This was done to explore and compare the computational and predictive performance enhancements of the base models (if any). We developed meta-models of the generated models by implementing various ensemble learning algorithms (bagging, boosting, and stacking).

In building the models, 80% of the data set was used for training and 20% of the data set for testing.Īnother unique aspect of this research is the examination of diverse ensemble learning techniques for improving computational performance. A data set containing 329 experimental data records was used in the study. Temperature, foam quality, pressure, salinity, shear rate, nanoparticle size, nanoparticle concentration, and surfactant concentration were identified as relevant input parameters using principal component analysis (PCA). Support vector regression (SVR), K-nearest neighbors (KNN), classification and regression trees (CART), feed-forward multilayer perceptron neural network (MLPNN), and multivariate polynomial regression (MPR) algorithms were used to create models. The predictive and computational performance of five nonlinear ML algorithms were first compared. The first novelty of our study is the investigation of the predictive performance of ML approaches as viable alternatives for predicting the apparent viscosity of NP-Surf-CO 2 foams. This paper investigates the computational behaviors of simple-to-use, relatively fast, and versatile machine learning (ML) methods to predict apparent viscosity, a key rheological property of nanoparticle-surfactant-stabilized CO 2 foam in unconventional reservoir fracturing.

How to test the viscosity of foam