Robust uncertainty quantification for green hydrocarbon production in carbonate reservoirs

The current situation with green gas emission requires the development of low carbon energy solutions. However, a significant part of the modern energy industry still relies on fossil fuels. To combine these two contradictory targets, we propose to develop a novel technology based on a combination of CO2 sequestration with Enhanced Oil Recovery (EOR) in the carbonate reservoirs. The carbonate reservoirs contain a substantial amount of hydrocarbons and propose a significant storage capacity for CO2 sequestration. However, the CO2 injection into carbonate hydrocarbon reservoirs poses important challenges for predictive simulation process. The main challenge corresponds to large uncertainties of the reservoir characterization. The low permeability of porous matrix and highly heterogeneous structure with fractures and caverns challenge the modeling process and require an accurate evaluation of uncertainties. Additional modeling challenges correspond to potential fracturing and carbonate dissolution process triggered by the injected CO2. In this proposal, we aim to develop a multi level optimization framework with the most innovative numerical and computational methods for uncertainty quantification for carbonate reservoirs. This framework is intended to be used to further improve and quantify the results from forward simulation for the end users. At one level, it addresses a multi objective optimization challenge where several targets have to be balanced in the desired solution (EOR and CO2 sequestration) coupled by the large uncertainty in the characterization for these reservoirs. At another level, it further optimizes the results by applying a reduction in physics complexity with advanced nonlinear solvers. An example of such reduction is an implementation of the prolongation operator, used in the multi-scale reconstruction of compositional transport. Finally, another level for optimization is applied for the spatial discretization for the governing equations and subsurface domain in combination with the reduce physics model. This will lead to a unique optimization software solution that can provide the end users with more reliable data and analysis in order to manage their resources at the most optimal level. The framework development will be conducted mainly in Qatar under supervision of Dr. Ahmad Abushaikha (HBKU) with contributions from Dr. Mayur Pal (North Oil Company), Dr. Denis Voskov (TU Delft), Dr. Vasily Demyanov (Heriot Watt University) and Prof. Hamdi Tchelepi (Stanford University). The group of Dr. Abushaikha will focus on project coordination, the general structure of the framework and discrete spatial optimization. Dr. Pal’s group will contribute to the fractures modeling framework where their employer is the end user. Dr. Demyanov’s team will develop the optimization framework. The data-driven reduction of physical complexity will be developed by Dr. Voskov’s team. Finally, an efficient non-linear solver will be developed by Prof. Tchelepi’s team. The proposed research project fits within the Oil and Gas and the Water production, storage, reuse, and management themes of the Energy and Environment pillar of the National Priorities Research Program (NPRP) and its goals are fully aligned with Qatar’s strategy for the responsible exploitation of oil and gas in Qatar’s National Vision 2030. This proposal will lead to the development of a unique optimization framework for carbonate reservoirs. The publications, based on this project, will be presented and published at highly prestigious conferences and journals. The proposal has co-funding in-cash and in-kind from North Oil Company (operator of Al-Shaheen field of Qatar) the end user for this project.

Hamad Bin Khalifa University (HBKU)