常用數(shù)學(xué)物理方法在測井解釋中的應(yīng)用
發(fā)布時(shí)間：2018-08-11 16:14
【摘要】：本文主要研究一些常用數(shù)學(xué)物理方法在地層劃分、巖性識別中的實(shí)際應(yīng)用效果。地層劃分是測井解釋中的一個(gè)重要環(huán)節(jié),人工分層不僅效率低下,而且易受解釋人員的主觀因素影響。本文利用極值方差聚類法、活度函數(shù)法、微商法并結(jié)合計(jì)算機(jī)來自動分層。這些分層方法的優(yōu)點(diǎn)是快速、定量、自動的進(jìn)行分層；還可以同時(shí)根據(jù)多條不同的測井曲線所提供的數(shù)據(jù)進(jìn)行綜合分層。當(dāng)然這些分層法效果的好壞,很大程度上取決于所選用的數(shù)值處理方法是否恰當(dāng)。 在巖性識別方法建立過程中,嘗試了交會圖法,但只能劃分巖性大類或指出變化趨勢,效果不佳。為此,本文采用貝葉斯判別法、支持向量機(jī)兩種算法,結(jié)合編程實(shí)現(xiàn)巖性的計(jì)算機(jī)自動識別,效果明顯,兩種方法的平均符合率都達(dá)到了80%以上,基本滿足勘探開發(fā)需要,可以作為測井巖性識別的主要方法在生產(chǎn)實(shí)際中應(yīng)用。 本文主要研究區(qū)塊及儲層為呼和諾仁油田(貝301區(qū)塊)南屯組二段砂礫巖儲層。該區(qū)塊儲層總體上屬于低孔、低滲、低含油飽和度,并且斷層多；儲層巖性主要為綠灰、灰色泥質(zhì)粉砂巖、粉砂巖、細(xì)砂巖、粗砂巖、砂礫巖、礫巖,呈不等厚互層。在了解貝301區(qū)塊的區(qū)域地質(zhì)概況的基礎(chǔ)上,本文從儲層測井響應(yīng)特征和儲層巖性特征入手,結(jié)合常用的數(shù)學(xué)物理方法對研究區(qū)塊的測井響應(yīng)參數(shù)建立數(shù)學(xué)模型,進(jìn)而進(jìn)行地層分層和巖性識別。如在用活度函數(shù)法分層中,選取在泥質(zhì)層和砂巖層的響應(yīng)特征比較明顯的自然伽馬曲線,并設(shè)置了窗長為3、7、9三組活度曲線來進(jìn)行比較識別,得出窗長7和9的分層效果更好。在用貝葉斯判別法識別巖性過程中,特征參數(shù)的選取至關(guān)重要,經(jīng)分析巖性響應(yīng)特征得出砂質(zhì)礫巖,細(xì)砂巖,泥質(zhì)粉砂巖的AC(聲波時(shí)差測井)、ILD(深感應(yīng)測井)、TLM(中感應(yīng)測井)、SFL(球形聚焦電阻率測井)、GR(自然伽馬測井)響應(yīng)均值在不同巖性段上變化最為明顯,故在此綜合這五條測井曲線值作為特征參數(shù)建立貝葉斯判別模型,并用來進(jìn)行巖性識別,判別結(jié)果經(jīng)與錄井巖性資料對比可得正判率超過80%,說明判別方法具有實(shí)用價(jià)值。通過本次研究,主要取得了以下成果與認(rèn)識： 1、測井曲線的形態(tài)與變化規(guī)律受巖性控制外,還與井下儀器類型、測井速度、井眼條件等有關(guān)�？蓪⑺鼈儦w總為與巖層中心對稱和不對稱兩大基本類型。 2、本文所采用的極值方差聚類分層法原理簡單,易于編程,運(yùn)算速度快,人工參與少,分層取值結(jié)果合理,效果明顯。此外其適用性較強(qiáng),對任何測井曲線(如SP,GR,AC,DEN,CNL,CAL,RA)都適用。 3、本文用活度極大值作為地層界面只適用于自然電位、自然伽馬這類半幅點(diǎn)分層的測井曲線,而不適用于側(cè)向、梯度這類不用半幅點(diǎn)分層的測井曲線。文中對選取了3、7、9三種活度窗長,處理結(jié)果與人工分層結(jié)果基本一致,體現(xiàn)了算法的有效性,能較好地達(dá)到測井自動地質(zhì)分層的目的。 4、本文在進(jìn)行巖性識別時(shí)需要注意對數(shù)據(jù)進(jìn)行歸一化處理,消除各測井參數(shù)間因量綱不同而引起的潛在問題,其次在選取測井參數(shù)時(shí),應(yīng)選用反映巖性變化能力大的測井信息。建立樣本模型后,為檢驗(yàn)巖性判別函數(shù)的可靠性,對確定巖性判別函數(shù)的樣品數(shù)據(jù)進(jìn)行回判,其結(jié)果如下：灰色泥質(zhì)粉砂巖、細(xì)砂巖、砂質(zhì)礫巖的正判率分別為85.05%,81.19%,85.7%,整個(gè)樣品層的正判率為83.33%。說明貝葉斯判別法在綜合運(yùn)用地質(zhì)、測井、巖心資料進(jìn)行巖性預(yù)測時(shí),能夠得到較好的結(jié)果。 5、支持向量機(jī)方法在解決模式識別小樣本、非線性及高維中表現(xiàn)出獨(dú)特的優(yōu)勢和良好的應(yīng)用前景。使用C語言編寫處理程序,對每種巖性樣本進(jìn)行SVM訓(xùn)練,然后利用學(xué)習(xí)后的SVM模型預(yù)測儲層巖性,可知,采用SVM識別貝301區(qū)塊地層的巖性與實(shí)際取心資料對比,符合率為85%,特別是對泥質(zhì)粉砂巖這種巖性的劃分可達(dá)到90%以上,說明利用該方法可識別油藏地質(zhì)中的復(fù)雜巖性,提高劃分精度。
[Abstract]:This paper mainly studies the practical application effect of some commonly used mathematical and physical methods in stratigraphic division and lithologic identification.Stratigraphic division is an important link in logging interpretation.Artificial stratification is not only inefficient but also susceptible to the subjective factors of interpreters.In this paper,extreme variance clustering method,activity function method and derivative method are combined. The advantages of these stratification methods are fast, quantitative, and automatic stratification, and they can also be synthetically stratified according to the data provided by several different logging curves. Of course, the effectiveness of these stratification methods depends largely on the appropriateness of the numerical processing methods selected.
In the process of establishing lithology identification method, the intersection diagram method is tried, but the effect is not good because it can only classify lithology into big categories or point out the changing trend. Therefore, this paper adopts Bayesian discriminant method and Support Vector Machine (SVM) algorithm to realize automatic lithology identification by computer, and the effect is obvious. The average coincidence rate of the two methods is 80%. On the other hand, it can basically meet the needs of exploration and development, and can be used as the main method of lithology identification in production practice.
This paper mainly studies the sandy conglomerate reservoir of the second member of Nantun Formation in Hohhot Noren Oilfield (Bei 301 Block).The reservoir in this block is generally of low porosity, low permeability, low oil saturation and many faults.The reservoir lithology is mainly composed of green ash, gray argillaceous siltstone, siltstone, fine sandstone, coarse sandstone, sandy conglomerate and conglomerate, which are interbedded with unequal thickness. On the basis of understanding the regional geological situation of Bei 301 block, this paper starts with the logging response characteristics and reservoir lithology characteristics of the reservoir, establishes a mathematical model for the logging response parameters of the study block in combination with common mathematical and physical methods, and then carries out stratigraphic stratification and lithology identification. Natural gamma-ray curves with obvious response characteristics of rock strata are set up, and three groups of activity curves with window lengths of 3,7,9 are set for comparison and identification. It is concluded that window lengths of 7 and 9 are better for stratification. Acoustic moveout logging, ILD, TLM, SFL and GR are the most obvious changes in different lithologic sections. Therefore, Bayesian discriminant model is established by synthesizing these five logging curves as characteristic parameters and used for lithologic identification. By comparing the discriminant results with logging lithologic data, the positive rate is more than 80%, which shows that the discriminant method is of practical value.
1. The shape and variation of logging curves are controlled by lithology, and are also related to downhole tool type, logging speed and borehole conditions. They can be grouped into two basic types: symmetrical and asymmetrical with the center of the strata.
2. The extremum variance clustering stratification method adopted in this paper is simple in principle, easy to program, fast in operation, less manual participation, reasonable in stratification and obvious in effect.
3. In this paper, the maximum activity is used as the formation interface, which is only suitable for half-amplitude stratified logging curves such as spontaneous potential and natural gamma, but not for lateral and gradient logging curves without half-amplitude stratification. It can achieve the purpose of automatic geological stratification.
4. In this paper, the data should be normalized to eliminate the potential problems caused by the different dimension of each logging parameter. Secondly, when choosing logging parameters, the logging information which reflects the large lithologic variation ability should be selected. Sample data of discriminant function are judged back, and the results are as follows: the positive rate of grey argillaceous siltstone, fine sandstone and sandy conglomerate are 85.05%, 81.19%, 85.7% respectively, and the positive rate of the whole sample layer is 83.33%. It shows that Bayesian discriminant method can get better results when comprehensive application of geology, logging and core data to predict lithology.
5. Support Vector Machine (SVM) has unique advantages and good application prospects in solving small sample, non-linearity and high dimension of pattern recognition. Each lithology sample is trained by SVM in C language, and then the reservoir lithology is predicted by SVM model. It is known that the lithology of Bei 301 block is recognized by SVM. Comparing the actual coring data, the coincidence rate is 85%, especially for argillaceous siltstone, which can be divided into more than 90%, indicating that the method can identify the complex lithology in reservoir geology and improve the division accuracy.
【學(xué)位授予單位】：長江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：P618.13;P631.81
[Abstract]:This paper mainly studies the practical application effect of some commonly used mathematical and physical methods in stratigraphic division and lithologic identification.Stratigraphic division is an important link in logging interpretation.Artificial stratification is not only inefficient but also susceptible to the subjective factors of interpreters.In this paper,extreme variance clustering method,activity function method and derivative method are combined. The advantages of these stratification methods are fast, quantitative, and automatic stratification, and they can also be synthetically stratified according to the data provided by several different logging curves. Of course, the effectiveness of these stratification methods depends largely on the appropriateness of the numerical processing methods selected.
In the process of establishing lithology identification method, the intersection diagram method is tried, but the effect is not good because it can only classify lithology into big categories or point out the changing trend. Therefore, this paper adopts Bayesian discriminant method and Support Vector Machine (SVM) algorithm to realize automatic lithology identification by computer, and the effect is obvious. The average coincidence rate of the two methods is 80%. On the other hand, it can basically meet the needs of exploration and development, and can be used as the main method of lithology identification in production practice.
This paper mainly studies the sandy conglomerate reservoir of the second member of Nantun Formation in Hohhot Noren Oilfield (Bei 301 Block).The reservoir in this block is generally of low porosity, low permeability, low oil saturation and many faults.The reservoir lithology is mainly composed of green ash, gray argillaceous siltstone, siltstone, fine sandstone, coarse sandstone, sandy conglomerate and conglomerate, which are interbedded with unequal thickness. On the basis of understanding the regional geological situation of Bei 301 block, this paper starts with the logging response characteristics and reservoir lithology characteristics of the reservoir, establishes a mathematical model for the logging response parameters of the study block in combination with common mathematical and physical methods, and then carries out stratigraphic stratification and lithology identification. Natural gamma-ray curves with obvious response characteristics of rock strata are set up, and three groups of activity curves with window lengths of 3,7,9 are set for comparison and identification. It is concluded that window lengths of 7 and 9 are better for stratification. Acoustic moveout logging, ILD, TLM, SFL and GR are the most obvious changes in different lithologic sections. Therefore, Bayesian discriminant model is established by synthesizing these five logging curves as characteristic parameters and used for lithologic identification. By comparing the discriminant results with logging lithologic data, the positive rate is more than 80%, which shows that the discriminant method is of practical value.
1. The shape and variation of logging curves are controlled by lithology, and are also related to downhole tool type, logging speed and borehole conditions. They can be grouped into two basic types: symmetrical and asymmetrical with the center of the strata.
2. The extremum variance clustering stratification method adopted in this paper is simple in principle, easy to program, fast in operation, less manual participation, reasonable in stratification and obvious in effect.
3. In this paper, the maximum activity is used as the formation interface, which is only suitable for half-amplitude stratified logging curves such as spontaneous potential and natural gamma, but not for lateral and gradient logging curves without half-amplitude stratification. It can achieve the purpose of automatic geological stratification.
4. In this paper, the data should be normalized to eliminate the potential problems caused by the different dimension of each logging parameter. Secondly, when choosing logging parameters, the logging information which reflects the large lithologic variation ability should be selected. Sample data of discriminant function are judged back, and the results are as follows: the positive rate of grey argillaceous siltstone, fine sandstone and sandy conglomerate are 85.05%, 81.19%, 85.7% respectively, and the positive rate of the whole sample layer is 83.33%. It shows that Bayesian discriminant method can get better results when comprehensive application of geology, logging and core data to predict lithology.
5. Support Vector Machine (SVM) has unique advantages and good application prospects in solving small sample, non-linearity and high dimension of pattern recognition. Each lithology sample is trained by SVM in C language, and then the reservoir lithology is predicted by SVM model. It is known that the lithology of Bei 301 block is recognized by SVM. Comparing the actual coring data, the coincidence rate is 85%, especially for argillaceous siltstone, which can be divided into more than 90%, indicating that the method can identify the complex lithology in reservoir geology and improve the division accuracy.
【學(xué)位授予單位】：長江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：P618.13;P631.81
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 馮敬英;肖慈s，
本文編號：2177520
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