地质统计地震反演方法及其在民丰断裂带封堵中的应用研究
详细信息 本馆镜像全文    |  推荐本文 | | 获取馆网全文
摘要
断层既可作为油气运移的通道 ,又可对油气形成封堵。在砂岩含量丰富、靠近油源的复杂断块区 ,断层封堵区域的分析与界定使我们能直接划分由封堵形成的油气藏范围。这类区域以断层两侧岩性配置封堵模式为主。分析岩性对接配置的理想方法是根据断层两侧钻井资料确定断层面的岩性对接 ,实际上常因钻井资料少而使此法难以施行。在分析、比较多种测井约束地震反演方法后 ,我们找到一种反演结果可靠的地质统计反演方法 ,依此研究断层面岩性对接关系。将其应用到民丰断裂带封堵分析研究中 ,反演出了较理想的岩性剖面
The fault can not only be as migration channel of petroleum, but also can seal petroleum. The analysis and the determination of fault closed area can directly divide the scope of petroleum formed by sealing in a complicated fault block zone which is near oil sources and has abundant content of sandstone. The sealing patterns of the areas mainly are lithologic seals on both side of fault. The ideal method for analysis of lithologic juxtaposition is determination of lighologic juxtaposition of fault plane by drilling data on the both sides of fault. But in practice, due to lack of drilling data, the work is difficult to carry out. Comparing different well log constrained seismic inversion by analysis, we found inversion by geologic statistics to be the method that the result of inversion is reliable, which can obtain a rather ideal lithologic section, from which we can study the lithologic juxtaposition relation of fault plane and first applied it to study the sealing analysis of Minfeng fault zone.
引文
[1] SchultzP S,RonenS,HattoriM andCorbettC.Seismic guided estim ation of log propties,Parts1,2&3.TheL eadingEdge,1994,13(5,6,7):305~310,674~678,770~776
    [2] SpechtD.Probabilistic neural networks.N euralN etworks.1990,3:109~118
    [3] SpechtD.Ageneral regression neural network.IEEE Transactions onN euralN etworks,1991,2(6):568~576
    [4] TanerM T,SchuelkeJS,O'DohertyR andBaysalE.Seism ic attributes revisited.ExpandedAbstractsof64 thSEG AnnualInternatMtg,1994,1104~1106
    [5] AndersonJK.L imitations of seism ic inversion forporosity and pore fluid:lessons from chalk reservoircharacterization exploration.ExpandedAbstracts of66 thSEG AnnualInternatMtg,1996,309~312
    [6] ChenQ &SidneyS.Seism ic attribute technology forreservoir forecasting and m onitoring.TheL eadingEdge,1997,16(5):445~456
    [7] L iuZ &L iuJ.Seismic- controlled nonlinearextrapolation of well param eters using neuralnetworks.Geophysics,1998,63(6):2035~2041
    [8] MastersT.Advanced algorithms for neural net-works.JohnWiley&SonsInc,1995
    [9] McCorm ackM D.Neural computing in geophysics.TheL eadingEdge,1991,10
    [10] SchuelkeJS,QuireinJA &SargJF.Reservoir ar-chitecture and porosity distribution,PegasusField,WestTexas:an integrated sequence stratigraphy-seism ic,attribute study using neural networks.ExpandedAbstracts of67thSEG AnnualInternatMtg,1997,668~671

版权所有:© 2023 中国地质图书馆 中国地质调查局地学文献中心