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

• The support vector machine (SVM) algorithm is used to calculate solubility product data of barite in brines.
• The coupled simulated annealing (CSA) optimization tool is applied to obtain the optimal model parameters.
• Acceptable agreement between the experimental and predictions is observed.

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.The model 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.The model perfectly fits the literature data with a squared correlation coefficient of 0.999.

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