本文選題：模式識(shí)別 + 模糊推理��； 參考：《東北石油大學(xué)》2017年碩士論文

【摘要】：大慶油田由于取心成本高,取心井?dāng)?shù)目少,因而通過(guò)分析取心樣品來(lái)判別沉積微相的微相類(lèi)型存在很大的局限性。本課題利用油田中已經(jīng)取心的油井的測(cè)井曲線(xiàn)數(shù)據(jù),結(jié)合專(zhuān)家知識(shí),研究過(guò)程神經(jīng)網(wǎng)絡(luò)的模式識(shí)別方法及應(yīng)用技術(shù),將研究成果應(yīng)用于非取心井的沉積微相判相中,實(shí)現(xiàn)對(duì)某一區(qū)塊的石油儲(chǔ)量及開(kāi)采價(jià)值評(píng)估。結(jié)合沉積微相實(shí)際分析發(fā)現(xiàn),就判相數(shù)據(jù)而言,除了測(cè)井曲線(xiàn)相關(guān)定量數(shù)據(jù)外,可能專(zhuān)家經(jīng)驗(yàn)等定性信息同樣會(huì)對(duì)判別結(jié)果產(chǎn)生影響,而且微相類(lèi)型本身也具有模糊性和隨機(jī)性,因此如果只采用定量識(shí)別的模型進(jìn)行判相,那么識(shí)別結(jié)果的誤差可能會(huì)較大。針對(duì)上述不足,本文題出了模糊推理過(guò)程神經(jīng)網(wǎng)絡(luò)模型和基于云變化的混合計(jì)算過(guò)程神經(jīng)網(wǎng)絡(luò)模型來(lái)實(shí)現(xiàn)對(duì)沉積微相的判別;一方面,針對(duì)有專(zhuān)家經(jīng)驗(yàn)的評(píng)判規(guī)則數(shù)據(jù),利用模糊推理可實(shí)現(xiàn)對(duì)測(cè)井相的定性信息進(jìn)行定量處理以簡(jiǎn)化判別規(guī)則,提取有效的判別數(shù)據(jù),進(jìn)而提高沉積微相判別的精度;另一方面,針對(duì)有模糊信息的數(shù)據(jù),利用云模型可將判相數(shù)據(jù)中的定性信息進(jìn)行轉(zhuǎn)化,從而納入計(jì)算過(guò)程,保證了原始數(shù)據(jù)的完整性與客觀性;考慮到測(cè)井相的數(shù)據(jù)是隨深度變化的曲線(xiàn)這一特征,因而采用過(guò)程神經(jīng)網(wǎng)絡(luò)的過(guò)程式輸入優(yōu)勢(shì),借助其具有對(duì)時(shí)、空二維信息處理能力和不斷優(yōu)化過(guò)程神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)機(jī)制來(lái)提高沉積微相判別的準(zhǔn)確度。課題以沉積微相數(shù)據(jù)要素為基礎(chǔ),以沉積微相識(shí)別問(wèn)題所建立的模型和算法為核心,按照軟件工程的開(kāi)發(fā)模式,進(jìn)行軟件設(shè)計(jì)和集成開(kāi)發(fā)后形成了沉積微相模式識(shí)系統(tǒng),并取得了較好的實(shí)驗(yàn)分析結(jié)果。課題的研究對(duì)于沉積微相判別資料實(shí)際處理,微相類(lèi)型準(zhǔn)確表達(dá)具有較高的實(shí)際價(jià)值和應(yīng)用前景。
[Abstract]:Because the cost of coring is high and the number of coring wells is small in Daqing Oilfield, it is very limited to judge the microfacies of sedimentary microfacies by analyzing coring samples. In this paper, using the logging curve data of well coring in oilfield, combining with expert knowledge, the pattern recognition method and application technology of neural network are studied, and the research results are applied to the sedimentary microfacies judgement of non-coring wells. To realize the evaluation of the oil reserves and exploitation value of a certain block. Combined with the actual analysis of sedimentary microfacies, it is found that, in terms of phase judgment data, besides the quantitative data related to logging curves, the qualitative information such as possible expert experience will also have an impact on the discriminant results. Moreover, the microphase type itself is fuzzy and random, so if only the quantitative recognition model is used to judge the phase, the error of the recognition result may be large. In order to solve the above problems, the fuzzy inference process neural network model and the mixed computing process neural network model based on cloud change are proposed to distinguish sedimentary microfacies, on the one hand, for the evaluation rule data with expert experience, the fuzzy inference process neural network model and the mixed computing process neural network model based on cloud variation are proposed. Fuzzy reasoning can be used to quantitatively process the qualitative information of logging facies in order to simplify the discriminant rules, extract effective discriminant data and improve the accuracy of sedimentary microfacies discrimination. On the other hand, for the data with fuzzy information, By using cloud model, the qualitative information in phase data can be transformed into the calculation process, which ensures the integrity and objectivity of the original data, considering the fact that the log phase data is a curve varying with depth. Therefore, the process input advantage of process neural network (PNN) is used to improve the accuracy of sedimentary microfacies discrimination by virtue of its ability to process the two dimensional information in time and space and to optimize the learning mechanism of process neural network continuously. Based on the data elements of sedimentary microfacies, the model and algorithm of sedimentary microfacies recognition are taken as the core. According to the development mode of software engineering, a sedimentary microfacies pattern recognition system is formed after software design and integrated development. Good experimental results are obtained. The research in this paper has a high practical value and application prospect for the actual processing of sedimentary microfacies discrimination data and the accurate expression of microfacies types.
【學(xué)位授予單位】：東北石油大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TE319;TP183

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