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Volume 67 Number 1
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Amplitude interpretation for hydrocarbon prediction is an important task in the oil and gas industry. Seismic amplitude is dominated by porosity, the volume of clay, pore‐filled fluid type and lithology. A few seismic attributes are proposed to predict the existence of hydrocarbon. This paper proposes a new fluid factor by adding a correct item based on the J attribute. The algorithm is verified through stochastic Monte Carlo modelling that contains various rock physical properties of sand and shale. Both gas and oil responses are separated by the new fluid factor. Furthermore, an approach based on the neural network model is trained using the deep learning method to predict the new fluid factor. The confusion matrix shows that this model performs well. This model allows the application of the new fluid factor in the seismic data. In this study, the Marmousi II data set is used to examine the performance of the new fluid factor, and the result is good. Most hydrocarbon reservoirs are identified in the shale–sandstone sequences. The combination of deep learning and the new fluid factor provides a more accurate way for hydrocarbon prediction.

© 2019 European Association of Geoscientists & Engineers © 2018 European Association of Geoscientists & Engineers
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/content/journals/10.1111/1365-2478.12712
2018-11-26
2024-04-19

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References

  1. AvsethP., MukerjiT. and MavkoG.2010. Quantitative Seismic Interpretation: Applying Rock Physics Tools to Reduce Interpretation Risk. Cambridge University Press.
     [Google Scholar]
  2. BiotM.A.1956. Theory of propagation of elastic waves in a fluid‐saturated porous solid. I. Low frequency range. Journal of the Acoustical Society of America, 28, 168–178.
     [Google Scholar]
  3. CastagnaJ.P., BatzleM.L. and EastwoodR.L.1985. Relationships between compressional‐wave and shear‐wave velocities in clastic silicate rocks. Geophysics, 50, 571–581.
     [Google Scholar]
  4. ChopraS. and MarfurtK.J.2007. Seismic Attributes for Prospect Identification and Reservoir Characterization, Vol. 11. Tulsa, OK: Society of Exploration Geophysicists.
     [Google Scholar]
  5. FattiJ.L., SmithG.C., VailP.J., StraussP.J. and LevittP.R.1994. Detection of gas in sandstone reservoirs using AVO analysis: a 3‐D seismic case history using the Geostack technique. Geophysics, 59, 1362–1376.
     [Google Scholar]
  6. FawcettT.2006. An introduction to ROC analysis. Pattern Recognition Letters, 27, 861–874.
     [Google Scholar]
  7. GassmannF.1951. Über die Elastizität poröser Medien. Vierteljahrs‐schrift der Naturforschenden Gesellschaft in Zürich, 96, 1–23.
     [Google Scholar]
  8. LeCunY., BengioY. and HintonG.2015. Deep learning. Nature, 521, 436–444.
     [Google Scholar]
  9. LiuC. and GhoshD.2017. A new seismic attribute for ambiguity reduction in hydrocarbon prediction. Geophysical Prospecting, 65, 229–239.
     [Google Scholar]
  10. MartinG.S., WileyR. and MarfurtK.J.2006. Marmousi2: An elastic upgrade for Marmousi. Leading Edge, 25, 156–166.
     [Google Scholar]
  11. MavkoG., MukerjiT. and DvorkinJ.2009. The Rock Physics Handbook: Tools for Seismic Analysis of Porous Media. Cambridge University Press.
     [Google Scholar]
  12. ShueyR.T.1985. A simplification of the Zoeppritz equations. Geophysics, 50, 609–614.
     [Google Scholar]
  13. SmithG.C. and GidlowP.M. (1987). Weighted stacking for rock property estimation and detection of gas. Geophysical Prospecting, 35, 993–1014.
     [Google Scholar]
  14. ZellA.1994. Simulation Neuronaler Netze, Vol. 1.Bonn: Addison‐Wesley.
     [Google Scholar]
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A new fluid factor and its application using a deep learning approach
Geophysical Prospecting 67, 140 (2019); https://doi.org/10.1111/1365-2478.12712
/content/journals/10.1111/1365-2478.12712
/content/journals/10.1111/1365-2478.12712
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  • Article Type: Research Article
Keyword(s): Attributes; AVO; Hydrocarbons; Neural network

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