Rami M. Younis, Ph.D.
Office Hours - Fall 2013
Tuesdays & Thursdays, 2:00 p.m. - 4:30 p.m.
Dr. Younis's primary research focus is to advance the state-of-the-art of Computational Predictive Simulation tools and technologies. His background is in the intersection of Reservoir Simulation, Computer Science, and Applied Mathematics.
Dr. Younis's goal is to advance simulation software, solver, and usage-paradigm technologies towards a future where we routinely use full-resolution full-physics field-scale simulations to successfully operate emerging unconventional subsurface resource and environmental applications. His specific application interests include nonlinearly coupled Enhanced Oil Recovery processes, Underground Coal Gasification, and geologic Carbon Sequestration. He joined The University of Tulsa in 2012 and is building a Reservoir Simulation research program with two broad research directions.
Software: Towards simulators that write themselves. The goal of this research direction is to rethink and reengineer the way that we develop, deliver, and maintain simulation software. Owing to the physical complexity inherent to current and emerging reservoir engineering applications, simulator developers have long recognized the pains and needs in this area. The vision is for a future where:
(a) Simulators are tailor-made, on-demand, and with unprecedented levels of customization.
(b) Engineers design simulators using a universal format and any graphical mathematics editor.
(c) Given specifications, vendors use Automated Simulator Generator Systems ASGS to automatically deliver a high-performance simulator.
Solvers: A future where solvers exploit an understanding of physics. There is a distinct nonlinear stiffness that arises when processes couple various physics that are each characterized by different scales. This research direction is pioneering a move towards porting a classic understanding of such physics at the continuous scale to the center-stage of modern discrete nonlinear solver design. Under this paradigm, insights into physics are adaptively acknowledged within the nonlinear solver of a fully-coupled method rather than by the numerical method itself, or by the spatial and temporal discretization. Emerging simulators will retain the accuracy and robustness properties of fully-coupled implicit methods while fully exploiting adaptivity within the solver in order to improve their computational efficiency.
Education and Degrees Earned
- Postdoctoral Research Fellow, Stanford University
- Ph.D., Petroleum Engineering, Stanford University
- M.S., Scientific Computing and Computational Mathematics, Stanford University
- M.S., Petroleum Engineering, Stanford University
- B.Eng., Honors in Mechanical Engineering, McGill University