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In the last decade, several developments in numerical simulations of rock properties known as Digital Rock Physics showed the ability to estimate reliably rock properties using images in sandstones but failed in several cases in carbonates. Since 2010, Khalifa University has developed software tools and expertise in this domain though projects funded by the ADNOC R&D Oil Subcommittee: Rock Physics Model for Fluid Substitution in Carbonates (2010-2013); Validation and Optimization of Digital Rock Physics in UAE Carbonates Project (2014-2015); Experimental and Digital Rock Physics: Mechanical Properties and Flow Properties from Nanotomography to Core Scale Analysis (2014-2017).

The 3D images obtained using X-ray Micro-tomography or Focused Ion Beam (FIB) allow investigating core samples to model solid frame and pore network at very high resolution. Due to physical limitations of detectors sizes in scanning devices, it is not possible to image macro, micro, and nano pores in the same acquisition. The general strategy to deal with this limitation is to use X-ray micro tomography to scan standard cylindrical rock core plug with a 1.5-inch diameter at a coarse scale (resolution 20 渭m). Then smaller subsets are extracted and scanned at fine scale (resolutions 1渭m to 4渭m). Numerical simulations allow estimating numerically rock properties on digital models of subsets. However, due to the very small size of subsets it is not possible to validate these simulations experimentally in laboratory. Also, producing numerical simulations at several scales for different locations can be time consuming.

In this project, we have two main objectives; the first consists of studying the feasibility of using 3D printing technology to print extracted subsets in larger scale to make laboratory experiments feasible. Producing these 3D print of rock subsets will allow validating our simulations experimentally at laboratory scale. The second objective is to use texture analysis and machine learning along with 3D printing results to simulate rock properties in complex carbonates rocks. The project will benefit geoscientist and petroleum engineers in the UAE as it will help understand the effect of micro and nanoscale structure in oilfield reservoir rocks.

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The reduced reaction mechanism will be used to simulate the Claus furnace and WHB. The predicted data will be validated against experimental data to ensure the high predictability and reliability of the reduced kinetic model. The robust Chemkin Pro software will be systematically coupled with HYSYS and MATLAB or HEEDS (an optimization software) to develop a user-friendly soft analyzer. The coupling of two process simulators offers a novel means of enhancing the computational accuracy of SRU simulations. Chemkin Pro, while handling the thermal section accurately, could not be used to model the catalytic section due to its complexity, and Aspen HYSYS (an equilibrium based process simulator) could not be used to capture the detailed chemistry of kinetically-limited reactions of BTEX destruction. Thus, the use of two process simulators is necessary for a reliable and complete evaluation of the SRU performance and fuel gas consumption in different Habshan processing units. This will assist in evaluating the reliability and potential benefit of the proposed soft analyzer that will be developed. An effort will be made to minimize the computational time of the soft analyzer to the minimum possible through the use of machine learning algorithms.

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In the UAE, the arguments are not different, and being one of the highest emitters of CO2 per capita, a lot of work lies ahead in this area. But the interest in CO2 capture locally goes beyond control of emissions to the atmosphere. To enhance the recovery/production of crude oil from existing wells, injection of CO2 is one of the available and preferred options. Large volumes are needed though; these may not be readily available and in some cases since even fossil fuels are burnt simply to generate it. Clearly this is not an advantageous solution, and technologies to effectively capture CO2 will be of continued interest. Reduction of CO2 emissions is directly influenced by the efficiency (read cost) by which carbon can be captured. Along these challenges, the project aims to develop energy鈥恊fficient and sustainable solutions based on hybrid capture using highly selective, high鈥恈apacity bi鈥恜hasic sorption systems and processes.

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