Principal component analysis-assisted selection of optimal denoising method for oil well transient data
- PDF / 6,742,975 Bytes
- 22 Pages / 595.276 x 790.866 pts Page_size
- 6 Downloads / 186 Views
ORIGINAL PAPER-PRODUCTION ENGINEERING
Principal component analysis‑assisted selection of optimal denoising method for oil well transient data Bing Zhang1 · Khafiz Muradov2 · Akindolu Dada2 Received: 25 May 2020 / Accepted: 19 September 2020 © The Author(s) 2020
Abstract Oil and gas production wells are often equipped with modern, permanent or temporary in-well monitoring systems, either electronic or fiber-optic, typically for measurement of downhole pressure and temperature. Consequently, novel methods of pressure and temperature transient analysis (PTTA) have emerged in the past two decades, able to interpret subtle thermodynamic effects. Such analysis demands high-quality data. High-level reduction in data noise is often needed in order to ensure sufficient reliability of the PTTA. This paper considers the case of a state-of-the-art intelligent well equipped with fiber-optic, high-precision, permanent downhole gauges. This is followed by screening, development, verification and application of data denoising methods that can overcome the limitation of the existing noise reduction methods. Firstly, the specific types of noise contained in the original data are analyzed by wavelet transform, and the corresponding denoising methods are selected on the basis of the wavelet analysis. Then, the wavelet threshold denoising method is used for the data with white noise and white Gaussian noise, while a data smoothing method is used for the data with impulse noise. The paper further proposes a comprehensive evaluation index as a useful denoising success metrics for optimal selection of the optimal combination of the noise reduction methods. This metrics comprises a weighted combination of the signal-to-noise ratio and smoothness value where the principal component analysis was used to determine the weights. Thus the workflow proposed here can be comprehensively defined solely by the data via its processing and analysis. Finally, the effectiveness of the optimal selection methods is confirmed by the robustness of the PTTA results derived from the de-noised measurements from the above-mentioned oil wells. Keywords Intelligent well · Downhole gauge · Pressure and temperature transient analysis (PTTA) · Data smoothing; wavelet threshold denoising · Principal component analysis (PCA) Abbreviations PDG Permanent downhole gauge WGN White Gaussian noise SNR Signal-to-noise ratio PCA Principal component analysis TTA Temperature transient analysis ICV Inflow control valve PSNR Power signal-to-noise ratio RMSE Root-mean-square error
* Bing Zhang [email protected] 1
Shaanxi Key Laboratory of Advanced Stimulation Technology for Oil and Gas Reservoirs, College of Petroleum Engineering, Xi’an Shiyou University, Xi’an, China
Heriot-Watt University, Edinburgh, UK
2
List of symbols h Reservoir thickness k Permeability K Thermal conductivity L Distance from well to reservoir boundary p Pressure Q Volumetric flow rate (surface) ε Joule–Thomson coefficient q Flow rate (downhole) S Saturation T Temperature
Data Loading...
