1887
Volume 65, Issue S1
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Seismic facies analysis is a well‐established technique in the workflow followed by seismic interpreters. Typically, huge volumes of seismic data are scanned to derive maps of interesting features and find particular patterns, correlating them with the subsurface lithology and the lateral changes in the reservoir. In this paper, we show how seismic facies analysis can be accomplished in an effective and complementary way to the usual one. Our idea is to translate the seismic data in the musical domain through a process called , mainly based on a very accurate time–frequency analysis of the original seismic signals. From these sonified seismic data, we extract several original musical attributes for seismic facies analysis, and we show that they can capture and explain underlying stratigraphic and structural features. Moreover, we introduce a complete workflow for seismic facies analysis starting exclusively from musical attributes, based on state‐of‐the‐art machine learning computational techniques applied to the classification of the aforementioned musical attributes. We apply this workflow to two case studies: a sub‐salt two‐dimensional seismic section and a three‐dimensional seismic cube. Seismic facies analysis through musical attributes proves to be very useful in enhancing the interpretation of complicated structural features and in anticipating the presence of hydrocarbon‐bearing layers.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.12504
2017-12-26
2024-03-28
Loading full text...

Full text loading...

References

  1. BarrassS. and ZehnerB.2000. Responsive sonification of well‐logs. International Conference on Auditory Display, Atlanta, GA.
    [Google Scholar]
  2. BrownA.R.1999. Interpretation of Three‐Dimensional Seismic Data, 5th edn. Society of Exploration Geophysicists.
    [Google Scholar]
  3. CamboropoulosE.1998. Towards a general computational theory of musical structures. PhD thesis, The University of Edinburgh, UK.
  4. ChopraS. and MarfurtK.J.2014. Seismic facies analysis using generative topographic mapping. 84th SEG annual meeting, Denver, USA, Expanded Abstracts, 1390–1394.
  5. ColéouT., PouponM. and AzbelK.2003. Interpreter's corner—Unsupervised seismic facies classification: a review and comparison of techniques and implementation. The Leading Edge22(10), 942–953.
    [Google Scholar]
  6. DaviesD.L. and BouldinD.W.1979. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence1, 224–227.
    [Google Scholar]
  7. Dell'AversanaP.2014. A bridge between geophysics and digital music—Applications to hydrocarbon exploration. First Break32(5), 51–56.
    [Google Scholar]
  8. Dell'AversanaP., GabbrielliniG. and AmendolaA.2014. Seismic data analysis using digital music technology—Applications in hydrocarbon exploration. 76th EAGE annual meeting, Amsterdam, The Netherlands, Expanded Abstracts.
  9. Dell'AversanaP., GabbrielliniG. and AmendolaA.2016a. Sonification of geophysical data: theory and examples. Geophysical Prospecting.
    [Google Scholar]
  10. Dell'AversanaP., GabbrielliniG., MariniA.J. and AmendolaA.2016b. Application of music information retrieval (MIR) techniques to seismic facies classification. Examples in hydrocarbon exploration. AIMS Geosciences2(4), 413–425.
    [Google Scholar]
  11. DumayJ. and FournierF.1988. Multivariate statistical analysis applied to seismic facies recognition. Geophysics53, 1151–1159.
    [Google Scholar]
  12. FournierF. and DerainJ.F.1995. A statistical methodology for deriving reservoir properties from seismic data. Geophysics60, 1437–1450.
    [Google Scholar]
  13. JohannP.R.S., CastroD.D. and BarrosoA.S.2001. Reservoir geophysics: seismic pattern recognition applied to ultra‐deepwater oilfield in Campos basin, offshore Brazil. SPE Latin America and Caribbean Petroleum Engineering Conference, Buenos Aires, Argentina.
  14. KnopoffL. and HutchinsonW.1983. Entropy as a measure of style: the influence of sample length. Journal of Music Theory27, 75–97.
    [Google Scholar]
  15. KohonenT.2001. Self Organizing Maps, 3rd edn. Springer‐Verlag.
    [Google Scholar]
  16. KrishnaK. and NarasimhaM.1999. Genetic K‐means algorithm. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics29, 433–439.
    [Google Scholar]
  17. LiJ. and CastagnaJ.2004. Support vector machine pattern recognition to AVO classification. Geophysical Research Letters31(2), L02609.
    [Google Scholar]
  18. MarfurtK.J.2014. Seismic attributes and the road ahead. 84th SEG annual meeting, Denver, USA, Expanded Abstracts, 4421–4426.
  19. MatosM.C., OsorioP.L.M. and JohannP.R.S.2003. Unsupervised seismic reservoir characterization using wavelet transform and self‐organizing maps of a deep‐water field, Campos basin, offshore Brazil. 73rd SEG annual meeting, Dallas, USA, Expanded Abstracts, 1458–1461.
  20. MatosM.C., OsorioP.L.M. and JohannP.R.S.2004a. Unsupervised seismic facies classification using matching pursuit and self‐organizing maps. 66th EAGE annual conference, Paris, France, Expanded Abstracts, C016.
  21. MatosM.C., OsorioP.L.M. and JohannP.R.S.2004b. Using matching pursuit and self‐organizing maps for seismic reservoir characterization of a deep‐water field, Campos basin, offshore Brazil. 74th SEG annual meeting, Denver, USA, Expanded Abstracts, 1611–1614.
  22. MatosM.C., OsorioP.L.M. and JohannP.R.S.2007. Unsupervised seismic facies analysis using wavelet transform and self‐organizing maps. Geophysics72(1), P9–P21.
    [Google Scholar]
  23. MeudicB. and St‐JamesE.2004. Automatic extraction of approximate repetitions in polyphonic MIDI files based on perceptive criteria. In: Computer Music Modeling and Retrieval, Lecture Notes in Computer Science, Vol. 2771 (ed. U.K.Wiil), pp. 124–142. Springer.
    [Google Scholar]
  24. MIDI 1.0.1995. Detailed Specification, Document Version 4.2, The MIDI Manufacturers Association, Los Angeles, CA.
  25. MitchumJr.R.M.1977. Seismic stratigraphy and global changes of sea level. Part 11: Glossary of terms used in seismic stratigraphy. In: Seismic Stratigraphy—Application to Hydrocarbon Exploration (ed. C.E.Peyton), pp. 205–212. American Association of Petroleum Geologists Memoir 26.
    [Google Scholar]
  26. OrioN.2006. Music retrieval: a tutorial and review. Foundations and Trends in Information Retrieval1(1), 1–90.
    [Google Scholar]
  27. OsorioP.L.M., MatosM.C. and JohannP.R.S.2003. Using wavelet transform and self‐organizing maps for seismic reservoir characterization of a deep‐water field, Campos basin, offshore Brazil. 65th EAGE annual conference, Stavanger, Norway, Expanded Abstracts, B29.
  28. ParkH.‐S. and JunC.‐H.2009. A simple and fast algorithm for K‐medoids clustering. Expert Systems with Applications36(2), 3336–3341.
    [Google Scholar]
  29. Ponce de LeónP.J., Pérez‐SanchoC. and IñestaJ.M.2004. A shallow description framework for musical style recognition. In: Structural, Syntactic, and Statistical Pattern Recognition, Lecture Notes in Computer Science, Vol. 3138 (eds. F.A.Caelli, R.P.W.Duin, A.Campilho and D.de Ridder), pp. 876–884. Springer.
    [Google Scholar]
  30. QuinteroG.2013. Sonification of oil and gas wireline well logs. Proceedings of the 19th International Conference on Auditory Display, Lodz, Poland, July 2013.
    [Google Scholar]
  31. RankeyE.C. and MitchellJ.C.2003. Interpreter's corner—That's why it's called interpretation: impact of horizon uncertainty on seismic attribute analysis. The Leading Edge22(9), 820–828.
    [Google Scholar]
  32. RonenS., SchultzP., HattoriM. and CorbertC.1994. Seismic‐guided estimation of log properties (Part 2: using artificial neural networks for nonlinear attribute calibration). The Leading Edge13(6), 674–678.
    [Google Scholar]
  33. RoyA., MatosM.C. and MarfurtK.J.2010. Automatic seismic facies classification with kohonen self organizing maps—A tutorial. Geohorizons Journal of Society of Petroleum Geophysicists, 6–14.
    [Google Scholar]
  34. RoyA., MarfurtK.J. and MatosM.C.2010. Applying self‐organizing maps of multiple attributes, an example from the Red‐Fork Formation, Anadarko Basin. 80th SEG annual meeting, Denver, USA, Expanded Abstracts, 1591–1595.
  35. RoyA., Romero‐PeláezS., KwiatkowskiT.J. and MarfurtK.J.2014. Generative topographic mapping for seismic facies estimation of a carbonate wash, Veracruz Basin, southern Mexico. Interpretation2(1), SA31–SA47.
    [Google Scholar]
  36. RoyA. and MarfurtK.J.2011. Cluster assisted 3D and 2D unsupervised seismic facies analysis, an example from the Barnett Shale Formation in the Fort Worth Basin, Texas. 81st SEG annual meeting, San Antonio, USA, Expanded Abstracts, 1734–1738.
  37. SaggafM.M., ToksözM.N. and MarhoonM.I.2003. Seismic facies classification and identification by competitive neural networks. Geophysics68, 1984–1999.
    [Google Scholar]
  38. SammutC. and WebbG.I.2010. Encyclopedia of Machine Learning. Springer.
    [Google Scholar]
  39. SaraswatP. and SenK.M.2012. Artificial immune‐based self‐organizing maps for seismic facies analysis. Geophysics77(4), O45–O53.
    [Google Scholar]
  40. SaueS.2000. A model for interaction in exploratory sonification display. Proceedings of the 6th International Conference on Auditory Display, Atlanta, GA, April 2000.
    [Google Scholar]
  41. ShannonC.E. and WeaverW.1949. The Mathematical Theory of Communication. University ofIllinois Press.
    [Google Scholar]
  42. StockwellR.G., MansinhaL. and LoweR.P.1996. Localization of the complex spectrum: the S transform. IEEE Transactions on Signal Processing44(4), 998–1001.
    [Google Scholar]
  43. TanerM.T., WallsJ.D., SmithM., TaylorG., CarrM.B. and DumasD.2001. Reservoir characterization by calibration of self‐organized map clusters. 71st SEG annual meeting, San Antonio, USA, Expanded Abstracts, 1552–1555.
  44. WangA.2003. An industrial‐strength Audio Search Algorithm. Proceedings of the 4th International Conference on Music Information Retrieval, Baltimore, MD, October 2003.
    [Google Scholar]
  45. ZhangL., QuierenJ. and SchuelkeJ.2001. Self‐organizing map (SOM) network for tracking horizons and classifying seismic traces. In: Computational Neural Networks for Geophysical Data Processing, Handbook of Geophysical Exploration: Seismic Exploration, Vol. 30 (ed. M.M.Poulton), pp. 155–170. Pergamon Press, Inc.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.12504
Loading
/content/journals/10.1111/1365-2478.12504
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Interpretation; Seismics; Signal Processing

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error