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 subsurface oil and gas production operations. Therefore, accurate estimation of this deposition type can result in increasing the efficiency of oil and gas production. In this work, a novel approach is implemented to develop a predictive model for the estimation of solubility product data of barite in oilfield brines. del is proposed using a robust soft computing approach, namely, least-squares support vector machine (LSSVM) modeling optimized with the coupled simulated annealing (CSA) optimization approach. Our results indicate that there is good agreement between predictions based on the CSA-LSSVM model and literature data on the solubility product of barite in oilfield brines. Furthermore, performance of the developed model is compared with the performance of an artificial neural network, available correlation in the literature and standard software (OLI Scalechem) for predicting barite deposition. del perfectly fits the literature data with a squared correlation coefficient of 0.999.