Wavelet Method for Denoising Pressure Data for Transient Analysis

Unlike the Fourier theory that lacks scaling and translation parameters, the wavelet theory provides a new platform for the so-called de-oising workflow where noise in a signal or set of data is reduced. This work demonstrates how the wavelet transform can be used as a de-noising tool to denoise pressure transient data for PTA. The target is to create a smoother yet representative data pattern out of a noisy set of data. Two flow regimes were clearly identified from a noisy set of pressure transient data – spherical and radial flow regimes. The data points were then fitted to simple mathematical models describing spherical and radial flows. The p-values from the Chi-squared distributions were less than 0.05, indicating a perfect fit of the data points to the models.

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Authors Information
Serial Name Affiliation Designation Email
1 RN Ayitey Akoto University of Professional Studies Accra; Takoradi Technical University; African University of Science and Technology Scientist; Lecturer; Engineer niiayiteyakoto@yahoo.com
2 BK Mussey Nigeria & Onyx Resources bkmussey77@gmail.com
3 AO Igbokoyi Canada alpade@yahoo.ca
Paper Type

Research Paper

Keywords
Algorithm Denoising Noise Radial Wavelets
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108

Paper Status
Volume

Volume 5

Issue

Issue 1

Status

Published

Wavelet Method for Denoising Pressure Data for Transient Analysis - TTUJOT-2019-48

Paper Abstract:

Unlike the Fourier theory that lacks scaling and translation parameters, the wavelet theory provides a new platform for the so-called de-oising workflow where noise in a signal or set of data is reduced. This work demonstrates how the wavelet transform can be used as a de-noising tool to denoise pressure transient data for PTA. The target is to create a smoother yet representative data pattern out of a noisy set of data. Two flow regimes were clearly identified from a noisy set of pressure transient data – spherical and radial flow regimes. The data points were then fitted to simple mathematical models describing spherical and radial flows. The p-values from the Chi-squared distributions were less than 0.05, indicating a perfect fit of the data points to the models.

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