基于LVQ过程神经元网络的储层岩性识别  

Reservoir Lithology Discrimination Based on LVQ Process Neural Network

作　　者：李学贵[1] 许少华 赵恩涛 赵玲[1] LI Xuegui XU Shaohua ZHAO Entao ZHAO Ling(School of Computer ＆ Information Technology, Northeast Petroleum University, Daqing 163318, China College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China Geological Brigade, No. 1 Oil Production Company of Daqing Oilfield Company Limited, Daqing 163111, China)

机构地区：[1]东北石油大学计算机与信息技术学院,黑龙江大庆163318 [2]山东科技大学计算机科学与工程学院,山东青岛266590 [3]大庆油田采油一厂地质大队,黑龙江大庆163111

出　　处：《吉林大学学报（信息科学版）》2017年第4期398-404,共7页Journal of Jilin University（Information Science Edition）

基　　金：国家自然科学基金资助项目(61402099)

摘　　要：针对基于取心井岩心分析数据和测井过程数据的储层岩性判别问题,建立了一类学习向量量化过程神经元网络模型(LVQ-PNN:Learning Vector Quantization Process Neural Network)。该模型通过增加输出层,扩展了自组织过程神经元网络的深度结构;采用无监督竞争与有教师示教相结合的算法策略,提高了多维信号特征的自适应提取和自组织综合能力。实验证明,该方法具有较好的岩性特征综合和辨识能力,岩性识别率达到了84.7%。Aiming at the reservoir lithology discrimination based analysis data of coring well and the logging process data, we proposed and established a learning vector quantization process neural network model （LVQ- PNN）. The model by increasing the output layer, expanded the depth of the self-organization process neural network, using the combination algorithm strategies with unsupervised competition and supervised learning, to improve the adaptive extraction of multidimensional signal feature and self-organization comprehensive ability, The experimental result shows that it has better recognition ability and comprehensive lithology. The recognition rate of lithology discrimination is 84.7%.

关 键 词：过程神经元网络 学习向量量化 岩性识别 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]