云模型在测井曲线分层中的应用

针对由模糊信息导致测井曲线人工分层的不准确,提出了一种基于云模型的测井曲线自动分层方法.而云模型能同时体现概念的随机性和模糊性,利用云变换从测井曲线中提取定性概念,然后对提取的概念进行概念跃升,使得算法输出的概念层次更符合实际.实验表明,该方法能够较准确地提取和表征定性概念,所提取的概念中心与传统测井曲线分层方法得到的概念中心吻合较好,验证了算法的有效性,提高了测井曲线分层的准确性.

Application of Cloud Model in Logging Curve Hierarchical

For the problem that the artificial layering of well logging curves is not accurate caused by the fuzzy information, an automatical layering method of well logging curves based on cloud model is proposed. Cloud model can describe the concepts with randomness and fuzziness at the same time. Cloud transformation is used to extract qualitative concepts from well logging curves and makes the concept zooming of the extracted concepts, making the output concept hierarchy more close to reality. Experimental results demonstrate that this method can represent and extract qualitative concepts exactly. The proposed method is in good agreement with that obtained by layering method of conventional well logging curves. It verifies the effectiveness of the algorithm and improves the layering accuracy of well logging curves.