摘要
塔河油田为碳酸盐岩缝洞型油藏,具有极强的非均质性,单纯用静态资料来认识这类油藏非常困难。提出利用生产动态资料和信息进行塔河油田油藏描述研究的新思路。利用人工神经网络技术在处理非线性相关参数预测方面的优势,以渗流理论为基础,结合试井成果,推导出影响油藏开发的重要参数(地层系数)与生产信息的关系;建立了人工神经网络预测储集层参数的结构模型。塔河油田研究证明,神经网络技术在塔河油田碳酸盐岩缝洞型油藏储集层评价预测和非均质性分析等方面具有较强的实用价值。
Tahe oilfield is a crack-cave carbonate reservoir with extremely serious heterogeneity. It is very difficult to recognize this type of reservoir only according to static behavior data. A new idea is put forward that this type of reservoir could be described by using production performance data or information. Artificial neural network technique is advantageous for dealing with and predicting the non-linear correlative parameters. This paper, based on percolation theory and integrated with well test result, presents the relations between formation factor and production performance information, develops the structural model for reservoir parameter prediction by artificial neural network. The field study shows that this technique is of more practical value in the proper recognition of Tahe field.
出处
《新疆石油地质》
CAS
CSCD
2004年第6期665-667,共3页
Xinjiang Petroleum Geology
