基于 LVQ 过程神经元网络的储层岩性识别

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%。

Reservoir Lithology Discrimination Based on LVQ Process Neural Network

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%.