Project description: This project examines introducing a problem-specific Genetic Algorithm (GA) for optimal well placement in oil fields. The outcome of this project is a framework that improves the convergence rate to the optimal solution, while maintianing a relativly less computational cost.
Funding: Schlumberger
Development Methods: Genetic Algorithm, Artificial Neural Network, Similarity Measures
Development Tools: MATLAB (Including: GUI design), Eclipse Reservoir Simulator
[2017]:
An efficient geometry-based optimization approach for well placement in oil fields
Z. Hamida, F. Azizi, G. Saad
Journal of Petroleum Science and Engineering 149, 383-392
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[2016]:
Hybrid Optimization Techniques for Oil Field Development.
Z. Hamida
M.Sc. Thesis, American University of Beirut, Lebanon.