Multivariate statistical analysis using principal components can reveal patterns and structures within a data set and give insights into process performance and operation. The output medium is usually a two dimensional screen, however, so it is a challenge to visualize the multidimensional structure of a data set by means of a two-dimensional plot. A method of visualization is described in the form of a hierarchical classification tree that can be used to view the structure within a multivariate principal component model of three or more dimensions. The tree is generated from an unsupervised agglomerative hierarchical clustering algorithm which operates in the score space of the principal component model, and a recursive algorithm to draw the tree. It is readily adaptable to a wide range of multivariate analysis applications including process performance analysis and process or equipment auditing. Its application are illustrated with industrial data sets.
Accurate metering of flow rates and prediction of water breakthrough are important issues in offshore oil production. The multiphase flow rates can be found for instance from frequent well testing, from multiphase flow meters, or from software simulations. Multiphase flow meters are very expensive and so are single-well tests, especially in cases with long tiebacks. On the contrary, software simulations are cheap, they can be made accurate, and simulations can usually be based on existing sensors. Also, software is easy to install, operate, and maintain compared to hardware multiphase meters.In this article we describe the main components needed in a flexible software system for flow metering that can handle sparsely instrumented production facilities. Several sources of implicit information are pointed out, and a tuning strategy that avoids single-well tests is described. We also present examples that show the power of advanced simulations in situations that are not well handled by multiphase flow meters and standard software because of very low flow rates. What makes the situation in the examples even harder is that: There is no available information about the choke, the bottom hole sensors fail, and the temperature measurements are influenced by the seawater temperature. Despite this, good results are obtained.
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