【摘要】：石油是當今世界不可或缺的非再生能源,提高石油的采集效率,及時識別采油設備系統(tǒng)的故障顯得尤為重要。目前,多數(shù)油田采用有桿泵抽油系統(tǒng)采集石油,該設備具有操作簡單,成本較低等優(yōu)點。在開采的過程中,設備經(jīng)常出現(xiàn)結蠟故障,使抽油機載荷增加,電動機電流增大,嚴重影響有桿抽油系統(tǒng)的采油效率。當前已有很多識別結蠟故障的方法,但識別準確率并不理想。本文運用統(tǒng)計學習理論的支持向量機方法對有桿抽油系統(tǒng)結蠟故障進行智能識別,該方法的泛化能力強,尤其適合小樣本的模式識別。論文主要進行以下研究:(1)介紹目前國內(nèi)外有桿抽油系統(tǒng)故障診斷的背景,對有桿抽油系統(tǒng)的結構,工作原理進行闡述,研究示功圖形成,并利用載荷傳感器對油井的載荷參數(shù)進行提取。示功圖含有諸多信息,可以根據(jù)它的圖像特征了解油井的生產(chǎn)狀況,所以選擇示功圖作為油井故障診斷的依據(jù)。(2)采集地面示功圖后,對系統(tǒng)建立數(shù)學模型,將地面示功圖轉化為井下泵功圖,更利于了解井下工況,并用MATLAB軟件進行計算,得到所需泵功圖。再將泵功圖進行MATLAB圖像處理,得知圖像的具體類型,運用大律法進行閾值分割,得到最佳的閾值后轉化為二值圖像,所得圖像利用數(shù)學形態(tài)學里的膨脹、腐蝕、細化、收縮進行處理,最終獲得所需圖像。(3)將處理好的泵功圖進行特征參數(shù)的提取,提取的參數(shù)應具備區(qū)分性、聚類性以及獨立性等,因此,選用不變矩理論提取泵功圖的7個不變矩參數(shù)來描述各種油井故障的情況,為分類器模式識別提供數(shù)據(jù)樣本。(4)選擇支持向量機這一方法對有桿抽油系統(tǒng)的故障進行智能識別,尤其是結蠟故障。對支持向量機進行理論分析并用MATLAB軟件進行仿真驗證。為了得到更好的識別效果,分別運用了交叉驗證法、粒子群算法和遺傳算法對支持向量機的參數(shù)進行尋優(yōu)。由于不同核函數(shù)識別效果不同,因此,對不同核函數(shù)的識別效果進行比較,尋得最優(yōu)參數(shù),達到良好的智能識別效果。
[Abstract]:Petroleum is an indispensable non-renewable energy in the world today. It is very important to improve the efficiency of oil collection and identify the fault of oil recovery equipment system in time. At present, the rod pump pumping system is used to collect oil in most oilfields, which has the advantages of simple operation and low cost. In the process of exploitation, the equipment often appears wax deposit fault, which makes the load of pumping unit increase and the electric current of motor increase, which seriously affects the oil recovery efficiency of the rod pumping system. At present, there are many methods to identify wax deposit fault, but the accuracy is not ideal. In this paper, the support vector machine (SVM) method based on statistical learning theory is used to identify waxing faults in rod pumping system. This method has strong generalization ability and is especially suitable for pattern recognition with small samples. The main contents of this paper are as follows: (1) the background of fault diagnosis of rod pumping system at home and abroad is introduced, the structure and working principle of rod pumping system are expounded, and the formation of indicator diagram is studied. The load parameters of oil well are extracted by load sensor. The indicator diagram contains a lot of information and can be used to understand the production condition of the oil well according to its image characteristics. Therefore, the indicator diagram is selected as the basis for fault diagnosis of the oil well. (2) after collecting the surface indicator diagram, the mathematical model of the system is established. If the surface indicator diagram is converted into the underground pump work diagram, it is more helpful to understand the downhole working conditions. The required pump power diagram is obtained by using MATLAB software. Then the pump work graph is processed by MATLAB image, and the specific type of image is obtained. The threshold value is segmented by using the great law, and the optimal threshold value is obtained. The image is transformed into a binary image by using the expansion, corrosion and thinning of mathematical morphology. The shrinkage is processed and the desired image is obtained. (3) the extracted parameters should be distinguished, clustered and independent, and so on. The moment invariant theory is used to extract 7 invariant moment parameters of pump power diagram to describe various oil well faults, and to provide data samples for classifier pattern recognition. (4) selecting support vector machine (SVM) to identify faults of sucker rod pumping system intelligently. Especially the waxing fault. The support vector machine is theoretically analyzed and simulated by MATLAB software. In order to obtain better recognition effect, the parameters of support vector machine are optimized by cross validation, particle swarm optimization and genetic algorithm respectively. Because different kernel functions have different recognition effects, the recognition effects of different kernel functions are compared, and the optimal parameters are found to achieve a good intelligent recognition effect.
【學位授予單位】：沈陽工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TE358.2

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