摘要
为了提高渗透率的解释精度 ,选择人工神经网络方法建立了储层渗透率解释模型 ,同时 ,为了能够更快地获得准确、稳定的解释模型 ,对BP网络进行了改进 ,编制了自检验遗传神经网络应用程序 ;然后利用改进的人工神经网络建立渗透率解释模型 ,对储层渗透率剖面进行了逐点解释 ,在此基础上进一步结合分形几何学提出了渗透率的韵律维数、韵律角、韵律高等能够更全面、准确描述储层非均质性的概念。
The artificial neural network methods are used in building of an interpretative model for reservoir permeability in order to improve the accuracy of permeability interpretation. Meanwhile, the BP network is improved and an application program for self - checking genetic neural networks is written to more rapidly acquire accurate and stable interpretative models. A permeability interpretative model is built by using the modified artificial neural network to perform level - by - level interpretation of reservoir permeability profile. Some new concepts that can describe more accurately the heterogeneity of reservoirs, including rhythmic dimensionality, rhythmic angle and rhythmic height, are presented in combination with fractal geometry.
出处
《河南石油》
CAS
2003年第2期1-3,共3页
Henan Petroleum
基金
油气藏地质及开发工程国家重点实验室开放基金项目 .PLN0 12 6.资助课题
