Influence of Representative Models in the Expected Value of Information Applied to Petroleum Fields Development

Abstract

Abstract The Expected Value of Information (EVoI) is a criterion to analyze the feasibility of acquiring new information to deal with uncertainties and improve decisions at any stage of an oil field. Here, we evaluate the influence of the use of representative models (RM) on the EVoI estimation and on the decision to develop the petroleum field. These RM are used to represent a large set of models that honor production data (FM), considering uncertainties in reservoir, fluid and economic parameters, enabling the following processes: (1) optimize production strategies (specialized for each RM and robust to all RM), (2) risk analysis, (3) select the strategy to develop the field based on risk analysis, and (4) estimate the EVoI. We evaluated the influence of the number of RM on these processes, observing the impacts on the results of reducing computational costs. For the EVoI, we applied a Complete (EVoI_FM) and a Simplified (EVoI_RM) methodology, where EVoI_FM was evaluated with all models (FM) while EVoI_RM used different groups with different numbers of RM (GR1, GR2 and GR3, ranging from 9 to 150 models in each group). To assess the quality of the results, we used the complete estimate (EVoI_FM) as a reference. The study was conducted on UNISIM-I-D, a benchmark oil reservoir in the development phase, taking an appraisal well as a source of information to clarify a structural uncertainty. Using RM to optimize specialized production strategies proved useful, since optimizing strategies for all FM would require high computational costs. Moreover, the RM could be used to represent risk curves and select production strategies under uncertainty, but less precisely, affecting directly the results of the EVoI (which is the difference between the expected values of the two curves). The precision of EVoI_RM results varied according to the number and group of RM employed, also varying the best strategies selected for field development. The choice of using simplifications or not will depend on the accuracy required or available resources. Variations in EVoI_RM may be tolerable when compared to the time saved, being the decision maker free for choosing the best estimation method.