本文選題：致密砂巖 + 測(cè)井�。� 參考：《長(zhǎng)江大學(xué)》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(chǔ)層愈來(lái)愈少,而低孔低滲的儲(chǔ)層成為了研究的重點(diǎn)；隨著資源的缺乏以及技術(shù)水平相應(yīng)提高,頁(yè)巖氣、砂巖致密氣也成為了如今探測(cè)開采的焦點(diǎn)。在這兩類氣藏開采的過(guò)程中,裂縫的評(píng)估與預(yù)測(cè)是重中之重。對(duì)于裂縫的預(yù)測(cè)評(píng)估而言,現(xiàn)在一般是通過(guò)成像測(cè)井來(lái)判斷裂縫發(fā)育程度,而成像測(cè)井因?yàn)槌杀驹?不能廣泛大量的在油田里運(yùn)用,因此開始尋求通過(guò)常規(guī)測(cè)井的各種手段與方法來(lái)判斷裂縫發(fā)育程度。目前大多數(shù)的常規(guī)測(cè)井手段基本上都是利用裂縫在各種曲線上的響應(yīng)特征諸如電阻率,聲波等來(lái)識(shí)別裂縫,都是利用測(cè)井曲線數(shù)值上的差異來(lái)進(jìn)行識(shí)別。但是單一的運(yùn)用常規(guī)測(cè)井資料來(lái)預(yù)測(cè)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(cè)井資料來(lái)識(shí)別又不現(xiàn)實(shí),因此需要更加實(shí)際有效的方法。本文以SH油田的區(qū)塊兩個(gè)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(huì)圖法、模糊數(shù)學(xué)法以及神經(jīng)網(wǎng)絡(luò)法識(shí)別巖性。交會(huì)圖法與神經(jīng)網(wǎng)絡(luò)法識(shí)別符合率均達(dá)到80%以上,運(yùn)用交會(huì)圖法模版能準(zhǔn)確快捷的識(shí)別出區(qū)域巖性,將區(qū)域內(nèi)的砂巖細(xì)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(guò)兩個(gè)不同的角度來(lái)預(yù)測(cè)裂縫發(fā)育程度。利用電成像測(cè)井資料來(lái)刻度常規(guī)測(cè)井資料上裂縫的響應(yīng)特征,總結(jié)出該區(qū)塊裂縫測(cè)井響應(yīng)特征；再利用對(duì)應(yīng)的測(cè)井曲線計(jì)算出裂縫參數(shù),通過(guò)R/S分形法計(jì)算對(duì)應(yīng)測(cè)井曲線的分維數(shù)值,將其與成像測(cè)井資料相對(duì)照得到裂縫發(fā)育不同程度的分維數(shù)值范圍以及對(duì)應(yīng)裂縫參數(shù)范圍；利用5口電成像測(cè)井資料的61個(gè)層位、巖心分析資料及常規(guī)測(cè)井資料利用R/S分形法對(duì)儲(chǔ)層裂縫進(jìn)行發(fā)育程度的劃分。得到相應(yīng)的圖版。研究區(qū)內(nèi)發(fā)育裂縫時(shí),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(shí)際處理11口井19個(gè)層位,平均符合率達(dá)到73.6%；利用偶極聲波成像測(cè)井資料建立不同巖性的縱橫波時(shí)差模型,通過(guò)縱橫波時(shí)差模型計(jì)算相應(yīng)的巖石力學(xué)參數(shù),再將巖石力學(xué)參數(shù)的值與裂縫參數(shù)結(jié)合計(jì)算出不同裂縫發(fā)育程度的巖石力學(xué)參數(shù)值的范圍標(biāo)準(zhǔn),充分利用3口偶極聲波井的56個(gè)層位的資料、巖心分析資料及對(duì)應(yīng)常規(guī)測(cè)井資料建立不同巖性的巖石力學(xué)參數(shù)模版,而在巖石力學(xué)參數(shù)里面,楊氏模量來(lái)預(yù)測(cè)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(shí),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(shí)際處理5口井15個(gè)層位,平均符合率達(dá)到66.7%,能夠初步滿足實(shí)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(xué)位授予單位】：長(zhǎng)江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：P618.13

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