Abstract
The description of chemical kinetics is very import to the simulation of reactive transport for enhanced oil recovery (EOR). Characterizing petroleum ignition is especially important for simulation and prediction of In-Situ Combustion (ISC). In order to model crude oil oxidation reactions accurately, an experimental workflow is introduced to obtain kinetic parameters for ISC chemical reaction models. An optimization algorithm assists to match the reaction model parameters to the experimental results, and this validated model is used to predict ignition of crude oil in porous media. Apparent activation energy is estimated from ramped temperature oxidation experiments under several heating rates, including 1.5, 2.0, 2.5, 3.0, 5, 10, 15, and 20 °C/min. These experiments are separated into a small heating rates group (1.5, 2.0, 2.5, 3.0 °/min) and large heating rates (5, 10, 15, 20 °/min). The results show that experiments with small heating rates capture the details of reaction kinetics such that the estimated activation energy is more accurate, with the validated simulation model able to make accurate predictions for this particular crude oil. After matching the kinetics parameters, we predict the ignition conditions as a function of the air flow rates and the heat loss rates. The ignition envelope indicates that the window for air flow rates to ignite the oil decreases if the heat loss rate is high. Greater heat losses require more thermal energy to be released from the reaction to overcome losses and for ignition to occur. This leads to a narrower range of ignition air flow rates due to convective heat transfer. The uncertainty quantification results provide a confidence region for the ignition envelope impacted by the threshold temperature of the ignition criterion. The novelty of this work is the description of optimized combustion reaction models with rigorous experimental verification and uncertainty quantification for reactive transport simulations.
Uncertainty Quantification for Atomistic Reaction Models: An Equation-Free Stochastic Simulation Algorithm Example
