Establishing a Robust Model Using Stacked Generalization for Gas Identification of Coal-Bearing Strata in Block A, Northeastern Ordos Basin, China

Abstract

Coal measure gas is a research hotspot in recent years. And yet the complexity of source-reservoir relationships and the ambiguity of the gas/water interface in coal measure reservoirs bring challenges to the traditional gas identification methods. With the development of intelligent computing, machine learning has shown good development prospects in the field of oil and gas exploration and development. However, on the one hand, the more capable the learning algorithm is, the greater the demand for data; on the other hand, traditional learning methods suffer from difficulties in hyperparameter tuning and generalization improvement when learning samples are insufficient. To perform intelligent and reliable gas identification in the coal measure reservoir, an ensemble learning-based gas identification method was proposed. The method models a two-layer structure. The first layer consists of multiple models that were trained by different learning algorithms, such as k-nearest neighbor (kNN), decision tree (DT), neural network (NN), and support vector machine (SVM). While the second layer was used to relearn the output of the first layer, which was implemented by logistic regression (LR). We tested and practically applied this method to real data from a coal measure reservoir in Block A of the Ordos Basin, China. The experimental results showed that our method significantly improved the learning ability of the individual learners on the small sample and performed most consistently when the hyperparameter changes. Moreover, random forest (RF) and deep NN (DNN), as the comparison methods in practical applications, were slightly inferior to ours due to greater computational effort and lower robustness and prediction accuracy. This demonstrates the superiority of our method for fast and effective log-based gas identification, and also suggests that stacking has great potential that is not limited to gas identification tasks.