薛亚东, 高德利. 基于人工神经网络的实钻地层可钻性预测[J]. 石油钻采工艺, 2001, 23(1): 26-28,83. DOI: 10.3969/j.issn.1000-7393.2001.01.007
引用本文: 薛亚东, 高德利. 基于人工神经网络的实钻地层可钻性预测[J]. 石油钻采工艺, 2001, 23(1): 26-28,83. DOI: 10.3969/j.issn.1000-7393.2001.01.007
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Xue Yadong, Gao Deli.et al. FORMATION DRILLABILITY PREDICTION METHOD BASED ON ARTIFICAL NERVE NETWORK[J]. Oil Drilling & Production Technology, 2001, 23(1): 26-28,83. DOI: 10.3969/j.issn.1000-7393.2001.01.007
Citation: Xue Yadong, Gao Deli.et al. FORMATION DRILLABILITY PREDICTION METHOD BASED ON ARTIFICAL NERVE NETWORK[J]. Oil Drilling & Production Technology, 2001, 23(1): 26-28,83. DOI: 10.3969/j.issn.1000-7393.2001.01.007
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基于人工神经网络的实钻地层可钻性预测

FORMATION DRILLABILITY PREDICTION METHOD BASED ON ARTIFICAL NERVE NETWORK

    摘要
  • 摘要: 通过对前人大量地层可钻性的研究成果的整理分析,得出影响地层可钻性的最主要因素是岩性和埋藏深度。在此基础上,提出应用人工神经网络技术计算预测地层可钻性,并针对已钻井段和未钻井段建立了 2个神经网络模型。通过实例验证,表明这是一种有效的确定可钻性的方法,其预测精度可以达到 90%以上。

     

    Abstract: The two most important factors influencing formation drillability are lithology and rock bury depth, and this has been proved in the state of the art. This paper presents a method of using artificial neural network to predict the formation drillability, and establishes two neural network models for the drilled and preceding formation. Test shows that it’s an effective model to predict the formation drillability, and its accuracy reaches as high as 90%.

     

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