Semi-supervised least squares support vector machine algorithm:application to offshore oil reservoir  

半监督最小二乘支持向量机的研究及其在海上油田储层预测中的应用（英文）

作　　者：罗伟平[1,2] 李洪奇 石宁[1,2] 

机构地区：[1]中国石油大学(北京)地球物理与信息工程学院 [2]石油数据挖掘北京市重点实验室(Z121109009212008)

出　　处：《Applied Geophysics》2016年第2期406-415,421,共11页应用地球物理（英文版）

基　　金：supported by the "12th Five Year Plan" National Science and Technology Major Special Subject:Well Logging Data and Seismic Data Fusion Technology Research(No.2011ZX05023-005-006)

摘　　要：At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine （LSSVM） algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine （SLSSVM） model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor （SKNN） method. The new semi- supervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area.勘探开发初期海上油田钻井少、井间距离大,在应用地震多属性分析技术预测储层参数的过程中,直接采用监督最小二乘支持向量机算法预测精度较低。本文将最小二乘支持向量机与半监督学习理论结合,提出基于最小二乘支持向量机协同训练的半监督回归模型,并在模型训练过程中引入矩阵迭代求逆的方法,提高模型训练速度。利用UCI数据集实验研究,对比了半监督与监督最小二乘支持向量机模型,结果表明,半监督学习机制能够有效地提高最小二乘支持向量机的泛化性能,且随着训练样本的减小,效果更加明显;同时对比了半监督最小二乘支持向量机与半监督k-临近算法,结果显示,在小样本建模中,半监督最小二乘支持向量机有着更高的预测精度。最终将半监督最小二乘支持向量机运用于锦州工区,预测该区的砂体及储层孔隙度的分布,获得了较好的地质效果。

关 键 词：Semi-supervised learning least squares support vector machine seismic attributes reservoir prediction 

分 类 号：P618.13[天文地球—矿床学]