Kan, Amy T. (Rice University) | Dai, Joey Zhaoyi (Rice University) | Deng, Guannan (Rice University) | Ruan, Gedeng (Rice University) | Li, Wei (Rice University) | Harouaka, Khadouja (Rice University) | Lu, Yi-Tsung (Rice University) | Wang, Xin (Rice University) | Zhao, Yue (Rice University) | Tomson, Mason B. (Rice University)
Numerous saturation indices and computer algorithms have been developed to determine if, when, and where scale will form, but scale prediction can still be challenging since the predictions from different models often differ significantly at extreme conditions. Furthermore, there is a great need to accurately interpret the partitioning of H2O, CO2, and H2S in different phases, and the speciations of CO2 and H2S. This presentation is to summarize current developments in the Equation of State and the Pitzer models to accurately model the partitioning of H2O, CO2, and H2S in hydrocarbon/aqueous phases and the aqueous ion activities at ultra high temperature, pressure and mixed electrolytes conditions. The equations derived from the Pitzer ion-interaction theory have been parametrized by regression of over 10,000 experimental data from publications in the last 170+ years using a genetic algorithm on the super computer, DAVinCI. With this new model, the 95% confidence intervals of the estimation errors for solution density are within 4*10'4 g/cm3. The relative errors of CO2 solubility prediction are within 0.75%. The estimation errors of the saturation index mean values for calcite, barite, gypsum, anhydrite, and celestite are within ± 0.1, and that for halite is within ± 0.01, most of which are within experimental uncertainties. This model accurately defines the pH of the production tubing at various temperature and pressure regimes and the risk of H2S exposure and corrosion. The developed model is able to predict the density of soluble chloride and sulfate salt solutions within ±0.1% relative error. The ability to accurately predict the density of a given solution at temperature and pressure allows one to deduce when freshwater breakthrough will occur. Lastly, accurate predictions can only be reliable with accurate data input. The need to improve accuracy of scale prediction with quality data will also be discussed.