本文選題：塔河油田 + 地震屬性��； 參考：《成都理工大學(xué)》2015年碩士論文

【摘要】：塔河油田碳酸鹽巖油藏埋藏較深,一般大于5300米,基巖不具備儲集性,儲集空間以溶洞、裂縫為主。在TH10前期研究中,主要以T74不整合面以下0~80m范圍內(nèi)的風(fēng)化殼巖溶及部分垂直巖溶段為主體,而針對更深層的巖溶及溶洞體很少涉及。然而鉆井等資料表明,在目前研究認(rèn)識的含油縫洞底界依然存在可開發(fā)的洞穴型儲集體。因此,加大中深部洞穴的儲層預(yù)測研究,能夠深化認(rèn)識巖溶發(fā)育規(guī)律,拓展中深部儲集體的資源領(lǐng)域,為塔河油田的產(chǎn)建轉(zhuǎn)型提供地質(zhì)依據(jù)。地震屬性分析技術(shù)在儲層預(yù)測中應(yīng)用較廣,然而利用常規(guī)地震屬性進行儲層預(yù)測時效果較差、精度較低,預(yù)測結(jié)果常常具有多解性。而信息融合方法可以解決多個屬性之間的相關(guān)性等矛盾,產(chǎn)生的融合屬性比單一的屬性數(shù)據(jù)更準(zhǔn)確更有效。因此,通過將信息融合技術(shù)應(yīng)用于地震屬性儲層預(yù)測中,可以更好檢測儲層溶洞體,同時對解決多解性問題具有重要的作用。本論文主要研究了主成分分析、核主成分分析、模糊C均值聚類、核模糊C均值聚類等四種信息融合算法的基本原理與計算步驟,并將其應(yīng)用到TH10中深部“串珠狀”溶洞儲集體的檢測中,并主要取得了方法、軟件、應(yīng)用等三個方面的研究成果。(1)方法成果方面。主成分分析是一種較好的信息融合方法,本質(zhì)上是一種線性方法,適用于屬性間相關(guān)性較強的情形;核主成分分析的非線性處理能力明顯強于主成分分析,在信息融合效果上也具有更好的檢測能力;模糊C均值聚類是一種基于模糊數(shù)學(xué)的聚類分析融合方法,非線性處理能力不強;對于非線性關(guān)系較強的屬性數(shù)據(jù),核模糊C均值算法能解決線性空間中不能被線性分割的問題,因此比模糊C均值聚類的融合效果更好。(2)軟件成果方面。本文主要基于Visual C++6.0平臺,對提取出的地震屬性數(shù)據(jù)分別進行四種信息融合算法的代碼實現(xiàn)。函數(shù)模塊在儲層預(yù)測中效果較好,具有一定的應(yīng)用開發(fā)價值。(3)應(yīng)用成果方面。本文應(yīng)用成果包括地質(zhì)模型、連井剖面、研究區(qū)域平面三個部分:(a)地質(zhì)模型上的成果。本文建立了多套不同的溶洞地質(zhì)模型,通過褶積得到正演結(jié)果,然后分析了地震響應(yīng)特征。當(dāng)溶洞規(guī)模較小時,地震波不能識別出溶洞;隨著溶洞規(guī)模的增大,地震響應(yīng)強度也隨之增大;當(dāng)充填物的速度減小時,地震響應(yīng)強度增強;在縱向可分辨情況下,溶洞間隔增大,地震響應(yīng)強度也隨之增大。(b)連井剖面上的應(yīng)用成果。將融合方法應(yīng)用于連井剖面,可以知道信息融合參數(shù)能準(zhǔn)確檢測出溶洞的位置和規(guī)模,且在一定程度上能檢測出溶洞體的流體屬性。從融合效果上看,核主成分分析的檢測能力比主成分分析強,核模糊聚類也比模糊聚類強。(c)研究區(qū)域平面上的應(yīng)用成果。以TH10鷹山組二段為研究目標(biāo),在精細(xì)層位追蹤后提取了多種屬性參數(shù),用信息融合方法進行地震屬性融合,獲得目的層溶洞平面分布特征:融合平面上儲層溶洞體主要呈短軸狀散亂分布,總體呈“東多西少,北多南少”的特征。綜合生產(chǎn)開發(fā)成果提出,“中等融合檢測值、溶洞集中且平面不連通”的地區(qū),為有利儲層和可能的油氣富集區(qū)。本次論文采用多元信息數(shù)據(jù)融合方法在該研究區(qū)首次進行了儲層溶洞檢測應(yīng)用,取得較好的預(yù)測效果,具有一定的創(chuàng)新性,對實際生產(chǎn)也具有一定的指導(dǎo)意義。
[Abstract]:The carbonate reservoir in Tahe oilfield is deep buried, generally more than 5300 meters, and the bedrock is not possessed of reservoir property. The reservoir space is mainly composed of karst caves and cracks. In the early study of TH10, the main body of the weathered crust karst and some vertical karst segments in the 0~80m range below the T74 unconformities is mainly, but it is rarely involved in the deeper karst and karst cave bodies. Drilling and other data show that there is still a developing cave type reservoir in the bottom boundary of the oil bearing seam. Therefore, increasing the reservoir prediction research in the middle and deep parts can deepen the understanding of the law of karst development, expand the resources of the middle and deep reservoirs, and provide geological basis for the transformation of Tahe oilfield. Analysis technology is widely used in reservoir prediction, however, the effect of reservoir prediction is poor, precision is low, and the prediction results often have multiple solutions. The information fusion method can solve the contradiction between multiple attributes, and the fusion is more accurate and effective than the single attribute data. By applying the information fusion technology to seismic attribute reservoir prediction, it can better detect the reservoir cavern and play an important role in solving the problem of multi solution. This paper mainly studies the basic principles and plans of four information fusion algorithms, such as principal component analysis, kernel principal component analysis, fuzzy C means clustering, and kernel fuzzy C mean clustering. It is applied to the detection of the deep "bead like" cave storage in TH10, and the main results are obtained in three aspects, such as method, software, application and so on. (1) the method results. The principal component analysis is a better information fusion method, which is essentially a linear method, which is suitable for the strong relationship among the attributes. The nonlinear processing ability of the kernel principal component analysis is stronger than the principal component analysis and has better detection ability in the information fusion effect. Fuzzy C means clustering is a clustering analysis fusion method based on fuzzy mathematics, and the nonlinear processing ability is not strong. For the attribute data with strong non linear relation, the kernel fuzzy C mean algorithm can be used. Solving the problem that linear space can not be segmented linearly, so the fusion effect is better than that of fuzzy C means clustering. (2) in the aspect of software achievement. This paper mainly based on the Visual C++6.0 platform, carries out the code realization of four information fusion algorithms for the extracted seismic attribute data. The function module has a good effect in the reservoir prediction, and has a good effect. Application development value. (3) application results. The application results include geological model, well section and three parts of regional plane: (a) geological model. In this paper, a number of different karst cave geological models are established, and the results are obtained by convolution, and then the characteristics of seismic response are analyzed. Seismic waves can not identify the karst cave; with the increase of the cave scale, the seismic response strength increases, and the seismic response strength increases when the velocity of the filling decreases. In the case of longitudinal resolution, the interval of the cave increases and the seismic response strength increases. (b) the application results on the well section are applied to the cross section, It can be known that the information fusion parameters can accurately detect the location and size of the cave, and to some extent can detect the fluid properties of the cavern. From the fusion effect, the detection ability of the nuclear principal component analysis is stronger than the principal component analysis, and the kernel fuzzy clustering is stronger than the fuzzy clustering. (c) the application results on the regional plane are studied. TH10 Yingshan is used. The two section of the group is the research target. After the tracking of the fine layer, a variety of attribute parameters are extracted. The information fusion method is used to fuse the seismic attributes and obtain the characteristics of the plane distribution of the target cave. The reservoir bodies in the fusion plane are mainly in the short axis and scattered distribution, and the overall production and development results are "East and West, and the north is more and more South". It is proposed that "medium fusion detection value, concentration of karst cave and unconnected plane" is a favorable reservoir and possible oil and gas enrichment area. This paper uses multiple information data fusion method in this study area for the first time to carry out the application of reservoir karst cave detection in this study area, which has obtained good prediction effect, and has certain innovation and is also of practical production. A certain guiding significance.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：P631.4;P618.13

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