【摘要】：在最近的幾年里,石油的消耗總量逐年增加,低滲透儲層油氣藏在整個油田的石油產(chǎn)量中所占的比重也在逐漸加大。水力壓裂可以改造地層結(jié)構(gòu),在原本封閉的儲層和井筒之間壓裂出一條新的裂縫作為流體的新通道。此方法可以大大提高低滲透儲層油氣藏的出油量,在當前的油氣田勘探開發(fā)中充當著一個不可或缺的重要角色。在水力壓裂施工的同時,需要使用微地震監(jiān)測技術觀測壓裂過程中地層裂縫的空間圖像,并對水力壓裂的壓裂效果做出符合實際情況的評判。影響評價結(jié)果的重要因素是：通過去噪處理后得到的微震數(shù)據(jù)信噪比的高低。由于微地震事件持續(xù)時間較短、聲波頻率較高、釋放能量較小的特點,在實際生產(chǎn)過程中采集到的微地震數(shù)據(jù)中摻雜著許多的干擾噪聲信號,更有甚者干擾噪聲信號完全淹沒了有效信號。因此,對采集到的微地震信號在自動拾取有效信號之前做去噪處理是非常有必要的。本文通過對微地震信號中干擾噪聲信號的類型進行分析,同時結(jié)合最近幾年國內(nèi)外的在微震信號去除噪聲方面的技術經(jīng)驗,對幾種地震數(shù)據(jù)去噪方法進行試驗與分析,并把改進后的去噪技術運用到微地震數(shù)據(jù)處理的實例上。本文從當前油田的實際生產(chǎn)現(xiàn)狀出發(fā)引出了微地震監(jiān)測在油田開采中后期的重要性,簡單介紹了國內(nèi)外微地震監(jiān)測方法從提出理論到應用到實際生產(chǎn)的過程、利用檢波器采集微地震監(jiān)測數(shù)據(jù)的方法(一般為井中監(jiān)測)、微地震監(jiān)測這一技術由哪些儀器所組成。從微地震數(shù)據(jù)干擾信號的類型和特點入手,主要研究了預測反褶積濾波、相關濾波、變模態(tài)分解、曲波變換等微震信號去噪方法。最后,使用MATLAB編寫了預測反褶積濾波、帶通濾波、相關濾波、變模態(tài)分解、曲波變換等程序,把前面提到的幾種濾波或重構(gòu)去噪方法在MATLAB的語言平臺上對現(xiàn)場采集的微地震數(shù)據(jù)進行了分析和處理。得到的處理結(jié)果表明：(1)在實際生產(chǎn)過程中,裂縫位置的實時精確成像和有效信號的自動拾取與微震資料的信噪比密切相關。(2)微地震信號震源位置的確定是去噪處理的一個關鍵問題,在微地震信號震源位置未知的情況下,很多完善的地震資料處理方法應用受到極大的限制,不能發(fā)揮其真正作用。(3)微震資料中包含有：有效波和干擾波,細分為直達波、折射波、透射波、多次波、轉(zhuǎn)換波、面波、管波和導波；純縱波和純橫波為線性極化波,面波、導波為非線性極化波。(4)主成分分析對壓制微震信號中的隨機干擾噪聲有一定的效果,壓制噪聲的效果比帶通濾波要好一些。(5)頻率濾波器是基于有效信號與噪聲在頻率域具有可分性這一特征,設定壓制某特定頻段的信號從而起到壓制噪聲的效果。因此,頻域濾波器的使用條件是,只有當有效信號和噪聲在頻率域內(nèi)存在較大差異的時侯,才能起到較好的壓制噪聲的效果。(6)預測反褶積濾波可以壓縮子波并有效去除微震資料中的多次波。預測反褶積濾波在處理包含多次波噪聲的微震數(shù)據(jù)時,不使用速度等輔助信息并且運行速度較快。(7)相關濾波方法是在隨機干擾背景上凸出微震信號同相軸比較有效的方法。根據(jù)實測微震資料選取合理的互相關參數(shù),可以有效去除隨機干擾噪聲,提高同相軸分辨率。(8)曲波變換擁有多尺度各向異性的特點,解決了小波變換在處理微震資料邊沿的方向特質(zhì)等方面的內(nèi)在不足,能夠在壓制隨機干擾信號的同時保留微震信號的細節(jié),實現(xiàn)能更去除噪聲的目的。(9)由于微震信號具有其特殊性和復雜性,單一的微震信號去噪方法難見其效,表明單一的方法都有其局限性。多種方法綜合使用才是今后微震信號去噪技術探索的方向。
[Abstract]:In recent years, the total consumption of oil has increased year by year, and the proportion of low permeability reservoirs in the oil production of the whole oilfield has gradually increased. Oil production of high and low permeability reservoirs plays an indispensable and important role in the current exploration and development of oil and gas fields. While hydraulic fracturing is being carried out, it is necessary to use microseismic monitoring technology to observe the spatial image of formation fractures in the process of fracturing, and to make a judgment of the fracturing effect in accordance with the actual situation. The important factors influencing the evaluation results are the signal-to-noise ratio of the microseismic data obtained by denoising. Because of the characteristics of short duration of microseismic events, high frequency of sound waves and low energy release, many interference noise signals are mixed with the microseismic data collected in the actual production process, and even more interference noise. The acoustic signal completely submerges the effective signal. Therefore, it is necessary to denoise the collected microseismic signal before picking up the effective signal automatically. In this paper, the types of interference noise signals in the microseismic signal are analyzed, and the technology of removing noise from the microseismic signal at home and abroad in recent years is combined. In this paper, the importance of microseismic monitoring in the middle and late period of oilfield production is introduced from the actual production situation of the current oilfield, and the microseismic monitoring methods at home and abroad are briefly introduced. In this paper, the method of acquiring microseismic monitoring data by geophone (usually in-well monitoring) is discussed, which instruments are used in microseismic monitoring. Starting with the types and characteristics of the interference signals of microseismic data, the main research contents are predictive deconvolution filtering, correlation filtering, variable mode decomposition and curved wave transformation. Finally, the program of predictive deconvolution filtering, band-pass filtering, correlation filtering, variable mode decomposition, curved wave transformation and so on are compiled by MATLAB to analyze and process the microseismic data collected on the spot on the language platform of MATLAB. The results show that: (1) In the actual production process, the real-time accurate imaging of fracture location and the automatic pick-up of effective signals are closely related to the signal-to-noise ratio of microseismic data. (2) The determination of the source location of microseismic signals is a key problem in the de-noising process. The application of this method is greatly limited and can not play its real role. (3) Microseismic data include: effective wave and interference wave, which are subdivided into direct wave, refraction wave, transmission wave, multiple wave, converted wave, surface wave, tube wave and guided wave; pure longitudinal wave and pure shear wave are linear polarized wave, surface wave and guided wave are nonlinear polarized wave. (4) Principal component analysis is used to counter-pressure. (5) Frequency filter is based on the separability of effective signals and noises in the frequency domain. It is set to suppress the signal of a certain frequency band so as to suppress noise. (6) Predictive deconvolution filtering can compress wavelets and effectively remove multiple waves from microseismic data. Predictive deconvolution filtering does not use velocity and other aids in processing microseismic data containing multiple noise. (7) Correlation filtering method is an effective method to convex microseismic signal in-phase axis on random interference background. Selecting reasonable cross-correlation parameters according to measured microseismic data can effectively remove random interference noise and improve the resolution of in-phase axis. (8) Curvilinear transform has the characteristics of multi-scale anisotropy. (9) Because of the particularity and complexity of microseismic signal, it is difficult to denoise a single microseismic signal, which indicates that the denoising method is ineffective. A single method has its limitations. The comprehensive use of multiple methods is the direction of future exploration of microseismic signal denoising technology.
【學位授予單位】：長江大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：P631.44

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