本文關(guān)鍵詞：石油鉆井過程井漏異常的預(yù)警技術(shù)研究　出處：《鄭州大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 石油鉆井工程預(yù)警系統(tǒng) 野值點(diǎn)剔除 自適應(yīng)滑動窗 門限值分類算法 多模核主元分析法

【摘要】：整個石油鉆井工程預(yù)警系統(tǒng)結(jié)構(gòu)復(fù)雜,操作環(huán)境惡劣且工況多變,對鉆井過程中各工況的實時監(jiān)測控制與故障診斷是目前鉆井工程預(yù)警系統(tǒng)研究的重要方向;其中實時監(jiān)測數(shù)據(jù)的采樣、處理、傳輸、野值點(diǎn)剔除、工況分類等技術(shù)是系統(tǒng)故障檢測與診斷的核心技術(shù)。本文以核主元分析方法為理論基礎(chǔ),以實現(xiàn)石油鉆井工程的自動化預(yù)警為目的,通過建立多個核主元分析模型,完成鉆井過程的信息自動獲取、特征量提取和故障診斷。本文針對石油鉆井這一復(fù)雜的多工況過程,在所選取“井漏事故”的故障點(diǎn)已知的情況下對工程預(yù)警系統(tǒng)的故障檢測與診斷進(jìn)行了深入研究。主要研究內(nèi)容如下:(1)數(shù)據(jù)處理:從鉆井現(xiàn)場獲得的原始運(yùn)行數(shù)據(jù)中存在大量的過程變量,比如立管壓力、總池體積、出口流量等,彼此之間具有很強(qiáng)的相關(guān)性,所以必須對這些數(shù)據(jù)進(jìn)行預(yù)處理才能用于過程分析。對樣本數(shù)據(jù)進(jìn)行標(biāo)準(zhǔn)化處理是數(shù)據(jù)處理的基礎(chǔ);野值點(diǎn)剔除算法主要是用來剔除數(shù)據(jù)中出現(xiàn)的孤立野值點(diǎn)和連續(xù)野值點(diǎn),驗證了以剔除率的拐點(diǎn)為標(biāo)準(zhǔn)進(jìn)行滑動窗口長度自適應(yīng)確定的可靠性,能夠有效剔除過程中的一些毛刺,以免影響檢測結(jié)果;除此之外,還綜合分析了各個變量間的相關(guān)性,提取出一些能夠描述變量變化趨勢的主要特征量(短期方差、長期方差、離差等)。(2)鉆井工況分類:針對石油鉆井工程預(yù)警系統(tǒng),本文提出了一種新的能將鉆井中的各個工況正確分類的門限值分類算法。該分類法不需要進(jìn)行繁瑣的計算,只需依據(jù)綜合錄井儀所記錄的鉆井過程數(shù)據(jù),準(zhǔn)確預(yù)置門限參量和參考數(shù)值,便可實現(xiàn)對各穩(wěn)態(tài)工況的正確分類。鉆井過程復(fù)雜多變,變量間存在很強(qiáng)的相關(guān)性,故障類型也呈現(xiàn)出多樣性,如果利用常用的K均值聚類方法對樣本數(shù)據(jù)進(jìn)行分類,無法根據(jù)鉆井?dāng)?shù)據(jù)準(zhǔn)確的計算出系統(tǒng)的穩(wěn)定度因子、分類指數(shù)以及隸屬度;門限值分類算法不需要計算這些量,它是通過預(yù)置過程變量的門限值來劃分工況的。(3)實例仿真:鑒于研究對象是非線性過程,需考慮將基于單一核主元分析模型的故障檢測方法擴(kuò)展為可以應(yīng)用于石油鉆井過程的多個核主元分析模型故障檢測方法。鉆井工程預(yù)警系統(tǒng)采用的多模核主元分析方法,不僅構(gòu)造了單一核主元分析模型貫穿整個鉆井過程,也構(gòu)造了多個核主元分析模型對應(yīng)過程中不同的工況;如果哪一個工況發(fā)生故障,那么基于門限值分類算法的多個核主元分析模型故障檢測方法能夠迅速的將發(fā)生故障的工況分離出來,并引入相對應(yīng)的故障檢測模塊。因此,可以實現(xiàn)在不同的變量空間和相應(yīng)的故障檢測統(tǒng)計量控制圖中的過程監(jiān)測,雖然它們不能直接的判斷出故障出現(xiàn)的原因,卻能夠通過統(tǒng)計圖顯示出過程變量是否超出了正常控制限,然后將檢測結(jié)果跟經(jīng)驗相結(jié)合,最終可以判定故障的類型和產(chǎn)生故障的原因,實現(xiàn)準(zhǔn)確、靈敏的故障檢測。
[Abstract]:The whole system of petroleum drilling engineering prediction of complex structure, poor operating environment and changing conditions of different conditions in the process of drilling, the real-time monitoring and fault diagnosis is an important direction of the present system of drilling engineering warning; including sampling, real-time monitoring data processing, transmission, eliminating outliers, condition classification technology is the core technology fault detection and diagnosis system. Based on kernel principal component analysis method as the theoretical basis, in order to achieve the automatic early warning of petroleum drilling engineering for the purpose, through the establishment of multiple kernel principal component analysis model, complete the drilling process information automatic acquisition, feature extraction and fault diagnosis. In this paper a more complicated process oil drilling, in the selected "fault point known well leakage accidents" in the case of fault detection and diagnosis of the early-warning system is deeply studied. The main research contents such as : (1) data processing: the existence of a large number of process variables of original operation data obtained from drilling site, such as standpipe pressure, total pool volume, export volume, has a strong correlation between each other, it is necessary to preprocess the data can be used to process analysis of the sample data standardization. Processing is the basis of data processing; outliers elimination algorithm is mainly used to isolate outliers in the data points and eliminate appear continuous outliers, to verify the inflection point rejection rate for the standard length of sliding window is adaptively determined by, can effectively eliminate the burr in the process, so as not to affect the test results; in addition, also analyzed the correlation between the variables, extract some main features which can describe the change trend of quantity variables (short term variance, variance, deviation, etc.). (2): according to the classification of drilling conditions Petroleum drilling engineering warning system, this paper proposes a new classification algorithm can correctly classify all conditions in drilling the threshold. The method does not require tedious calculations, only on the basis of drilling data of comprehensive logging instrument recorded accurately, the preset threshold parameter and a reference value, it can achieve correct the classification of the steady state conditions. The drilling process is complicated, there is a strong correlation between variables, the fault type is also showing diversity, if using K means clustering method used to classify the sample data, not according to the calculation of drilling data accurate system stability factor, classification index and membership threshold value; the classification algorithm does not need to compute these quantities, it is through the preset process variable threshold division condition. (3) the simulation: in view of the research object is nonlinear process, must be considered based on single A fault detection method of kernel principal component analysis model for the expansion of oil drilling process can be applied to a plurality of kernel principal component analysis model of fault detection method. The early warning system for drilling multi kernel principal component analysis method, not only the structure of single kernel principal component analysis model through the whole process of drilling, but also constructed a multiple different conditions a kernel principal component analysis model corresponding to the process; if a fault condition occurs, then the threshold condition classification algorithm of multiple kernel principal component analysis model of fault detection method can quickly turn fault separation based on fault detection module and introduce corresponding. Therefore, can be implemented in different variable space and the corresponding fault detection statistics process control monitoring map, although they can not directly determine the cause of the failure, but can through the statistical chart shows the process variable Whether it exceeds the normal control limit, and then combine the test results with experience, we can ultimately determine the type of failure and cause the cause of failure, so as to achieve accurate and sensitive fault detection.

【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TE28;TP277

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本文編號：1390336