Combination of Experimental Design and Joint Modeling Methods for Quantifying the Risk Associated With Deterministic and Stochastic Uncertainties - An Integrated Test Study

摘要

This paper presents a fully-integrated methodology forrnmanaging reservoir uncertainties during history matching,rnproduction forecasting and production scheme optimization.rnBased on the traditional experimental design methodology,rnthis innovative approach, called the Joint Modeling Method,rnallows to model the production recovery as a function of bothrnthe deterministic uncertain parameters, such as petrophysicalrnand production parameters, as well as non-continuousrnparameters such as geostatistical realizations and equiprobablernmatched models. In this new approach, the dispersion due tornthe non-continuous uncertainties is modeled in a rigorousrnstatistical framework through the variance of the productionrnrecovery.rnThe method was successfully applied on data derived fromrna North Sea real field case. The objective was to quantify thernimpact of the principle reservoir uncertainties on therncumulative oil production and to optimize future fieldrndevelopment in a risk analysis approach. The uncertaintiesrnwere mainly on petrophysical data, geostatistical faciesrndistribution and aquifer strength. The study was performed inrnthe following steps:rn- sensitivity study. The most influential parameters werernidentified and the impact of geostatistical uncertainties wasrnhighlighted.rn- history matching. The influential parameters werernconstrained to the available production data. In particular, therngeostatistical model was locally modified using both FFTMArntechnique and the gradual deformation method.rn- production scheme optimization. Experimental design andrnjoint modeling were used to obtain probabilistic distributionsrnof the optimized location of new wells in a non-producingrnzone.rn- risk analysis: Finally, probabilistic incremental oilrnproduction was obtained using Monte-Carlo technique.rnResults show that this integrated methodology successfullyrnenables to quantify the risk associated with the main reservoirrnuncertainties during the whole process of a reservoirrnengineering study (sensitivity, history match, productionrnoptimization and forecast).