本文關(guān)鍵詞： Hilbert-Huang變換 測井曲線 去噪分析 層序地層 流體識別　出處：《西安石油大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：目前,油氣田地質(zhì)研究依據(jù)的大多為常規(guī)測井資料。為了突破常規(guī)測井曲線“視覺域”分析的局限性,從常規(guī)測井?dāng)?shù)據(jù)中“挖掘”出更多的地質(zhì)信息,本文將Hilbert-Huang變換應(yīng)用于常規(guī)測井資料處理研究,以期為測井資料的處理解釋提供新的方法,從而服務(wù)于測井地質(zhì)研究。本文首先介紹了Hilbert-Huang變換的基本原理,重點闡述了EMD算法的分解過程、停止準(zhǔn)則,總結(jié)了Hilbert-Huang變換的優(yōu)缺點,并對仿真信號進(jìn)行了分析。其次開展了基于Hilbert-Huang變換的測井曲線去噪分析,對比分析了HHT減性去噪、小波閾值去噪及HHT-WT聯(lián)合去噪三種方法,并改變噪聲的強度開展研究�；贖ilbert-Huang變換開展了層序地層劃分,利用EMD分解得到的IMF分量較好地劃分了層序界面及地層旋回。針對儲層流體識別,對深感應(yīng)電阻率曲線開展分析,利用得到的IMF2和Amp2的組合特征較好地識別了上油下水流體界面。最后對Hilbert-Huang變換在測井資料處理中的應(yīng)用效果進(jìn)行了分析總結(jié)。研究結(jié)果表明,Hilbert-Huang變換的核心部分是經(jīng)驗?zāi)B(tài)分解(EMD),該方法簡單、分解效率高,具有自適應(yīng)性;當(dāng)噪聲強度較小時,小波閾值去噪和HHT-WT聯(lián)合去噪具有較好的效果,當(dāng)噪聲強度中等時,三種去噪方法均能獲得較理想的結(jié)果,當(dāng)噪聲強度較大時,小波閾值去噪為首選方法;高頻IMF可劃分小層序(短期旋回),中頻IMF可劃分中等層序(中期旋回),低頻IMF可劃分大層序(長期旋回)。IMF分量相對于原始曲線,更加清晰地反映了層序界面及地層層序級別;油水界面處,IMF2分量達(dá)到峰值,Amp2分量達(dá)到階段幅值最大。使用該方法時要注意剔除夾層對測井曲線的干擾,并注重多種測井系列的綜合應(yīng)用。
[Abstract]:At present, the geological research of oil and gas fields is mostly based on conventional logging data. In order to break through the limitations of "visual domain" analysis of conventional logging curves, more geological information is "mined" from conventional logging data. In this paper, Hilbert-Huang transform is applied to the study of conventional logging data processing, in order to provide a new method for processing and interpretation of logging data. In this paper, the basic principle of Hilbert-Huang transform is introduced, and the decomposition process and stop criterion of EMD algorithm are expounded. The advantages and disadvantages of Hilbert-Huang transform are summarized, and the simulation signal is analyzed. Secondly, the denoising analysis of logging curve based on Hilbert-Huang transform is carried out. The three methods of HHT de-noising, wavelet threshold de-noising and HHT-WT combined de-noising are compared and analyzed. And change the intensity of noise to carry out research. Based on Hilbert-Huang transform to develop sequence stratigraphy. The sequence interface and stratigraphic cycle are well divided by the IMF component obtained by EMD decomposition. The deep induction resistivity curve is analyzed for reservoir fluid identification. The combined features of IMF2 and Amp2 are used to identify the interface of oil and water fluid well. Finally, the application effect of Hilbert-Huang transform in logging data processing is analyzed. Summary. Research results show that. The core part of Hilbert-Huang transform is empirical mode decomposition (EMD), which is simple, efficient and adaptive. When the noise intensity is small, wavelet threshold denoising and HHT-WT combined denoising have a better effect. When the noise intensity is medium, the three denoising methods can obtain better results, when the noise intensity is high. Wavelet threshold denoising is the first choice; High frequency IMF can be divided into small sequence (short cycle cycle), intermediate frequency IMF can be divided into medium sequence (middle cycle) and low frequency IMF can be divided into large sequence (long term cycle. IMF component is relative to original curve). The sequence interface and stratigraphic sequence level are more clearly reflected; At the oil-water interface, the IMF2 component reaches the peak value and the Amp2 component reaches the maximum amplitude. When using this method, attention should be paid to eliminating the interference of intercalation to the logging curve and to the comprehensive application of various logging series.
【學(xué)位授予單位】：西安石油大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：P618.13;P631.81

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