本文關(guān)鍵詞： 泥頁巖儲層 有機質(zhì) 非均質(zhì)性 ΔlogR方法 人工神經(jīng)網(wǎng)絡(luò)(RBF)　出處：《東北石油大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：測井方法評價有機碳含量及儲層非均質(zhì)性具有經(jīng)濟、快捷、縱向分辨率高的特點,應(yīng)用價值和推廣潛力極大。要利用測井資料對泥頁巖層進行評價首先要掌握其地球物理測井相應(yīng)特征,并了解其與常規(guī)砂巖儲層有本質(zhì)區(qū)別的儲層空間及儲集特性。本文對頁巖儲層特征進行細致分析,總結(jié)出其區(qū)別于常規(guī)儲層的顯著特征:①頁巖儲層中富含干酪根等有機質(zhì);②頁巖油氣儲層中礦物組分復(fù)雜,非均質(zhì)性強;③頁巖儲層滲透率極低,孔滲關(guān)系差;④頁巖儲層儲集空間類型多樣;⑤泥頁巖巖石類型多樣,縱向非均質(zhì)性明顯;⑥流體賦存方式多樣。本文介紹了多種利用測井資料計算有機碳含量及無機非均質(zhì)性評價的方法,有機非均質(zhì)性評價方法包括聲波電阻率幅度差法及新方法人工神經(jīng)網(wǎng)絡(luò)法;無機非均質(zhì)性評價方法有經(jīng)驗統(tǒng)計法、全體積模型法及多礦物體積模型法,文中對各方法的原理及步驟進行介紹,并進行簡單的應(yīng)用。本文利用改進的Δlog R方法和人工神經(jīng)網(wǎng)絡(luò)法對渤南洼陷進行實際應(yīng)用,分析對比各方法的適用性與優(yōu)缺點。在實測點中選取162個代表性樣本,其中149個點用于RBF神經(jīng)網(wǎng)絡(luò)訓(xùn)練和Δlog R法的模型建立,用其余13個點對RBF神經(jīng)網(wǎng)絡(luò)和Δlog R模型評價效果進行驗證。對比研究結(jié)果表明,RBF神經(jīng)網(wǎng)絡(luò)法和Δlog R法在對單井進行測井評價TOC的建模和驗證過程中均有較好的效果,當聲波或電阻率曲線與TOC的線性關(guān)系不明顯時,RBF神經(jīng)網(wǎng)絡(luò)法優(yōu)于Δlog R法;RBF神經(jīng)網(wǎng)絡(luò)法對于S1的測井評價具有較好效果,基本可以達到外推應(yīng)用的要求,對于泥頁巖含油性的評價具有重要意義。本文還利用羅69井數(shù)據(jù)進行無機建模,并在縱向上對礦物含量進行連續(xù)計算,效果較理想,為后續(xù)可采性的評價工作做鋪墊。
[Abstract]:The evaluation of organic carbon content and reservoir heterogeneity by logging method has the characteristics of economy, rapidity and high vertical resolution. The application value and popularization potential are great. In order to evaluate shale formation with logging data, it is necessary to master the corresponding geophysical logging characteristics. And understand the reservoir space and reservoir characteristics which are essentially different from the conventional sandstone reservoir. This paper makes a detailed analysis of the shale reservoir characteristics. It is concluded that there are abundant organic matter such as kerogen in the Wei 1 shale reservoir, which is different from the conventional reservoir. (2) the mineral composition in shale oil and gas reservoir is complex and heterogeneity is strong; (3) the permeability of shale reservoir is extremely low and the relationship between pore and permeability is poor; (4) the reservoir space types of shale reservoir are various; (5) the types of shale rocks are various and the longitudinal heterogeneity is obvious; (6) there are many ways to store fluids. This paper introduces a variety of methods for evaluating organic carbon content and inorganic heterogeneity by using logging data. The evaluation methods of organic heterogeneity include acoustic resistivity amplitude difference method and artificial neural network method. The evaluation methods of inorganic heterogeneity include empirical statistical method, full-volume model method and multi-mineral volume model method. The principles and steps of each method are introduced in this paper. In this paper, the modified 螖 log R method and artificial neural network method are applied to Bonan sag. 162 representative samples were selected from the measured points, 149 of which were used for RBF neural network training and 螖 log R modeling. The evaluation results of RBF neural network and 螖 log R model were verified with the other 13 points. RBF neural network method and 螖 log R method have good results in the modeling and verification of TOC for single well logging evaluation, when the linear relationship between acoustic or resistivity curves and TOC is not obvious. RBF neural network method is superior to 螖 log R method. RBF neural network method has a good effect on the logging evaluation of S1, and can basically meet the requirements of extrapolation. It is of great significance to evaluate the oil content of shale. This paper also uses the data of Luo 69 well to build inorganic model and calculate the mineral content continuously in longitudinal. The result is satisfactory. For the follow-up evaluation of admissibility to do the groundwork.
【學(xué)位授予單位】：東北石油大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：P618.13

【參考文獻】

相關(guān)期刊論文 前1條

1 程順國;侯讀杰;肖建新;;利用測井與地震技術(shù)評價優(yōu)質(zhì)烴源巖[J];西部探礦工程;2009年01期

，

本文編號：1487457