Abstract
Lithofacies identification is critical to energy exploration and reservoir evaluation. Machine learning provides a way to use logging data for lithofacies intelligence identification. However, labeled logging data are usually scarce, which makes the currently used supervised algorithms less effective, so semi-supervised methods have received attention from researchers. In this paper, we propose to apply Tri-Training to the field of lithofacies recognition. The framework used Random Forest (RF), Gradient-Boosted Decision Trees (GBDT), and Support Vector Machine (SVM), as the baseline supervised classifiers, and based on the idea of inductive semi-supervised methods and ensemble learning. Baseline classifiers are trained and iterated using unlabeled data to obtain effect improvement. The final results are output in an ensemble paradigm. We used seven logging parameters from two wells as input and divide the data randomly 10 times for training and testing. With only five samples of each lithology, the prediction accuracy improved by the average of 2.1% and 14.5% in both wells compared to the baseline methods. In addition, we also compared two commonly used semi-supervised methods, label propagation algorithm (LPA) and Co-Training. The experimental results also confirm that Tri-training has the better and more stable performance. The Tri-training method in this paper can be effectively applied to lithofacies identification under scarce labeled logging data.
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Data availability
The data are not publicly available due to Privacy of data. Other relevant materials during the current study are available from the corresponding author on reasonable request.
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Funding
The research was supported by National Natural Science Foundation of China (No. 41374116 and 41674113).
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All authors contributed to the study conception and design. Xinyi Zhu: Methodology, Software, Visualization, Writing—original draft. Hongbing Zhang: Data collection, Conceptualization, Writing—review & editing. Quan Ren: Validation, Software, Data curation. Dailu Zhang: Supervision, Parameter analysis. Fanxing Zeng: Resources, Data curation. Xinjie Zhu: Resources, Data curation. Lingyuan Zhang: Programing, Visualization. All authors read and approved the final manuscript.
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Communicated by: H. Babaie
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Zhu, X., Zhang, H., Ren, Q. et al. A Tri-Training method for lithofacies identification under scarce labeled logging data. Earth Sci Inform 16, 1489–1501 (2023). https://doi.org/10.1007/s12145-023-00986-w
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DOI: https://doi.org/10.1007/s12145-023-00986-w