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Bayesian network is a popular tool for uncertainty process in Artificial Intelligence.
In this work, we quantify the effect of factors such as constraints, modelling uncertainty, disturbance uncertainty, process nonlinearity on the achieved closed loop performance.
The proposed methodology also provides correlation information between all outputs, thus providing information not easily obtained using the traditional uncertainty process based on analyzing one data reduction equation (DRE /model at a time.
The present study distinguishes between two types of ontic uncertainty: process variability and normative uncertainty.
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To continuously study the effect of the reservoir heterogeneity on IOR processes, a closed-loop uncertainty analysis process is introduced here (Fig. 1).
It eliminates delays and interruptions caused by the uncertainty in process operations, and increases the availability of the process.
The resulting uncertainty propagation process is non-intrusive, requiring immaterial modification of the deterministic analysis spreadsheet.
The sensitivity of the costs to uncertainty in process conditions are explored following the ADL design.
Performance degradation due to uncertainty in process and measurement noise statistics is discussed.
It is set in the context of an iterative approach to an overall uncertainty management process.
Analyses as presented explicitly specify uncertainty of process knowledge and data used in future-oriented studies.
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