Automatic well test data processing: a time series wavelet analysis approach

Authors: I.S. Afanasiev, A.V. Sergeychev (Rosneft Oil Company OJSC, RF, Moscow), R.N. Asmandiyarov (RN-Yuganskneftegas LLC, RF, Nefteyugansk), V.A. Baikov, D.S. Ivaschenko, A.Ya. Davletbaev (RN-UfaNIPIneft LLC, RF, Ufa), A.I. Sakhanenko (Sobolev Institute of Mathematics, Siberian Branch of the RAS, RF, Novosibirsk)

Key words: well test, wavelet analysis, time series.

This paper discusses perspectives of automatic well test data processing.  Well test data treated as a non-stationary time series can be studied by means of wavelet analysis. The approach suggested allows one to carry out well test data denoising and smoothing, recognize informative intervals in telemetry system curves and flow regimes in pressure response plots. In order to illustrate the method efficiency, field examples are presented.

References

1. Athichanagorn S., Horne R.N., Kikani J., Processing and interpretation of long-term data from permanent downhole pressure gauges, SPE 56419, 1999.

2. Daubechies I., Ten lectures on wavelets, Philadelphia (PA) SIAM, 1992.

3. Chui C.K., An introduction to wavelets, Academic Press, Boston, 1992.

4. Mallat S., A wavelet tour of signal processing: The sparse way, 3rd Ed., Academic Press - Elsevier, 2009.

5. Donoho D.L., Johnstone I.M., Adapting to unknown smoothness via wavelet shrinkage, Journal of American Statistical Association, 1994, V. 90, no. 432, pp. 1200-1224.

6. Ortiz C.E.P., Aguiar R.B., Pires A.P., Wavelet filtering of permanent downhole gauge data, SPE 123028, 2009.

7. Nomura M., Processing and interpretation of pressure transient data from permanent downhole gauges: dissertation for the degree of Doctor of Phylosophy, Stanford University, USA, 2006.

8. Bourdet D., Well test analysis: the use of advanced interpretation models, Paris, 2002.

9. Bilen C., Huzurbazar S., Wavelet-based outlier detection in time series, Journal of Computational & Graphical Statistics, 2002, V. 11, no.2, pp. 311-327.

Key words: well test, wavelet analysis, time series.

This paper discusses perspectives of automatic well test data processing.  Well test data treated as a non-stationary time series can be studied by means of wavelet analysis. The approach suggested allows one to carry out well test data denoising and smoothing, recognize informative intervals in telemetry system curves and flow regimes in pressure response plots. In order to illustrate the method efficiency, field examples are presented.

References

1. Athichanagorn S., Horne R.N., Kikani J., Processing and interpretation of long-term data from permanent downhole pressure gauges, SPE 56419, 1999.

2. Daubechies I., Ten lectures on wavelets, Philadelphia (PA) SIAM, 1992.

3. Chui C.K., An introduction to wavelets, Academic Press, Boston, 1992.

4. Mallat S., A wavelet tour of signal processing: The sparse way, 3rd Ed., Academic Press - Elsevier, 2009.

5. Donoho D.L., Johnstone I.M., Adapting to unknown smoothness via wavelet shrinkage, Journal of American Statistical Association, 1994, V. 90, no. 432, pp. 1200-1224.

6. Ortiz C.E.P., Aguiar R.B., Pires A.P., Wavelet filtering of permanent downhole gauge data, SPE 123028, 2009.

7. Nomura M., Processing and interpretation of pressure transient data from permanent downhole gauges: dissertation for the degree of Doctor of Phylosophy, Stanford University, USA, 2006.

8. Bourdet D., Well test analysis: the use of advanced interpretation models, Paris, 2002.

9. Bilen C., Huzurbazar S., Wavelet-based outlier detection in time series, Journal of Computational & Graphical Statistics, 2002, V. 11, no.2, pp. 311-327.


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