用集成分类器预测格罗宁根气田岩石含气概率，精度高且数据增广效果显著。
Groningen: Spatial Prediction of Rock Gas Saturation by Leveraging Selected and Augmented Well and Seismic Data with Classifier Ensembles
- 通过筛选与增强井位和地震数据，构建1481个属性并保留63个关键特征。
- 数据增广使训练样本量提升9倍，盲测42口井的准确率达F1=0.7949，MCC=0.7689。
- 适合油气勘探、地质建模及机器学习在能源领域应用的研究者参考。
本文以大型格罗宁根气田为例，展示了利用分类器集成方法进行岩层含气概率空间预测的可行性。文中详细描述了生成1481个地震场属性并筛选出63个显著属性的过程。所提出的井位与地震数据增强方法有效将训练样本扩大9倍。在42口井的盲测样本上，集成分类器表现优异：马修斯相关系数为0.7689，'气藏'类别的F1分数达0.7949。同时对气藏厚度在区块及邻近区域进行了预测。
原文摘要 · Abstract (English)
This paper presents a proof of concept for spatial prediction of rock saturation probability using classifier ensemble methods on the example of the giant Groningen gas field. The stages of generating 1481 seismic field attributes and selecting 63 significant attributes are described. The effectiveness of the proposed method of augmentation of well and seismic data is shown, which increased the training sample by 9 times. On a test sample of 42 wells (blind well test), the results demonstrate good accuracy in predicting the ensemble of classifiers: the Matthews correlation coefficient is 0.7689, and the F1-score for the "gas reservoir" class is 0.7949. Prediction of gas reservoir thicknesses within the field and adjacent areas is made.
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