摘要
本文以松辽盆地SP地区营城组上部旋回火山岩为例,利用自组织神经网络方法进行波形分类计算,采用时窗、振幅、频率和相位参数进行训练,经过30次迭代计算,划分了15种模型道。由计算得到的地震相图可观察到地震波形呈块状或沿断裂呈条带状分布,与地质背景相吻合。然后遵循岩相命名原则,根据钻井岩相标定单井火山岩相并进行岩相平面预测。预测的火山岩相分布规律与钻井岩相统计规律一致。此岩相预测结果应用于SP地区火山岩气藏开发井网部署,取得了较好效果,表明该方法预测火山岩相是可行的。
Taking the volcanic in upper cycle of Yingcheng Formation in SP area of Songliao basin, the paper utilizes self-organization neural network approach to carry out waveform classification, uses such parameters as time-window, amplitude, frequency and facies to implement training and divides 15 kinds of model traces after 30 iterative computations. The seismic waveforms can be seen as pieces distribution or strips distribution along the faults on the resulted seismic facies map, which is coincident with geologic background. Then, following the lithofacies-naming principle, we calibrated volcanic facies at single well based on drilling lithofacies and conducted planar prediction of lithofacies. The distribution law of predicted volcanic facies is consistent with the statistical raw of drilling lothofacies. Application of the predicted lithofacies results to deploy the development well network of volcanic gas reservoir in SP area achieved good effects, showing the feasibility using the approach to predict the volcanic facies.
出处
《石油地球物理勘探》
EI
CSCD
北大核心
2007年第4期440-444,共5页
Oil Geophysical Prospecting
基金
国家自然科学基金资助(40372066号)
高等学校博士学科点专项科研基金资助(20030183042)课题
关键词
松辽盆地
营城组
波形分类
火山岩相
Songliao basin, Yingcheng Formation, waveform classification, volcanic facies
