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In this paper, a new generalized sensitivity analysis is developed with a focus on parameter interaction. The proposed method is developed to apply to complex reservoir systems. Most critical in many engineering applications is to find which model parameters and parameter combinations have a significant impact on the decision variables. There are many types of parameters used in reservoir modeling, e.g., geophysical, geological and engineering. Some parameters are continuous, others discrete, and others have no numerical value and are scenario-based. The proposed generalized sensitivity analysis approach classifies the response/decision variables into a limited set of discrete classes. The analysis is based on the following principle: if the parameter frequency distribution is the same in each class, then the model response is insensitive to the parameter, while differences in the frequency distributions indicate that the model response is sensitive to the parameter. Based on this simple idea, a new general measure of sensitivity is developed. This sensitivity measure quantifies the sensitivity to parameter interactions, and incorporates the possibility that these interactions can be asymmetric for complex reservoir modeling. The approach is illustrated using a case study of a West Africa offshore oil reservoir. [ABSTRACT FROM AUTHOR]

The article presents a study which uses the distance kernel method (DKM) to determine petroleum reservoir models that will be assessed by flow simulation to identify reservoir uncertainty and was applied in Deepwater Turbidite reservoir in West Africa. It mentions the use of multidimensional scaling (MDS) to translate the dissimilarity of distance matrix into arrangement of points. It notes that streamline simulation correlates with the distance differences in flow simulation.