Abstract:

Based on the traditional well test and wavelet theory，it is found the that types of wavelet families are particularly useful in the processing of long-term pressure data by iterative attempt.The iterative thresholding method to detect outliers from the data based on wavelet analysis is developed.This method is applied to actual field data and the method is successful in identifying outliers.Based on the linear regression method from Chee Kin (2001)，a new method to remove noise from the data is developed.The denoising process from simulated and field examples is successful in suppressing the noise while the data features are still preserved.The pressure set is discretized into individual transient successfully by using the wavelet modulus maxima.The size of original data is reduced considerably by using the maximum-pressure-maximum-time criterion while the most representative points are kept.Based on the data processing，the moving data window approach，together with the orthogonal experimental method is used to interpret long-term pressure set.It is found that the interpreting result is consistent with traditional well testing interpretation.

Key words: long-term pressure data, wavelet transform, noise, data reduction, orthogonal experiment, moving window analysis