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多波联合属性提取及油气预测

Multi-wave Joint Attributes Extraction and Hydrocarbon Prediction

【作者】 白清云

【导师】 杜启振;

【作者基本信息】 中国石油大学 , 地球探测与信息技术, 2010, 硕士

【摘要】 多波多分量地震勘探技术是油藏综合地球物理研究的一种重要的技术手段,其优势不仅仅是多波资料的相互验证,更体现在综合利用多波多分量地震数据直接提取与油气有关的联合属性,进行储层预测和油气预测。传统的地震属性提取大都是针对单道地震信号进行的,没有考虑储层的横向变化,而且传统的属性提取只是截取了地震波形的某一个或几个特征,没有全面的利用地震波形,为了满足油藏开发后期进一步提高采收率的需求,传统的属性提取技术需要进行改进来适应越来越复杂的油气勘探环境。针对传统的属性提取没有考虑横向变化的不足,本文将常规Hilbert变换由单纯时间域变换扩展到地震剖面的二维时间-空间? t ,x ?域,对地震剖面进行二维解析信号的处理,即考虑地质结构的空间相变造成的地震剖面的横向变化来进行油气预测;结合油气对地震信号影响的分析以测井信息为约束,利用二维时频分析方法,寻找油气和水差异性最大的频段,称之为“有利频段”,提取有利频段内的地震多波属性,进而进行油气预测;依据该地区油气在多波地震资料上的表现特点,采用了的相对变化,即这个函数组合达到突出油气异常进而进行油气预测的目的。针对传统的地震属性提取没有充分利用地震波形的不足,结合地球物理勘探的“灰色”的本质通过灰色预测方法,直接利用地震波波形信息提取灰异常属性全面的开发地震波形所携带的地下储层的信息来进行油气预测。针对多波联合属性运用没有理论基础的不足,结合该地区目的层的岩石物理分析,采用具有物理意义的多波联合比值类属性进行油气预测。将上述多波联合属性运用于胜利油田某薄互层区块多波实际资料,实际应用效果表明,通过横向信息的加入和全面波形信息的利用,对于平均砂体厚度只有十米的薄互层目的层,多波联合属性预测的效果无论是与构造图、小层含油面积图的吻合程度,还是与多井测井解释结果的对比验证都取得了不错的应用效果,验证了以上多波联合属性在该地区的可行性和实用性。

【Abstract】 The multi-wave and multi-component seismic exploration is an important technology applied to the study of comprehensive reservoir geophysics. This new geophysical technology of multi-wave and multi-component can not only bring us the mutual verification of multi-wave information, but its deeper effect is integrated utilization of multi-wave and multi-component data to directly extract joint attributes related to hydrocarbon for reservoir prediction and hydrocarbon prediction. The traditional method of attributes extraction is only applied to one single seismic trace, without consideration of the transverse variation of reservoir, and just exacts one or several characteristics of seismic waveform. Therefore, it can not comprehensively exploit the seismic waveform. For the raised recovery in the latter period of oilfield development, we need to improve the traditional method for the more and more complicated and difficult exploration.To solve the lack of transverse changes in conventional attributes extraction, we attend the conventional Hilbert transform from single time domain to time-spatial domain make 2-D analytical signal measures applied to seismic data, and contain the lateral variation of seismic data resulted from spatial change of sedimentary facies of geological structure for hydrocarbon prediction. Combined with the influence of hydrocarbon to seismic signal and logging information, we use 2-D time frequency analysis to find a certain frequency band in which the difference between hydrocarbon and water is the biggest, named“the favorable frequency band”, and then extract multi-wave seismic attributes from the seismic data in the favorable frequency band for hydrocarbon prediction. Based on the reflection feature of hydrocarbon on practical multi-wave data, we introduced the relative change of R p Rps, i.e., to outburst the hydrocarbon anomaly for hydrocarbon prediction. For the comprehensive use of seismic waveform, combined with grey inherent of geophysical exploration, we adopt the method of extracting the grey abnormal attribute from the seismic waveform directly to comprehensively exploit the waveform information for the purpose of hydrocarbon prediction. To solve the lack of theoretical foundation of multi-wave combination, based on the petrophysical analysis of target zone, we use multi-wave joint ratio attributes with physical significance for hydrocarbon prediction.Applying the above-mentioned multi-wave joint attributes to the practical multi-wave data of some interbedded thin layers zone in Shengli Oilfield, the actual data processing results show that the prediction of multi-wave joint attributes not only inosculates the structural map and oil area map of substratum, but also corresponds well with logging interpretation to the target zone of interbedded thin layers of only ten meters thick with the help of lateral information and comprehensive utilization of seismic waveform. Therefore, it verifies the feasibility and validity of the multi-wave joint attributes.

【关键词】 属性油气预测多波联合薄互层
【Key words】 AttributeHydrocarbon predictionMulti-waveJointInterbedded thin layer
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