Grant: Discovery Grants Program - Individual

Asphaltenes are chemical aromatic compounds that may precipitate at certain thermodynamic conditions and fluid characteristics. Oil production and transportation operations can be affected by asphaltene precipitation/deposition through wettability alteration and blockage mechanisms. This undesirable behavior is found in many oil reservoirs across Canada, resulting in significant capital / operating costs and environmental issues. Further understanding and more accurate prediction of asphaltene characteristics and precipitation/deposition processes in reservoir rocks are vital for better design, implementation, and control of oil production operations.This research program aims to develop better understanding of asphaltene characterization, precipitation, and inhibition mechanisms through experimental studies combined with theoretical modeling by Lattice Boltzmann method, Computational Fluid Dynamics, Equation of State (EOS), statistical tools, and smart techniques. The proposed research aims to reduce or eliminate the existing drawbacks (overfitting, non-feasible retraining and being stuck in local optima) with conventional methods and offer better accuracy and reliability in terms of thermodynamic behavior, extent of precipitated asphaltene, and asphaltene properties. Microfluidic chips will enable studies of the flow in porous media of random pore structures at microscale. After a characterization stage, flow tests will be conducted in porous media to track the likelihood of asphaltene precipitation, deposition or/and dissolution. Gas Chromatograph (GC), viscometer, and High Performance Liquid Chromatography (HPLC) will be employed for the purposes of asphaltene characterization and precipitation phenomena identification. In addition, visual and quantitative methodologies (Near Infra Red (NIR) and High Pressure Microscopy (HPM)) will be used to study the impacts of key factors (pressure and temperature) on the onset and morphological behavior of asphaltene.Utilizing the experimental and literature data, better statistical and connectionist models (non-linear regressive equations) will be developed to relate temperature, pressure, and molecular weight to asphaltene precipitation/deposition. This research will also examine constants and adjustable parameters of the EOS model in order to better characterize asphaltenes, study their thermodynamic behavior, and predict precipitation/deposition occurrence. After the asphaltene precipitation/deposition investigations, chemical methods and ultrasonic irradiation for prevention/removal of asphaltene deposition will be examined under static and dynamic conditions. The experimental and modeling studies will be used to find optimal operating conditions (temperature and composition) in asphaltene deposition and inhibition operations using genetic algorithm method.