基于BiLSTM-XGBoost混合模型的储层岩性识别

储层岩性分类是地质研究基础, 基于数据驱动的机器学习模型虽然能较好地识别储层岩性, 但由于测井数据是特殊的序列数据, 模型很难有效提取数据的空间相关性, 造成模型对储层识别仍存在不足. 针对此问题, 本文结合双向长短期循环神经网络(bidirectional long short-term memory, BiLSTM)和极端梯度提升决策树(extreme gradient boosting decision tree, XGBoost), 提出双向记忆极端梯度提升(BiLSTM-XGBoost, BiXGB)模型预测储层岩性. 该模型在传统XGBoost基础上融入了BiLSTM, 大大增强了模型对测井数据的特征提取能力. BiXGB模型使用BiLSTM对测井数据进行特征提取, 将提取到的特征传递给XGBoost分类模型进行训练和预测. 将BiXGB模型应用于储层岩性数据集时, 模型预测的总体精度达到了91%. 为了进一步验证模型的准确性和稳定性, 将模型应用于UCI公开的Occupancy序列数据集, 结果显示模型的预测总体精度也高达93%. 相较于其他机器学习模型, BiXGB模型能准确地对序列数据进行分类, 提高了储层岩性的识别精度, 满足了油气勘探的实际需要, 为储层岩性识别提供了新的方法.

Reservoir Lithology Identification Using Hybrid Model BiLSTM-XGBoost

Reservoir lithology classification is the foundation of geological research. Although data-driven machine learning models can effectively identify reservoir lithology, the special nature of well logging data as sequential data makes it difficult for the model to effectively extract the spatial correlation of the data, resulting in limitations in reservoir identification. To address this issue, this study proposes a bidirectional long short-term memory extreme gradient boosting (BiLSTM-XGBoost, BiXGB) model for predicting reservoir lithology by combining bidirectional long short-term memory (BiLSTM) and extreme gradient boosting decision tree (XGBoost). By integrating BiLSTM into the traditional XGBoost, the model significantly enhances the feature extraction capability for well logging data. The BiXGB model utilizes BiLSTM to extract features from well logging data, which are then input into the XGBoost classification model for training and prediction. The BiXGB model achieves an overall prediction accuracy of 91% when applied to a reservoir lithology dataset. To further validate its accuracy and stability, the model is tested on the publicly available UCI Occupancy dataset, achieving an overall prediction accuracy of 93%. Compared to other machine learning models, the BiXGB model accurately classifies sequential data, improving the accuracy of reservoir lithology identification and meeting the practical needs of oil and gas exploration. This provides a new approach for reservoir lithology identification.