Advertisement

Qualitative and quantitative comparison of geostatistical techniques of porosity prediction from the seismic and logging data: a case study from the Blackfoot Field, Alberta, Canada

  • S. P. Maurya
  • K. H. Singh
  • N. P. Singh
Original Research Paper
  • 97 Downloads

Abstract

In present study, three recently developed geostatistical methods, single attribute analysis, multi-attribute analysis and probabilistic neural network algorithm have been used to predict porosity in inter well region for Blackfoot field, Alberta, Canada, an offshore oil field. These techniques make use of seismic attributes, generated by model based inversion and colored inversion techniques. The principle objective of the study is to find the suitable combination of seismic inversion and geostatistical techniques to predict porosity and identification of prospective zones in 3D seismic volume. The porosity estimated from these geostatistical approaches is corroborated with the well log porosity. The results suggest that all the three implemented geostatistical methods are efficient and reliable to predict the porosity but the multi-attribute and probabilistic neural network analysis provide more accurate and high resolution porosity sections. A low impedance (6000–8000 m/s g/cc) and high porosity (> 15%) zone is interpreted from inverted impedance and porosity sections respectively between 1060 and 1075 ms time interval and is characterized as reservoir. The qualitative and quantitative results demonstrate that of all the employed geostatistical methods, the probabilistic neural network along with model based inversion is the most efficient method for predicting porosity in inter well region.

Keywords

Seismic inversion Model-based inversion Colored inversion Single attribute analysis Multi-attribute analysis Probabilistic neural network 

Notes

Acknowledgements

One of the author (S.P. Maurya) is indebted to Science and Engineering Research Board, Department of Science and Technology, New Delhi for financial supports in form of research project (Grant No. PDF/2016/000888) under National Post-doctoral Fellowship scheme. Authors also acknowledge the CGG Veritas for providing seismic and well log data of Blackfoot field, Alberta, Canada.

References

  1. Ansari HR (2014) Use of seismic colored inversion and power law committee machine based on imperial competitive algorithm for improving porosity prediction in a heterogeneous reservoir. J Appl Geophys 108:61–68CrossRefGoogle Scholar
  2. Bhatt A, Hell HB (2002) Committee neural networks for porosity and permeability prediction from well logs. Geophys Prospect 50(6):645–660CrossRefGoogle Scholar
  3. Bosch M, Mukerji T, Gonzalez EF (2010) Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: a review. Geophysics 75(5):75A165–75A176CrossRefGoogle Scholar
  4. Brossier R, Operto S, Virieux J (2015) Velocity model building from seismic reflection data by full-waveform inversion. Geophys Prospect 63(2):354–367CrossRefGoogle Scholar
  5. Brown AR (2004) Interpretation of three-dimensional seismic data. AAPG Tulsa OK 6(42):514Google Scholar
  6. Chambers RL, Yarus JM (2002) Quantitative use of seismic attributes for reservoir characterization. CSEG Rec 27(6):14–25Google Scholar
  7. Chen Q, Sidney S (1997) Seismic attribute technology for reservoir forecasting and monitoring. Lead Edge 16(5):445–448CrossRefGoogle Scholar
  8. Clochard V, Delépine N, Labat K, Ricarte P (2009) January. Post-stack versus pre-stack stratigraphic inversion for CO2 monitoring purposes: a case study for the saline aquifer of the Sleipner field. In SEG Annual Meeting. Society of Exploration GeophysicistsGoogle Scholar
  9. Daniel PH, James SS, John AQ (2001) Use of multi-attribute transforms to predict log properties from seismic data. SEG 66(1):220–236Google Scholar
  10. Doyen PM (1988) Porosity from seismic data: a geostatistical approach. Geophysics 53(10):1263–1275CrossRefGoogle Scholar
  11. Eskandari H, Rezaee MR, Mohammadnia M (2004) Application of multiple regression and artificial neural network techniques to predict shear wave velocity from well log data for a carbonate reservoir, south-west Iran. CSEG Rec 29(7):42–48Google Scholar
  12. Gardner GHF, Gardner LW, Gregory AR (1974) Formation velocity and density—the diagnostic basics for stratigraphic traps. Geophysics 39(6):770–780CrossRefGoogle Scholar
  13. Hampson DP, Schuelke JS, Quirein JA (2001) Use of multi-attribute transforms to predict log properties from seismic data. Geophysics 66(1):220–236CrossRefGoogle Scholar
  14. Iturrarán VU, Parra JO (2014) Artificial neural networks applied to estimate permeability, porosity and intrinsic attenuation using seismic attributes and well-log data. J Appl Geophys 107:45–54CrossRefGoogle Scholar
  15. Lancaster S, Whitcombe D (2000) Fast-track “colored” inversion. SEG Expand Abstr 19:1572–1575Google Scholar
  16. Leiphart DJ, Hart BS (2001) Comparison of linear regression and a probabilistic neural network to predict porosity from 3-D seismic attributes in Lower Brushy Canyon channeled sandstones, southeast New Mexico. Geophysics 66(5):1349–1358CrossRefGoogle Scholar
  17. Leite EP, Vidal AC (2011) 3D porosity prediction from seismic inversion and neural networks. Comput Geosci 37(8):1174–1180CrossRefGoogle Scholar
  18. Mallick S (1995) Model-based inversion of amplitude-variations-with-offset data using a genetic algorithm. Geophysics 60(4):939–954CrossRefGoogle Scholar
  19. Matlab and Statistics Toolbox Release 2015b, The MathWorks, Inc., Natick, Massachusetts, United StatesGoogle Scholar
  20. Maurya SP, Sarkar P (2016) Comparison of post stack seismic inversion methods: a case study from Blackfoot Field. Canada IJSER 7(8):1091–1101Google Scholar
  21. Maurya SP, Singh KH (2015) LP and ML sparse spike inversion to characterize reservoir: a case study. 77th EAGE Conference and Exhibition, Madrid, Spain.  https://doi.org/10.3997/2214-4609.201412822
  22. Maurya SP, Singh KH (2017) Band limited impedance inversion of Blackfoot field, Alberta, Canada. J Geophys 38(1):57–61Google Scholar
  23. Miller S (1996) Multicomponent seismic data interpretation: M.Sc. thesis, University of CalgaryGoogle Scholar
  24. Naeem M, El-Araby HM, Khalil MK, Jafri MK, Khan F (2015) Integrated study of seismic and well data for porosity estimation using multi-attribute transforms: a case study of Boonsville Field, Fort Worth Basin, Texas, USA. Arab J Geosci 8(10):8777–8793CrossRefGoogle Scholar
  25. Pendrel J (2006) Seismic inversion-still the best tool for reservoir characterization. CSEG Rec 26(1):5–12Google Scholar
  26. Pramanik AG et al (2004) Estimation of effective porosity using geostatistics and multi-attribute transforms: a case study. SEG 69(2):352–372Google Scholar
  27. Russell B (1988) Introduction to seismic inversion methods: The SEG Course Notes Series 2Google Scholar
  28. Russell B, Hampson D (1991) Comparison of post-stack seismic inversion methods, SEG Expanded Abstracts: 876–878Google Scholar
  29. Russell B, Dan H, Jim S, John Q (1997) Multi-attribute seismic analysis. Lead Edge 16(10):1439–1444CrossRefGoogle Scholar
  30. Simin V, Harrison MP, Lorentz GA (1996) Processing the Blackfoot 3C-3D seismic survey. CREWES Res Rep 8:39-1-39-11Google Scholar
  31. Singh V et al (2007) Neural networks and their applications in litho stratigraphic interpretation of seismic data for reservoir characterization. Lead Edge 26(10):1244–1260CrossRefGoogle Scholar
  32. Singh S, Kanli AI, Sevgen S (2016) A general approach for porosity estimation using artificial neural network method: a case study from Kansas gas field. Stud Geophys Geod 60(1):130–140CrossRefGoogle Scholar
  33. Swisi A (2009) Post-and pre-stack attribute analysis and inversion of Blackfoot 3D seismic dataset. MSc, Thesis, University of SaskatchewanGoogle Scholar
  34. Vestergaard PD, Mosegaard K (1991) Inversion of post-stack seismic data using simulated annealing. Geophys Prospect 39(5):613–624CrossRefGoogle Scholar
  35. Wood JM, Hopkins JC (1992) Traps associated with Paleo valleys and interfluves in an unconformity bounded sequence: lower Cretaceous Glauconitic member, Southern Alberta, Canada. AAPG Bull 76(6):904–926Google Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Geophysics, Institute of ScienceBanaras Hindu UniversityVaranasiIndia
  2. 2.Department of Earth SciencesIndian Institute of Technology BombayMumbaiIndia

Personalised recommendations