A major challenge to the successful full-scale development of modem aerospace

The proposed ARV index is a more reliable representation of time series variability than SD and may be less sensitive to the relative low sampling frequency of the ambulatory blood pressure monitoring devices. The results suggest that ARV adds prognostic value to the ABPM and could prompt the use of therapeutic measures to control BPV.

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractAfter conventional waterflood processes the residual oil in the reservoir remains as a discontinuous phase in the form of oil drops trapped by capillary forces and is likely to be around 70% of the original oil in place (OOIP). The EOR method socalled alkaline-surfactant-polymer (ASP) flooding has been proved to be effective in reducing the oil residual saturation in laboratory experiments and field projects through reduction of interfacial tension and mobility ratio between oil and water phases.A critical step to make ASP floodings more effective is to find the optimal values of design variables that will maximize a given performance measure (e.g. net present value, cumulative oil recovery) considering a heterogeneous and multiphase petroleum reservoir. Previously reported works using reservoir numerical simulation have been limited to sensitivity analyses at core and field scale levels because the formal optimization problem includes computationally expensive objective function evaluations (field scale numerical simulation).The proposed methodology estimates the optimal values for a set of design variables (slug size and concentration of the chemical agents) to maximize the cumulative oil recovery from a heterogeneous and multiphase petroleum reservoir subject to an ASP flooding. The surrogate-based optimization approach has been shown to be useful in the optimization of computationally expensive simulation-based models in the aerospace, automotive, and oil industries. In this work we have extended this idea along two directions: i) using multiple surrogates for optimization, and ii) incorporating an adaptive weighted average model of the individual surrogates.The proposed approach involves the coupled execution of a global optimization algorithm and fast surrogates (ì.e. based on Polynomial Regression, Kriging, and a Weighted Average Model) constructed from field scale numerical simulation data. The global optimization program implement the DIRECT algorithm and the reservoir numerical simulations are conducted using the UTCHEM program from the University of Texas at Austin.The effectiveness and efficiency of the proposed methodology is demonstrated using a well-known field scale case study.