摘要
中东M油田是以碳酸盐岩储层为主的双重介质油藏,裂缝发育、非均质性较强。由于目前常规单一的地震属性无法精细刻画裂缝分布规律,因此建立了基于神经网络的多信息融合裂缝建模技术,首先利用神经网络预测无成像测井资料的单井裂缝密度;其次将纵波方位各向异性、地震不连续检测叠前地震属性,基于神经网络非线性融合进行三维裂缝密度发育概率预测;以单井裂缝密度作为井上硬数据,在严格的变差函数分析和裂缝密度概率体双重约束条件下建立裂缝密度模型;最后通过地质统计学建模方法建立离散裂缝网络模型并将其粗化等效成裂缝属性模型。模型拟合率应用于M油田开发决策优化,优选裂缝较为发育的区域采用水平井或大斜度井进行开发,平均单井日产油量达上千桶。新井揭示的裂缝发育情况与钻前预测一致,并且投产井单井产量均明显高于先期开发井。
The Oilfield M in the Middle East is a dominated by dual-medium carbonate reservoir with well-developed fractures and strong heterogeneity.However,the conventional single method cannot finely characterize the distribution of fractures.The multi-information fusion modeling technology for fractures based on the neural network firstly depends on the neural network to predict the fracture density in a single well without imaging logging data.Secondly,the nonlinear fusion of multiple pre-stack seismic attributes including P-wave azimuthal anisotropy and seismic discontinuity detection is performed on the basis of the neural network to predict the development probability of 3D fracture density.The fracture density of a single well is taken as hard data,and the fracture density model is constructed within the dual constraints of strict variogram analysis and fracture density probability volume.Finally,the discrete fracture network model is construced with the geostatistical modeling method,which is coarsened to be equivalent to the fracture attribute model.The model fitting rate is applied to the decision-making optimization for Oilfield M development.It is preferable to use horizontal wells or highly deviated wells for development in the areas with relatively developed fractures,and the average daily oil production per well reaches thousands of barrels.The fracture development revealed by the new well is consistent with the results of pre-drilling prediction,and the output of a single production well is significantly higher than that of a previous development well.
作者
但玲玲
史长林
文佳涛
魏莉
张学敏
张剑
DAN Lingling;SHI Changlin;WEN Jiatao;WEI Li;ZHANG Xuemin;ZHANG Jian(CNOOC Energy Technology-Drilling&Production Company,Tianjing City,300452,China;Tianjin Branch,China National Offshore Oil Corporation(CNOOC)Limited,Tianjin City,300459,China)
出处
《油气地质与采收率》
CAS
CSCD
北大核心
2022年第1期46-52,共7页
Petroleum Geology and Recovery Efficiency
关键词
碳酸盐岩
双重介质
裂缝建模
神经网络
地震属性
carbonate reservoir
dual-medium
fracture modeling
neural network
seismic attribute
