摘要
目的研究储层精细评价技术中的储层参数井间预测方法。方法基于人工神经网络模型,结合油藏微相研究成果,采用井位和微相信息作为神经网络的输入信息,采用神经网络模型对储层参数进行空间预测。结果利用空间分散井位点的孔隙度资料和地区沉积微相信息,对孤岛油田渤21断块油藏进行井间孔隙度内插预测,其井间参数的预测精度得到明显提高,为油藏建模提供了可靠的基础。结论基于神经网络模型的井间参数预测方法,可以为储层精细评价提供高质量的油藏地质模型。
Aim In order to improve the inter-well interpolation precision of reservoir parameters. Methods Using neural network model based on depositional mierofaeies for inter-well interpolation. The well location and mierofaeies are used as input parameters of neural network model for reservoir parameter Bo 21 block, Gudao field has been studied for inter-well porosity prediction combining porosity data of wells with depositional microfacies information. It is shown that the method can improve obviously the prediction accuracy. Conclusion The method of parameter predict can provide a high quality oil pool geological model for reservoir fine evaluation.
出处
《西北大学学报(自然科学版)》
CAS
CSCD
北大核心
2007年第1期95-98,共4页
Journal of Northwest University(Natural Science Edition)
基金
国家重点基础研究发展规划基金资助项目(2003CB214607)
关键词
神经网络
井间参数预测
沉积微相
孔隙度
neural network
model inter-well interpolation
depositional microfacies
porosity