本文選題：致密儲層 + 巖性識別��； 參考：《東北石油大學》2015年碩士論文

【摘要】：隨著油田勘探開發(fā)程度的不斷深入,復雜油氣藏逐漸成為油田增儲上產(chǎn)的主要來源。大民屯凹陷沙四段致密油儲層屬于低孔隙度、低滲透率的致密儲層,巖性多樣,孔隙結構復雜,泥質(zhì)含量高,束縛水飽和度高。為后續(xù)的巖性劃分及飽和度求取增加了難度。本文在巖電實驗數(shù)據(jù)和測井資料的基礎上,采用統(tǒng)計方法對大民屯凹陷沙四段致密油儲層的巖性、物性和孔隙結構等特征進行了研究,表明該區(qū)塊為巖性多樣,物性差,孔隙結構復雜的致密儲層�；谠搮^(qū)塊不同巖性的地質(zhì)及測井響應特征,將該區(qū)塊致密油儲層巖性劃分為油頁巖、粉砂巖和泥質(zhì)云巖3類。通過統(tǒng)計方法對3類巖性測井曲線的敏感性進行分析,優(yōu)選出聲波時差、密度和自然伽馬等測井曲線�；诿舾袦y井響應,分別建立了測井響應交會圖巖性識別方法以及決策樹和量子神經(jīng)網(wǎng)絡巖性識別模型,結果顯示基于數(shù)據(jù)挖掘方法的決策樹模型和量子神經(jīng)網(wǎng)絡模型的巖性識別效果優(yōu)于傳統(tǒng)的測井響應交會圖法�；趲r心分析資料和測井資料,分別建立了致密砂巖儲層、泥質(zhì)云巖儲層和油頁巖的泥質(zhì)含量、孔隙度等參數(shù)解釋模型�；趲r電實驗數(shù)據(jù),結合致密砂巖儲層、泥質(zhì)云巖儲層和油頁巖特征研究成果,采用改進的等效巖石元素理論結合通用阿爾奇理論建立了大民屯凹陷沙四段致密砂巖儲層導電模型,應用連通導電方程結合混合導電定律建立了大民屯凹陷沙四段泥質(zhì)云巖儲層導電模型,利用有效介質(zhì)對稱導電理論建立了大民屯凹陷沙四段油頁巖導電模型。對模型中參數(shù)的敏感性進行了分析,利用巖電實驗數(shù)據(jù)和測井資料確定了模型中的參數(shù),并給出了模型求取含水飽和度的方法。利用本文所建立的參數(shù)解釋模型和導電模型對該區(qū)塊致密砂巖儲層、泥質(zhì)云巖儲層和油頁巖的實際測井資料進行了處理,結果表明,該模型能夠很好地用于大民屯凹陷沙四段致密油儲層的測井解釋評價。
[Abstract]:With the deepening of exploration and development, complex reservoirs are becoming the main source of increasing reservoir production. The tight oil reservoir of the fourth member of Shahejie formation in Damingtun depression belongs to the tight reservoir with low porosity and low permeability. The lithology is diverse, the pore structure is complex, the content of mud is high, and the irreducible water saturation is high. It increases the difficulty for the subsequent lithologic division and saturation calculation. Based on the experimental data of rock and electricity and logging data, the lithology, physical properties and pore structure of the tight oil reservoir of the fourth member of Shahejie formation in Damingtun depression are studied by using statistical method. The results show that the block is of diverse lithology and poor physical property. Tight reservoir with complex pore structure. Based on the geological and logging response characteristics of different lithology in this block, the lithology of tight oil reservoir in this block is divided into three categories: oil shale, siltstone and shaly dolomite. The sensitivity of three kinds of lithologic logging curves is analyzed by statistical method, and acoustic moveout, density and natural gamma logging curves are selected. Based on the sensitive logging response, the lithologic identification method of cross plot, decision tree and quantum neural network lithology identification model are established, respectively. The results show that the lithology recognition effect of decision tree model and quantum neural network model based on data mining method is better than that of traditional well logging response cross plot method. Based on the core analysis data and logging data, the interpretation models of shale content and porosity of tight sandstone reservoir, argillaceous dolomite reservoir and oil shale are established respectively. Based on the experimental data of rock and electricity, combined with the research results of tight sandstone reservoir, argillaceous dolomite reservoir and oil shale, Based on the improved equivalent rock element theory and the universal Archie theory, a conductive model for the tight sandstone reservoir of the fourth member of Sha in the Damingtun Sag is established. The conductive model of shaly dolomite reservoir in the fourth member of Shahejie formation in Damingtun sag is established by using the connected conductive equation and the mixed conductive law. The oil shale conductive model of the fourth member of Shahejie formation in Damingtun sag is established by using the theory of symmetric conductivity of effective medium. The sensitivity of the parameters in the model is analyzed, the parameters in the model are determined by using the experimental data of rock electricity and logging data, and the method of calculating the saturation of water in the model is given. The actual logging data of tight sandstone reservoir, argillaceous dolomite reservoir and oil shale in this block are processed by using the parameter interpretation model and conductive model established in this paper. The results show that, The model can be applied to the logging interpretation and evaluation of the tight oil reservoirs in the fourth member of Shahejie formation in Damingtun Sag.
【學位授予單位】：東北石油大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：P618.13;P631.81

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