摘要
针对柴西北风西地区超薄藻灰岩油气藏勘探需求,以地质任务为导向,开展了采集、处理、解释一体化技术应用。采集方面,采用“高精度可控震源组合”与“单个高灵敏检波器”相结合,获得宽频、高信噪比地震资料;处理方面,运用网格层析反演速度建模及TTI叠前深度偏移技术,提高地质体成像精度与水平井储层钻遇率;解释方面,优选地震分频属性开展薄层藻灰岩预测,结合已钻井定性认识碳酸盐岩藻灰岩地震响应模式与平面展布规律。研究表明:藻灰岩沉积厚度在一定程度上受微地貌控制;利用残余厚度法恢复微层沉积前古地貌可预测优势储层分布;运用BP神经网络地震反演技术能够精细刻画藻灰岩发育区。运用多种地震勘探技术开展优势储层预测结果与实钻吻合率达到80%。
Considering the exploration demand of ultra-thin algal limestone oil&gas reservoirs in the Fengxi area of northwestern Qaidam Basin,and guided by geological tasks,researches on the application of the integrated technology of acquisition,processing,and interpretation was carried out.Regarding the acquisition,the acquisition technology combining the“high-precision vibroseis array”and“single highlysensitive geophone”is adopted to obtain broadband and high signal-to-noise ratio(SNR)seismic data.In terms of processing,the grid tomography inversion for velocity modeling and TTI pre-stack depth migration technology are used to improve the imaging accuracy of geological bodies and the probability of penetration of horizontal well reservoirs.In terms of interpretation,the seismic frequency-division attribute is selected for thin-layer algal limestone prediction,and the response mode and plane distribution law of the seismic attributes of carbonate algal limestone are qualitatively recognized in combination with the drilled wells.The research results show that the sedimentary thickness of algal limestone is controlled by the micro geomorphic feature to a certain extent.The distribution of dominant reservoirs can be predicted by the residual thickness method to restore the ancient landform before micro-layer sedimentation.The seismic inversion technology of BP neural networks can be used to accurately depict the algal limestone development area.The coincidence rate between the prediction of dominant reservoirs by various seismic exploration technology and the actual drilling results reaches 80%.
作者
王海峰
李万中
朱波
李祥
熊业刚
何湘
WANG Haifeng;LI Wanzhong;ZHU Bo;LI Xiang;XIONG Yegang;HE Xiang(Research Institute of Exploration and Development,Qinghai Oilfield Company,PetroChina,Dunhuang,Gansu 736202,China;No.2 Oil Production Plant of Qinghai Oilfield Company,PetroChina,Dunhuang,Gansu 736202,China)
出处
《石油地球物理勘探》
EI
CSCD
北大核心
2022年第S02期123-129,230,共8页
Oil Geophysical Prospecting
基金
中国石油天然气股份有限公司重大科技专项“柴达木盆地建设高原大油气田勘探开发关键技术研究与应用”子课题“柴达木盆地复杂油气藏地震配套技术研究”(2016E-01)资助
关键词
藻灰岩
可控震源
网格层析反演
分频属性
残余厚度
BP神经网络
algal limestone
vibroseis
grid tomography inversion
frequency division attribute
residual thickness
BP neural network
作者简介
王海峰,高级工程师,1980年生,2003年毕业于西南石油大学,获勘查地球物理专业学士学位,现就职于中石油青海油田勘探开发研究院,主要从事地震储层预测综合研究工作。甘肃省敦煌市七里镇青海油田勘探开发研究院,736202。Email:whfqh@petrochina.com.cn
