摘要
锦270井区大凌河油层是盆地大幅度沉降条件下的产物,以深陷湖环境为主,发育湖底扇浊积岩。准确预测大凌河油层的砂体分布和泥质含量,是勘探目标选择的关键。由于钻井资料少,不能反映储层横向变化。为了准确预测储层的横向变化,综合钻井地质资料和地震勘探资料,并为了反映地震资料的多个参数与储层横向变化间的非线性相关关系,采用人工神经网络模型进行预测,计算了大凌河油层砂层厚度和泥质含量的平面分布。依据计算结果,分析了有利油气区的分布,并给出了几点结论。
Dalinghe oil layer is the result of basin's large extent subsiding, with environments of deep rift lake, and turbidite of sublacustrine fan were deposited. Prediction of sand thickness distribution and shale content exactly is the key for exploration targets selection. For the lack of drilling data, it is difficult to predict the lateral variety of reservoir. In order to predict the lateral variety of reservoir exactly, both geological data and seismic data are used, and neural network model is used to analyze the nonlinear relations between the factors of seismic data and the lateral variety of reservoir. The distributions of sand thickness and shale content are calculated. According to the calculating results, the distributions of favorable area for petroleum exploration are analyzed, and some conclusions are given.
出处
《资源调查与环境》
2002年第4期281-287,共7页
Resources Survey & Environment
