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首页> 外文会议>SPE Asia Pacific Oil and Gas Conference and Exhibition >Investigation on Automatic Recognition of Stratigraphic Lithology Based on Well Logging Data Using Ensemble Learning Algorithm
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Investigation on Automatic Recognition of Stratigraphic Lithology Based on Well Logging Data Using Ensemble Learning Algorithm

机译：基于井测井数据的基于井测井数据的地层岩性自动识别研究

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摘要

The automation of lithology identification based on natural gamma,resistivity,neutron density and other well logging data is an important step to perform intelligent drilling/geo-steering. The current identification of lithology normally is based on the statistical results of the previous well logging data or on empirical methods,which may not be efficient or accurate. Therefore,a machine learning method is introduced here to improve the efficiency and accuracy of lithologic identification. With the development of a classification algorithm,the ensemble learning method becomes more influential since it can compensate the weak learning algorithm by using multiple learning algorithms to obtain better performance. The present research tries to identify different strata in complex sedimentary environment underground during the drilling process with a typical integrated learning method,the Adaboost algorithm,based on three wells in the Daan section,Longqian area of China. Typical single classification algorithms are used to identify the lithology,such as decision trees,SVMs(Support Vector Machines),and Bayes,etc. Comparing the results of single classifiers,the results of ensemble learning algorithm performed better than the selected single classifier. As such,the accuracy rate of lithology prediction can be increased from 66% to 90%.
机译：基于天然伽马，电阻率，中子密度和其他井测井数据的岩性识别自动化是执行智能钻孔/地理转向的重要步骤。目前岩性的识别通常是基于先前井测井数据的统计结果或对经验方法进行有效或准确的。因此，这里介绍了一种机器学习方法以提高岩性识别的效率和准确性。随着分类算法的开发，集合学习方法变得更具影响力，因为它可以通过使用多学习算法来补偿弱学习算法来获得更好的性能。本研究试图在钻探过程中识别地下复杂沉积环境中的不同地层，典型的综合学习方法，达到龙桥区龙桥区三井的综合算法。典型的单分类算法用于识别岩性，例如决策树，SVMS（支持向量机）和贝叶斯等。比较单分类器的结果，组合学习算法的结果比所选择的单分类器更好。因此，岩性预测的精度率可以从66％增加到90％。

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