Tabnet efficiency for facies classification and learning feature embedding from well log data

The well log data is represented as raw tabular data with diverse and nonlinear features. This poses a challenge for feature learning by machine learning models. The recent popular decision tree-based algorithms, such as random forest (RF), extreme gradient boosting (XGB) are prominent for learning nonlinear relationships of well log data in comparison with other methods of support vector machines (SVMs) and even deep learning models. In this work, we proposed using Tabnet model for direct learning tabular data of well logs. To our knowledge, this is the first time a state-of-the-art transformer-based model of Tabnet has been utilized for this task. The efficiency of Tabnet-based feature embedding is evaluated in two tasks of rock facies classification and learning feature embedding. We prove the efficiency of Tabnet model by experimental results on two small datasets of public Kansas dataset, which has nine wells for training and two wells for testing, and our own-built dataset, which has four wells for training and one well for testing. Although training on the modest amount of well log data, the proposed Tabnet model still promotes better classification efficiency than tree-based models of RF, XGBoost, LightGBM and deep learning models of MLP, CNN-1D, and ResNet-1D. KEY POINTS:Tabnet efficiency for facies classification and learning feature embedding from well log data.A challenge to learn these raw features directly for separating classes of facies.The superiority of the Tabnet network in comparison with other ruling tree-based methods and deep learning models.Facies classification and learning feature embeddings for categorical variables of well logs.