such as eased expert assessment and getting past discretization of variables that are inherently continuous. The paper also places the VOI approach in a risk management context, thus extending VOI methodology beyond the pure calculation of a VOI number.