| 随钻测井中岩性识别方法的对比及应用 |
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| 引用本文： | 陈刚,汪凯斌,蒋必辞,王小龙.随钻测井中岩性识别方法的对比及应用[J].煤田地质与勘探,2018,46(1):165-169. |
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| 作者姓名： | 陈刚 汪凯斌 蒋必辞 王小龙 |
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| 作者单位： | 中国石油大学(华东)地球科学与技术学院;中煤科工集团西安研究院有限公司; |
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| 基金项目： | 国家科技重大专项课题（2016ZX05045-003）；国家自然科学基金项目（51774320） |
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| 摘 要： | 岩性识别是对地层认识及储层参数求解的基础，受沉积环境复杂性和非均质性影响，传统岩性识别方法已不能满足实际生产需要。针对传统识别方法容错能力差、自动化程度低和解释精度低的问题，通过应用神经网络自主学习预测分析手段，对比分析当下几种流行的岩性识别方法，选出更为适合现场实用的方法应用到随钻测井系统中。经研究发现，在预测方法及测井曲线相同的情况下，获得标准层段训练样本越多，准确率越高。通过对比得出结果:PNN概率神经网络方法在生产应用中效果更佳、识别准确率高、训练识别用时最短，在获取较少测井资料信息时，仍能保持较高的识别水平。
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| 关 键 词： | 随钻测井 岩性识别 神经网络 PNN |
| 收稿时间： | 2016-12-12 |
Comparison and application of LWD lithology identification method |
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| Abstract: | Lithology identification is the basis of formation recognition and reservoir parameter calculation, and the traditional lithology identification method can not meet the needs of actual production because of the complexity and heterogeneity of sedimentary environment. Aiming at the problem of traditional identification method such as the fault tolerance ability is poor, the degree of automation is low and the interpretation accuracy is low. By using the neural network autonomous learning prediction analysis method, the comparison study of several popular lithologic identification methods, a more suitable field practical method was applied to the drilling system. The study found that in the case of the same prediction method and well logging curve, the more the number of standard stratigraphic samples is, the higher the correct rate. By comparing probabilistic neural networks method in the application in the production of better effect, the recognition accuracy rate was high, training and recognition time was the shortest, a high level of recognition can be still maintained when less logging data are got. |
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