Abstract
Foam has many applications in the oil and gas industry, either in hydraulic fracturing, enhanced oil recovery, or drilling operations. The success of these operations depends largely on understanding the behavior of foam rheology, which is complex. The literature contains many models used to estimate the effective bulk foam viscosity; most were based on fitting parameters estimated from limited-experimental data. Nevertheless, the fitting parameters are not valid at different operating conditions such as temperature, pressure, and shear rate. This results in models with limited applicability as the laboratory conditions are hardly replicated. In this study, we generated 360 data points of effective bulk foam viscosity using the high pressure high temperature (HPHT) foam rheometer device. A wide range of conditions was examined, such as temperature, pressure, shear rate, foam quality, and composition. The gas-phase consisted of either CO2 or N2, while four types of water representing different salinities were used in the liquid phase. The foam was generated using seven different commercial surfactants at different concentrations. Also, low pH chelating agent and corrosion inhibitor were added in some experiments. The data pool was analyzed using four machine learning techniques: Artificial Neural Network (ANN), Decision Trees (DT), Random Forest Regressor (RFR), and K-Nearest Neighbor (KNN). ANN showed the highest accuracy with R2 of 0.972 and 0.985 on the training and testing datasets, respectively. Also, the relative importance of features was examined using Pearson, Spearman, and Kendall correlation coefficients. The most significant parameters in reducing foam viscosity were temperature, corrosion inhibitor, and shear rate, respectively. On the contrary, foam quality positively impacted the foam viscosity, where 80% foam quality was the maximum tested condition. The impact of pressure, surfactant concentration, water type, and chelating agents were complex. This paper provides a simplified ANN-based model which can be used on the fly to predict the effective bulk foam viscosity in both laboratory and field conditions.
•This research generated 360 data points of effective foam viscosity utilizing the HPHT foam rheometer.•The data pool was analyzed using four machine learning techniques.•The performance of ANN was much better than the other three techniques.•The sensitivity analysis with the proposed ANN model showed that it can predict the physics-based behavior.•An empirical model was provided in this study to predict the effective foam viscosity.