Individual well flowing rate recovery from PDG transient pressure with either assigned daily rate or total cumulative production of the well or group of wells through wavelet approach

Zheng, Shi-Yi; Li, Xiaogang

doi:10.1016/j.petrol.2009.06.014

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…The median filter replaces the center signal value with the signal value from the middle of the sorted list, which will lose a lot of useful information, causing a huge deviation between the filtered signal and the original signal. Zheng [ 22 ] presented a wavelet transform-based approach for recovering the production history from the measured permanent downhole gauge (PDG) pressure. By assuming that the reservoir system is linear, they realized the production evaluation of the reservoir.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-Based Kalman Smoothing Method for Uncertain Parameters Processing: Applications in Oil Well-Testing Data Denoising and Prediction

Feng

et al. 2020

Sensors

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The low-distortion processing of well-testing geological parameters is a key way to provide decision-making support for oil and gas field development. However, the classical processing methods face many problems, such as the stochastic nature of the data, the randomness of initial parameters, poor denoising ability, and the lack of data compression and prediction mechanisms. These problems result in poor real-time predictability of oil operation status and difficulty in offline interpreting the played back data. Given these, we propose a wavelet-based Kalman smoothing method for processing uncertain oil well-testing data. First, we use correlation and reconstruction errors as analysis indicators and determine the optimal combination of decomposition scale and vanishing moments suitable for wavelet analysis of oil data. Second, we build a ground pressure measuring platform and use the pressure gauge equipped with the optimal combination parameters to complete the downhole online wavelet decomposition, filtering, Kalman prediction, and data storage. After the storage data are played back, the optimal Kalman parameters obtained by particle swarm optimization are used to complete the data smoothing for each sample. The experiments compare the signal-to-noise ratio and the root mean square error before and after using different classical processing models. In addition, robustness analysis is added. The proposed method, on the one hand, has the features of decorrelation and compressing data, which provide technical support for real-time uploading of downhole data; on the other hand, it can perform minimal variance unbiased estimates of the data, filter out the interference and noise, reduce the reconstruction error, and make the data have a high resolution and strong robustness.

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Section: Introductionmentioning

confidence: 99%

Wavelet-Based Kalman Smoothing Method for Uncertain Parameters Processing: Applications in Oil Well-Testing Data Denoising and Prediction

Feng

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

Unknown Rate History Calculation from Down-hole Transient Pressure Data Using Wavelet Transform

Wang

Zheng

2012

Transp Porous Med

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New approach for interpreting pressure and flow rate data from permanent downhole gauges, least square support vector machine approach

Khazali

Sharifi

2019

Journal of Petroleum Science and Engineering

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Individual well flowing rate recovery from PDG transient pressure with either assigned daily rate or total cumulative production of the well or group of wells through wavelet approach

Cited by 4 publications

References 11 publications

Wavelet-Based Kalman Smoothing Method for Uncertain Parameters Processing: Applications in Oil Well-Testing Data Denoising and Prediction

Wavelet-Based Kalman Smoothing Method for Uncertain Parameters Processing: Applications in Oil Well-Testing Data Denoising and Prediction

Unknown Rate History Calculation from Down-hole Transient Pressure Data Using Wavelet Transform

New approach for interpreting pressure and flow rate data from permanent downhole gauges, least square support vector machine approach

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