岩性-物性高分辨率非线性联合反演技术及其应用
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摘要
在沉积环境复杂和钻井资料少的勘探地区,有效地进行地震反演和储层预测是油气勘探和开发中的关键。针对我国近海岩性和物性高度非均质复杂隐蔽岩性储层的勘探实际,提出了基于非线性统计地震褶积模型和非线性储层岩性物性统计褶积模型的联合反演技术,实现了确定性反演、统计性反演和非线性理论三者的有机结合。南黄海盆地北部坳陷北凹陷A构造、辽东湾渤中25-1勘探区和旅大勘探区的目的层是以曲流河与辫状河为主的河流相沉积和以三角洲前缘与浅湖为主的湖泊相沉积,储层的岩性和物性高度非均质,岩石物理关系复杂。将岩性—物性高分辨率非线性联合反演技术应用于这些地区,反演结果刻画了储层岩性和物性的横向变化细节。
In exploration areas with complicated sedimentary environment and few drilling wells,effective seismic inversion and reservoir prediction is the key to exploration.Aiming at the actual exploration of serious heterogeneous and complex lithological reservoirs in our coastal sea.a joint inversion technique based on nonlinear statistical seismic convolution model and nonlinear lithology petrophysical statistical convolution model was proposed,which combined deterministic inversion,statistical inversion and nonlinear theory.The exploration targets of A structure at the north depression in South Yellow Sea Basin,Bozhong25-l exploration area and Lvda exploration area at Liaodong Gulf are fluvial facies sediments dominated by meandering river and braid river,and lacustrine facies sediments dominated by delta front and shallow lake,where reservoir lithology is highly heterogeneous and petrophysical relation is complex.The high-resolution nonlinear inversion technique was applied to these areas;the result fine describes the lateral changes of lithology and petrophysical properties.
In exploration areas with complicated sedimentary environment and few drilling wells,effective seismic inversion and reservoir prediction is the key to exploration.Aiming at the actual exploration of serious heterogeneous and complex lithological reservoirs in our coastal sea.a joint inversion technique based on nonlinear statistical seismic convolution model and nonlinear lithology petrophysical statistical convolution model was proposed,which combined deterministic inversion,statistical inversion and nonlinear theory.The exploration targets of A structure at the north depression in South Yellow Sea Basin,Bozhong25-l exploration area and Lvda exploration area at Liaodong Gulf are fluvial facies sediments dominated by meandering river and braid river,and lacustrine facies sediments dominated by delta front and shallow lake,where reservoir lithology is highly heterogeneous and petrophysical relation is complex.The high-resolution nonlinear inversion technique was applied to these areas;the result fine describes the lateral changes of lithology and petrophysical properties.
引文
1李庆忠.走向精确勘探的道路—高分辨率地震勘探系统工程剖析[M].北京:石油工业出版社,1993.154- 174
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3张永刚.地震波阻抗反演技术的现状和发展[J].石油物探,2002,41(4):385-390
4马丽娟,郑和荣,陈霞.隐蔽油气藏地震预测技术研究新进展[J].地球物理学进展,2007,22(1):294-300
5李爱山,印兴耀,张繁昌,等.叠前AVA多参数同步反演技术在含气储层预测中的应用[J].石油物探,2007, 46(1):64-93
6李廷辉,陈洪涛,孔凡东,等.储层综合预测技术在隐蔽油气藏勘探中的应用—以板桥凹陷东斜坡为例[J].石油物探,2003,42(3):340-345
7黄花香,曾涛,唐必锐,等.地质统计分析方法在鲕滩储层预测中的应用[J].石油物探,2004,43(3):275-277
8 van der Baan M,Christian J.Neural networks in geophysical applications[J].Geophysics,2000,65(4): 1 032-1 047
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10 Hampson D P,Schuelke J S,Quirein J A.Use of multiattribute transforms to predict log properties from seismic data[J].Geophysics,2001,66(1):220-236
11 Anstey N A,O'Doherty R F.Cycles,layers,and reflections [J].The Leading Edge,2002,21:44-51,152- 158
12崔炯成,吴媚,李维新,等.高分辨率非线性地震波阻抗反演方法和应用[J].石油物探,2007,46(1):1-12
13 Caianiello E R.Outline of a theory of thought-processes and thinking machines[J].Theoret Biol,1961,2 (3):204-235
14 Robinson E A,Treitel S.Geophysical signal analysis [M].New Jersey,USA:Prentice-Hall Inc,1980.56- 84
15 Lines L R,Treitel S.A review of least square inversion and its application to geophysical problems[J].Geophysical Prospecting,1984,32(2):159-186
16 Fu L Y.A neuron filtering model and its neural network for space-and time-varying signal processing [R].Boston,USA:Third International Conference on Cognitive and Neural Systems,1999,174-178
17 Fu L Y.Caianiello neural network method for geophysical inverse problems[A].In Poulton M,ed.Computational Neural Networks for Geophysical Data Processing [C].Netherlands,Amsterdam:Pergamon Press,2001.187-215
18 Fu L Y.Joint inversion of seismic data for acoustic impedance [J].Geophysics,2004,69(4):994-1 004
19吴媚,符力耘,李维新.高分辨率非线性储层物性参数反演方法和应用.地球物理学报,2008,51(2):546- 557
20 Wyllie M R J,Gregory A R,Gardner L W.Elastic wave velocities in heterogeneous and porous media[J]. Geophysics,1956,21(1):41-70
21 Nur A,Mavko G,Dvorkin J,et al.Critical porosity:A key to relating physical properties to porosity in rocks [J].The Leading Edge,1998,17:357-362
22 Han D H,Nur A,Morgan D.Effects of porosity and clay content on wave velocities in sandstones[J].Geophysics, 1986,51(11):2 093-2 107
23 Fu L Y.Joint lithologic inversion[A].In:Wong P, Aminzade F,Nikravesh M,ed.Soft computing for reservoir characterization and modeling[C].Heidelberg, Germany:Springer-Verlag Publishers,2002.511-530
2杨文采.神经网络算法在地球物理反演中的应用[J].石油物探,1995,34(2):116-120
3张永刚.地震波阻抗反演技术的现状和发展[J].石油物探,2002,41(4):385-390
4马丽娟,郑和荣,陈霞.隐蔽油气藏地震预测技术研究新进展[J].地球物理学进展,2007,22(1):294-300
5李爱山,印兴耀,张繁昌,等.叠前AVA多参数同步反演技术在含气储层预测中的应用[J].石油物探,2007, 46(1):64-93
6李廷辉,陈洪涛,孔凡东,等.储层综合预测技术在隐蔽油气藏勘探中的应用—以板桥凹陷东斜坡为例[J].石油物探,2003,42(3):340-345
7黄花香,曾涛,唐必锐,等.地质统计分析方法在鲕滩储层预测中的应用[J].石油物探,2004,43(3):275-277
8 van der Baan M,Christian J.Neural networks in geophysical applications[J].Geophysics,2000,65(4): 1 032-1 047
9 Poulton M M.Neural networks as an intelligence amplification tool:a review of applications[J].Geophysics, 2002,67(3):979-993
10 Hampson D P,Schuelke J S,Quirein J A.Use of multiattribute transforms to predict log properties from seismic data[J].Geophysics,2001,66(1):220-236
11 Anstey N A,O'Doherty R F.Cycles,layers,and reflections [J].The Leading Edge,2002,21:44-51,152- 158
12崔炯成,吴媚,李维新,等.高分辨率非线性地震波阻抗反演方法和应用[J].石油物探,2007,46(1):1-12
13 Caianiello E R.Outline of a theory of thought-processes and thinking machines[J].Theoret Biol,1961,2 (3):204-235
14 Robinson E A,Treitel S.Geophysical signal analysis [M].New Jersey,USA:Prentice-Hall Inc,1980.56- 84
15 Lines L R,Treitel S.A review of least square inversion and its application to geophysical problems[J].Geophysical Prospecting,1984,32(2):159-186
16 Fu L Y.A neuron filtering model and its neural network for space-and time-varying signal processing [R].Boston,USA:Third International Conference on Cognitive and Neural Systems,1999,174-178
17 Fu L Y.Caianiello neural network method for geophysical inverse problems[A].In Poulton M,ed.Computational Neural Networks for Geophysical Data Processing [C].Netherlands,Amsterdam:Pergamon Press,2001.187-215
18 Fu L Y.Joint inversion of seismic data for acoustic impedance [J].Geophysics,2004,69(4):994-1 004
19吴媚,符力耘,李维新.高分辨率非线性储层物性参数反演方法和应用.地球物理学报,2008,51(2):546- 557
20 Wyllie M R J,Gregory A R,Gardner L W.Elastic wave velocities in heterogeneous and porous media[J]. Geophysics,1956,21(1):41-70
21 Nur A,Mavko G,Dvorkin J,et al.Critical porosity:A key to relating physical properties to porosity in rocks [J].The Leading Edge,1998,17:357-362
22 Han D H,Nur A,Morgan D.Effects of porosity and clay content on wave velocities in sandstones[J].Geophysics, 1986,51(11):2 093-2 107
23 Fu L Y.Joint lithologic inversion[A].In:Wong P, Aminzade F,Nikravesh M,ed.Soft computing for reservoir characterization and modeling[C].Heidelberg, Germany:Springer-Verlag Publishers,2002.511-530
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