Ali Sadeghkhani

Profile

Passionate about the fundamental principles of Porous Medium, my academic journey and professional experiences have been a continuous exploration into computational modeling and fluid dynamics in porous materials. During previous Academic studying I've engaged deeply with subjects spanning Physics, Fluid Mechanics, and Computational Mathematics especially in the cases of fluid-solid and fluid-fluid micro interactions. As a Log-Data Analyzer in the Geoscience department, I analyzed the porous media and the fluid(s) inside from diverse perspectives via the logging tools passed through the well to record different matrix and fluid properties. Various properties which are inherent-responses of bulk volume to radiated waves/ particles of far apart technologies like Sonic, Electric, and Radioactive instruments.
Currently as a Postgraduate Researcher at the University of Leeds, I'm immersed in a groundbreaking project titled "Reconstruction of 3D images of porous media from 2D images using Conditional Generative Adversarial Neural Networks." Here, I contribute my expertise to develop a computational framework using machine learning methods, specifically Conditional Generative Adversarial Neural Networks, aiming to generate 3D realizations from 2D images. It's an exhilarating experience being part of the Computing Group, addressing real-world micro-scale fluid-solid interactions, and providing an opportunity to enhance both my porous media knowledge and programming skills. Excited about the dynamic challenges ahead, I'm keen on contributing to collaborative endeavors and leveraging my background to make meaningful contributions in the realm of porous media and computational modeling.
 

Qualifications

• M.Sc. Petroleum (Drilling) Engineering, Petroleum University of Technology
• B.Sc. Petroleum Engineering, Petroleum University of Technology