基于层序地层学的地质统计学反演
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摘要
地质统计学反演具有较高的空间分辨率,在薄储层预测中应用广泛,其反演精度受地质框架模型、纵/横向变程和岩性空间展布趋势的影响较大.传统地质框架模型、变程及岩性空间展布趋势的选取主要通过参数测试来实现,效率低,反演精度不高,并不能完全反应储层空间变化特征,尤其是对于储层横向变化较快的曲流河沉积,其储层叠置特征和空间展布特征刻画难度更大.为此,本文以渤海W油田为例,将层序地层学理论引入地质统计学反演中,以层序地层学理论为指导,建立精细的层序地层格架,并构建相应的地质框架模型,借助储层沉积学分析,进而开展层序地层格架约束下变程及地质框架模型约束下岩性空间展布趋势选取方式及其对反演精度影响的研究.研究认为,基于层序地层学选取反演参数效率高、精度高,所选参数地质含义明确;反演结果空间分辨率高,与井上信息吻合度更高,储层内部砂体的叠置特征及空间展布特征更加清晰,从而为油田开发提供了高品质数据支撑.
At present, geostatistical inversion has been widely used in the thin reservoir predicting for its high transverse and longitudinal resolution. There are multiple factors for inversion accuracy, above all,the geological framework model, longitudinal/lateral variation and lithology ratio. However, the traditional method to build geological framework model and choose longitudinal/lateral variation and lithology ratio by testing is low efficiency. Besides, the inversion result can not completely describe reservoir characteristics precisely. Especially to meandering river sediments, it's more difficult to characterize its reservoir and spatial distribution feature for lithology fast changing on the transverse. Therefore, it's necessary to solve the problem that how chooses inversion parameters accurately and then improves spatial resolution of reservoir prediction to meet the needs of oilfield development on resolution. Taking W oilfield in Bohai for example,this paper introduces the sequence stratigraphy into geostatistical inversion for the first time. Sequence stratigraphy framework is built guided by the theory of sequence stratigraphy. Based on framework stratigraphy theory, the geological framework model is built. Combined with reservoir sedimentary features, it discusses how to choose longitudinal/lateral variation and lithology ratio under controlling by sequence stratigraphy and geological framework model, respectively. Besides, their influence on inversion precision are also analyzed. The result shows that the method, using sequence stratigraphy to guide the inversion parameters selection, has the following advantages: high efficiency, high precision and clear geological meaning for selected parameters; high resolution for inversion results, mostly in accordance with well information, accurately characterizing sand body superimposed within the reservoir and meeting accuracy requirement of reservoir characterization in development stage and it provides high quality data for oilfield development.
At present, geostatistical inversion has been widely used in the thin reservoir predicting for its high transverse and longitudinal resolution. There are multiple factors for inversion accuracy, above all,the geological framework model, longitudinal/lateral variation and lithology ratio. However, the traditional method to build geological framework model and choose longitudinal/lateral variation and lithology ratio by testing is low efficiency. Besides, the inversion result can not completely describe reservoir characteristics precisely. Especially to meandering river sediments, it's more difficult to characterize its reservoir and spatial distribution feature for lithology fast changing on the transverse. Therefore, it's necessary to solve the problem that how chooses inversion parameters accurately and then improves spatial resolution of reservoir prediction to meet the needs of oilfield development on resolution. Taking W oilfield in Bohai for example,this paper introduces the sequence stratigraphy into geostatistical inversion for the first time. Sequence stratigraphy framework is built guided by the theory of sequence stratigraphy. Based on framework stratigraphy theory, the geological framework model is built. Combined with reservoir sedimentary features, it discusses how to choose longitudinal/lateral variation and lithology ratio under controlling by sequence stratigraphy and geological framework model, respectively. Besides, their influence on inversion precision are also analyzed. The result shows that the method, using sequence stratigraphy to guide the inversion parameters selection, has the following advantages: high efficiency, high precision and clear geological meaning for selected parameters; high resolution for inversion results, mostly in accordance with well information, accurately characterizing sand body superimposed within the reservoir and meeting accuracy requirement of reservoir characterization in development stage and it provides high quality data for oilfield development.
引文
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Dong Q,Lu S F,Zhang X J,et al. 2013. Selection of Parameters and Reliability Analysis of Results in Geostatistical Inversion Method [J]. Geophysical & Geochemical Exploration (in Chinese),37(2): 328-332,337.董奇,卢双舫,张学娟,等. 2013. 地质统计学反演参数选取及反演结果可靠性分析[J]. 物探与化探,37(2): 328-332,337.
Dubrule O,Thibaut M,Lamy P,et al. 1998. Geostatistical reservoir characterization constrained by 3D seismic data [J]. Petroleum Geoscience,4(2): 121-128.
Fan G J,LI X Y,ZHAO Y J,et al. 2014. Point Bar parameters’ Quantitative Relationship Research and Application Based on Satellite Images [J]. Mathematics in Practice and Theory (in Chinese),44(3): 62- 67.范广娟,李新宇,赵跃军,等. 2014. 基于卫星图像的点坝参数定量关系研究与应用[J]. 数学的实践与认识,44(3): 62- 67.
Gale F J,Gary M B. 2000 Stochastic inversion [C]. SEG Expanded Abstracts.
Gambus M,Torres-Verdín C. 2002. High-resolution geostatistical inversion of a seismic data set acquired in a Gulf of Mexico gas reservoir [C]. SEG 2002 Expanded Abstract. Tulsa: 1785-1788.
Gary Robinson. 2001. Stochastic seismic inversion applied to reservoir characterization [C]. CSEG Recorder January.
Haas A,Dubrule O. 1994. Geostatistical inversion-A sequential metood of stochastic reservoir modeling constrained by seismic data [J]. First Break,12(11): 561-569.
Hu G Y,Chen F,Fan T E,et al. 2014. Analysis of Fluvial Facies Compound Sandbody Architecture of the Neogene Minghuazhen Formation of S Oilfield in the Bohai Bay [J]. ACTA Sedimentologica Sinica (in Chinese),32(3): 586-592.胡光义,陈飞,范廷恩,等. 2014. 渤海海域S油田新近系明化镇组河流相复合砂体叠置样式分析[J]. 沉积学报,32(3): 586-592.
Huang Z Y,Gan L D,Dai X F,et al. 2012. Key parameter optimization and analysis of stochastic seismic inversion [J]. Applied Geophysics (in Chinese),9(1): 49-56.黄哲远,甘利灯,戴晓峰,等. 2012. 随机地震反演关键参数优选和效果分析(英文)[J]. Applied Geophysics,9(1): 49-56.
Jiang W L,Yang K. 2012. High-resolution Geostatistical Petrophysical-parameter Inversion [J]. Geophysical Prospective for Petroleum (in Chinese),51(6): 638- 648.姜文龙,杨锴. 2012. 岩石物理参数高分辨率地质统计学反演[J]. 石油物探,51(6): 638- 648.
Leeder M R. 1973. Fluviatile fining-upwards cycles and the magnitude of palaeochannels [J]. Geological Magazine,110(3): 265-276.
Lei X,LI Z,Wu F R,et al. 2003. Application of high-resolution sequence stratigraphy in inversion of seismic data in western Sichuan [J]. Geophysical Prospective for Petroleum (in Chinese),42(4): 493-500.雷雪,李忠,巫芙蓉,等. 2003. 高分辨率层序地层学在川西地震资料反演中的应用[J]. 石油物探,42(4): 493-500.
Li F M,Ji Z F,Zhao G L,et al. 2007. Methodology and application of stochastic seismic inversion: A case from P Oilfield,M Basin,Sudan [J]. Petroleum Exploration and Development (in Chinese),34(4): 451- 455.李方明,计智锋,赵国良,等. 2007. 地质统计反演之随机地震反演方法-以苏丹M盆地P油田为例[J]. 石油勘探与开发,34(4): 451- 455.
Li J. 2012. Research on Seismic Inversion in the Second Member of Shahejie Formation Dongying Formation of Wen’an slope,Baxian Depression [Master’s thesis]. Jingzhou: Yangtze University.李进. 2012. 霸县凹陷文安斜坡沙二段-东营组地震反演研究[硕士论文]. 荆州:长江大学.
Liang H W,He B,Fan Z F,et al. 2015. Distribution pattern of compound bars based on Base-level cyclic [J]. Journal of Central South University (Science and Technology) (in Chinese),46(12): 4602- 4609.梁宏伟,何彬,范子菲,等. 2015. 基准面旋回控制下的复合点坝分布模式[J]. 中南大学学报(自然科学版),46(12): 4602- 4609.
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