Machine learning-based steam channeling identification for steam injection of heavy oil reservoirs

Occurring of steam channeling during cyclic steam injection of heavy oil reservoirs is attributed to both geological and engineering factors. The current methods for identifying steam channeling are limited to the reservoir engineering approach and numerical simulation, which fail to capture the uncertainty and correlation between factors. Nevertheless, machine learning can recognize implicit correlations among massive data and has high accuracy and low maintenance. This research investigated the factors affecting steam channeling and performed the feature engineering processing after building the base dataset, including data reconstruction, dealing with missing values, dimension transformation and similarity analysis to build the feature attribute set for steam channeling. Subsequently, the dimensionality reduction of the dataset was carried out via the Wrapper method, Embedded method and principal component analysis to deliver three schemes of feature combinations. The steam channeling prediction models were built using the random forest, support vector machine (SVM), neural network, and XGBoost algorithms, respectively, of which the prediction accuracies and predicted steam channeling pathway distributions were presented. The research showed that the steam injection intensity, permeability extreme value of layers and well spacing have the largest influences on steam channeling. The data-algorithm combination with the best performance is the PCA dataset with the XGBoost model, which precisely predicts steam channeling with an accuracy of 97.20% for the training set and 96.11% for the validation set.