Download Petroleum Engineering 284

15
14
13
12
11
10
9
8
7
6
15
14
13
12
11
10
9
8
7
6
100
200
0
100
-100
0
-200
-100
Petroleum Engineering 284
Optimization
Fall Quarter 2005
Mon-Wed 1:15-3:05, Green 104
-300
-200
-300
-400
Objective:
This course will examine techniques of optimization and parameter estimation useful in petroleum
engineering and geosciences. We will look at linear and nonlinear parameter estimation, model fitting,
history matching and the computation of confidence intervals on estimates parameter values. Specific
parameter estimation techniques to be examined will 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. We will look also at methods
to optimize goal-oriented projects, for example production or profit maximization, environmental impact
minimization and cost reduction. Optimization techniques we will consider will include direct search
methods (line search, conjugate gradient and polytope), gradient-based methods such as Newton, variational
approaches such as optimal control theory, and exploration methods such as simulated annealing, taboo
search and genetic algorithm. We will look at both constrained and unconstrained methods.
Applications:
Typical examples of the uses of these methods will be used in assignments and projects in this course.
These may include: computer-aided well test analysis, tracer analysis, automated history matching of
reservoir simulations, computation of stabilized flow in pipe networks, production optimization, optimal
development scheduling, optimal well design, estimation of reservoir parameter distributions.
Prerequisites:
Linear algebra (e.g. CME200), some statistics experience will be an advantage, programming fluency (C,
C++, Matlab or Fortran).
Suggested Texts (none required):
“Practical Optimization”, Gill, Murray and Wright, Academic Press, 1981
“Numerical Recipes” (second edition), Press, Teukolsky, Vetterling and Flannery, Cambridge Press, 1992
[your choice of Fortran or C]
“Nonlinear Parameter Estimation”, Bard, Academic Press, 1974 (read in library)
“Application of Optimal Control Theory to Enhanced Oil Recovery”, Ramirez, Elsevier, 1987 (read in
library)
“Genetic Algorithms”, Goldberg, Addison-Wesley, 1989 (read in library)
Course Outline:
1.
2.
Parameter Estimation: linear regression, orthogonal polynomials, chi-squared fitting, nonlinear
regression, confidence intervals, Newton, Gauss-Newton, Levenberg-Marquardt, Greenstadt methods,
automated history matching (sensitivity matrix estimation).
Optimization: line searching, Newton and quasi-Newton (BFGS), polytope, conjugate gradient,
singular value decomposition, simulated annealing, genetic algorithm, taboo search, Fang algorithm,
variational techniques, artifical neural networks (ANN), ant colony oprimization, gradual deformation.