| 梁海波,马睿.基于Stacking集成学习的孔隙度预测方法[J].电子测量与仪器学报,2024,38(12):202-210 |
| 基于Stacking集成学习的孔隙度预测方法 |
| Porosity prediction method based on stacking ensemble learning |
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| DOI： |
| 中文关键词: 孔隙度 预测 Optuna优化 Stacking 集成学习 测井数据 |
| 英文关键词:porosity prediction Optuna optimization Stacking ensemble learning logging data |
| 基金项目:四川省自然科学基金创新研究群体项目（2023NSFSC1981）资助 |
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| 中文摘要: |
| 储层孔隙度的预测准确性决定了评估地下储层的储集空间和储层质量的可靠性。然而，现有孔隙度预测的方法存在模型算法单一、精度不高和泛化性差等问题。为了提高孔隙度预测的精度，提出了一种基于Optuna优化的Stacking集成学习方法。首先，采用灰色关联度选取声波时差、井深、岩石密度、井斜角和光电截面吸收指数作为输入参数。然后，对输入数据进行归一化处理，并通过Optuna优化模型参数。根据均方根误差、平均绝对误差和定系数选取随机森林（RF）、支持向量回归（SVM）和k-近邻算法（KNN）作为Stacking的基学习器，以及弹性网络回归（ENet）作为Stacking元学习器。各主流模型预测结果与Stacking模型比较发现：RF在处理非线性数据时表现优异，但预测结果不稳定，Stacking模型相较RF降低了约10%的均方根误差。SVM具备较强的泛化能力，但参数调优复杂，Stacking模型相较SVM降低了约39%的均方根误差。KNN对异常值不敏感，但对高维数据效果较差，Stacking模型相比KNN降低了约21%的误差。Xgboost能够较好地避免过拟合，但对异常值敏感而且参数调优复杂，Stacking模型相比Xgboost降低了约30%的误差。最终结果表明，基于Optuna优化的Stacking模型显著提高了孔隙度预测的准确性，为反应储层油气储存能力提供重要参考。 |
| 英文摘要: |
| The accuracy of reservoir porosity prediction is crucial for assessing the storage capacity and quality of underground reservoirs. However, existing methods for porosity prediction face challenges such as limited model algorithms, low accuracy, and poor generalization. To enhance the precision of porosity prediction, this study proposes a Stacking ensemble learning method optimized by Optuna. First, gray relational analysis is used to select input parameters, including acoustic time difference, well depth, rock density, dip angle, and photoelectric absorption index. The input data is then normalized, and Optuna is employed to optimize the model parameters. Based on metrics like root mean square error, mean absolute error, and determination coefficient, random forest, support vector regression, and K-nearest neighbors are chosen as base learners for the Stacking model, with elastic net regression serving as the meta-learner. Comparative results reveal that while RF excels in handling nonlinear data, it shows instability in predictions; the Stacking model reduces RMSE by approximately 10% compared to RF. SVM demonstrates strong generalization ability but requires complex parameter tuning, with the Stacking model achieving about a 39% reduction in RMSE compared to SVM. KNN is insensitive to outliers but performs poorly on high-dimensional data, with the Stacking model lowering the error by about 21% compared to KNN. Additionally, XGBoost effectively avoids overfitting but is sensitive to outliers and requires complex tuning, with the Stacking model reducing the error by approximately 30% compared to XGBoost. Overall, the results indicate that the Optuna-optimized Stacking model significantly improves the accuracy of porosity prediction, providing a valuable reference for evaluating the oil and gas storage capacity of reservoirs. |
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