Regression of Dynamic Tube Blocking Test Data to Study the Kinetics of Calcite Scale Precipitation and Inhibition

Abstract The thermodynamics of scale precipitations have been extensively studied over the past century but integrating kinetics is essential to accurately predict real behavior of produced fluids. This integration allows for a more comprehensive understanding of the dynamic processes that govern scale formation in practical applications. This work aims at improving the understanding of calcite scale precipitation and inhibition kinetics by modelling dynamic tube blocking test data using a new "scale kinetics and inhibition tool" (SKIT). By regressing a few selected parameters, as needed, it is possible to match experimental data for scale precipitation with and without the presence of scale inhibitor. The Mixed Solvent Electrolyte (MSE) thermodynamic model and Classical nucleation theory (CNT) are the basis of the predictions. The MSE model computes the driving force for precipitation whilst CNT predicts the induction time of the precipitate. The main limitation of any kinetic model is the limited published data on the precipitation kinetics, particularly in the presence of scale inhibitors. This problem is addressed here by adding a user function that enables the regression of proprietary experimental data to produce kinetic parameters specific to that experiments, scale inhibitor or field/plant condition. Dynamic tube blocking test data for calcite (CaCO3) precipitation at variable temperature, salinity, ionic composition and inhibitor concentration were used in this study. The measured induction times were inputs to the SKIT tool along with compositions and test conditions. Up to five kinetic parameters for calcite and two parameters for each dissociated form of the scale inhibitor were regressed to match experimental data. All newly regressed parameters were stored in a private database which was subsequently utilized to calculate scale induction times under specific well conditions, from downhole to separator, and to evaluate CaCO3 kinetics with and without inhibitors. The results guided the selection of optimal scale inhibitor concentrations for a chemical field trial. This work demonstrates, for the first time, how proprietary experimental data on scale inhibition can be utilized to create a private database for modeling the kinetics of scale precipitation and inhibition. Furthermore, it illustrates how this data can be leveraged to enhance the modeling of scale behavior in real field scenarios, ultimately guiding more informed decisions on strategizing scale management through optimal chemical injection concentrations.