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A variety of algorithms that show the mathematical model for obtaining gas rate correction factor that appeared in the book entitled "Well Productivity Handbook: Vertical, Fractured, Horizontal, Multilateral, and Intelligent Wells" are presented.

This chapter provides background information and problem descriptions of the applications treated in this book. Most of the applications include real data and many of them are used as case studies examined throughout the book with different algorithms. This chapter serves both as a reference chapter and as a motivation for the area of adaptive filtering and change detection. The applications are divided here according to the model structures that are used. The real data applications include signals from: airbag, paper refinery, photon emission, econometrics, altitude sensors, EEG from rats and humans, EKG, ash recycling, earthquake and speech data, electronic nose and DC motor. The examples are used as benchmarks throughout the book. [ABSTRACT FROM PUBLISHER]

In the preceding chapters, the authors introduced several different estimation principles and algorithms for independent component analysis (ICA). In this chapter, they provide an overview of these methods. First, they show that all these estimation principles are intimately connected, and the main choices are between cumulant-based vs. negentropy/likelihood-based estimation methods, and between one-unit vs. multi-unit methods. They compare the algorithms experimentally, and show that the main choice here is between on-line (adaptive) gradient algorithms vs. fast batch fixed-point algorithms. At the end of the chapter, they provide a short summary of basic ICA estimation. [ABSTRACT FROM PUBLISHER]