Geological Carbon Storage (GCS) is an important geo-solution to mitigate the increasing global carbon emission in order to meet the goal of Paris Climate Agreement. For the purpose of GCS management, numerical simulators with coupled physics (flow/reactive/geo-mechanics/thermal) have been developed and often applied to predict the long-term fate of the injected carbon dioxide to ensure its secure containment. The prohibitively high computational cost of such simulations necessitates the development of efficient and robust surrogate models for general GCS modeling tasks, especially when inverse modeling tasks require high-frequency evaluations of forward models, in order to quantify the uncertainties of rock and fluid properties in the subsurface formations.
Therefore, the objectives of this research project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict the coupled physical process of GCS, such that we can efficiently forecast the spatial-temporal patterns of the subsurface response variables, e.g., pressure, saturation, minerals etc.; (2) integrate the surrogate forward models with a Bayesian inverse modeling framework to achieve real-time or near-real-time uncertainty quantification, such that we can efficiently resolve the uncertainties rising from rock and fluid, and thus improve our understanding about of GCS systems.
Education:
A Ph.D. degree in computational mathematics, reservoir/petroleum engineering, machine learning or closely related fields. The candidate must have completed all Ph.D. requirements by commencement of the appointment and be within 5 years of completion of the Ph.D.
Minimum Job Requirements:
(1) Experience in computational subsurface flow and transport modeling
(2) Experience with code development
(3) Publications in refereed journals and record of successful research in a collaborative team environments
Desired Qualification:
(1) Experience with Python
(2) Experience in scientific machine learning, deep learning and computer vision
(3) Experience in CCUS modeling with numerical reservoir simulators such as CMG, Eclipse, MRST or TOUGH-React
(4) Experience in inverse modeling and uncertainty quantification
Applications are sought for a one-year postdoc position (extendable) and will work closely with an industrial 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 be affiliated with the Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC) at KAUST.
King Abdullah University of Science and Technology (KAUST)
