走滑断层对我国东部陆相盆地油气勘探具有重要意义,以往研究多关注大型走滑断层,忽视了小型或者隐性走滑断层。近年来在济阳坳陷陆续发现了一系列NNE向和NNW向的小型或隐性走滑断层,研究形成了此类断层的识别方法。利用走滑断层近等间距分布、多发生在构造性质转换处以及多条近平行断层收敛处的特点,首先识别出可能存在隐性断层的区域,再采用相干分析等方法进行走滑断层的精细解释,然后采用垂直断层两侧地震剖面处滑动扫描拼接方法、紧邻断层两侧平行地震剖面反射特征对比方法、平面地震属性切片、早期构造的切割、断层两侧伸展量差异估算等方法,定量地计算走滑量,进一步确定隐性走滑断层。将上述方法分别应用于济阳坳陷车镇凹陷车57地区、大王庄次洼PX722井区以及临南洼陷兴隆寺地区,识别出了相应的隐性走滑断层,而后依据不同地区的地质情况,采用不同的方法计算得到误差较小的走滑量,最后结合油气勘探实践,确定了隐性走滑断层,并证实隐性走滑断层对油气聚集具有重要的控制作用。上述隐性走滑断层的识别方法及其走滑量的计算方法对类似地区隐性走滑断层的识别具有借鉴意义。
Strike-slip structures are of great significance in oil and gas exploration in continental basins in eastern China.Previous studies have mostly focused on large strike-slip faults,neglecting those that are small or concealed.A method to identify a series of NNE and NNW trending small or recessive strike-slip faults discovered in the Jiyang Depression was developed.First,areas where concealed faults may develop on the plane were identified based on the characteristics of strike-slip faults,such as equidistant distribution,frequent occurrence at structural property transformation,and convergence of several near-parallel faults.A detailed interpretation of strike-slip faults was then performed with coherent analysis.Finally,the number of strike slips was quantitatively calculated to identify concealed strike slip faults,using slip scanning splicing of seismic sections on both sides of the vertical fault,the comparison of reflection characteristics of parallel seismic sections on both sides of the adjacent fault,plane seismic attribute slicing,cutting of early structures,and the estimation of extension differences on both sides of the fault.This method was applied in field data from the Che57 area of Chezhen sag,the Dawangzhuang sub-sag and Xinglongsi areas of Linnan,and the sub-sag in the Jiyang depression.The corresponding concealed strike-slip faults were identified in the results.This method can be used for reference to identify concealed strike-slip faults in similar areas,but the methods for calculating strike-slip amount varies with different regions.
国家科技重大专项(2017ZX05049004)资助。