多点地质统计学在河流相储层建模中的应用
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摘要
多点地质统计学综合了基于象元方法以及基于目标方法两者的优点,对于河流相等具有复杂地质形态的储层精确建模具有较强的优势。在对传统建模方法综合分析的基础上,介绍了多点地质统计学的基本理论及SNESIM算法,并应用该技术对大牛地气田某开发井区的辫状分流河道相进行了实际建模。研究结果表明,在河流相储层建模中,该方法比传统的建模方法更具优越性。最后,进一步综合讨论了多点地质统计学目前面临的主要问题(包括训练图像、目标体连续性、数据样板选择、综合地震信息等方面)的改进方法。
Multiple-point geostatistics is a promising discipline in reservoir stochastic modeling. This approach combines the advantages of two methods: pixel-based two-point simulation and object-based simulation, which is able to make a more exact reservoir modeling for the reservoir with complex variability, especially the fluvial reservoir. With the analysis of the traditional modeling methods, the paper presents the principle of multiple-point geostatistics and SNESIM algorithms, and simulates the sand distribution of a braided distributary channel reservoir in one development block, Daniudi gas field. The result indicates that this approach is better than the traditional methods for the fluvial reservoir modeling. Finally, the paper discusses some main problems, including the training images, object continuity, data template, and integration of seismic information.
Multiple-point geostatistics is a promising discipline in reservoir stochastic modeling. This approach combines the advantages of two methods: pixel-based two-point simulation and object-based simulation, which is able to make a more exact reservoir modeling for the reservoir with complex variability, especially the fluvial reservoir. With the analysis of the traditional modeling methods, the paper presents the principle of multiple-point geostatistics and SNESIM algorithms, and simulates the sand distribution of a braided distributary channel reservoir in one development block, Daniudi gas field. The result indicates that this approach is better than the traditional methods for the fluvial reservoir modeling. Finally, the paper discusses some main problems, including the training images, object continuity, data template, and integration of seismic information.
引文
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[14]Strebelle S.Sequential Si mulation drawing structures from training i mages[D].Stanford:Stanford University,2000:187.
[15]冯国庆,陈浩,张烈辉,等.利用多点地质统计学方法模拟岩相分布[J].西安石油大学学报:自然科学版,2005,20(5):9-11.
[16]Ortiz J M,Steven L,Clayton V D.Scaling multiple-point sta-tistics to different univariate proportions[J].Computers&Ge-osciences,2007,33:191-201.
[17]Caers J M.Multiple-point geostatistics:Aquantitative vehicle for integrating geologic analogs into multiple reservoir models[C]∥Grammer G M.AAPG memoir:Integration of out crop and modern analog data in reservoir models.[S.l.]:[s.n.],2004:383-394.
[18]Liu Y.Downscaling seismic data into a geologically sound numerical model[D].Stanford:Stanford University,2003:202.
[19]Journel A G.Multiple-point Geostatistics:Astate of the art[R].[S.l.]:Stanford Center for Reservoir Forecasting,2003.
[20]Apart B G,Caers J.A multi-scale pattern based approach to sequential si mulation[R].Stanford:Stanford Center for Reser-voir Forecasting,2003.
[21]Qiu WY,Kelar MG.Si mulationof geological models using multipoint histogram[C].SPE30601,1995:771-780.
[2]吴胜和,李文克.多点地质统计学——理论、应用与展望[J].古地理学报,2005,7(1):137-144.
[3]Liu Yuhong.Usingthe Snesi mprogramfor multiple-point sta-tistical si mulation[J].Computers&Geosciences,2006,32:1544-1563.
[4]尹艳树,吴胜和.储层随机建模研究进展[J].天然气地球科学,2006,17(2):210-216.
[5]白鹤翔,葛咏,李德玉.多点模拟算法与试验对比分析[J].地球信息科学,2006,8(4):117-121.
[6]XU Wenlong.Conditional curvilinear stochastic si mulation u-sing pixel-based algorithms[J].Mathematic Geology,1996,28(7):937-943.
[7]Deutsch C V.Annealingtechniques appliedto reservoir mod-eling andthe integration of geological and engineering(well test)data[D].Stanford:Stanford University,1992:306.
[8]Ortiz J M.Characterizationof high order correlation for en-hancedindicator si mulation[D].Edmonton:University of Al-berta,2003:255.
[9]Ortiz J M,Deutsch C V.Indicator si mulation accounting for multiple-point statistics[J].Mathematical Geology,2004,36(6):545-565.
[10]Strebelle S.Conditional si mulation of complex geological struc-tures using multiple-point statistics[J].Mathematical Geolo-gy,2002,34(1):1-21.
[11]Liu Y,Journel A.I mproving si mulation with a structured path guided by information content[J].Mathematical Geology,2004,36(8),945-964.
[12]Liu Y,Harding A,Abriel W,et al.Multiple point si mulation integrating wells,3D seismic data and geology[J].American Association of Petroleum Geologists Bulletin,2004,88(7):905-922.
[13]Strebelle S,Journel A.Reservoir modeling using multiple-point statistics[C].SPE71324,2001:1-11.
[14]Strebelle S.Sequential Si mulation drawing structures from training i mages[D].Stanford:Stanford University,2000:187.
[15]冯国庆,陈浩,张烈辉,等.利用多点地质统计学方法模拟岩相分布[J].西安石油大学学报:自然科学版,2005,20(5):9-11.
[16]Ortiz J M,Steven L,Clayton V D.Scaling multiple-point sta-tistics to different univariate proportions[J].Computers&Ge-osciences,2007,33:191-201.
[17]Caers J M.Multiple-point geostatistics:Aquantitative vehicle for integrating geologic analogs into multiple reservoir models[C]∥Grammer G M.AAPG memoir:Integration of out crop and modern analog data in reservoir models.[S.l.]:[s.n.],2004:383-394.
[18]Liu Y.Downscaling seismic data into a geologically sound numerical model[D].Stanford:Stanford University,2003:202.
[19]Journel A G.Multiple-point Geostatistics:Astate of the art[R].[S.l.]:Stanford Center for Reservoir Forecasting,2003.
[20]Apart B G,Caers J.A multi-scale pattern based approach to sequential si mulation[R].Stanford:Stanford Center for Reser-voir Forecasting,2003.
[21]Qiu WY,Kelar MG.Si mulationof geological models using multipoint histogram[C].SPE30601,1995:771-780.
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