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首页> 外文会议>2009年中国控制与决策会议（2009 Chinese Control and Decision Conference）论文集 >Studying the Lithology Identification Method from Well logs Based on DE-SVM
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Studying the Lithology Identification Method from Well logs Based on DE-SVM

机译：基于DE-SVM的测井岩性识别方法研究

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摘要

Identify the rock lithology has important meaning for estimating the reserve of petroleum, adopting proper drilling technology and improving recovery.The lithology identification from well log based on DE-SVM was proposed and studied. After digitization and collection the data of the well logs and cores observation results, the mapping model between well logs and strata lithology is established by Support vector machine(SVM), so the strata lithology of wells without rock cores can be automatically gotten by well logs. Because the penal factor c and kernel parameter affect the identification accuracy evidently, the global optimization arithmetic-difference evolutionary (DE) is coupled with SVM to optimize above parameters in order to improve the performance of SVM model. The model theory and algorithm are discussed as well as the true example is calculated, it is stated that the proposed method is feasible and can get satisfied results.
机译：岩石岩性的识别对于评价石油储量，采用适当的钻井技术和提高采收率具有重要意义。提出并研究了基于DE-SVM的测井岩性识别方法。数字化采集测井资料和岩心观察结果后，利用支持向量机（SVM）建立测井与地层岩性的映射模型，可以通过测井自动获得无岩心的地层岩性。 。由于惩罚因子c和核参数明显地影响了识别精度，因此将全局优化算法-差分进化算法（DE）与支持向量机相结合，对上述参数进行优化，以提高支持向量机模型的性能。讨论了模型理论和算法，并计算了实际例子，表明所提方法是可行的，可以得到满意的结果。

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