本文選題：經(jīng)驗(yàn)?zāi)B(tài)分解 + 測(cè)井相��； 參考：《成都理工大學(xué)》2016年博士論文

【摘要】：在巖性地層油氣藏勘探中,由于低滲透儲(chǔ)層物性相對(duì)較差,導(dǎo)致測(cè)井曲線信噪比低,儲(chǔ)層流體識(shí)別較為困難,存在多解性�；谠颇Ｐ偷腅MD相控測(cè)井儲(chǔ)層預(yù)測(cè)研究是在確立優(yōu)勢(shì)測(cè)井相序集的基礎(chǔ)上,探索經(jīng)驗(yàn)?zāi)B(tài)分解(簡(jiǎn)稱EMD)提高信噪比的方法,達(dá)到提高測(cè)井?dāng)?shù)據(jù)分辨率之目的;以層點(diǎn)為樣本,研究時(shí)頻分析技術(shù)在測(cè)井層序地層學(xué)中應(yīng)用的新方法;在對(duì)主力油層精細(xì)分層的基礎(chǔ)上,提出了基于EMD分解的儲(chǔ)層流體識(shí)別的新算法和新參數(shù);針對(duì)儲(chǔ)層產(chǎn)能評(píng)價(jià)中的多解性問(wèn)題,建立了儲(chǔ)層產(chǎn)能評(píng)估的二維正態(tài)云模型判定標(biāo)準(zhǔn),實(shí)現(xiàn)了測(cè)井產(chǎn)能評(píng)價(jià)過(guò)程的可視化;通過(guò)對(duì)低滲透致密砂巖儲(chǔ)層預(yù)測(cè)實(shí)例的分析,從科學(xué)上,客觀的給出測(cè)井解釋結(jié)果,用于指導(dǎo)油氣藏勘探的進(jìn)程。具體來(lái)說(shuō),主要研究?jī)?nèi)容及成果有下幾個(gè)方面:(1)從多角度、多方面探討用于測(cè)井?dāng)?shù)據(jù)處理的算法理論。在時(shí)頻分析技術(shù)方面,分析了傳統(tǒng)傅里葉變換、小波變換及EMD分解算法的優(yōu)缺點(diǎn)及局限性,指出:傅里葉變換適合于分析頻率固定的平穩(wěn)信號(hào);小波變換和EMD分解更適合于分析非線性、非平穩(wěn)的測(cè)井序列。在空間數(shù)據(jù)挖掘理論研究方面,介紹了不確定性人工智能中用于定性概念與其定量數(shù)據(jù)之間相互轉(zhuǎn)換的“云模型”理論,討論了描述“云”的三個(gè)基本數(shù)字特征的意義,給出了“正態(tài)云”云模型發(fā)生器的實(shí)現(xiàn)算法,為解決測(cè)井儲(chǔ)層產(chǎn)能評(píng)價(jià)中的多解性問(wèn)題奠定了理論基礎(chǔ)。(2)深入探討了多參數(shù)雷達(dá)圖在相控測(cè)井?dāng)?shù)據(jù)處理中的應(yīng)用技巧,編寫了雷達(dá)圖繪圖程序,為用戶提供便利的使用環(huán)境。以四川盆地大邑構(gòu)造須家河組為例,詳細(xì)介紹了雷達(dá)圖用于測(cè)井?dāng)?shù)據(jù)處理的三種典型方法與操作步驟;建立了該地區(qū)巖性和流體識(shí)別的雷達(dá)圖標(biāo)準(zhǔn)模板;利用雷達(dá)圖繪圖程序有效地幫助數(shù)據(jù)解釋人員較為精確的實(shí)現(xiàn)了分選敏感測(cè)井指標(biāo)、識(shí)別未知井剖面地層巖性以及初步判定儲(chǔ)集層的流體性質(zhì)。(3)深入分析了三種利用測(cè)井資料進(jìn)行層序界面劃分和旋回識(shí)別的方法,給出了新的認(rèn)識(shí)。以鄂爾多斯盆地子北采油廠L井區(qū)為例,通過(guò)對(duì)比傳統(tǒng)的測(cè)井曲線形態(tài)識(shí)別法、小波變換精細(xì)分層法及經(jīng)驗(yàn)?zāi)B(tài)分解旋回識(shí)別法的理論基礎(chǔ)、應(yīng)用方法與技巧,指出:傳統(tǒng)的測(cè)井曲線形態(tài)識(shí)別法所投入的工作量大,難度高,并且受人為主觀性因素影響較大;小波變換是一種時(shí)頻局部分析方法,它通過(guò)兩個(gè)互補(bǔ)的濾波器產(chǎn)生低頻的近似信號(hào)和高頻的細(xì)節(jié)信號(hào),分別對(duì)應(yīng)于信號(hào)中的穩(wěn)定變化趨勢(shì)和突變點(diǎn),利用這一特性,可以較為準(zhǔn)確的實(shí)現(xiàn)小層的精細(xì)劃分;而emd分解函數(shù)中,每一個(gè)分量都包含了信號(hào)的完整信息,反映出信號(hào)的瞬時(shí)頻率特征,更適合于識(shí)別地層不同級(jí)次的沉積旋回。(4)提出了基于emd分解的測(cè)井地層旋回識(shí)別的新方法。概括出五種emd分解曲線響應(yīng)特征在各種沉積環(huán)境中的基本形態(tài);以利薩如圖(lissajousfigures)理論為基礎(chǔ),指出了在深度域上作imfs分量的散點(diǎn)圖時(shí)出現(xiàn)的光滑閉合曲線正是地層沉積旋回特征的表現(xiàn);并以imfs瞬時(shí)頻率主值區(qū)間推算可識(shí)別旋回級(jí)次,證明了新方法的科學(xué)性。結(jié)合四川盆地川西坳陷中段孝泉-豐谷構(gòu)造帶層序地層研究的實(shí)例,詳細(xì)介紹了emd分解用于測(cè)井地層旋回識(shí)別的新方法和操作步驟,深入探討了不同尺度下本征固有模態(tài)函數(shù)的地質(zhì)意義,指出:對(duì)地區(qū)敏感參數(shù)作emd分解,依據(jù)相關(guān)性原理優(yōu)化組合imfs分量可以強(qiáng)化反映地層旋回特征的較為穩(wěn)定的信息;在層序地層學(xué)研究中,地層的旋回是以分布時(shí)限為標(biāo)準(zhǔn)劃分的,由瞬時(shí)頻率的主值區(qū)間推算出測(cè)井?dāng)?shù)據(jù)在深度域的可分辨深度范圍,據(jù)此可以很好的識(shí)別不同級(jí)次的地層旋回特征。(5)提出了基于emd的儲(chǔ)層流體識(shí)別的新算法和新參數(shù)。將經(jīng)驗(yàn)?zāi)B(tài)分析方法用于低滲透致密砂巖儲(chǔ)層的天然氣識(shí)別當(dāng)中,通過(guò)對(duì)適合地區(qū)特征的天然氣敏感參數(shù)作經(jīng)驗(yàn)?zāi)B(tài)分解,展開(kāi)多尺度的相關(guān)性對(duì)比分析,選擇能較好反映流體性質(zhì)變化的imfs函數(shù)分量作交會(huì)圖,揭示出非平穩(wěn)的測(cè)井信號(hào)在微觀上隱藏的局部曲線相關(guān)和局部線性相關(guān)的規(guī)律,找出了新的水層指示參數(shù)pw、氣層指示參數(shù)pα及優(yōu)質(zhì)氣層指示參數(shù)hgr,并給出依據(jù)新參數(shù)進(jìn)行儲(chǔ)層流體判別的方法和計(jì)算公式。(6)發(fā)現(xiàn)了一個(gè)適用于致密砂巖儲(chǔ)層流體判別的新規(guī)律。以川西凹陷須家河組氣藏識(shí)別為例,揭示了優(yōu)勢(shì)測(cè)井相序集信號(hào)中隱藏的局部相關(guān)性規(guī)律,驗(yàn)證了水層和氣層的新指示參數(shù)和新算法在低滲透性致密砂巖儲(chǔ)層中流體識(shí)別的可行性。詳細(xì)介紹了在常規(guī)方法無(wú)法識(shí)別氣藏的情況下,應(yīng)用emd分解法進(jìn)行氣、水界面識(shí)別的新算法與步驟,證實(shí)了新算法和新參數(shù)的有效性,指出:在依據(jù)相關(guān)性原理,對(duì)優(yōu)選的天然氣敏感參數(shù)ac和cnl的經(jīng)驗(yàn)?zāi)B(tài)分解函數(shù)作交會(huì)圖時(shí),天然氣儲(chǔ)層呈現(xiàn)出局部曲線相關(guān)的特征,表現(xiàn)為明顯的弧線,并且儲(chǔ)層物性越好,弧線的曲率越大;而水層和干層則呈現(xiàn)出局部線性相關(guān)或可近似為線性相關(guān)的特征。(7)提出了基于云模型的測(cè)井儲(chǔ)層產(chǎn)能評(píng)價(jià)的新方法。針對(duì)油氣儲(chǔ)層產(chǎn)能評(píng)估過(guò)程中廣泛存在的不確定性因素,以及測(cè)井?dāng)?shù)據(jù)與專家意見(jiàn)之間存在的定量與定性變量轉(zhuǎn)換和映射的需求,深入分析云模型的數(shù)字特征,以鄂爾多斯盆地子北采油廠X井區(qū)延長(zhǎng)組地層為例,結(jié)合試油數(shù)據(jù),建立儲(chǔ)層不同產(chǎn)能標(biāo)準(zhǔn)的二維正態(tài)云圖,分別從定性和定量上給出了判定儲(chǔ)層產(chǎn)能的方法,實(shí)現(xiàn)了數(shù)據(jù)挖掘的可視化解釋。該方法從多角度研究?jī)?chǔ)層數(shù)據(jù)的分布特征,最大限度的保留了評(píng)估過(guò)程中概念描述的不確定性因素,建立了專家定性意見(jiàn)的定量可比性,客觀的對(duì)儲(chǔ)層產(chǎn)能作出評(píng)價(jià),從而保證了測(cè)井解釋的可信度。
[Abstract]:In the exploration of lithostratigraphic reservoir, due to the relatively poor physical property of low permeability reservoir, it leads to low signal-to-noise ratio of logging curves, and the reservoir fluid recognition is difficult and multi solvable. The research of EMD phase controlled logging reservoir prediction based on cloud model is based on the establishment of the predominant log phase sequence set, exploring the empirical mode decomposition (EMD) to improve the signal to noise ratio. In order to improve the resolution of log data, a new method of applying time frequency analysis technique to log sequence stratigraphy is studied with stratified sampling. On the basis of fine stratification of main oil layers, a new algorithm and new parameters for reservoir fluid identification based on EMD decomposition are put forward. The two dimensional normal cloud model criterion for reservoir productivity evaluation has been established, and the visualization of logging productivity evaluation process is realized. Through the analysis of the low permeability and dense sandstone reservoir prediction example, the results of logging interpretation are objectively given and used to guide the process of oil and gas exploration. The following aspects: (1) the algorithm theory of logging data processing is discussed from many angles and many aspects. In the aspect of time-frequency analysis, the advantages and disadvantages of traditional Fourier transform, wavelet transform and EMD decomposition are analyzed. It is pointed out that Fourier transform is suitable for stationary signals with fixed frequency, and the wavelet transform and EMD decomposition are more suitable. In the analysis of nonlinear and non-stationary log sequences, in the field of spatial data mining theory, the "cloud model" theory used for the conversion between qualitative concepts and quantitative data in uncertain artificial intelligence is introduced, and the significance of describing the three basic digital characteristics of "cloud" is discussed, and the occurrence of "normal cloud" cloud model is given. The realization algorithm of the device has laid the theoretical foundation for solving the multi solvability problem in the logging reservoir productivity evaluation. (2) the application techniques of the multi parameter radar map in the phase controlled logging data processing are deeply discussed, and the radar drawing program is written to provide the convenient use environment for the users. The Xujiahe Formation in Dayi of the Sichuan basin is taken as an example. This paper introduces three typical methods and operation steps of radar map for logging data processing, establishes a standard template for radar map of lithology and fluid identification in this area, and effectively helps the data interpreters to achieve more accurate identification of sensitive logging indicators, identify the lithology of unknown well profile and preliminary identification. The fluid properties of the reservoir are determined. (3) three methods of sequence boundary division and cycle identification using logging data are deeply analyzed. A new understanding is given. Taking the L well area of the Erdos Basin subarea oil production plant as an example, the traditional logging curve shape recognition method, the fine stratification method of wavelet transform and the empirical mode decomposition cycle are compared. The theoretical basis, the application method and the technique of the recognition method, point out that the traditional logging curve shape recognition method has a large amount of work, high difficulty and influenced by the subjective subjective factors. The wavelet transform is a time-frequency local analysis method, which produces low frequency approximate signal and high frequency detail signal through two complementary filters. In the EMD decomposition function, each component contains the complete information of the signal, reflects the instantaneous frequency characteristic of the signal, and is more suitable for identifying the sedimentary cycles of different gradation. (4) proposed A new method of identification of log stratigraphic cycles based on EMD decomposition. The basic morphology of the response characteristics of five EMD decomposition curves in various sedimentary environments is summarized. Based on the theory of lissajousfigures, the smooth and closed curve of the formation of the stratigraphic sedimentary cycles is pointed out when the scatter plot of the IMFs component in the depth domain is the characteristic of the sedimentary cycle. It is proved that the new method is scientific, and the new method is proved to be scientific by using the main interval of IMFs instantaneous frequency. The new method and operation steps of EMD decomposition for identification of log stratigraphic cycle are introduced in detail, combined with the example of the sequence stratigraphy of the middle section of the Sichuan basin in the middle section of the West Sichuan depression. The geological significance of the intrinsic intrinsic modal function indicates that the EMD decomposition of sensitive parameters of the region and the optimization of the combined IMFs component according to the principle of correlation can strengthen the more stable information reflecting the characteristics of the stratigraphic cycle. In the sequence stratigraphic study, the formation cycle is divided by the time limit of distribution and is pushed by the main interval of the instantaneous frequency. The distinguishable depth range of the log data in the depth domain can be calculated and the stratigraphic cycle characteristics of different grades can be identified well. (5) a new algorithm and new parameters for reservoir fluid identification based on EMD are proposed. The empirical mode analysis method is applied to the identification of the sky gas in the low permeability and dense sandstone reservoir, and the characteristics are suitable for the region. The sensitive parameters of natural gas are decomposed by empirical mode, and multiscale correlation analysis is carried out. The IMFs function component, which can better reflect the change of fluid properties, is selected as the rendezvous graph, and the laws of local curve correlation and local linear correlation hidden by non-stationary logging signals are revealed, and the new water layer indication parameter PW, gas layer is found. The indicator parameter p alpha and the indicator parameter HGR of high quality gas reservoir are given, and the method and calculation formula of reservoir fluid discrimination according to the new parameters are given. (6) a new law for identifying the fluid in the tight sandstone reservoir is discovered. The local correlation hidden in the phase sequence set signal of the best potential well log is revealed with the identification of the Xujiahe gas reservoir in the West Sichuan depression as an example. The feasibility of the new indicator parameters of water layer and gas reservoir and the new algorithm in the low permeability tight sandstone reservoir are verified. The new algorithm and steps of gas and water interface identification by the EMD decomposition method are introduced in detail, and the validity of the new algorithm and the new parameters are confirmed. On the basis of the principle of correlation, when the selected natural gas sensitive parameters AC and CNL are rendezvous with the empirical mode decomposition function, the natural gas reservoir shows the characteristics related to the local curve, showing the obvious arc, and the better the physical property of the reservoir, the greater the curvature of the arc line, while the water layer and the dry layer show a local linear correlation or can be approximated as a line. (7) a new method of logging reservoir productivity evaluation based on cloud model is proposed. The uncertain factors which exist widely in the process of oil and gas reservoir productivity evaluation, and the requirement of quantitative and qualitative variable transformation and mapping between logging data and expert opinion are needed, and the digital characteristics of the cloud model are analyzed in depth, and the ERL is analyzed. As an example of the Yanchang Formation in the X well area of the duo basin sub oil production plant, combining with the oil test data, the two-dimensional normal cloud map of the reservoir with different productivity standards is established. The method of determining the reservoir productivity is given from the qualitative and quantitative terms, and the visual interpretation of the data mining is realized. The method is used to study the distribution characteristics of the reservoir data from multiple angles and maximize the distribution characteristics. The uncertainty factors of the conceptual description in the evaluation process are retained, the quantitative comparability of the expert qualitative opinion is established, and the reservoir productivity is evaluated objectively, thus ensuring the credibility of the log interpretation.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：P618.13;P631.81

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