Prediction of igneous lithology and lithofacies based on ensemble learning with data optimization

作者：Ruiyi Han, Zhuwen Wang, Zhitao Zhang, Xinru Wang, Yitong Cui, Yuhang Guo · 发表于：Geophysics · 年份：2024 · DOI：10.1190/geo2022-0782.1 · 被引用次数：9 · 研究领域：Reservoir Engineering and Simulation Methods、Mineral Processing and Grinding、Imbalanced Data Classification Techniques

ABSTRACT Igneous rocks are widely developed in various Mesozoic and Cenozoic continental and marine basins. Igneous reservoirs are the key reservoirs for current oil and gas development. Accurate prediction of lithology and lithofacies is a prerequisite for the effective exploration of igneous reservoirs. Igneous lithology and lithofacies are complex and correlated. The existing single-label igneous rock identification methods only consider the prediction of individual properties, and less consideration is given to the correlation of reservoir properties. Therefore, lithology and lithofacies prediction based on conventional logging data is regarded as a typical class-imbalanced multilabel classification problem when considering both attribute correlation in algorithms and evaluation metrics. To solve this problem, an ensemble method of data optimization combined with multigrained cascade forest (CF) is used in this study to develop a new multilabel lithology and lithofacies prediction model based on data from nine conventional logs in the eastern depressional reservoirs of the Liaohe Basin, and satisfactory results are obtained. The imbalance problem of the conventional logging data sets is first solved by using K-means and synthetic minority oversampling technique methods; then, the model is trained by scenario transformation and stripping with multigrained CF; and next, a multilabel classification evaluation index with multiple perspectives is introduced. The differences be...