What is about Petroleum Engineering Optimization?
Petroleum Engineering Optimization examines techniques of reservoir optimization and parameter estimation that is useful in petroleum engineering and geosciences. In regard to reservoir optimization, there are several parameters to look at such as matching reservoir models, linear and nonlinear parameter estimation, history matching and the computation of confidence intervals on estimates parameter values.
Specific parameter estimation techniques include Newton type methods (Gauss-Newton, Newton, Levenberg-Marquardt, eigenvalue modification approaches), quasi-Newton (Broyden’s algorithm), singular value decomposition (SVD) and robust (LAV and MLAV) methods.
Methods to optimize goal-oriented projects are also emphasized on this course, for instance environmental impact minimization and cost reduction, production or profit maximization, etc. Optimization techniques includes gradient-based methods such as Newton, variational approaches such as optimal control theory, direct search methods (line search, conjugate gradient and polytope), and exploration methods such as simulated annealing, taboo search and genetic algorithm. What’s more, both constrained and unconstrained methods are also studied.
What are you required for Petroleum Engineering Optimization?
If you are interested in this subject, you are required to pass several courses in regard to mathematics and computer programming for example Linear algebra, computer language C, C++, Matlab or Java.
Suggested Books for Petroleum Engineering Optimization Topic
“The Basics of Practical Optimization”, Adam B. Levy, Society for Industrial and Applied Mathematics
“Numerical Recipes with Source Code CD-ROM 3rd Edition: The Art of Scientific Computing”, William H. Press, Saul A. Teukolsky, William T. Vetterling, Brian P. Flannery, Cambridge Press
“Genetic Algorithms in Search, Optimization, and Machine Learning”, Michael Affenzeller, Stephan Winkler, Stefan Wagner, Andreas Beham,Chapman & Hall Publisher
"Genetic Algorithms in Search, Optimization", and Machine Learning,David E. Goldberg, Addison-Wesley Professional
"Nonlinear Regression (Wiley Series in Probability and Statistics)",George A. F. Seber, C. J. Wild,Wiley-Interscience
"Nonlinear Regression Analysis and Its Applications (Wiley Series in Probability and Statistics)",Douglas M. Bates, Donald G. Watts,Wiley
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