Pore-Scale modeling for NMR and PC measurements using Random-Walk, Finite-Elements and Machine-Learning

The wire-line nuclear magnetic resonance (NMR) tool is a well-borehole logging technology that is routinely used for exploration and reservoir characterization. NMR logging is a fast imaging technology that can be applied while drilling within the wellbore to provide various spatially continuous measurements, such as formation porosity, permeability, water, and hydrocarbon distributions. This technology has enabled major advances in the oil & gas industry related to exploration, reservoir characterization, and management. However, its capability is not fully exploited. 

The objective of this project is to develop a modeling framework including the use of Random-Walk method to predict NMR measurements, pore-scale finite-element modeling on 3D digital models, generated from CT-images to predict capillary pressure, and data-driven, physics-driven machine-learning.    

Applications are sought for a two-year postdoc position, and will work closely with an industry partner. The position will include a competitive salary based on the candidate’s qualifications; benefits include medical and dental insurance, free furnished housing on the KAUST campus, annual travel allowance to visit home country, annual paid vacation, and other generous benefits. The successful applicant will become a part of the Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC) at KAUST.