基于深度學習的抽油機井工況診斷方法
信息技術(shù)與網(wǎng)絡(luò)安全
肖 翔
(中國科學技術(shù)大學 信息科學技術(shù)學院，安徽 合肥230026)
摘要： 基于示功圖對抽油機井下工況進行自動診斷是數(shù)字油田不可或缺的環(huán)節(jié)。通常通過人工提取示功圖的特征向量，然后輸入機器學習算法分類器識別井下工況。然而，特征的選擇需要借助經(jīng)驗和先驗知識，并且直接影響后續(xù)分類器的最終性能。而人工提取特征易受知識干擾，且在特征提取的過程中存在信息丟失，這決定了識別結(jié)果的上限。為此，受深度神經(jīng)網(wǎng)絡(luò)自動特征提取的啟發(fā)，提出基于卷積神經(jīng)網(wǎng)絡(luò)的示功圖的離線訓練與在線診斷的方法。首先將挑選后的信號數(shù)據(jù)轉(zhuǎn)換為圖像數(shù)據(jù)，然后將圖像二值化降低計算復(fù)雜度，最后基于改進的LeNet-5網(wǎng)絡(luò)探究最適合模型的網(wǎng)絡(luò)結(jié)構(gòu)。最終通過實驗與目前先進的算法進行對比，驗證了本方法的有效性和可行性。
基于示功圖對抽油機井下工況進行自動診斷是數(shù)字油田不可或缺的環(huán)節(jié)。通常通過人工提取示功圖的特征向量，然后輸入機器學習算法分類器識別井下工況。然而，特征的選擇需要借助經(jīng)驗和先驗知識，并且直接影響后續(xù)分類器的最終性能。而人工提取特征易受知識干擾，且在特征提取的過程中存在信息丟失，這決定了識別結(jié)果的上限。為此，受深度神經(jīng)網(wǎng)絡(luò)自動特征提取的啟發(fā)，提出基于卷積神經(jīng)網(wǎng)絡(luò)的示功圖的離線訓練與在線診斷的方法。首先將挑選后的信號數(shù)據(jù)轉(zhuǎn)換為圖像數(shù)據(jù)，然后將圖像二值化降低計算復(fù)雜度，最后基于改進的LeNet-5網(wǎng)絡(luò)探究最適合模型的網(wǎng)絡(luò)結(jié)構(gòu)。最終通過實驗與目前先進的算法進行對比，驗證了本方法的有效性和可行性。
Research on diagnostic method for working conditions of pumping unit wells based on deep learning
Xiao Xiang
(School of Information Science and Technology，University of Science and Technology of China，Hefei 230026，China)
Abstract： It is an indispensable link to automatically diagnose downhole working conditions of pumping unit wells on dynamometer cards in digital oilfields. The feature vector of the dynamometer card is usually extracted manually and then identified downhole working conditions by the machine learning algorithm classifier as input. However, the selection of features requires experience and prior knowledge, then it directly affects the final performance of subsequent classifier. Features extracted manually are easily disturbed by knowledge, lead to the loss of key information, which determines the upper limit of the recognition result. Therefore, inspired by the automatic feature extraction of deep neural networks, this paper proposes the method of offline training and online diagnosis of dynamometer cards based on convolutional neural networks. Firstly the selected signal data is converted to image data. Then the image is binarized to reduce the complexity of computation. Finally based on the improved LeNet-5 network, we explore the network structure that is most suitable for the model. Compared with the current advanced algorithm, the validity and feasibility of this method are verified.
Key words : dynamometer card；downhole working conditions；automatic diagnosis；convolutional neural networks
0 引言
基于游梁式抽油機井的人工舉升法[1]是目前主流的采油法。人工舉升法以其結(jié)構(gòu)簡單、成本低廉、適應(yīng)性強等優(yōu)點著稱。據(jù)不完全統(tǒng)計[2]，全球超過90%的油田以及國內(nèi)超過85%的油田都是采用這種方式進行原油開采。在油田開采的過程中，地下深井作業(yè)的環(huán)境較為復(fù)雜，容易引起井下抽油泵從正常的狀態(tài)轉(zhuǎn)變?yōu)楣收系臓顟B(tài)。若抽油泵長期處于故障狀態(tài)，抽油泵設(shè)備會加速磨損使其生命周期驟縮，進一步影響油田的開采效率。由實時監(jiān)測的數(shù)據(jù)快速、準確地診斷識別出抽油機井的工作狀態(tài)，會給實際的開采提供有價值的信息，實現(xiàn)高效開采的同時將損耗降至最小。
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作者信息：
肖 翔
(中國科學技術(shù)大學 信息科學技術(shù)學院，安徽 合肥230026)
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