本文選題：致密砂巖 + 細(xì)菌覓食算法�。� 參考：《吉林大學(xué)》2015年碩士論文

【摘要】：隨著大型油氣田勘探開(kāi)發(fā)程度的不斷深入，常規(guī)油氣資源儲(chǔ)量日益減少，人們對(duì)非常規(guī)油氣資源的關(guān)注越來(lái)越多。致密砂巖氣是一種典型的非常規(guī)油氣資源，資源儲(chǔ)量巨大，具有廣闊的發(fā)展前景。致密砂巖氣藏具有非均質(zhì)性強(qiáng)、低孔低滲、孔隙結(jié)構(gòu)復(fù)雜且多種泥質(zhì)分布形式共存等特點(diǎn)，這些特點(diǎn)造成其測(cè)井評(píng)價(jià)困難，儲(chǔ)層參數(shù)計(jì)算準(zhǔn)確率不高。 最優(yōu)化測(cè)井解釋方法是評(píng)價(jià)致密砂巖儲(chǔ)層的一種有效途徑。與利用有限測(cè)井曲線信息的傳統(tǒng)順序式測(cè)井解釋方法不同，最優(yōu)化測(cè)井解釋方法依據(jù)地球物理反演理論并綜合利用多種測(cè)井信息、地質(zhì)資料和工作經(jīng)驗(yàn)，運(yùn)用最優(yōu)化方法計(jì)算儲(chǔ)層參數(shù)。最優(yōu)化測(cè)井解釋方法對(duì)測(cè)井信息的利用率高，可以靈活地變化解釋模型和解釋方法。它還可以進(jìn)行自我質(zhì)量檢測(cè)，在生產(chǎn)實(shí)踐中展示了獨(dú)特的優(yōu)勢(shì)，并得到了廣泛的應(yīng)用。 本文以蘇里格致密砂巖儲(chǔ)層盒8及山1層段為研究對(duì)象。整理并分析研究區(qū)內(nèi)的測(cè)井?dāng)?shù)據(jù)、壓汞和相滲實(shí)驗(yàn)數(shù)據(jù)、巖心物性分析以及試氣結(jié)論等資料，確定致密砂巖儲(chǔ)層含氣特征及有效儲(chǔ)層下限值，對(duì)有效儲(chǔ)層進(jìn)行基于結(jié)構(gòu)約束的BFA-CM混合最優(yōu)化測(cè)井解釋方法評(píng)價(jià)。 泥質(zhì)的分布形式對(duì)儲(chǔ)層參數(shù)具有重要影響，因此可以將其考慮到儲(chǔ)層測(cè)井解釋模型中以提高儲(chǔ)層未知參數(shù)的計(jì)算精度。依據(jù)所建立的測(cè)井解釋模型，可以得到中子、密度和聲波的測(cè)井響應(yīng)方程。在求解過(guò)程中需要對(duì)未知參數(shù)進(jìn)行限制，以便求取得到的結(jié)果具有一定的合理性。除基本的數(shù)學(xué)物理約束條件外，將Thomas-Stieber結(jié)構(gòu)推導(dǎo)得到的兩個(gè)響應(yīng)方程作為結(jié)構(gòu)約束條件引入到最優(yōu)化測(cè)井解釋方法中，同時(shí)解決體積分?jǐn)?shù)和巖石泥質(zhì)結(jié)構(gòu)。這樣，成分體積和泥質(zhì)結(jié)構(gòu)分析可以在一個(gè)步驟中進(jìn)行，實(shí)現(xiàn)了更全面更合理的巖石物理解釋。 建立最優(yōu)化測(cè)井解釋方法的數(shù)學(xué)模型后，需要選擇合適的最優(yōu)化方法求解數(shù)學(xué)模型的最優(yōu)解。細(xì)菌覓食算法(BFA)是新興的一種仿生類優(yōu)化算法，通過(guò)模擬細(xì)菌在生物體內(nèi)的生存過(guò)程來(lái)迭代尋求最優(yōu)解，還未被應(yīng)用于最優(yōu)化測(cè)井解釋方法中。實(shí)踐表明，細(xì)菌覓食算法在尋優(yōu)后期收斂速度變慢。為提高計(jì)算精度和效率，，本文將其與具有極強(qiáng)局部搜索能力的復(fù)合形算法(CM)相結(jié)合構(gòu)成BFA-CM混合算法，作為最優(yōu)化測(cè)井解釋方法中的尋優(yōu)方法。 應(yīng)用結(jié)合結(jié)構(gòu)約束條件的BFA-CM混合最優(yōu)化測(cè)井解釋方法對(duì)蘇里格致密砂巖儲(chǔ)層實(shí)際資料進(jìn)行處理。與沒(méi)有結(jié)構(gòu)約束條件的BFA-CM混合最優(yōu)化測(cè)井解釋方法相比，結(jié)合結(jié)構(gòu)約束條件的處理結(jié)果更加穩(wěn)定，與巖心分析及薄片數(shù)據(jù)吻合程度更好。
[Abstract]:With the deepening of exploration and development of large oil and gas fields, the reserves of conventional oil and gas resources are decreasing day by day, and people pay more and more attention to unconventional oil and gas resources. Tight sandstone gas is a typical unconventional oil and gas resource with huge reserves and broad development prospects. The tight sandstone gas reservoir is characterized by strong heterogeneity, low porosity and low permeability, complex pore structure and coexistence of various shaly distribution forms. These characteristics make the logging evaluation difficult and the accuracy of reservoir parameter calculation not high. Optimization logging interpretation method is an effective way to evaluate tight sandstone reservoir. Different from the traditional sequential logging interpretation method using finite log curve information, the optimal logging interpretation method is based on geophysical inversion theory and synthetically utilizes various logging information, geological data and working experience. The reservoir parameters are calculated by optimization method. The optimal logging interpretation method has a high utilization rate of logging information and can flexibly change the interpretation model and interpretation method. It can also be used for self-quality testing, showing unique advantages in production practice, and has been widely used. In this paper, Sulige tight sandstone reservoir box 8 and Shan 1 are taken as research objects. Well logging data, experimental data of mercury injection and phase permeability, core physical properties analysis and gas test conclusion were collected and analyzed to determine the gas-bearing characteristics of tight sandstone reservoir and the lower limit value of effective reservoir. The effective reservoir is evaluated by BFA-CM mixed optimization logging interpretation method based on structural constraints. The distribution of mudstone has an important influence on reservoir parameters, so it can be taken into account in the reservoir log interpretation model to improve the calculation accuracy of unknown reservoir parameters. The log response equations of neutron density and acoustic wave can be obtained according to the log interpretation model. In the process of solution, the unknown parameters should be restricted so that the obtained results are reasonable. In addition to the basic mathematical and physical constraints, the two response equations derived from the Thomas-Stieber structure are introduced into the optimal logging interpretation method as structural constraints, and the volume fraction and the muddy structure of the rock are solved at the same time. In this way, the analysis of composition volume and muddy structure can be carried out in one step, and a more comprehensive and reasonable interpretation of rock physics can be achieved. After establishing the mathematical model of the optimal logging interpretation method, it is necessary to select a suitable optimization method to solve the optimal solution of the mathematical model. Bacterial foraging algorithm (BFA) is a new kind of bionic optimization algorithm. It can find the optimal solution iteratively by simulating the survival process of bacteria in organism. It has not been applied to the optimal logging interpretation method. The practice shows that the convergence rate of bacterial foraging algorithm slows down in the later stage of optimization. In order to improve the calculation accuracy and efficiency, this paper combines it with the complex algorithm with strong local search ability to form the BFA-CM hybrid algorithm, which is used as the optimization method in the optimization logging interpretation method. The practical data of Sulige tight sandstone reservoir are processed by BFA-CM mixed optimization logging interpretation method combined with structural constraints. Compared with the BFA-CM mixed optimization logging interpretation method without structural constraints, the processing results with structural constraints are more stable, and are in better agreement with core analysis and sheet data.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：P618.13;P631.81

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