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Determination of the optimal filtering parameters to find close associations of seismic inversion attributes

UDK: 550.834.01
DOI: 10.24887/0028-2448-2020-4-27-29
Key words: seismic attribute associations, frequency range, matrix of the Pearson correlation coefficients, total weighted correlation matrix
Authors: A.V. Novoyavchev (Moscow State University Seismic Data Analysis Center LLC, RF, Moscow), A.A. Kleimenov (Moscow State University Seismic Data Analysis Center LLC, RF, Moscow), M.Yu. Tokarev (Moscow State University Seismic Data Analysis Center LLC, RF, Moscow), K.M. Myatchin (Moscow State University Seismic Data Analysis Center LLC, RF, Moscow)

Seismic survey data is the basis for geologic modeling and reservoir characterization since they are most evenly and relatively tightly distributed in the zone of interest. The use of modern computational methods in the interpretation process generates a huge amount of secondary data referred to as seismic attributes. The total volume of this data may be hundreds of times greater than the amount of post-processing data. Attributes hide large potentially useful components. Attributes help to more accurately outline faults, selvages, fractured zones, lithological facies and etc. Some of the most useful attributes are the elastic parameters models of the rocks obtained as a result of inverse computational methods. As a result of further mathematical transformations of these models, together with petrophysical models, experts can obtain models of useful engineering and exploration parameters. A huge number of attributes as well as their hidden linear and non-linear dependencies create the Big Data problem. In this article, we propose methods for searching for associations and the corresponding optimal filtering ranges (bandwidth or statistical) of attributes that significantly reduce future computational costs. Despite the fact that the algorithms are quite demanding on computing resources, their efficiency over time can be significantly increased through the use of parallelization methods.

References

1. Yunsong Huang, Full waveform inversion with multisource frequency selection for marine-streamer or land-streamer data, LAP Lambert, 2017, 116 p.

2. Pisupati P.B., Seismic waveform inversion, Geophysical Journal International, 2017, pp. 1076–1092.

3. Chopra S., Castagna J.P., AVO, Tulsa, Oklahoma, USA: Society of Exploration Geophysics, 2014, 304 р., https://doi.org/10.1190/1.9781560803201

4. Buland A., Kolbjornsen A., Omre H., Rapid spatially coupled AVO inversion in the Fourier domain, Geophysics, 2003, V. 68 (3), pp. 824–836.

5. Francis A. Understanding stochastic inversion. Part 1, First Break, 2006, V. 24, pp. 69–77.

6. Francis A., Limitations of deterministic and advantages of stochastic seismic inversion, Canadian Society of Exploration Geophysicists, 2005, V. 2, pp. 1–12.

7. Andrieu C.A., Djuric P.M., Doucet A., Model selection by MCMC computation,  Signal Processing, 2001, V. 81, pp. 19–37.

8. Brooks S.P., Markov chain Monte Carlo and its application, Journal of the Royal Statistical Society. Series D (The Statistician), 1998, V. 47, no. 1, pp. 69–100.

9. Kemper M.A.C., Waters K., Somoza A. et al., Introducing Ji-Fi – Joint impedance & facies inversion, Proceedings of 6th EAGE Saint Petersburg International Conference and Exhibition, 2014, https://doi.org/10.3997/2214-4609.20140151

10. Colombo D., De Stefano M., Geophysical modeling via simultaneous joint inversion of seismic, gravity and electromagnetic data: application to prestack depthimaging, The Leading Edge, 2007, March, pp. 326–331.

11. Kubyshta I.I., Pavlovskiy Yu.V., Emel'yanov P.P., Efficient 3D seismic inversion technologies as a basis for creating and updating geoseismic model of the Vendian deposits (in terms of Eastern Siberia oil-and-gas fields) (In Russ.), PROneft', 2016, no. 1, pp. 27–37.

12.  Ampilov Yu.P., Barkov A.Yu., Yakovlev I.V. et al., Almost everything about the seismic inversion. Part 1 (In Russ.), Tekhnologii seysmorazvedki, 2009, no. 4, pp. 3–16.

13. Eremin N.A., Kondratyuk A.T., Eremin Al. N., About the hydrocarbon resource base in Russian Arctic shelf (In Russ.), URL: http://www.http://oilgasjournal.ru/2009-1/3-rubric/eremin.pdf

14. Kuznetsov V.G., Shcherbich N.E., Sazonov A.I., Kuz'menko S.E., Osobennosti bureniya skvazhin na arkticheskom shel'fe (Features of drilling on the Arctic shelf), Tyumen': TSPU, 2016, 52 p.

15. Castagna J.P., Bazle M.L., Kan T.K., Rock physics – The link between rock properties and AVO response: edited by Castagnaand J.P., Backus M.M., In: Off-set-dependent reflectivity, Theory and practice of AVO analysis, Soc. Expl. Geophys., 1993, pp. 135–171.

16. Sakhautdinov I.R., Vakhitova G.R., Analysis of the results of the restoration and correction of the density properties of rocks (In Russ.), Vestnik Bashkirskogo universiteta, 2018, V. 23, no. 2, pp. 299–304.

17. Vedernikov G.V., Maksimov L.A., Chernyshova T.I., Prognoz zalezhey uglevodorodov po kharakteristikam mikroseysm (Prediction of hydrocarbon deposits by microseismic characteristics), URL: http://geovers.com/base/files/gr11/papers/15_Vedernikov_GV.pdf

18. Voronov M.V., Pimenov V.I., Suzdalov E.G., Prikladnaya matematika: tekhnologii primeneniya (Applied mathematics: Application technologies), Moscow: Yurayt Publ. 2017, 168 p.

19. Anderson T.W., The statistical analysis of time series, Wiley-Interscience, Hoboken, NJ, 1994.

20. Höppner F., Kruse R., Klawonn F., Runkler T., Fuzzy cluster analysis: Methods for classification, Wiley, 1999, 289 p.

21. Patrascu V., A generalization of Gustafson-Kessel algorithm using a new constraint parameter, Proceedings of the Joint 4th Conference of the European Society for Fuzzy Logic and Technology and the 11th Rencontres Francophones sur la Logique Floue et ses Applications, Barcelona, Spain, 7–9 September 2005, pp. 1250–1255. 

Seismic survey data is the basis for geologic modeling and reservoir characterization since they are most evenly and relatively tightly distributed in the zone of interest. The use of modern computational methods in the interpretation process generates a huge amount of secondary data referred to as seismic attributes. The total volume of this data may be hundreds of times greater than the amount of post-processing data. Attributes hide large potentially useful components. Attributes help to more accurately outline faults, selvages, fractured zones, lithological facies and etc. Some of the most useful attributes are the elastic parameters models of the rocks obtained as a result of inverse computational methods. As a result of further mathematical transformations of these models, together with petrophysical models, experts can obtain models of useful engineering and exploration parameters. A huge number of attributes as well as their hidden linear and non-linear dependencies create the Big Data problem. In this article, we propose methods for searching for associations and the corresponding optimal filtering ranges (bandwidth or statistical) of attributes that significantly reduce future computational costs. Despite the fact that the algorithms are quite demanding on computing resources, their efficiency over time can be significantly increased through the use of parallelization methods.

References

1. Yunsong Huang, Full waveform inversion with multisource frequency selection for marine-streamer or land-streamer data, LAP Lambert, 2017, 116 p.

2. Pisupati P.B., Seismic waveform inversion, Geophysical Journal International, 2017, pp. 1076–1092.

3. Chopra S., Castagna J.P., AVO, Tulsa, Oklahoma, USA: Society of Exploration Geophysics, 2014, 304 р., https://doi.org/10.1190/1.9781560803201

4. Buland A., Kolbjornsen A., Omre H., Rapid spatially coupled AVO inversion in the Fourier domain, Geophysics, 2003, V. 68 (3), pp. 824–836.

5. Francis A. Understanding stochastic inversion. Part 1, First Break, 2006, V. 24, pp. 69–77.

6. Francis A., Limitations of deterministic and advantages of stochastic seismic inversion, Canadian Society of Exploration Geophysicists, 2005, V. 2, pp. 1–12.

7. Andrieu C.A., Djuric P.M., Doucet A., Model selection by MCMC computation,  Signal Processing, 2001, V. 81, pp. 19–37.

8. Brooks S.P., Markov chain Monte Carlo and its application, Journal of the Royal Statistical Society. Series D (The Statistician), 1998, V. 47, no. 1, pp. 69–100.

9. Kemper M.A.C., Waters K., Somoza A. et al., Introducing Ji-Fi – Joint impedance & facies inversion, Proceedings of 6th EAGE Saint Petersburg International Conference and Exhibition, 2014, https://doi.org/10.3997/2214-4609.20140151

10. Colombo D., De Stefano M., Geophysical modeling via simultaneous joint inversion of seismic, gravity and electromagnetic data: application to prestack depthimaging, The Leading Edge, 2007, March, pp. 326–331.

11. Kubyshta I.I., Pavlovskiy Yu.V., Emel'yanov P.P., Efficient 3D seismic inversion technologies as a basis for creating and updating geoseismic model of the Vendian deposits (in terms of Eastern Siberia oil-and-gas fields) (In Russ.), PROneft', 2016, no. 1, pp. 27–37.

12.  Ampilov Yu.P., Barkov A.Yu., Yakovlev I.V. et al., Almost everything about the seismic inversion. Part 1 (In Russ.), Tekhnologii seysmorazvedki, 2009, no. 4, pp. 3–16.

13. Eremin N.A., Kondratyuk A.T., Eremin Al. N., About the hydrocarbon resource base in Russian Arctic shelf (In Russ.), URL: http://www.http://oilgasjournal.ru/2009-1/3-rubric/eremin.pdf

14. Kuznetsov V.G., Shcherbich N.E., Sazonov A.I., Kuz'menko S.E., Osobennosti bureniya skvazhin na arkticheskom shel'fe (Features of drilling on the Arctic shelf), Tyumen': TSPU, 2016, 52 p.

15. Castagna J.P., Bazle M.L., Kan T.K., Rock physics – The link between rock properties and AVO response: edited by Castagnaand J.P., Backus M.M., In: Off-set-dependent reflectivity, Theory and practice of AVO analysis, Soc. Expl. Geophys., 1993, pp. 135–171.

16. Sakhautdinov I.R., Vakhitova G.R., Analysis of the results of the restoration and correction of the density properties of rocks (In Russ.), Vestnik Bashkirskogo universiteta, 2018, V. 23, no. 2, pp. 299–304.

17. Vedernikov G.V., Maksimov L.A., Chernyshova T.I., Prognoz zalezhey uglevodorodov po kharakteristikam mikroseysm (Prediction of hydrocarbon deposits by microseismic characteristics), URL: http://geovers.com/base/files/gr11/papers/15_Vedernikov_GV.pdf

18. Voronov M.V., Pimenov V.I., Suzdalov E.G., Prikladnaya matematika: tekhnologii primeneniya (Applied mathematics: Application technologies), Moscow: Yurayt Publ. 2017, 168 p.

19. Anderson T.W., The statistical analysis of time series, Wiley-Interscience, Hoboken, NJ, 1994.

20. Höppner F., Kruse R., Klawonn F., Runkler T., Fuzzy cluster analysis: Methods for classification, Wiley, 1999, 289 p.

21. Patrascu V., A generalization of Gustafson-Kessel algorithm using a new constraint parameter, Proceedings of the Joint 4th Conference of the European Society for Fuzzy Logic and Technology and the 11th Rencontres Francophones sur la Logique Floue et ses Applications, Barcelona, Spain, 7–9 September 2005, pp. 1250–1255. 


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