本文選題：智能井 + 井下監(jiān)測(cè)數(shù)據(jù)��； 參考：《西安石油大學(xué)》2017年碩士論文

【摘要】：智能井井下監(jiān)測(cè)設(shè)備獲得的生產(chǎn)數(shù)據(jù)攜帶了大量的油藏信息,這些信息對(duì)我們認(rèn)識(shí)油藏、解釋油藏狀態(tài)以及生產(chǎn)優(yōu)化控制具有不可估量的價(jià)值。但由于各種因素的影響,數(shù)據(jù)中會(huì)存在一些噪音,甚至是異常的數(shù)據(jù)。為了提高數(shù)據(jù)的真實(shí)性,必須對(duì)監(jiān)測(cè)數(shù)據(jù)進(jìn)行處理,從而得到較為準(zhǔn)確的數(shù)據(jù)。準(zhǔn)確的監(jiān)測(cè)數(shù)據(jù)才能真實(shí)可靠地反映油藏動(dòng)態(tài)變化,將這些數(shù)據(jù)應(yīng)用到油藏實(shí)時(shí)擬合中,可及時(shí)地更新油藏模型,有利于獲取油藏的流動(dòng)狀態(tài),更好地實(shí)現(xiàn)智能井的優(yōu)化控制。本文以處理智能井井下監(jiān)測(cè)數(shù)據(jù)為基礎(chǔ),以智能井油藏實(shí)時(shí)擬合為手段,為真實(shí)而準(zhǔn)確地獲取油藏參數(shù)提供了一種新思路。首先,利用Thompson的異常值檢測(cè)算法檢測(cè)數(shù)據(jù)中的奇異值點(diǎn);其次,基于小波理論,結(jié)合MATLAB軟件用wden函數(shù)對(duì)去除異常值后的數(shù)據(jù)做降噪處理;接著,使用MATLAB軟中的自帶函數(shù)wdencmp函數(shù)對(duì)數(shù)據(jù)做壓縮處理;最后,深入剖析集合卡爾曼濾波擬合技術(shù),通過序貫高斯模擬方法生成初始油藏模型集合,并結(jié)合Eclipse設(shè)計(jì)智能井油藏實(shí)時(shí)擬合軟件,利用該軟件驗(yàn)證EnKF在智能井油藏實(shí)時(shí)擬合中的作用,同時(shí)研究了影響擬合效果的敏感性因素。結(jié)果表明:井下監(jiān)測(cè)數(shù)據(jù)經(jīng)處理后,噪音水平明顯降低,數(shù)據(jù)冗余度大幅減小,且數(shù)據(jù)的真實(shí)變化趨勢(shì)和細(xì)節(jié)特征都被完整地保留了下來,為油藏實(shí)時(shí)擬合提供了可靠的數(shù)據(jù)基礎(chǔ)。通過油藏實(shí)時(shí)擬合可得到更為真實(shí)的油藏參數(shù),使我們更正確地了解油藏,為油藏的生產(chǎn)優(yōu)化控制奠定堅(jiān)實(shí)的基礎(chǔ),同時(shí)也對(duì)合理油藏開發(fā)方案的設(shè)計(jì)提供了重要依據(jù)。這些工作有助于我們進(jìn)一步認(rèn)識(shí)油藏,改善油藏模型,使其能更正確地反映油藏動(dòng)態(tài),對(duì)合理、準(zhǔn)確制定油藏生產(chǎn)優(yōu)化控制策略起著至關(guān)重要的作用。
[Abstract]:The production data obtained by intelligent downhole monitoring equipment carry a large amount of reservoir information, which is of inestimable value for us to understand the reservoir, explain the reservoir state and optimize the control of production. However, due to the influence of various factors, there will be some noise in the data, even abnormal data. In order to improve the authenticity of the data, the monitoring data must be processed to obtain more accurate data. The accurate monitoring data can truly and reliably reflect the reservoir dynamic change. Applying these data to the reservoir real-time fitting can update the reservoir model in time, which is helpful to obtain the flow state of the reservoir and to realize the optimization control of the intelligent well better. Based on the data processing of intelligent well downhole monitoring and real-time fitting of intelligent well reservoir, this paper provides a new way to obtain reservoir parameters truthfully and accurately. Firstly, the outlier value detection algorithm of Thompson is used to detect the singular value points in the data. Secondly, based on the wavelet theory and MATLAB software, wden function is used to reduce the noise of the data after removing the outlier value. The wdencmp function in MATLAB software is used to compress the data. Finally, the set Kalman filter fitting technique is deeply analyzed, and the initial reservoir model set is generated by sequential Gao Si simulation. Combining with Eclipse to design the real-time fitting software of intelligent well reservoir, the function of EnKF in real-time fitting of intelligent well reservoir is verified by the software. The sensitive factors that affect the fitting effect are also studied. The results show that the noise level is obviously reduced, the data redundancy is greatly reduced, and the true trend and detail characteristics of the data are completely preserved after the downhole monitoring data is processed. It provides a reliable data base for reservoir real-time fitting. Through real-time reservoir fitting, we can get more real reservoir parameters, make us understand the reservoir more correctly, lay a solid foundation for reservoir production optimization and control, and provide an important basis for the design of reasonable reservoir development plan. These works are helpful for us to further understand the reservoir, improve the reservoir model, make it more accurate to reflect reservoir performance, and play an important role in the rational and accurate formulation of reservoir production optimization control strategy.
【學(xué)位授予單位】：西安石油大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TE151

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