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首页> 外文期刊>Journal of natural gas science and engineering >Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization
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Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization

机译：基于非线性特征选择的极限学习机的集成模型，用于改进天然气藏特征

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The deluge of multi-dimensional data acquired from advanced data acquisition tools requires sophisticated algorithms to extract useful knowledge from such data. Traditionally, petroleum and natural gas engineers rely on "rules-of-thumb" in the selection of optimal features with much disregard to the hidden patterns in operational data. The traditional multivariate method of feature selection has become grossly inadequate as it is incapable of handling the non-linearity embedded in such natural phenomena. With the application of computational intelligence and its hybrid techniques in the petroleum industry, much improvement has been made. However, they are still incapable of handling more than one hypothesis at a time. Ensemble learning offers robust methodologies to handle the uncertainties in most complex industrial problems. This learning paradigm has not been well embraced in petroleum reservoir characterization despite the persistent quest for increased prediction accuracy. This paper proposes a novel ensemble model of Extreme Learning Machine (ELM) in the prediction of reservoir properties while utilizing the non-linear approximation capability of Functional Networks to select the optimal input features. Different instances of ELM were fed with features selected from different bootstrap samplings of the real-life field datasets. When benchmarked against existing techniques, our proposed ensemble model outperformed the multivariate regression-based feature selection, the conventional bagging and the Random Forest methods with higher correlation coefficient and lower prediction errors. This work confirms the huge potential in the capability of the new ensemble modeling paradigm to improve the prediction of reservoir properties. (C) 2015 Elsevier B.V. All rights reserved.
机译：从高级数据采集工具采集的大量多维数据需要复杂的算法，才能从此类数据中提取有用的知识。传统上，石油和天然气工程师在选择最佳特征时依靠“经验法则”，而无视运营数据中的隐藏模式。传统的多元特征选择方法已经变得严重不足，因为它无法处理嵌入在这种自然现象中的非线性。随着计算智能及其混合技术在石油工业中的应用，已经取得了很大的进步。但是，它们仍然无法一次处理多个假设。集成学习提供了强大的方法来处理大多数复杂的工业问题中的不确定性。尽管人们一直在寻求增加预测精度的方法，但在石油储层表征中并没有很好地采用这种学习范例。本文提出了一种新型的极限学习机（ELM）集成模型，用于预测储层物性，同时利用功能网络的非线性逼近能力来选择最佳输入特征。 ELM的不同实例具有从现实生活数据集的不同引导采样中选择的特征。当以现有技术为基准时，我们提出的集成模型优于基于多元回归的特征选择，传统的装袋方法和随机森林方法，具有较高的相关系数和较低的预测误差。这项工作证实了新的整体建模范式在改善储层物性预测方面的巨大潜力。 （C）2015 Elsevier B.V.保留所有权利。

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