Geological well section correlation is one of the most important geological problems, since its results are used for further construction of geological models. The results of manual correlation are subjective and depend on the specialist's qualifications performing it. The correlation process is a routine and time-consuming work which requires processing big data sets, therefore automatic well correlation methods are necessary for better performance. In practice, as a rule, the boundaries of the layers in neighboring wells are found by paired correlation method of the corresponding well logging data based on a well with known reservoir boundaries. The sequential correlation on a large number of wells leads to the fact that the result significantly depends on the order of their bypass. This is the main problem with automatic correlation methods. Usually the triangulation networks method is used as a verification method of well correlation results. This approach is implemented in a number of domestic software products. It should be noted that this method also depends on the order of well bypass in which they are correlated.

In this paper, we propose a verification method of well logging correlation results for a given pair of wells, based on the statistical evaluation along different wells pathways. We assume that each pathways starts and ends at the specified wells and passes through various intermediate wells. The Dynamic Time Warping (DTW) method and the method based on the wavelet analysis are used as pair correlation algorithms. One part of the developed method is an algorithm for generating a set of paths connecting the wells under consideration and passing in some limited area. Also we propose correctness verification procedure for the developed method. Examples are given to demonstrate the developed approach and its comparison with the well-known verification algorithm based on triangulation networks.

References

1. Dolitskiy V.A., Geologicheskaya interpretatsiya materialov geofizicheskikh issledovaniy skvazhin (Geological interpretation of well logging data), Moscow: Nedra Publ., 1966, 387 p.

2. Metodicheskie rekomendatsii po podschetu zapasov nefti i gaza ob’emnym metodom. Otsenka kharaktera nasyshchennosti po dannym GIS (Guidelines for the calculation of reserves of oil and gas by volumetric method. Assessment of the nature of saturation according to well logging): edited by Petersil’e V.I., Poroskun V.I., Yatsenko G.G., Moscow – Tver: Publ. of VNIGNI, 2003. 261 p.

3. Shaybakov R.A., Obosnovanie kompleksnoy metodiki identifikatsii trekhmernykh geologicheskikh ob"ektov (Substantiation of an integrated technique for identifying three-dimensional geological objects): thesis of candidate of geological and mineralogical science, Ufa, 2014, 190 p.

4. Shi Y., Wu X., Fomel S., Finding an optimal well-log correlation sequence using coherence-weighted graphs, Proceedings of Conference: SEG Technical Program Expanded Abstracts 2017, 2017, pp. 1982–1987, DOI:10.1190/segam2017-17746336.1

5. Gutman I.S., Balaban I.Yu., Kuznetsova G.P., Staroverov V.M., Reservoir modeling. Automatic well log data correlation using "AutoCorr" software (In Russ.), SPE-104343-MS, 2006, DOI: https://doi.org/10.2118/104343-MS

6. Salvador S., Chan P., FastDTW: Toward accurate dynamic time warping in linear time and space, Intelligent Data Analysis, 2004, no. 11(5), pp. 70–80.

7. Lineman D.J., Mendelson J.D., Toksoz M.N., Well-to-well log correlation using knowledge-based systems and dynamic depth warping: Technical report, Massachusetts Institute of Technology, Earth Resources Laboratory, 1987, pp. 421–459.

8. Keogh E. J., Pazzani M.J., Derivative dynamic time warping, Proceedings of the 2001 SIAM International Conference on Data Mining, Chicago, 2001, DOI:10.1137/1.9781611972719.1

9. Mirowski P., Herron M., Seleznev N., Fluckiger S., McCormick D., New software for well-to-well correlation of spectroscopy logs, URL: https://www.searchanddiscovery.com/documents/abstracts/2005intl_paris/mirowski.htm

10. Jiang Y., Qi Y., Wang W.K. et al., EventDTW: An improved Dynamic Time Warping algorithm for aligning biomedical signals of nonuniform sampling frequencies, Sensor, 2020, V. 20(9), pp. 1–13, DOI:10.3390/s20092700.

11. Mallat S.G., A wavelet tour of signal processing, Academic Press, 1999.

12. Preston F.W. Henderson J., Fourier series characterization of cyclic sediments for stratigraphic correlation, Kansas Geological Survey, 1964, pp. 415–425.

13. Gutman I.S., Kuznetsova G.P., Saakyan M.I., Detailed correlation of drill sections with the help of the “AUTOCORR” program complex (In Russ.), Geoinformatika, 2009, no. 2, pp. 86–97.Geological well section correlation is one of the most important geological problems, since its results are used for further construction of geological models. The results of manual correlation are subjective and depend on the specialist's qualifications performing it. The correlation process is a routine and time-consuming work which requires processing big data sets, therefore automatic well correlation methods are necessary for better performance. In practice, as a rule, the boundaries of the layers in neighboring wells are found by paired correlation method of the corresponding well logging data based on a well with known reservoir boundaries. The sequential correlation on a large number of wells leads to the fact that the result significantly depends on the order of their bypass. This is the main problem with automatic correlation methods. Usually the triangulation networks method is used as a verification method of well correlation results. This approach is implemented in a number of domestic software products. It should be noted that this method also depends on the order of well bypass in which they are correlated.

In this paper, we propose a verification method of well logging correlation results for a given pair of wells, based on the statistical evaluation along different wells pathways. We assume that each pathways starts and ends at the specified wells and passes through various intermediate wells. The Dynamic Time Warping (DTW) method and the method based on the wavelet analysis are used as pair correlation algorithms. One part of the developed method is an algorithm for generating a set of paths connecting the wells under consideration and passing in some limited area. Also we propose correctness verification procedure for the developed method. Examples are given to demonstrate the developed approach and its comparison with the well-known verification algorithm based on triangulation networks.

References

1. Dolitskiy V.A., Geologicheskaya interpretatsiya materialov geofizicheskikh issledovaniy skvazhin (Geological interpretation of well logging data), Moscow: Nedra Publ., 1966, 387 p.

2. Metodicheskie rekomendatsii po podschetu zapasov nefti i gaza ob’emnym metodom. Otsenka kharaktera nasyshchennosti po dannym GIS (Guidelines for the calculation of reserves of oil and gas by volumetric method. Assessment of the nature of saturation according to well logging): edited by Petersil’e V.I., Poroskun V.I., Yatsenko G.G., Moscow – Tver: Publ. of VNIGNI, 2003. 261 p.

3. Shaybakov R.A., Obosnovanie kompleksnoy metodiki identifikatsii trekhmernykh geologicheskikh ob"ektov (Substantiation of an integrated technique for identifying three-dimensional geological objects): thesis of candidate of geological and mineralogical science, Ufa, 2014, 190 p.

4. Shi Y., Wu X., Fomel S., Finding an optimal well-log correlation sequence using coherence-weighted graphs, Proceedings of Conference: SEG Technical Program Expanded Abstracts 2017, 2017, pp. 1982–1987, DOI:10.1190/segam2017-17746336.1

5. Gutman I.S., Balaban I.Yu., Kuznetsova G.P., Staroverov V.M., Reservoir modeling. Automatic well log data correlation using "AutoCorr" software (In Russ.), SPE-104343-MS, 2006, DOI: https://doi.org/10.2118/104343-MS

6. Salvador S., Chan P., FastDTW: Toward accurate dynamic time warping in linear time and space, Intelligent Data Analysis, 2004, no. 11(5), pp. 70–80.

7. Lineman D.J., Mendelson J.D., Toksoz M.N., Well-to-well log correlation using knowledge-based systems and dynamic depth warping: Technical report, Massachusetts Institute of Technology, Earth Resources Laboratory, 1987, pp. 421–459.

8. Keogh E. J., Pazzani M.J., Derivative dynamic time warping, Proceedings of the 2001 SIAM International Conference on Data Mining, Chicago, 2001, DOI:10.1137/1.9781611972719.1

9. Mirowski P., Herron M., Seleznev N., Fluckiger S., McCormick D., New software for well-to-well correlation of spectroscopy logs, URL: https://www.searchanddiscovery.com/documents/abstracts/2005intl_paris/mirowski.htm

10. Jiang Y., Qi Y., Wang W.K. et al., EventDTW: An improved Dynamic Time Warping algorithm for aligning biomedical signals of nonuniform sampling frequencies, Sensor, 2020, V. 20(9), pp. 1–13, DOI:10.3390/s20092700.

11. Mallat S.G., A wavelet tour of signal processing, Academic Press, 1999.

12. Preston F.W. Henderson J., Fourier series characterization of cyclic sediments for stratigraphic correlation, Kansas Geological Survey, 1964, pp. 415–425.

13. Gutman I.S., Kuznetsova G.P., Saakyan M.I., Detailed correlation of drill sections with the help of the “AUTOCORR” program complex (In Russ.), Geoinformatika, 2009, no. 2, pp. 86–97.