Estimation of effective porosity using geostatistics and multiattribute transforms: A case study

GEOPHYSICS

Volumn 69, Issue 2, 2021, Pages 352-372

  Pramanik, A G ^a   Singh, V ^a   Vig, Rajiv ^a   Srivastava, A K ^a   Tiwary, D N ^a  

^a ONGC   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC IMPEDANCE; HYDROCARBONS; LITHOLOGY; MATHEMATICAL MODELS; POROSITY; SEISMIC PROSPECTING; STATISTICS; STRATIGRAPHY; FOSSIL FUELS; GEOPHYSICAL PROSPECTING; IMAGING TECHNIQUES; MATHEMATICAL TRANSFORMATIONS; NEURAL NETWORKS; PETROLEUM PROSPECTING; PETROLEUM RESERVOIRS; POROUS MATERIALS; SAND; SEISMIC RESPONSE; SEISMIC WAVES; TARGET TRACKING; THREE DIMENSIONAL COMPUTER GRAPHICS;

GEOSTATISTICS; KALOL FORMATION; STRUCTURAL MAPPING; EXPLORATION AND DEVELOPMENT; GEOSTATISTICAL METHOD; GEOSTATISTICAL TECHNIQUES; MODEL BASED INVERSION; MULTI-LINEAR REGRESSION; POROSITY DISTRIBUTIONS; PROBABILISTIC NEURAL NETWORKS; RESERVOIR CHARACTERIZATION;

GEOPHYSICS; POROSITY;

EOCENE; HYDROCARBON EXPLORATION; LITHOLOGY; POROSITY; SEISMIC METHOD;

ASIA; CAMBAY BASIN; EURASIA; GUJARAT; INDIA; SOUTH ASIA;

EID: 84995355512     PISSN: 00168033     EISSN: None     Source Type: Journal    
DOI: 10.1190/1.1707054     Document Type: Article

References (44)

1. Anderson, J. K., 1996, Limitations of seismic inversion for porosity and pore fluid: Lesson from chalk reservoir characterization and exploration: 66th Ann, Internat. Mtg., Soc. Explo. Geophys., Expanded Abstracts, 309-312.
2. Angelier, G. P., and Carpi, R., 1982, Porosity prediction from seismic data: Geophys. Prosp., 30, 580-607.
3. Banchs, R. E., and Michelena, R. J., 2002, From 3D seismic attributes to pseudo-well log volumes using neural networks: Practical considerations. The Leading Edge, 21, 996-1001.
4. Biswas, S. K., 1987, Regional tectonic framework, structure and evolution of the western marginal basins of India: Tectonophysics, 135, 307-327.
5. Brac, J., Dequirez, P. Y., Herve, F., Jacques, C., Lailly, P., Richard, V., and Van Nhieu, D. T., 1988, Inversion with a priori information: An approach to integrated stratigraphic interpretation, in Sheriff, R. E., Ed., Reservoir geophysics: Soc. Expl. Geophys., 251-258.
6. Brown, A. R., 2001a, Data polarity for the interpreter: The Leading Edge, 20, 549.
7. Brown, A. R., 2001b, Understanding seismic attributes: Geophysics, 66, 47-48.
8. Chen, Q., and Sydney, S., 1997, Seismic attribute technology for reservoir forecasting and monitoring: The Leading Edge, 16, 445-456.
9. Connolly, P., 1999, Elastic impedance: The Leading Edge, 19, 438-452.
10. deBuyl, M., Guidish, T., and Bell, F., 1986. Statistical inference of spatial random functions: Water Resources Research, 22, 935-942.
11. Doyen, P. M., 1988, Porosity from seismic data-A geostatistical approach: Geophysics, 53, 1263-1275.
12. Dubrule, O., 2000, Geostatistics in Petroleum geology: AAPG Distinguished Lecture Course Notes, 4-52.
13. Fogg, A. N., 2000. Petro-seismic classification using neural networks: UK onshore: 70th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 1426-1429.
14. Francis, A., 1997, Acoustic impedance inversion pitfalls and some fuzzy analysis: The Leading Edge, 16, 275-278.
15. Gelfand, V. A., and Larner, K. L., 1984, Seismic lithologic modeling: The Leading Edge, 3, No. 11, 30-34.
16. Hampson, D., Schuelke, J., and Quirein, J., 2001, Use of multiattribute transforms to predict log properties from seismic data: Geophysics, 66, 220-236.
17. Leiphart, D. J., and Hart, B. S., 2001, Comparison of linear regression and a probabilistic neural network to predict porosity from 3D seismic attributes in Lower Brushy Canyon channeled sandstones, southeast New Mexico: Geophysics, 66, 1349-1358.
18. Lindseth, R. O., 1979, Synthetic sonic logs-A process for stratigraphic interpretation: Geophysics, 44, 3-26.
19. Liu, Z. and Liu, J., 1998, Seismic controlled nonlinear extrapolation of well parameters using neural networks: Geophysics, 63, 2035-2041.
20. Mallick, S., 1999, Some practical aspects of prestack waveform inversion using a genetic algorithm: An example from east Texas Wood-bine gas sand: Geophysics, 64, 326-336.
21. ++ source book: John Wiley & Sons, Inc.
22. Mathur, L. P., Rao, K. L. N., and Chaube, A. N., 1980, Tectonic framework of Cambay, India: Bull. Oil and Natural Gas Corporation, 17, 203-222.
23. McCormack, M. D., Stoisits, R. F., MacAllister, D. J., and Crawford, K. D., 1999, Applications of genetic algorithms in exploration and production: The Leading Edge, 18, 716-718.
24. Neff, D. B., 1990, Estimated pay mapping using three-dimensional seismic data and incremental pay thickness modeling, Geophysics, 55, 567-575.
25. Oldenburg, D. W., Levy, S., and Stinson, K., 1986, Inversion of bandlimited reflection seismograms: Theory and practice: Proc. IEEE, 74, 487-497.
26. Pramanik, A. G., Singh, V., Srivastava, A. K., and Rakesh, K., 2002a, Post stack seismic inversion for enhancing vertical resolution: Geo-horizons, 7, 8-18.
27. Pramanik, A. G., Singh, V., Vig, R., Srivastava, A. K., and Tiwari, D. N., 2002b, Estimation of effective porosity using geostatistics and multiattribute transforms: A case study, 72nd Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 1758-1761.
28. Ronen, S., Schultz, P. S., Hattori, M., and Corbett, C., 1994, Seismic guided estimation of log properties, Part 2: Using artificial neural networks for nonlinear attribute calibration: The Leading Edge, 13, 674-678.
29. Russell, B., Hampson, D., and Todorov, T., 2001, Combining geo-statistics and multiattribute transforms-A channel sand case study: 71st Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 638-641.
30. Russell, B., Hampson, D., Schuelke, J., and Quirein, J., 1997, Multiattribute seismic analysis: The Leading Edge, 16, 1439-1443.
31. Schuelke, J. S., Quirein, J. A., and Sarg, J. F., 1998, Reservoir architecture and porosity distribution, Pegasus field, west Texas-An integrated sequence stratigraphic-seismic attribute study using neural networks: Geotriad, Expanded Abstracts, 142-145.
32. Schultz, P. S., Ronen, S., Hattori, M., and Corbett, C., 1994a, Seismic guided estimation of log properties, Part 1: A data-driven interpretation technology: The Leading Edge, 13, 305-315.
33. Schultz, P. S., Ronen, S., Hattori, M., Mantran, P., and Corbett, C., 1994b, Seismic guided estimation of log properties, Part 3: A controlled study: The Leading Edge, 13, 770-776.
34. Sen, M. K., and Stoffa, P. L., 1995, Global optimization methods in geophysical inversion: Elsevier Sci. Publ. Co.
35. Specht, D., 1990. Probabilistic neural networks: Neural Networks, 3, 109-118.
36. Specht, D., 1991, A general regression neural network: IEEE Trans. Neural Networks, 2, 568-576.
37. Todorov, T., Stewart, R., Hampson, D., and Russell, B., 1998, Well log prediction using attributes from 3C-3D seismic data: 68th Ann. Internat. Mtg.. Soc. Expl. Geophys., Expanded Abstracts, 1574-1576.
38. Tonn, R., 2002, Neural network seismic reservoir characterization in a heavy oil reservoir: The Leading Edge, 21, 309-312.
39. Trappe, H., and Hellmich, C., 2000, Using neural networks to predict porosity thickness from 3D seismic: First Break, 18, 377-384.
40. Van Riel, P., 2000, The past, present, and future of quantitative reservoir characterization: The Leading Edge, 19, 878-881.
41. Verwest B., 2000, Elastic impedance inversion: 70th Ann. Internat. Mtg., Soc. Expl. Geophys. Expanded Abstracts, 1580-1582.
42. Vig, R., Singh, V., Kharoo, H. L., Tiwari, D. N., Verma, R. P., Chandra, M., and Sen, G., 2002, Post stack seismic inversion for delineating thin reservoirs: A case study: 4th Conf. and Expo. in Petroleum Geophysics, Soc. Petroleum Geophys., India, Proc., 287-291.
43. Walls, J. D., Taner, M. T., Taylor, G., Smith, M., Carr, M., Derzhi, N., Drummond, J., McGuire, D., Morris, S., Bregar, J., and Lakings. J., 2002, Seismic reservoir characterization of a U.S. Midcontinent fluvial system using rock physics, poststack seismic attributes, and neural networks: The Leading Edge, 21, 428-436.
44. Wolf, D.J., Withers, K. D., and Burman, M. D., 1994, Integration of well and seismic data using geostatistics: AAPG Computer Applications in Geology, 177-199.