本文選題：預測方法 + 井下復雜情況��； 參考：《中國石油大學(華東)》2015年碩士論文

【摘要】：井下復雜情況直接關系到鉆井的成敗,消除鉆井過程中的井下復雜問題是安全鉆完井的最重要任務之一。提前預測鉆井井下復雜情況、采取適當措施能確保鉆井施工安全,同時可以節(jié)約鉆進時間和成本。人工神經網絡方法具有解決需要復雜模式識別鉆井井下復雜問題預測的巨大潛力,本文基于人工神經網絡方法理論,開展鉆井井下復雜情況預測方法研究。本文首先歸納總結了鉆井過程中的各類井下復雜情況,重點分析了各類鉆井井下復雜情況的影響因素,從而準確地為人工神經網絡預測方法選擇合適的參數(shù)。引用人工神經網絡方法,對比選用一種新型高效數(shù)學算法,基于C++程序設計語言,開發(fā)鉆井井下復雜情況預測診斷應用程序,形成了鉆井井下復雜情況人工神經網絡預測診斷方法。結合油田井下復雜情況實例數(shù)據(jù),驗證人工神經網絡算法和鉆井井下復雜情況預測診斷方法的可靠性。預測結果和實際結果對比分析表明,該人工神經網絡算法和鉆井井下復雜情況預測診斷方法具有較高的精度和準確性,建立的鉆井井下復雜情況預測診斷方法可行、結果可靠,能應用于鉆井實際來預測和確認鉆井中可能出現(xiàn)的井下復雜問題。這種基于人工神經網絡的鉆井井下復雜情況及其應用計算程序預測診斷精確度高,具有很大應用潛力,對鉆井井下復雜問題診斷和預測具有重要的意義。
[Abstract]:The downhole complex situation is directly related to the success or failure of drilling. It is one of the most important tasks for safe drilling and completion to eliminate the downhole complex problem during drilling. The complex condition of drilling well can be predicted ahead of time, and appropriate measures can be taken to ensure the safety of drilling operation, and at the same time, the drilling time and cost can be saved. The artificial neural network (Ann) method has great potential to solve the complex problems in drilling wells which need complex pattern recognition. Based on the theory of artificial neural network (Ann), the prediction method of drilling downhole complex situation is studied in this paper. In this paper, we first summarize the various downhole complex conditions in drilling process, and analyze the influencing factors of various drilling downhole complex conditions, so as to accurately select the appropriate parameters for the artificial neural network prediction method. By using artificial neural network method, a new and efficient mathematical algorithm is used to develop a prediction and diagnosis program for complex conditions in drilling wells based on C programming language. The artificial neural network prediction and diagnosis method for complex conditions in drilling well is formed. The reliability of artificial neural network algorithm and drilling downhole complex condition prediction and diagnosis method is verified by combining with the data of oilfield downhole complex case. The comparison and analysis between the prediction results and the actual results show that the artificial neural network algorithm and the drilling downhole complex situation prediction and diagnosis method have high accuracy and accuracy, the established prediction and diagnosis method for the drilling downhole complex situation is feasible and the results are reliable. It can be used in drilling practice to predict and confirm complex downhole problems that may occur in drilling. This kind of artificial neural network based drilling downhole complex situation and its application calculation program has high accuracy of prediction and diagnosis, and has great application potential, which is of great significance to the diagnosis and prediction of drilling downhole complex problems.
【學位授予單位】：中國石油大學(華東)
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP183;TE28

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