Kinetic Modeling of Hydrogen Generation via In Situ Combustion Gasification of Heavy Oil

Abstract

In the global push for sustainable energy, in situ combustion gasification (ISCG) has emerged as a transformative technology to leverage the world’s abundant heavy oil reserves for producing carbon-zero hydrogen. Chemical kinetics are crucial for modeling subsurface hydrogen generation and optimizing production schemes to maximize hydrogen yield, which are however currently lacking. This study aims to develop the first experimentally validated kinetic model for hydrogen generation during ISCG of heavy oil. To accurately model ISCG reactions, particularly hydrogen generation, we combined kinetic cell experiments with numerical modeling to history match the experimental results. The temporal variation of generated gases, such as hydrogen, measured in laboratory experiments, served as the baseline for history matching. A differential evolution optimization algorithm was employed to calibrate the kinetic parameters of the numerical model with experimental results. The kinetic model for combustion reactions was accurately calibrated after 454 optimization runs with a history-matching error of 3.46%. This accuracy is attributed to the well-studied nature of heavy oil oxidation and the comprehensive reaction scheme employed. Conversely, calibrating the kinetic model for gasification reactions with kinetic cell experimental results proved more challenging yielding a history-matching error of 22.19% after 488 optimization runs. Despite significant uncertainties in hydrogen generation and consumption reactions due to limited knowledge of the gasification process, our proposed kinetic model can still predict hydrogen generation with a simplified but powerful reaction scheme, compared to previously proposed ISCG models that involve numerous reactions. This work introduces the first kinetic model to describe the hydrogen generation process during ISCG of heavy oil with rigorous experimental validation. This reliable kinetic model establishes a solid foundation for future multiscale reservoir simulation and further optimization of the field development for enhanced hydrogen production in a more sustainable manner.