本文選題：道集優(yōu)化 + 同步反演　； 參考：《成都理工大學(xué)》2017年碩士論文

【摘要】：隨著地震勘探技術(shù)的飛速發(fā)展,提高復(fù)雜隱蔽油氣藏勘探成功率已經(jīng)日益重要,而運(yùn)用疊前地震資料進(jìn)行儲(chǔ)層流體識(shí)別在儲(chǔ)層特征描述中的作用日益凸顯,充分挖掘地震資料的有用信息,提高儲(chǔ)層流體類型識(shí)別的準(zhǔn)確度能夠有效的降低勘探開發(fā)的風(fēng)險(xiǎn)與成本。疊前CRP道集的質(zhì)量直接關(guān)乎疊前反演和AVO分析結(jié)果的精度和可靠性。然而在實(shí)際工作中,由于受地震采集激發(fā)接收條件、觀測(cè)系統(tǒng)、噪音干擾、地層橫向各向異性和常規(guī)處理流程針對(duì)性不強(qiáng)等因素影響,如果將常規(guī)疊前偏移處理生成的CRP道集直接用于AVO分析或疊前反演等工作,常常會(huì)導(dǎo)致AVO特征不明顯以及AVO屬性分析和疊前反演效果欠佳,嚴(yán)重影響到儲(chǔ)層預(yù)測(cè)和烴類檢測(cè)成果質(zhì)量。因此,有必要在進(jìn)行AVO分析及疊前反演之前,根據(jù)實(shí)際資料情況對(duì)疊前道集進(jìn)行一系列有針對(duì)性的優(yōu)化處理。文中以疊前道集的保幅處理為核心,建立了一個(gè)針對(duì)實(shí)際資料的優(yōu)化處理流程,對(duì)疊前CRP道集進(jìn)行優(yōu)化處理,分析各種道集優(yōu)化技術(shù)及流程對(duì)識(shí)別AVO異常的影響。同時(shí)筆者采用疊前道集分析、疊前屬性分析和疊前同步反演技術(shù)確定其地震道集含氣響應(yīng)類型,由疊前同時(shí)反演同時(shí)得到縱波阻抗、橫波阻抗兩個(gè)屬性體,再計(jì)算得到一些其他彈性參數(shù)屬性體,分析含油氣儲(chǔ)層的分布。此外,構(gòu)建更加靈敏可靠的流體因子屬性來直接檢測(cè)油氣是一條人們一直努力嘗試的途徑,文中構(gòu)建的流體識(shí)別因子,在流體檢測(cè)中表現(xiàn)出良好的識(shí)別能力,有一定的實(shí)際應(yīng)用價(jià)值。簡而言之,本文涉及的儲(chǔ)層含油氣性的研究就是利用疊前地震資料分析含油氣儲(chǔ)層的存在性和其含油氣地震響應(yīng)類型、含油氣平面分布、并對(duì)該方法在實(shí)際資料應(yīng)用中的應(yīng)用效果進(jìn)行探討,為實(shí)際的儲(chǔ)層預(yù)測(cè)提供可靠依據(jù)。
[Abstract]:With the rapid development of seismic exploration technology, it is increasingly important to improve the success rate of exploration for complicated subtle reservoirs, and the application of pre-stack seismic data in reservoir fluid identification plays an increasingly important role in reservoir characterization. Fully mining the useful information of seismic data and improving the accuracy of reservoir fluid type identification can effectively reduce the risk and cost of exploration and development. The quality of prestack CRP gathers is directly related to the accuracy and reliability of prestack inversion and AVO analysis. However, in the actual work, due to the seismic acquisition excitation and reception conditions, observation system, noise interference, lateral anisotropy of formation and the lack of pertinence of conventional processing flow and other factors, If the CRP gathers generated by conventional prestack migration are directly used in AVO analysis or prestack inversion, the AVO features will not be obvious and the effect of AVO attribute analysis and prestack inversion will be poor. The quality of reservoir prediction and hydrocarbon detection is seriously affected. Therefore, it is necessary to carry out a series of targeted optimization processing of prestack gathers according to the actual data before AVO analysis and prestack inversion. In this paper, based on the amplitude preserving processing of prestack gathers, an optimized processing flow for practical data is established. The prestack CRP gathers are optimized, and the effects of various gathers optimization techniques and processes on the identification of AVO anomalies are analyzed. At the same time, the author uses prestack gather analysis, prestack attribute analysis and prestack synchronous inversion technology to determine the gas response type of seismic gather. Two attribute bodies, P-wave impedance and S-wave impedance, are obtained simultaneously by simultaneous pre-stack inversion. Then some other elastic parameter attribute bodies are obtained to analyze the distribution of oil and gas reservoirs. In addition, building a more sensitive and reliable fluid factor attribute to directly detect oil and gas is a way that people have been trying hard to try. The fluid recognition factor constructed in this paper shows good recognition ability in fluid detection. It has certain practical application value. In short, the research of reservoir oil and gas bearing property in this paper is to use prestack seismic data to analyze the existence of oil and gas reservoir, the type of seismic response of hydrocarbon bearing reservoir, and the plane distribution of oil and gas. The application effect of this method in the application of practical data is discussed, which provides a reliable basis for the actual reservoir prediction.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P618.13;P631.4

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本文編號(hào)：1793905