基于径向基过程神经网络的储层岩性识别  

Lithology Identification Based on Radial Basis Process Neural Network

作　　者：秦研博 许少华[1,2] QIN Yan-Bo XU Shao-Hua(Institute of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

机构地区：[1]东北石油大学计算机与信息技术学院,大庆163318 [2]山东科技大学信息科学与工程学院,青岛266590

出　　处：《计算机系统应用》2017年第3期271-274,共4页Computer Systems & Applications

摘　　要：识别并评价油气储层是油田勘探开发工作中至关重要的部分,而目前现有的岩性识别方法一般不能表述地层的非均质性,也没有考虑到地层参数随着深度而变化所产生的影响.本文提出一种基于径向基过程神经网络的岩性识别模型,并用实际数据进行了验证.实验结果表明,所提出的方法有着较高的识别率,是一种可以实际应用的方法.Identification and evaluation of oil and gas reservoirs is an essential part in the work of oil exploration and development. Generally speaking, the existing lithology identification methods can＇t be expressed in formation heterogeneity, the impact of layer parameters varies with depth arising is not taken into account. This paper presents a model of lithologic identification based on radial basis process neural network, which is verified by the actual data. The experimental result shows that the proposed method has a high recognition rate, and it is a practical application method.

关 键 词：径向基过程神经元 神经网络 岩性识别 

分 类 号：TE311[石油与天然气工程—油气田开发工程] TP183[自动化与计算机技术—控制理论与控制工程]