Full
LIAO Yan,born in 2001,is a master's student in Southwest Petroleum University. The research direction is logging geology,logging artificial intelligence processing and interpretation. E-mail: 18283426959@163.com.
Outline
As the exploration and development of shale gas continue to deepen,the accurate and efficient division and prediction of lithofacies have become key factors in evaluating the quality of shale gas reservoirs. Traditional lithofacies logging identification methods are limited by their strong subjectivity,inability to handle large-scale datasets,and difficulty in effectively capturing the complex nonlinear relationships in the data. This study takes the Lower Cambrian Qiongzhusi Formation shale gas reservoir in the Sichuan Basin as an example. It utilizes precise data from rock core testing and logging curves to establish a lithofacies-labeled dataset. A novel ensemble learning(Voting)model is proposed,integrating traditional machine learning models(Decision Tree,Support Vector Machine(SVM),and K-Nearest Neighbors(KNN))and a deep learning model(1D Convolutional Neural Network,1D-CNN)for lithofacies classification and prediction. The study also compares the predictive performance of traditional machine learning and deep learning models with that of the ensemble learning model,assessing its application potential in complex shale lithofacies prediction. The results indicate that the ensemble learning(Voting)model achieves the highest lithofacies prediction accuracy of 94.0%,significantly outperforming individual base learners(Decision Tree,KNN,SVM,and 1D-CNN). The proposed ensemble learning method effectively overcomes the recognition bias caused by sample imbalance in traditional classification algorithms by leveraging the strengths of the four base learner models. This improvement enhances the ability to identify lithofacies with limited samples,providing technical support for the high-precision reconstruction of logging-based lithofacies profiles and sweet spot prediction in the deeply buried,over-mature Qiongzhusi Formation shale gas reservoirs in the Sichuan Basin.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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