Rigorous modeling for prediction of barium sulfate (barite) deposition in oilfield brines

Barium sulfate (barite) has been recognized to be a major operational problem in surface and subsurfaceoil and gas production operations. Therefore, accurate estimation of this deposition type can result inincreasing the efficiency of oil and gas production. In this work, a novel approach is implemented todevelop a predictive model for the estimation of solubility product data of barite in oilfield brines.The model is proposed using a robust soft computing approach, namely, least-squares support vec-tor machine (LSSVM) modeling optimized with the coupled simulated annealing (CSA) optimizationapproach. Our results indicate that there is good agreement between predictions based on the CSA-LSSVMmodel and literature data on the solubility product of barite in oilfield brines. Furthermore, performanceof the developed model is compared with the performance of an artificial neural network, availablecorrelation in the literature and standard software (OLI Scalechem) for predicting barite deposition.The model perfectly fits the literature data with a squared correlation coefficient of 0.999.