本文選題：儲層預(yù)測　切入點：地震屬性　出處：《西南石油大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：河流相儲層是我國廣泛發(fā)育的儲集類型,主要特點是巖性變化大,連通性差等。蘇里格氣田59區(qū)盒8段屬于典型的河流相儲層,儲層內(nèi)氣藏的分布主要受砂巖橫向展布及物性變化的影響。該工區(qū)具有儲層較薄且縱向上砂體相互疊置,非均質(zhì)性強,氣水分布規(guī)律十分復(fù)雜等特點。通過儲層預(yù)測弄清該工區(qū)的主河道空間展布及氣水分布規(guī)律,劃分有利勘探區(qū),選取目標(biāo)井位是對我們最大的挑戰(zhàn)。 本論文從儲層預(yù)測技術(shù)的研究出發(fā),提取了地震屬性,研究了多種數(shù)學(xué)法屬性優(yōu)選技術(shù)及神經(jīng)網(wǎng)絡(luò)儲層預(yù)測技術(shù),并重點討論了針對樣本數(shù)分布不均或儲層非均質(zhì)性強等可能引起儲層預(yù)測結(jié)果產(chǎn)生較大誤差等問題。同時結(jié)合地震屬性分析技術(shù)、多元屬性綜合分析等方法,創(chuàng)新性地提出了地震敏感參數(shù)模板這一全新的概念。并通過正演模擬中理論模型的檢驗及在實際工區(qū)中的應(yīng)用,證明了該方法有助于提高儲層預(yù)測精度。 對于本論文研究區(qū)的目標(biāo)儲層,結(jié)合所掌握的有限資料,工區(qū)地質(zhì)特征、儲層特征及勘探情況等。首先,選擇合適的時窗,通過提取相應(yīng)的地震屬性、應(yīng)用多種方法組合優(yōu)選敏感屬性,并使用神經(jīng)網(wǎng)絡(luò)儲層預(yù)測,初步劃分出勘探有利區(qū)域。但神經(jīng)網(wǎng)絡(luò)對于樣本分布有一定的要求,特別是在少井區(qū)或井間距離較遠(yuǎn)時預(yù)測精度將受到一定的影響。結(jié)合目標(biāo)儲層段非均質(zhì)性較強,橫向變化快,氣水分布復(fù)雜等特點,借助地震敏感參數(shù)模板來解決神經(jīng)網(wǎng)絡(luò)在少井區(qū)的預(yù)測精度不高等問題,劃分有利勘探區(qū)域,優(yōu)選目標(biāo)井位,提高鉆井成功率,降低勘探風(fēng)險。 目前,該方法已經(jīng)被應(yīng)用于實際工區(qū)的生產(chǎn)研究中,并通過實鉆表明該方法能夠在一定程度上有效地提高儲層預(yù)測精度,具有較強的實用價值和研究潛力。
[Abstract]:Fluvial reservoir is a widely developed reservoir type in China, which is characterized by great lithologic change and poor connectivity. The distribution of gas reservoirs in the reservoir is mainly affected by the transverse distribution of sandstone and the variation of physical properties. The reservoir is thin and the vertical sand bodies overlap with each other, and the heterogeneity is strong. The distribution of gas and water is very complicated. It is the biggest challenge for us to make clear the spatial distribution of the main channel and the distribution of gas and water through reservoir prediction, to divide the favorable exploration area and to select the target well location. Based on the research of reservoir prediction technology, the seismic attributes are extracted in this paper, and various mathematical attribute optimization techniques and neural network reservoir prediction techniques are studied. This paper also focuses on the problems that may result in large errors in reservoir prediction results due to the uneven distribution of samples or strong heterogeneity of reservoir. At the same time, combined with seismic attribute analysis technology and multivariate attribute comprehensive analysis methods, A new concept of seismic sensitive parameter template is put forward creatively, and it is proved that this method is helpful to improve reservoir prediction accuracy through the testing of theoretical model in forward modeling and its application in practical working area. For the target reservoir in this study area, combined with the limited data, geological characteristics, reservoir characteristics and exploration conditions. Firstly, the appropriate time window is selected and the corresponding seismic attributes are extracted. Several methods are used to select sensitive attributes, and neural network is used to predict reservoir, and the favorable exploration area is preliminarily divided. However, the neural network has certain requirements for sample distribution. In particular, the prediction accuracy will be affected when there are few well areas or longer inter-well distances. Combined with the characteristics of high heterogeneity, fast lateral change and complicated gas-water distribution in the target reservoir section, the prediction accuracy will be affected to a certain extent. By means of seismic sensitive parameter template, the problem of low prediction accuracy of neural network in the area of fewer wells is solved, the favorable exploration area is divided, the target location is selected, the success rate of drilling is improved, and the exploration risk is reduced. At present, this method has been applied to the production research of practical work area, and it is proved by real drilling that the method can effectively improve the reservoir prediction accuracy to a certain extent, and has strong practical value and research potential.
【學(xué)位授予單位】：西南石油大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：P618.13;P631.4

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