Speakers - Honggang Wang

Honggang Wang
Rudgers University, USA

Title: "Stochastic Modeling and Optimization for Oil/Gas Field Development Under Uncertainty"

Abstract: In this talk, I will discuss three major research topics: (i) optimal well placement under geological uncertainty. I will introduce a sequential uncertainty sampling and optimization framework, retrospective optimization (RO), for well placement optimization under uncertainty. Embedded in RO, a variety of sampling methods and nonlinear optimizers can be used to enhance modeling and optimization efficiency. Numerical results based on some field cases suggest that RO provides efficient solution techniques for practical problems. (ii) joint
optimization of well placement and well controls. We propose a new numerical optimization method for mixed integer simulation optimization, based on subspace simplex interpolation search. This new approach can effectively handle poor-scaling problems such as joint optimization problems in oil/gas projects. (iii) multi-objective optimization (MOO)
using zigzag search. Zigzag search method is first developed by me and is the current state-of-the-art for continuous MOO problems. Recently I designed the direct zigzag search for integer or mixed integer simulation optimization problems. I apply direct zigzag search to optimal development of carbon dioxide sequestration project cases considering multiple decision criteria, such as secure storage volume and leakage risk. The promising results suggest that this new algorithm outperforms the existing methods significantly.

Bio-sketch:
Honggang Wang is an assistant professor in Industrial and Systems Engineering at Rutgers University. He received his Bachelor of Science degree in Power Engineering from Shanghai Jiao Tong University, Shanghai, China, in 1996, Master of Science in Manufacturing Engineering from University of Missouri-Rolla, in 2004, and Ph.D. in Operations
Research from Purdue University, West Lafayette IN, in 2009. He has worked as a Postdoctoral Scholar in Energy Resources Engineering at Stanford University for two years before he joined Rutgers, NJ in 2011.

Dr. Wang's research and teaching interests lie in system uncertainty modeling and analysis, stochastic optimization, operations research, and their applications in energy production, sustainable energy, and manufacturing systems. Dr. Wang has won IBM faculty award 2012 for his work in oil/gas projects.