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Volume 70, Issue 4
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Cumulative oil predictions are made from stochastically inverted earth attributes. The inverted attributes are from the SEAM Life of Field model and an offshore field in West Africa. To perform the prediction, we use a naïve Bayesian classifier for its transparency in methodology, computational efficiency and flexibility. The inverted properties we use for classification consist of porosity, P‐S wave velocity ratio (/), acoustic impedance and density which are extracted from within a set radius around the well‐path. The production from the wells in both SEAM and the West African field serve as the labels for the naïve Bayesian classifier, which are ultimately placed into two classes: the ‘high’ and ‘low’ producers. For calculating the accuracy of the classifier, we perform full cross‐validation with a set number of training wells. The cross‐validation accuracy is 78.0% and 83.3% for the SEAM model and the West African Field respectively. The classifier's sensitivity to the chosen radius around the wells and its inherent independence assumption are investigated and shown to change the bulk accuracy by less than 3.0%. We implement full reservoir classifications on SEAM to highlight potential drilling locations and show the capability of the classifier. Overall, we demonstrate how the naïve Bayesian classifier can efficiently and transparently synthesize the entire distribution of stochastic inversions of seismic data to predict oil production.

© 2022 European Association of Geoscientists & Engineers © 2022 European Association of Geoscientists & Engineers
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/content/journals/10.1111/1365-2478.13190
2022-04-14
2024-04-26

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References

  1. Bize‐Forest, N., Lima, L., Baines, V., Boyd, A., Abbots, F. & Barnett, A. Using Machine‐Learning for Depositional Facies Prediction in a Complex Carbonate Reservoir, SPWLA‐2018‐NNN, presented at SPWLA 59th Annual Logging Symposium, London, UK, June 2018.
  2. Chen, J. and Hoversten, G.M. (2012) Joint inversion of marine seismic AVA and CSEM data using statistical rock‐physics models and Markov random fields. Geophysics, 77, R65–R80.
     [Google Scholar]
  3. Contreras, A., Gerhardt, A., Spaans, P. and Docherty, M. (2020) Characterization of fluvio‐deltaic gas reservoirs through AVA deterministic, stochastic, and wave‐equation‐based seismic inversion: A case study from the Carnarvon Basin, Western Australia. The Leading Edge, 39(2), 92–101.
     [Google Scholar]
  4. Eberhardt, J.J. (2015) Bayesian spam detection. Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal, 2(1), 2.
     [Google Scholar]
  5. Feng, H. and Bancroft, J.C. (2006) AVO principles, processing and inversion. CREWES Research Report, 18, 1–19.
     [Google Scholar]
  6. Feng, X., Li, S., Yuan, C., Zeng, P. and Sun, Y. (2018) Prediction of slope stability using naïve Bayes classifier. KSCE Journal of Civil Engineering, 22(3), 941–950.
     [Google Scholar]
  7. Hampson, D.P., Russell, B.H. and Bankhead, B. (2005) Simultaneous inversion of pre‐stack seismic data. In SEG Technical Program Expanded Abstracts 2005 . Society of Exploration Geophysicists, pp. 1633–1637.
  8. Hoversten, G., Royle, A., Chen, J. and Myer, D. (2017) MCMC inversion of offshore West Africa AVA data. In 79th EAGE Conference and Exhibition 2017. EAGE, Vol. 2017, pp. 1–5.
  9. Knott, C. (1899) Reflection and refraction of elastic waves with seismological applications. Philosophical Magazine, 48, 64–97.
     [Google Scholar]
  10. Li, Y. (2005) Facies identification from well logs: A comparison of discriminant analysis and naÃŕve Bayes classifier. Journal of Petroleum Science and Engineering, 53, 149–157.
     [Google Scholar]
  11. Mosteller, F. and Tukey, J.W. (1968) Data analysis, including statistics. Handbook of Social Psychology, 2, 80–203.
     [Google Scholar]
  12. Mukherjee, S. and Sharma, N. (2012) Intrusion detection using naïve Bayes classifier with feature reduction. Procedia Technology, 4, 119–128.
     [Google Scholar]
  13. Oliver, D.S., Fossum, K., Bhakta, T., Sandø, I., Nævdal, G. and Lorentzen, R.J. (2021) 4D seismic history matching. Journal of Petroleum Science and Engineering, 207, 109119. https://doi.org/10.1016/j.petrol.2021.109119.
     [Google Scholar]
  14. Ostrander, W. (1984) Plane‐wave reflection coefficients for gas sands at nonnormal angles of incidence. Geophysics, 49(10), 1637–1648.
     [Google Scholar]
  15. Rish, I. (2001) An empirical study of the naive Bayes classifier. Empirical Methods in Artificial Intelligence Workshop, IJCAI. New York: IBM, pp. 41–46. Available at: http://www.cc.gatech.edu/home/isbell/classes/reading/papers/Rish.pdf. Accessed November 2018.
     [Google Scholar]
  16. Rutherford, S.R. and Williams, R.H. (1989) Amplitude versus offset variations in gas sands. Geophysics, 54, 680–688.
     [Google Scholar]
  17. SEG (2015) Life of field project prospectus. Society of Exploration Geophysics.
  18. Sharma, A., Srinivasan, S. and Lake, L. (2010) Classification of oil and gas reservoirs based on recovery factor: A data‐mining approach. SPE‐130257‐MS. In SPE Annual Technical Conference and Exhibition, Florence, Italy, 2010. SPE.
  19. Simmons and Backus (1996) Waveform based AVO inversion and AVO predictionerror. Geophysics, 61(6), 1575–1588. https://doi.org/10.1190/1.1444077.
     [Google Scholar]
  20. Stuart, G.K., Minkoff, S.E. and Pereira, F. (2019) A two‐stage Markov chain Monte Carlo method for seismic inversion and uncertainty quantification. Geophysics, 84(6), R1003–R1020.
     [Google Scholar]
  21. Tarantola, A. (2005) Inverse Problem Theory and Methods for Model Parameter Estimation. Philadelphia: SIAM.
     [Google Scholar]
  22. Trainor‐Guitton, W.J., Hoversten, G.M., Ramirez, A., Roberts, J., Juliusson, E., Key, K. and Mellors, R. (2014) The value of spatial information for determining well placement: a geothermal example. Geophysics, 79(5), W27–W41.
     [Google Scholar]
  23. Wei, W., Visweswaran, S. and Cooper, G.F. (2011) The application of naive Bayes model averaging to predict Alzheimer's disease from genome‐wide data. Journal of the American Medical Informatics Association, 18(4), 370–375.
     [Google Scholar]
  24. Zoeppritz, K. (1919) Erdbebenwellen, On the reflection and penetration of seismic waves through unstable layers. Nachrichte, 48(7B), 66–84.
     [Google Scholar]
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Naïve Bayesian classification of cumulative oil production from stochastic amplitude variation with angle inversion attributes: As applied to SEAM and a West Africa Field
Geophysical Prospecting 70, 801 (2022); https://doi.org/10.1111/1365-2478.13190
/content/journals/10.1111/1365-2478.13190
/content/journals/10.1111/1365-2478.13190
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  • Article Type: Research Article
Keyword(s): AVA; Bayes; Production; Statistical Learning

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