本文選題：凝灰質(zhì)儲(chǔ)層 + 測(cè)井響應(yīng)差異��； 參考：《吉林大學(xué)》2016年博士論文

【摘要】：隨著人們對(duì)油氣資源需求的日益增加和油氣勘探開(kāi)發(fā)的不斷深入,復(fù)雜油氣藏已經(jīng)成為研究熱點(diǎn)。其中,火山巖碎屑巖與火山碎屑沉積巖等儲(chǔ)層評(píng)價(jià)受到廣泛關(guān)注,但研究程度仍然不夠深入。該類儲(chǔ)層在本文中稱為凝灰質(zhì)儲(chǔ)層,由于普遍含有凝灰質(zhì)等火山碎屑物質(zhì),和正常的泥質(zhì)砂巖儲(chǔ)層相比,不僅物源多樣、礦物成分復(fù)雜、非均質(zhì)性強(qiáng)而且成巖過(guò)程更為復(fù)雜,使得儲(chǔ)層孔隙結(jié)構(gòu)復(fù)雜。尤其是凝灰質(zhì)和泥質(zhì)在許多性質(zhì)上具有相似性,導(dǎo)致了區(qū)分的難度,因此凝灰質(zhì)儲(chǔ)層測(cè)井評(píng)價(jià)一直是一個(gè)難題。本文通過(guò)凝灰質(zhì)儲(chǔ)層中泥質(zhì)和凝灰質(zhì)的測(cè)井響應(yīng)特征分析,建立了凝灰質(zhì)儲(chǔ)層的體積模型和導(dǎo)電模型。并利用多種方法求解飽和度、評(píng)價(jià)含油性,為這類儲(chǔ)層的測(cè)井評(píng)價(jià)提供了一定依據(jù),對(duì)凝灰質(zhì)儲(chǔ)層的勘探、開(kāi)發(fā)具有重要意義。論文研究的內(nèi)容主要有以下幾個(gè)方面:綜合利用常規(guī)測(cè)井曲線結(jié)合電成像測(cè)井和ECS等測(cè)井資料識(shí)別火山巖、火山碎屑巖、火山碎屑沉積巖和沉火山碎屑巖等巖性。理論分析結(jié)合巖心實(shí)驗(yàn)數(shù)據(jù),得到了泥質(zhì)與凝灰質(zhì)在放射性元素含量、密度等測(cè)井響應(yīng)上的差異,進(jìn)而建立了考慮泥質(zhì)和凝灰質(zhì)的儲(chǔ)層體積模型。利用ECS資料獲得了隨深度變化的骨架密度和骨架中子,解決了成分復(fù)雜的儲(chǔ)層孔隙度的計(jì)算,并利用粒子群和細(xì)菌覓食的混合優(yōu)化算法計(jì)算凝灰質(zhì)和泥質(zhì)含量。利用CEC實(shí)驗(yàn)數(shù)據(jù),分析了泥質(zhì)和凝灰質(zhì)的導(dǎo)電性差異。繼而借用W-S模型的思想推導(dǎo)并得到了CEC與電阻率之間的關(guān)系,將這種轉(zhuǎn)換關(guān)系應(yīng)用到飽和度計(jì)算中。將W-S模型進(jìn)行改進(jìn),將其推廣到凝灰質(zhì)儲(chǔ)層的飽和度評(píng)價(jià)中,并取得了良好的應(yīng)用效果。利用常規(guī)測(cè)井資料建立了流體識(shí)別圖版并結(jié)合SVM分類算法進(jìn)行儲(chǔ)層流體識(shí)別,建立了一套適合凝灰質(zhì)儲(chǔ)層的測(cè)井評(píng)價(jià)方法。論文的有以下幾個(gè)創(chuàng)新點(diǎn):1、論證了泥質(zhì)與凝灰質(zhì)的測(cè)井響應(yīng)差異。2、基于粒度對(duì)凝灰質(zhì)放射性元素含量的影響,建立了含粗、細(xì)凝灰質(zhì)的凝灰質(zhì)儲(chǔ)層的體積模型。3、利用粒子群和細(xì)菌覓食的混合優(yōu)化算法,計(jì)算泥質(zhì)和凝灰質(zhì)含量。4、基于W-S模型的思想推導(dǎo)了CEC與電阻率的關(guān)系,并將這種轉(zhuǎn)換關(guān)系應(yīng)用到飽和度計(jì)算中。5、在W-S模型的基礎(chǔ)上得到了計(jì)算凝灰質(zhì)儲(chǔ)層的含水飽和度的方法,并取得了良好的應(yīng)用效果應(yīng)用前景:本文通過(guò)論證泥質(zhì)和凝灰質(zhì)的測(cè)井響應(yīng)差異,解決了兩者難以區(qū)分的問(wèn)題;提出了利用CEC比值計(jì)算儲(chǔ)層飽和度的方法以及沒(méi)有CEC實(shí)驗(yàn)資料時(shí)的基于W-S模型的飽和度計(jì)算方法。解決了巖性識(shí)別、儲(chǔ)層參數(shù)計(jì)算和含油性評(píng)價(jià)等難題,對(duì)凝灰質(zhì)儲(chǔ)層的勘探、開(kāi)發(fā)提供了參考價(jià)值,經(jīng)濟(jì)意義較為明顯。
[Abstract]:With the increasing demand for oil and gas resources and the deepening of oil and gas exploration and development, complex oil and gas reservoirs have become a research hotspot. Among them, the evaluation of volcanic clastic rock and pyroclastic sedimentary rock has received extensive attention, but the degree of research is still not deep enough. This kind of reservoir is called tuffaceous reservoir in this paper. Because of the common volcanic clastic material such as tuff, compared with normal argillaceous sandstone reservoir, this kind of reservoir is not only rich in material source, complex in mineral composition, strong in heterogeneity, but also more complicated in diagenetic process. The pore structure of the reservoir is complicated. In particular, many properties of tuff and mud are similar, which leads to the difficulty of distinguishing. Therefore, logging evaluation of tuff reservoir is always a difficult problem. In this paper, the volume model and conductive model of tuff reservoir are established by analyzing the logging response characteristics of the clay and tuff in the tuff reservoir. Several methods are used to calculate saturation and evaluate oil content, which provides a certain basis for logging evaluation of this kind of reservoir, and is of great significance for exploration and development of tuff reservoir. The main contents of this paper are as follows: the lithology of volcanic rocks, pyroclastic sedimentary rocks and sedimentary pyroclastic rocks are identified by using conventional logging curves combined with electrical imaging logging and ECS logging data. Based on the theoretical analysis and core experimental data, the difference of log responses between mud and tuff in terms of radioactive element content and density is obtained, and a reservoir volume model considering mud and tuff is established. By using ECS data, the skeleton density and skeleton neutron varying with depth are obtained, and the calculation of reservoir porosity with complex composition is solved, and the content of tuff and mud is calculated by the hybrid optimization algorithm of particle swarm and bacterial foraging. The difference of electrical conductivity between clay and tuff is analyzed by using CEC experimental data. Then, the relationship between CEC and resistivity is derived by using W-S model, and the transformation relation is applied to saturation calculation. The W-S model has been improved and extended to the evaluation of tuff reservoir saturation, and good results have been obtained. Based on the conventional logging data, the fluid identification chart is established and the SVM classification algorithm is used to identify the reservoir fluid. A set of logging evaluation methods suitable for the tuff reservoir are established. This paper has the following innovations: 1, proves the difference of logging response between mud and tuff. Based on the effect of particle size on the content of radioactive elements in tuff, the coarse content is established. The volume model of tuffaceous reservoir of fine tuff. 3, using the mixed optimization algorithm of particle swarm and bacteria foraging, calculates the content of clay and tuff. Based on W-S model, the relationship between CEC and resistivity is deduced. The transformation relation is applied to saturation calculation. On the basis of W-S model, the method of calculating water saturation of tuff reservoir is obtained. And has obtained the good application effect application prospect: this article through the demonstration mud and the tuff logging response difference, has solved the two difficult to distinguish the question; The calculation method of reservoir saturation using CEC ratio and the saturation calculation method based on W-S model without CEC experimental data are put forward. The problems of lithology identification, reservoir parameter calculation and oil-bearing evaluation are solved, which provides a reference value for the exploration and development of tuff reservoir, and the economic significance is obvious.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：P618.13;P631.81

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