本文選題：致密砂巖 + 測井��； 參考：《長江大學》2015年碩士論文

【摘要】：隨著油氣的不斷開采,目前高孔高滲的儲層愈來愈少,而低孔低滲的儲層成為了研究的重點；隨著資源的缺乏以及技術(shù)水平相應提高,頁巖氣、砂巖致密氣也成為了如今探測開采的焦點。在這兩類氣藏開采的過程中,裂縫的評估與預測是重中之重。對于裂縫的預測評估而言,現(xiàn)在一般是通過成像測井來判斷裂縫發(fā)育程度,而成像測井因為成本原因,不能廣泛大量的在油田里運用,因此開始尋求通過常規(guī)測井的各種手段與方法來判斷裂縫發(fā)育程度。目前大多數(shù)的常規(guī)測井手段基本上都是利用裂縫在各種曲線上的響應特征諸如電阻率,聲波等來識別裂縫,都是利用測井曲線數(shù)值上的差異來進行識別。但是單一的運用常規(guī)測井資料來預測裂縫發(fā)育與否仍然具有一定的局限性,而單一的依靠成像測井資料來識別又不現(xiàn)實,因此需要更加實際有效的方法。本文以SH油田的區(qū)塊兩個層段作為研究區(qū),研究區(qū)內(nèi)平均孔隙度為7.51%,平均滲透率為0.20mD,屬于致密砂巖氣藏。首先以區(qū)塊內(nèi)的薄片資料分析研究目的層的巖性,分別利用交會圖法、模糊數(shù)學法以及神經(jīng)網(wǎng)絡(luò)法識別巖性。交會圖法與神經(jīng)網(wǎng)絡(luò)法識別符合率均達到80%以上,運用交會圖法模版能準確快捷的識別出區(qū)域巖性,將區(qū)域內(nèi)的砂巖細化分為巖屑砂巖、巖屑石英砂巖以及石英砂巖三種巖性。在不同巖性的基礎(chǔ)上,分別通過兩個不同的角度來預測裂縫發(fā)育程度。利用電成像測井資料來刻度常規(guī)測井資料上裂縫的響應特征,總結(jié)出該區(qū)塊裂縫測井響應特征；再利用對應的測井曲線計算出裂縫參數(shù),通過R/S分形法計算對應測井曲線的分維數(shù)值,將其與成像測井資料相對照得到裂縫發(fā)育不同程度的分維數(shù)值范圍以及對應裂縫參數(shù)范圍；利用5口電成像測井資料的61個層位、巖心分析資料及常規(guī)測井資料利用R/S分形法對儲層裂縫進行發(fā)育程度的劃分。得到相應的圖版。研究區(qū)內(nèi)發(fā)育裂縫時,巖屑石英砂巖分維數(shù)大于1.15,石英砂巖與巖屑砂巖分維數(shù)大于1.1,實際處理11口井19個層位,平均符合率達到73.6%；利用偶極聲波成像測井資料建立不同巖性的縱橫波時差模型,通過縱橫波時差模型計算相應的巖石力學參數(shù),再將巖石力學參數(shù)的值與裂縫參數(shù)結(jié)合計算出不同裂縫發(fā)育程度的巖石力學參數(shù)值的范圍標準,充分利用3口偶極聲波井的56個層位的資料、巖心分析資料及對應常規(guī)測井資料建立不同巖性的巖石力學參數(shù)模版,而在巖石力學參數(shù)里面,楊氏模量來預測裂縫發(fā)育在該區(qū)塊最為適用,研究區(qū)內(nèi)存在裂縫時,巖屑石英砂巖的楊氏模量小于31,巖屑砂巖的楊氏模量小于36；實際處理5口井15個層位,平均符合率達到66.7%,能夠初步滿足實際需求。
[Abstract]:With the continuous exploitation of oil and gas, there are fewer and fewer reservoirs with high porosity and high permeability, and the reservoirs with low porosity and low permeability become the focus of research. Tight sandstone gas has also become the focus of exploration and mining. In the process of producing these two types of gas reservoirs, the evaluation and prediction of fractures is the most important. For the prediction and evaluation of fractures, it is generally used now to judge the degree of fracture development through imaging logging, and imaging logging cannot be widely used in oil fields due to cost reasons. Therefore, we began to look for various means and methods of conventional logging to judge the degree of fracture development. At present, most conventional logging methods use the response characteristics of fractures on various curves, such as resistivity and sound waves, to identify fractures, and use the difference of logging curves to identify fractures. However, the single use of conventional logging data to predict fracture development or not still has some limitations, but it is not realistic to rely solely on imaging logging data to identify fractures, so a more practical and effective method is needed. In this paper, two layers in the block of SH oilfield are taken as the study area. The average porosity and permeability in the study area are 7.51 and 0.20mDrespectively, which belong to the tight sandstone gas reservoir. Firstly, the lithology of the target layer is analyzed with the slice data of the block, and the lithology is identified by cross plot method, fuzzy mathematics method and neural network method respectively. The coincidence rate of cross plot method and neural network method is more than 80%. The lithology of the area can be identified accurately and quickly by using the cross plot template. The sandstone in the area can be divided into three types: lithic sandstone, lithic quartz sandstone and quartz sandstone. On the basis of different lithology, the degree of fracture development is predicted from two different angles. The response characteristics of fractures on conventional logging data are calibrated by using electrical imaging logging data, and the logging response characteristics of fractures in this block are summarized, and the fracture parameters are calculated by using the corresponding logging curves. The fractal dimension of the corresponding logging curve is calculated by the R / S fractal method, and compared with the imaging logging data, the fractal dimension value range and the corresponding fracture parameter range of different degrees of fracture development are obtained, and the 61 layers of 5 electrical imaging logging data are used. Core analysis data and conventional logging data are used to divide the development degree of reservoir fractures by using R / S fractal method. Get the corresponding plates. In the development of fractures, the fractal dimension of lithic quartz sandstone is greater than 1.15, the fractal dimension of quartz sandstone and lithic sandstone is greater than 1.1, and the 19 layers of 11 wells are actually treated. The average coincidence rate is 73.6.The longitudinal and shear wave moveout models with different lithology are established by using dipole acoustic imaging data, and the corresponding rock mechanics parameters are calculated by the P-S wave moveout model. Then combining the values of rock mechanics parameters with fracture parameters to calculate the range standard of rock mechanics parameters with different fracture development degrees, the data of 56 layers of 3 dipolar acoustic wells are fully utilized. The core analysis data and the corresponding conventional logging data are used to establish the lithologic parameters template of different lithology. Among the rock mechanics parameters, Young's modulus is the most suitable for predicting fracture development in this block. When there are fractures in the study area, The Young's modulus of lithic quartz sandstone is less than 31, the Young's modulus of lithic sandstone is less than 36, and the average coincidence rate of treating 5 wells and 15 layers is 66.7, which can meet the actual demand.
【學位授予單位】：長江大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：P618.13

【參考文獻】

相關(guān)期刊論文 前10條

1 李會軍,吳泰然,郝銀全,張秀珠,張文才;港深78井裂縫發(fā)育影響因素、成因及其在油氣勘探中的意義[J];斷塊油氣田;2004年03期

2 魏陽慶,劉鋒,孫彩云;利用六臂地層傾角測井資料分析地應力[J];斷塊油氣田;2005年05期

3 劉平;淺談主成分分析與因子分析的異同[J];遼寧師專學報(自然科學版);2004年03期

4 崔大慶,楊永超,譚國華,梁成蔚;東濮凹陷深層致密砂巖氣層識別方法研究[J];內(nèi)蒙古石油化工;2005年09期

5 周燦燦,楊春頂;砂巖裂縫的成因及其常規(guī)測井資料綜合識別技術(shù)研究[J];石油地球物理勘探;2003年04期

6 王學軍,陳鋼花,張家震,朱家俊,楊海霞;古潛山油藏裂縫性儲層的測井評價[J];石油大學學報(自然科學版);2003年05期

7 曹延旭;用測井方法識別致密砂巖天然氣層[J];石油儀器;2005年03期

8 劉吉余;馬志欣;孫淑艷;;致密含氣砂巖研究現(xiàn)狀及發(fā)展展望[J];天然氣地球科學;2008年03期

9 張博;袁文芳;曹少芳;秦紅;魏燕萍;趙新艷;王忠武;;庫車坳陷大北地區(qū)砂巖儲層裂縫主控因素的模糊評判[J];天然氣地球科學;2011年02期

10 林海明;;主成分分析與初始因子分析的異同——兼與盧紋岱《SPSS for Windows統(tǒng)計分析》商榷[J];統(tǒng)計與決策;2006年08期

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