Calcium ions are usually present at high concentrations in brines produced with oil and gas. Such brines are typically saturated with respect to CaCO3. Consequently, precipitation of CaCO3 as scale on the internal wall of the pipeline can readily occur due to changes in operational and environmental parameters as produced fluids are transferred from downhole to surface facilities. Despite its importance, there is minimal research in the literature addressing the effect of calcium ions, and specifically CaCO3 scale, on the CO2 corrosion mechanism. The main objectives of this research are to further broaden the mechanistic understanding of CO2 corrosion of mild steel in the presence of high concentrations of calcium ions and evaluate the protectiveness that FexCayCO3 and CaCO3 scale confers against further corrosion. The corrosion behavior was studied in situ by electrochemical methods, including linear polarization resistance and open-circuit potential, along with weight loss using a UNS G10180 steel with ferritic-pearlitic microstructure. Surface characterization of the scale and corrosion product was performed using scanning electron microscopy, energy dispersive x-ray spectroscopy, and x-ray diffraction. A descriptive model is proposed for the CO2 corrosion mechanism of mild steel in the presence of high concentrations of calcium ions. Unprotective FexCayCO3 (y>x) and CaCO3 scales were observed to act as a mass transfer barrier that could promote surface conditions favoring FeCO3 precipitation. The presence of uniform CaCO3 scale did not result in the onset of localized corrosion at the conducted experimental conditions.

Wax deposition in petroleum industry is one of the major problems requiring accurate predictive procedures to reduce the deficiencies and effective designing of the process. An adaptive neuro fuzzy inference system (ANFIS) model is proposed to predict the wax deposition in oily systems. Parameters of the ANFIS model are optimized using the particle swarm optimization (PSO) method. Results are then compared to those previously reported by Kamari et al., demonstrating better performance of the proposed ANFIS model. Statistical and graphical approaches are employed to investigate the reliability of the proposed model, illustrating the model's capability of precise estimation of the wax deposition. Coefficient of determination (R²) and mean squared error (MSE) values of 0.994 and 0.053 are obtained for the proposed ANFIS model, revealing the reliable prediction of wax deposition by the proposed ANFIS model.