三维地震约束多点建模降低井间砂体预测的不确定性
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摘要
本文利用垦71断块的测井数据和三维地震数据,运用多点统计建模方法,和软硬数据结合的原理,进行了油藏建模的井间砂体预测研究。本文的研究包括,三维地震数据的质量控制,软硬数据结合,多点统计学建模应用,训练图象的制作,砂体概率生成曲线的选用。在这基础上,本文利用测井数据和三维地震数据结合的建模结果,与仅用测井数据的建模结果进行了对比。这种分析和对比,以地震泥质含量剖面图为对比根据,分为三个层次:研究层段的上部和下部的砂泥岩分布对比,不同井及其周围地区的砂泥岩分布对比,不同随机种子产生的多个测井砂体预测剖面图之间、和多个井震砂体预测剖面图之间分别对比。本文结果可以说明,地震约束的多点统计建模结果明显地提高了井间砂体预测的合理性,并降低了油藏建模的不确定性。
A study of reservoir modeling is present with 3D seismic and logging data by applying of multi-point statistical modeling,and by the combination of seismic and logging data. The results indicate that the combination of seismic and logging data can reduce uncertainty of reservoir modeling. The study includes the quality control of seismic data,the combination of soft and hard data,the application of multi-point modeling,the making of training image,and the selection of curves for sand body probability making. At last,the result obtained by the combination of 3D seismic and logging data makes a contrast with the result by only logging data. Seismic shale content cross sections are used as a criterion for these analysis and contrast,which include 3 levels: ① contrast of distributions of sand and shale for upper part and low part of the reservoir,② contrast of distributions of sand and shale for different wells and their neighboring areas,③ contrast of among well logging sand prediction cross sections,and both well logging and seismic sand prediction cross sections,produced by different random seeds. As a result of this paper,multi-point statistical modeling constrained by 3D seismic data obviously increases reasonableness of sand prediction inter-wells,and reduces uncertainty of reservoir modeling.
A study of reservoir modeling is present with 3D seismic and logging data by applying of multi-point statistical modeling,and by the combination of seismic and logging data. The results indicate that the combination of seismic and logging data can reduce uncertainty of reservoir modeling. The study includes the quality control of seismic data,the combination of soft and hard data,the application of multi-point modeling,the making of training image,and the selection of curves for sand body probability making. At last,the result obtained by the combination of 3D seismic and logging data makes a contrast with the result by only logging data. Seismic shale content cross sections are used as a criterion for these analysis and contrast,which include 3 levels: ① contrast of distributions of sand and shale for upper part and low part of the reservoir,② contrast of distributions of sand and shale for different wells and their neighboring areas,③ contrast of among well logging sand prediction cross sections,and both well logging and seismic sand prediction cross sections,produced by different random seeds. As a result of this paper,multi-point statistical modeling constrained by 3D seismic data obviously increases reasonableness of sand prediction inter-wells,and reduces uncertainty of reservoir modeling.
引文
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9 Yuhong Liu,Andrew Harding,William Abriel,et al.Mulpti-point simulation integration wells,three-dimensional seismic data,and geology[J].AAPG-Bulletin,2004,88,905-921
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16 李阳.油藏综合地球物理技术研究进展[A].中国地球物理学会第22届年会论文集[C],2006
2 Bortoli L J,Alabert F,Haas A,et al.Constraining stochastic Images to Seismic Data[M],Troia:International Geostatistics Congress,1992
3 Haas A,Dubrule O.Geostatistical inversion:A sequential method of stochastic reservoir modeling constrained by seismic data[J].First Break,1994,13(12):561-566
4 Dubrule O,Geostatistics for seismic data integration in earth models,SEG/EAGE distinguished instructor short course No.6,2003
5 Journel A G.Geostatistics:roadblocks and challenges,Soares,A.,Geostatistics-Troia,Dordrecht:Kluwer Academic,1992,1:475-489
6 Guardiano F,Srivastava R M.Multivariate geostatistics:beyond bivariate moments.Soares,A.,Geostatistics-Troia,Dordrecht:Kluwer Academic,1993,1:133-144
7 Strebelle S.Conditional simulation of complex geological structures using multiple-point statistics[J].Mathmatical Geology,2002,34:1-
22 8 Journel A G,Zhang T.The necessity of a prior multiple-point model,Mathematical Geology,2006,38(5):591-610
9 Yuhong Liu,Andrew Harding,William Abriel,et al.Mulpti-point simulation integration wells,three-dimensional seismic data,and geology[J].AAPG-Bulletin,2004,88,905-921
10 T.Zhang,D.McCormick,N.Hurley,et al.Applying multiplepoint geostatistics to reservoir modeling-a practical perspective[J].Petroleum Geostatistics,10-14,September,2007,Cascais,Portugal
11 Zhang Tuanfeng.Numerical Modeling of Heterogeneous Carbonates and Multi-Scale Dynamics[C].SPWLA 50th Annual Logging Symposium,June 21-24,2009,The Woodlands,Texas
12 Zhang T,et al.An Integrated Multi-point Statistics Modeling Workflow Driven by Quantification of Comprehensive Analogue Database[C].73rd EAGE Conference&Exhibition incorporating SPE EUROPEC 2011 Vienna,Austria,May,23-26,2011
13 Journel A G.Combining Knowledge From Diverse Sources:An Alternative to Traditional Data Independence Hypotheses[J].Mathematical Geology,2002,34(5):573-596
14 Henning M.Bayesian Kriging-Merging observation and qualified guesses in Kriging[J].Mathematical Geology,1987,19(1):25-39
15 裘怿楠.储层地质模型[J].石油学报,1991,12(4):55-62[Qiu Yinan.Geological models of petroleum reservoir[J].Acta petrolei sinica,1991,12(4):55-62]
16 李阳.油藏综合地球物理技术研究进展[A].中国地球物理学会第22届年会论文集[C],2006
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