多点地质统计学在苏49-01井区沉积微相建模中的应用
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摘要
鉴于两点地质统计学在沉积相建模中存在不能模拟多种微相空间接触关系的缺点,尝试用多点地质学建立苏49-01区块辫状河沉积微相模型。首先结合地震属性、露头观察、相似沉积环境密井网区地质认识和相关文献等资料分析了辫状河各微相的地质要素和空间接触关系,建立了研究区石盒子组盒8下段辫状河三维训练图像。然后以井上解释的沉积微相作为建模硬数据、地震反演成果作为软数据,利用多点地质统计学建模方法建立了苏49-01区块河流相三维沉积微相模型。研究结果表明,用多点地质统计学整合地质模式和地震数据建立的沉积微相模型能更准确地反映辫状河各沉积微相的空间展布,为苏里格气田有效砂体分布预测这一地质难题的解决提供了新的手段,对苏里格气田开发具有重要指导意义。
Provided that sedimentary facies modeling based on two-point geostatistics is unable to present complex space relationship of multiple microfacies, in this paper multiple-points geostatistics is introduced to the 3D braided river sedimentary facies model of Block SU49-01 of Sulige gas field. The geologic element and space contact relationships of different microfacies of braided river is established through comprehensive analysis of all kinds of materials including seismic attributes, outcrop observation, geological understanding of similar sedimentary environment in dense well net block and related literature material, and then the 3D braided channel microfacies training image of 8th lower member of Shihezi Formation is made. With the hard data of well point microfacies interpretation and soft data of P-sonic impedance, three-dimensional microfacies model is created with the method of multiple-point geostatistics constrained with seismic data. Integrating geological model and seismic data, the sedimentary facies model built with multiple-point geostatistics reflects the sedimentary microfacies space distribution in braided river more accurately, which provides a new measurement to predict the distribution of effective sand-body and has a guiding significance for gas development at Sulige gas field.
Provided that sedimentary facies modeling based on two-point geostatistics is unable to present complex space relationship of multiple microfacies, in this paper multiple-points geostatistics is introduced to the 3D braided river sedimentary facies model of Block SU49-01 of Sulige gas field. The geologic element and space contact relationships of different microfacies of braided river is established through comprehensive analysis of all kinds of materials including seismic attributes, outcrop observation, geological understanding of similar sedimentary environment in dense well net block and related literature material, and then the 3D braided channel microfacies training image of 8th lower member of Shihezi Formation is made. With the hard data of well point microfacies interpretation and soft data of P-sonic impedance, three-dimensional microfacies model is created with the method of multiple-point geostatistics constrained with seismic data. Integrating geological model and seismic data, the sedimentary facies model built with multiple-point geostatistics reflects the sedimentary microfacies space distribution in braided river more accurately, which provides a new measurement to predict the distribution of effective sand-body and has a guiding significance for gas development at Sulige gas field.
引文
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[16]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.
[17]Liu Y,Harding A G,Gilbert R.A workflow for multiple-point geostatistical simulation.Quantitative Geology and Geostatistics,2005,14(1):245～254.
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[2]Strebelle S.Sequential Simulation drawing structures from training images.Stanford:Stanford University,2000:187.
[3]Strebelle S.Conditional simulation of complex geological structures using multiple-point statistics.Mathematical Geology,2002,34(1):1～21.
[4]Liu Y,Harding A,Abriel W,et al.Multiple point simulation integrating wells,3D seismic data and geology.American Association of Petroleum Geologists Bulletin,2004,88(7):905～922.
[5]王大兴,赵玉华,周义军,等.苏里格气田多波地震处理与储层预测技术研究及应用[J].中国石油勘探,2011,16(5-6):95～102.
[6]明治良,贺新蔚,孙进,等.苏里格地区低渗透砂岩气藏多波地震勘探技术及应用[J].中国石油勘探,2011,16(5-6):103～110.
[7]兰朝利,何顺利,张君峰,等.苏里格气田储层“甜点”控制因素探讨[J].西安石油大学学报(自然科学版),2007,22(1):45～48.
[8]何东博,贾爱林,田昌炳,等.苏里格气田储集层成岩作用及有效储集层成因[J].石油勘探与开发,2004,31(3):69～71.
[9]朱怡翔,张明禄,王根久,等.苏里格气田相控建模及有利储层预测[J].中国石油勘探,2004,9(1):52～57.
[10]贾爱林,唐俊伟,何东博,等.苏里格气田强非均质致密砂岩储层的地质建模[J].中国石油勘探,2007(1):12～17.
[11]王大兴,史松群,赵玉华.苏里格庙储层预测技术及效果[J].中国石油勘探.2001,6(3):32～43.
[12]Campbell C V.Reservoir geometry of a fluvial sheet sandstone.AAPG Bulletin,1976,60(7):1009～1020.
[13]裘怿楠.我国河道砂体储层沉积特征和非均质模式、碎屑岩沉积物研究[M].北京:石油工业出版社,1988.
[14]Strebelle S,Journel A.Rerervior modeling using multiple-point statistics.SPE71324,2001:1～11.
[15]Zhang Tuanfeng.Incorporating geological conceptual models and interpretations into reservoir modeling using multiple-point geostatistics.Earth Science Frontiers,2008,15(1):26～35.
[16]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.
[17]Liu Y,Harding A G,Gilbert R.A workflow for multiple-point geostatistical simulation.Quantitative Geology and Geostatistics,2005,14(1):245～254.
[18]Liu Y.Downscaling seismic data into a geologically sound numerical model.Stanford:Stanford University,2003:134～207.
[19]Caers J,Avseth P,Mukerji T.Geostatistical integration of rock physics,seismic amplitudes and geological models in North-Sea turbidite systems.The Leading Edge,2001,20(3):308～312.
[20]Arpat B G,Cacrs J.A Multi-scale,Pattern-based Approach to Sequential Simulation.Geostatistics Banff.2004,1(10):255～264.
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