摘要
浊积砂岩储层为典型的重力流沉积,储层横向突变,纵向多期叠置,迁移摆动频繁。地震反演是进行储层精细描述的主要方法之一,但E油田复杂的断裂系统、储层的横向突变以及超限厚度层的发育均制约了储层反演的精度及后续应用。为此,针对E油田,重点研究并提出了复杂断裂系统约束下的自相控叠前反演方法,首先,利用基于断层接触关系图版库的深度学习算法构建复杂断裂系统模型,进而构建高精度地震地层格架;其次,利用自相控低频模型构建方法,构建高精度自相控低频模型;最后,在高精度地层格架和自相控低频模型的约束下实现自相控叠前反演,有效提高了断层附近砂体预测、超限厚储层刻画及储层横向边界的识别精度。E油田实践表明,该方法取得了较好的应用效果,16口新钻开发井水平段厚度预测与实钻结果吻合率为91%。
Turbidite sandstone reservoir is a typical gravity flow deposit,which is characterized by lateral variation,vertical multi-stage superposition and frequent migration.Seismic inversion is one of the main methods for fine reservoir description,but the complex fault system,lateral abrupt variation and overlimit thickness of the reservoir in E Oilfield restrict the accuracy of reservoir inversion and its subsequent application.In order to solve the problem of reservoir prediction in E Oilfield,a selffacies-control pre-stack inversion technology with complex fault system is proposed in this paper.Firstly,the deep learning algorithm based on the fault contact relationship chart library is used to construct the complex fault system model,and then the high-precision seismic stratigraphic framework is constructed.Secondly,a high-precision self-facies-control low-frequency model is built using the self-facies-control low-frequency model construction technology.Finally,under the constraints of high-precision stratigraphic framework and self-facies-control low-frequency model,self-facies-control pre-stack inversion is realized,which effectively improves the accuracy of sand body prediction near the fault,overlimit thick reservoir characterization and reservoir lateral boundary identification.The application in E Oilfield shows that this method has achieved good results.The thickness coincidence rate of the horizontal length of 16 new drilled development wells is 91%.
作者
王宗俊
田楠
范廷恩
高云峰
WANG Zongjun;TIAN Nan;FAN Ting′en;GAO Yunfeng(State Key Laboratory of Offshore Oil Exploitation,Chaoyang,Beijing 100028,China;CNOOC Research Institute Co.Ltd.,Chaoyang,Beijing 100028,China)
出处
《西南石油大学学报(自然科学版)》
CAS
CSCD
北大核心
2023年第2期43-57,共15页
Journal of Southwest Petroleum University(Science & Technology Edition)
关键词
深度学习
复杂断层
超限厚储层
储层边界
自相控低频模型
deep learning
complex fault
overlimit thick reservoir
reservoir boundary
self-facies-control low frequency model