Predicting the surfactant-polymer flooding performance in chemical enhanced oil recovery: Cascade neural network and gradient boosting decision tree

Abstract

Surfactant-polymer flooding is one of the most important enhanced oil recovery (EOR) techniques, which refers to the injection of surfactant slugs and polymer drives. Two crucial decision-making parameters in EOR operations are net present value (NPV) and oil recovery factor (RF). Herein, various intelligent models, based on multilayer perceptron (MLP), cascade neural network (CNN), radial basis function (RBF), neural networks as well as support vector regression (SVR), and decision tree (DT) algorithms are proposed toward estimating these two parameters with respect to polymer drive size, surfactant slug size, the salinity of polymer drive, Kv/Kh ratio, surfactant concentration, and polymer concentration in polymer drive and surfactant slug. The results exhibited the outperformance of the CNN model trained with the Levenberg Marquardt algorithm in forecasting the RF and NPV with average absolute errors of 0.66% and 1.95%, respectively. Moreover, the results of the sensitivity analysis reflected that the most effective inputs on the predicted value of RF were surfactant concentration and surfactant slug size, while surfactant concentration and polymer concentration in surfactant slug could considerably affect the NPV model’s output. Lastly, the outlier detection analysis revealed that the employed data is valid and only two points were detected as outliers.

Keywords

• Enhanced oil recovery
• Artificial neural networks
• Surfactant-polymer flooding
• Intelligent model
• Recovery factor
• Net present value