【摘要】：目前在油田測井作業(yè)中,工人采用在井口聽爆破聲音和用手觸摸油管壁感覺振動判斷射孔彈是否起爆,通過將射孔器提出地面數(shù)彈孔計算射孔數(shù),這種方法是十分落后的。經(jīng)常會發(fā)生因作業(yè)油井過深導致振動不明顯,人員在井口感覺不到振動,從而無法判斷射孔彈是否起爆。如果對起爆信號判斷失誤,就會給接下來的作業(yè)帶來很大影響,甚至造成人員傷亡。所以需要一種有效的方法,對射孔彈起爆信號進行分析,判斷射孔彈是否起爆并計算有多少彈起爆。通過對大量油管傳輸射孔信號作傅里葉分析,確定射孔信號的有效頻率范圍,根據(jù)這些參數(shù)設計巴特沃斯帶通濾波器濾除有效頻率之外的噪聲部分。小波變換具有良好的時頻局部化特性,利用小波變換對有效信號進行降噪處理,去除夾雜在有效信號中的高頻噪聲,重構出射孔信號,此時的信號就十分接近原始信號。同時利用信號經(jīng)過小波變換后的分量確定射孔彈的起爆開始和結束點,利用這段時間差計算射孔數(shù),結合每次作業(yè)中設定的射孔總數(shù)可以計算射孔率。通過不斷調(diào)整BP網(wǎng)絡參數(shù)得到神經(jīng)網(wǎng)絡模型,此模型經(jīng)過大量樣本數(shù)據(jù)的訓練就具備識別射孔信號的能力,經(jīng)過實驗證明,此網(wǎng)絡可以識別一級起爆的油管傳輸射孔信號。本文將數(shù)字濾波器、小波變換和神經(jīng)網(wǎng)絡方法結合,提出基于神經(jīng)網(wǎng)絡的油管傳輸射孔信號分析,經(jīng)實驗證明此方法有效解決了油田測井作業(yè)中對射孔彈起爆判斷問題和射孔彈數(shù)的計算問題,同時可以利用BP神經(jīng)網(wǎng)絡識別油管傳輸射孔信號,基于神經(jīng)網(wǎng)絡的油管傳輸射孔信號分析方法為油田工作人員判斷油井射孔質量優(yōu)劣提供了較為準確的依據(jù)。
[Abstract]:At present, in oil field logging, workers use the sound of blasting at the well head and the sensory vibration of touching the oil pipe wall to judge whether the perforator is initiating or not. This method is very backward by putting forward the surface number of perforators to calculate the number of perforations. It often happens that the vibration is not obvious because the working well is too deep, and the personnel can not feel the vibration at the well head, so it is impossible to judge whether the perforating projectile is detonated or not. If the initial signal is misjudged, it will have a great impact on the next operation, and even cause casualties. Therefore, an effective method is needed to analyze the ejection signal of perforation, to judge whether the projectile is primed and to calculate the number of ejection. The effective frequency range of perforation signal is determined by Fourier analysis of a large number of perforation signals transmitted by tubing. According to these parameters, Butterworth bandpass filter is designed to filter the noise part beyond the effective frequency. Wavelet transform has good time-frequency localization property. Wavelet transform is used to reduce the noise of the effective signal, remove the high frequency noise in the effective signal, and reconstruct the perforated signal. The signal is very close to the original signal. At the same time, the signal components after wavelet transform are used to determine the initiation point and the end point of the projectile, the perforation number is calculated by using this time difference, and the perforation rate can be calculated by combining the total number of perforations set in each operation. The neural network model is obtained by constantly adjusting the parameters of BP network. The model is trained by a large number of sample data and has the ability to recognize perforation signal. The experiment shows that the neural network can recognize the perforation signal transmitted by the tubing that is primed by one stage. In this paper, the digital filter, wavelet transform and neural network are combined to analyze the perforation signal of tubing transmission based on neural network. The experiments show that this method can effectively solve the problem of judging the ejection of perforation and calculating the number of perforated projectiles in oil field logging operations. At the same time, the BP neural network can be used to identify the perforating signals transmitted by tubing. The neural network based perforation signal analysis method provides a more accurate basis for oil field workers to judge the perforation quality of oil wells.
【學位授予單位】：沈陽理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：P631.81;TP183

【參考文獻】

相關期刊論文 前3條

1 Saeed Chehreh Chelgani;Behzad Shahbazi;Bahram Rezai;;Estimation of froth flotation recovery and collision probability based on operational parameters using an artificial neural network[J];International Journal of Minerals Metallurgy and Materials;2010年05期

2 楊建廣,呂紹林;地球物理信號處理技術的研究及進展[J];地球物理學進展;2002年01期

3 胡麗瑩;肖蓬;;快速傅里葉變換在頻譜分析中的應用[J];福建師范大學學報(自然科學版);2011年04期

，

本文編號：2249327