Proposal
Petroleum field decision-making process is associated to high risks due to geological, economic and technological uncertainties, and high investments, mainly in the appraisal and development phases of petroleum fields. In these phases, it is necessary to model the recovery process with higher precision increasing the computation time to represent all possible scenarios. The necessity to speedup the process demands simplification of the process. The use of Monte Carlo technique, for instance, is normally not viable when numerical flow simulation is used to model the recovery process due to the high number of simulations required. The use of the derivative tree technique can be an alternative in such a case but it also yields a high number of simulations when several attributes have to be considered. An alternative, in such cases, is to use fewer attributes or to use a lower number of discretization levels. Another alternative is to simplify the reservoir modeling process with faster models. Several works are being presented recently about these techniques but normally they show applications but not a comparison among alternatives. The objective of this work is to compare these techniques taking into account the reliability and precision of the results and speed up of the process due to the simplifications. Monte Carlo and derivative tree techniques are compared using reservoir simulation, sensitivity analysis, experimental design and response surface method as supporting tools. These techniques are applied to an offshore field to quantify the risk in economic and technical parameters. The results show that it is possible to reduce significantly the number of flow simulation runs maintaining the precision of the results.
