A methodology to evaluate and reduce reservoir uncertainties using multivariate distribution

Abstract

History matching is a challenging and time-consuming task related to reservoir simulation. Probabilistic approaches using dynamic data are often used to reduce reservoir uncertainties and improve matching. This work presents a new process to evaluate and reduce reservoir uncertainties using multivariate analysis incorporating the interaction between reservoir properties. The proposed uncertainty reduction workflow provides a multivariate approach without the use of proxy models, allowing understanding of the reservoir response through the R2 matrix as well as more reliable reservoir predictions. The methodology offers a quantitative analysis and a new tool to evaluate and reduce uncertainties. The process uses a Latin Hypercube (LHC) to sample the reservoir attribute range and a smoothed mismatch data set from the LHC selected objective functions. The attribute interval, which minimizes the mismatch, is identified through polynomial fitting. The main objective is to reduce uncertainties considering the reservoir attributes range and a multivariate sensitivity matrix. The methodology was firstly applied to a simple synthetic reservoir simulation model with 20 uncertainty attributes and we drew the following conclusions: (1) R2 sensitivity matrix clearly showed the key physical features of the reservoir model; (2) all reservoir attributes ranges were reduced, providing a set of simulation models with improved history matching. We successfully applied to the UNISIM-I-H reservoir model based on Namorado field, Campos basin, Brazil.