长时井下压力监测数据流动过程识别方法研究

doi:10.11885/j.issn.1674-5086.2012.06.01.01

摘要/Abstract

摘要：

利用长时井下压力计监测井下生产在提高油藏和油井管理方面正发挥着越来越重要的作用。在长时井下压
力监测数据的解释过程中，准确地确定流动变化过程起始时间至关重要，但由于其庞大的数据量而使得手工划分和处
理这些数据不切实际。基于小波模极值理论和噪声鲁棒微分算法，利用模拟的井下压力数据对比研究了长时井下压
力监测数据中流动变化过程（突变点）的识别方法。结果表明：小波模极值方法的误识别率相对较高，对噪声比较敏
感，在采用该方法之前必须对数据进行降噪处理；而基于噪声鲁棒微分算法的二阶导数识别方法可以准确、有效地识
别出流动变化过程，并且对噪声具有稳健性，可以在不对信号进行降噪处理的情况下识别流动过程。研究结果为自动
处理长时井下监测数据提供了一种新的手段。

关键词: 长时井下压力数据, 噪声, 小波模极值方法, 噪声鲁棒微分算法, 流动过程识别

Abstract:

The real-time monitoring technology of the downhole conditions with permanent downhole pressure gauge is playing
an important role in improving reservoir and well management. During the interpretation of permanent downhole pressure data，
it is vital to acquire a good well-test result that accurately identifies the beginning time of new transient flow. Due to the large
volume of the collected data by permanent downhole gauge，it is impractical to partition and process these data manually. Based
on wavelet transform module maximum theory and noise-robust differentiator，the identification methods of transient flow from
permanent downhole pressure data are investigated with the synthetic data. The results show that the wavelet transform module
maximum method may not identify some key transient flows，and it is sensitive to the noise. The data must be de-noised before
using this method. But with the noise-robust differentiator，the transient flow can be identified accurately and effectively using
its 2nd derivative. It is robust to noise and can be used to identify the transient flow without data de-noising. The study provides
a new automatic method to process the permanent downhole data.

Key words: permanent downhole pressure data, noise, wavelet transform module maximum, noise-robust differentiator,
transient flow identification

刘均荣，姚军，于伟强. 长时井下压力监测数据流动过程识别方法研究[J]. 西南石油大学学报(自然科学版), DOI: 10.11885/j.issn.1674-5086.2012.06.01.01.

Liu Junrong, Yao Jun, Yu Weiqiang. Study of Identification Method of Transient Flow from Permanent
Downhole Pressure Data[J]. 西南石油大学学报(自然科学版), DOI: 10.11885/j.issn.1674-5086.2012.06.01.01.

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参考文献

[1] Goh K F G，Gee M J，Zaini M Z，et al. Application of

permanent reservoir monitoring data（DTS & PDG）in a

mature offshore Malaysian oil field，for production optimisation

and workover control[C]. SPE 14274，2011.

[2] Liu Yang，Horne R N. Interpreting pressure and flow rate

data from permanent downhole gauges using data mining

approaches[C]. SPE 147298，2011.

[3] Zheng Shiyi，Wang Fuyong. Recovering flowing history

from transient pressure of permanent down-hole gauges

（PDG）in oil and water two-phase flowing reservoir[C].

SPE 149100，2011.

[4] 张海燕，熊国荣，方伟，等. 井下永置式测试技术在

普光气田的应用[J]. 天然气工业，2011，31（5）：64–66.Zhang Haiyan，Xiong Guorong，Fang Wei，et al. Application

of downhole implanted gauge testing technology in

the Puguang Gas Field[J]. Natural Gas Industry，2011，

31（5）：64–66.

[5] Elsherif T，Balto A，Baez F，et al. The use of real-time

downhole pressure and distributed temperature surveying

in quantifying the skin evolution and zonal coverage in

horizontal well stimulation[C]. SPE 155723，2012.

[6] Li Z，Zhu D. Predicting flow profile of horizontal well

by downhole pressure and distributed-temperature data for

waterdrive reservoir[J]. SPE Production & Operations，

2010，25（3）：296–304.

[7] Aggrey G，Davies D，Skarsholt L. A novel approach of

detecting water influx time in multi-zone and multilateral

completions using real-time downhole pressure data[C].

SPE 105374，2007.

[8] Suzuki S，Chorneyko D. Automatic detection of pressurebuildup

intervals from permanent downhole pressure data

using filter convolution[C]. SPE 125240，2009.

[9] Pico C，Aguiar R，Pires A. Wavelet filtering of permanent

downhole gauge data[C]. SPE 123028，2009.

[10] Ribeiro P，Pires A，Oliveira E A P，et al. Use of wavelet

transform in pressure-data treatment[J]. SPE Production &

Operations，2008，23（1）：24–31.

[11] Zheng Shiyi，Li Xiaogang. Analyzing transient pressure

from permanent downhole gauges (PDG) using wavelet

method[C]. SPE 107521，2007.

[12] 樊启斌. 小波分析[M]. 武汉：武汉大学出版社，2008.

[13] 刘伟，曹思远. 基于小波变换的信号奇异性检测在层

位识别中的应用[J]. 石油地球物理勘探，2010，45（4）：

530–533.

Liu Wei，Cao Siyuan. Application of wavelet transform

based signal singularity detection in horizon identification[

J]. Oil Geophysical Prospecting，2010，45（4）：

530–533.

[14] 吴凡，熊高君，叶志婵. 小波变换在信号突变点检测中

的应用[J]. 计算机与现代化，2008（8）：133–135.

Wu Fan，Xiong Gaojun，Ye Zhichan. Applications of

wavelet transform on signals catastrophe-points detection[

J]. Computer and Modernization， 2008（8）：

133–135.

[15] 权建峰，李艳，郭东敏，等. 小波变换模极大值方法对

信号的奇异性检测[J]. 探测与控制学报，2009，31（2）：

46–49.

Quan Jianfeng，Li Yan，Guo Dongmin，et al. Signal singularity

detection with modulus maxima method based

on wavelet transform[J]. Journal of Detection & Control，

2009，31（2）：46–49.

[16] Pavel Holoborodko. Smooth noise-robust differentiators[

EB/OL]. http://www.holoborodko.com/pavel/numeri

cal-methods/numerical-derivative/smooth-low-noisedifferentiators，

2012–12–15.