【摘要】：示功圖是進行抽油機井工況診斷的主要依據(jù),而示功圖的特征值提取是診斷的關(guān)鍵步驟。目前各大油田現(xiàn)場仍然通過對采集到的示功圖進行人工分析,往往受到人工主觀因素的影響,導致診斷結(jié)果有偏差。由于抽油機井下工作環(huán)境非常復雜,抽油設(shè)備會經(jīng)常遭到破壞,故障的判斷越來越難。如果能夠及時了解和掌握有桿抽油系統(tǒng)的工況,對實現(xiàn)抽油機的遠程自動化管理和科學監(jiān)控具有很重要的意義。根據(jù)電流曲線圖,用時間 電流關(guān)系代替位移 載荷描述示功圖,即通過電流法間接測量等效示功圖。根據(jù)能量守恒定律,利用復變矢量法對游梁式抽油機的運動規(guī)律進行分析,建立了電流和光桿載荷及懸點位移之間的數(shù)學模型。在示功圖特征值的提取方面,使用Freeman鏈碼對等效示功圖提取特征參數(shù),進行預處理,建立抽油機典型工況的鏈碼特征樣本庫。利用PNN網(wǎng)絡(luò)對抽油機井工況進行診斷,建立了抽油機井工況診斷的概率神經(jīng)網(wǎng)絡(luò)模型。本文首先介紹了抽油機的工作原理和示功圖的相關(guān)概念,描述了示功圖的形成過程;然后介紹電流法間接測量示功圖的基本原理,以及通過建立起的數(shù)學模型如何繪制出等效示功圖;接著介紹Freeman鏈碼的相關(guān)概念,以及示功圖的預處理和Freeman鏈碼特征值的提取方法;最后分析了BP神經(jīng)網(wǎng)絡(luò)和PNN神經(jīng)網(wǎng)絡(luò)的特點,比較了BP神經(jīng)網(wǎng)絡(luò)的不足,利用PNN神經(jīng)網(wǎng)絡(luò)對樣本訓練確定分類故障。將Freeman鏈碼作為特征向量,利用MATLAB對網(wǎng)絡(luò)進行訓練。實驗結(jié)論:用Freeman鏈碼可以準確表達出示功圖的特征,并且該PNN網(wǎng)絡(luò)模型學習速度快、診斷準確率高,可用于抽油機井工況的實時監(jiān)測和診斷。
[Abstract]:Indicator diagram is the main basis for working condition diagnosis of pumping well, and eigenvalue extraction of indicator diagram is the key step of diagnosis. At present, the field of major oil fields is still through the manual analysis of the collected indicator diagram, which is often affected by artificial subjective factors, which leads to the deviation of diagnosis results. Because the underground working environment of pumping unit is very complex, the pumping equipment will often be destroyed, so it is more and more difficult to judge the fault. If we can understand and master the working condition of rod pumping system in time, it is of great significance to realize the remote automatic management and scientific monitoring of pumping unit. According to the current curve, the time current relation is used instead of the displacement load to describe the indicator diagram, that is, the equivalent indicator diagram is measured indirectly by the current method. According to the law of conservation of energy, the motion law of beam pumping unit is analyzed by using complex variable vector method, and the mathematical model between current and beam load and suspension point displacement is established. In the aspect of characteristic extraction of indicator diagram, Freeman chain code equivalent indicator diagram is used to extract feature parameters, preprocessing is carried out, and the chain code feature sample database of typical working conditions of pumping unit is established. The PNN network is used to diagnose the working condition of the pumping well, and the probabilistic neural network model of the working condition diagnosis of the pumping well is established. This paper first introduces the working principle of pumping unit and the related concepts of indicator diagram, describes the formation process of indicator diagram, then introduces the basic principle of indirect measurement of indicator diagram by current method, and how to draw the equivalent indicator diagram through the established mathematical model, and then introduces the related concepts of Freeman chain code, as well as the preprocessing of indicator diagram and the extraction method of Freeman chain code eigenvalues. Finally, the characteristics of BP neural network and PNN neural network are analyzed, and the shortcomings of BP neural network are compared. PNN neural network is used to determine the classification faults by sample training. The Freeman chain code is used as the eigenvector, and MATLAB is used to train the network. The experimental results show that the Freeman chain code can accurately express the characteristics of the power map, and the PNN network model has the advantages of fast learning speed and high diagnostic accuracy, so it can be used for real-time monitoring and diagnosis of pumping well working conditions.
【學位授予單位】：安徽工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TE933.1;TP183

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本文編號：2505469