Advertisement

Robust Rule-Based Aggradational Lobe Reservoir Models

  • Honggeun JoEmail author
  • Michael J. Pyrcz
Original Paper

Abstract

Stratigraphic rule-based reservoir models approximate sedimentary dynamics to generate numerical models of reservoir architecture with realistic spatial distributions of petrophysical properties for reservoir forecasting and to support development decision making. A few intuitive rules for the sequential placement of surfaces bounding reservoir units render realistic reservoir heterogeneity, continuity, and spatial organization of petrophysical property distributions that are difficult to obtain using conventional geostatistical pixel- and object-based subsurface methods. While these methods are emerging in applications specifically for deepwater and fluvial clastic reservoirs, there are some remaining obstacles to broad application, such as selection of rule parameters and addressing emergent non-stationarities over the sequence of the placed surfaces. Firstly, there is a need to tune rule parameters to ensure the models honor natural heterogeneities. We demonstrate this for the compensational rule. Secondly, invariants over model sequence (from the base to the top of the model) may occur with respect to shape, volume, undulation, and gradients of surfaces. For example, for a stack of compensational lobes, the volume of individual lobes may decrease due to the onlapping of previous bathymetry and also increasing undulation over model sequence may occur. In addition, for stacking of compensational lobes, the interfacial width and average gradient of the composite surface may initially increase, but then saturate and stabilize. Such non-stationarities represent numerical artifacts that may bias the results from these rule-based models. It is essential that these features are quantified and mitigated as a prerequisite for robust application of rule-based aggradational lobe methods for reservoir modeling.

Keywords

Geostatistics Reservoir characterization Aggradational lobe reservoir model Rule-based modeling Emergent artifact Submarine lobes 

References

  1. Barabási, A.-L., & Stanely, H. E. (1995). Fractal concepts in surface growth. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  2. Bertoncello, A., Sun, T., Li, H., Mariethoz, G., & Caers, J. (2013). Conditioning surface-based geological models to well and thickness data. Mathematical Geosciences, 45(7), 873–893.CrossRefGoogle Scholar
  3. Caers, J. (2011). Modeling uncertainty in the earth sciences. Chichester: Wiley.CrossRefGoogle Scholar
  4. Cojan, I., Fouche, O., & Lopez, S. (2005). Process-based reservoir modelling in the example meandering channel. In O. Leuangthong & C. V. Deutsch (Eds.), Geostatistics Banff 2004 (Vol. 14, pp. 611–620), Quantitative geology and geostatistics. Dordrecht: Springer.CrossRefGoogle Scholar
  5. Cox, D. L., Lindquist, S. J., Bargas, C. L., Havholm, K. G., & Srivastava, R. M. (1994). Integrated modeling for optimum management of a giant gas condensate reservoir, Jurassic eolian nugget sandstone, Anschutz Ranch East Field, Utah overthrust (USA). Stochastic modeling and geostatistics: Principles, methods, and case studies. AAPG Computer Applications in Geology, 3, 287–321.Google Scholar
  6. Deptuck, M. E., Piper, D. J. W., Savoye, B., & Gervas, A. (2008). Dimensions and architecture of late Pleistocene submarine lobes off the northern margin of East Corsica. Sedimentology, 55, 869–898.CrossRefGoogle Scholar
  7. Deutsch, C. V., & Journel, A. (1998). GSLIB: Geostatistical software library and user’s guide (2nd ed.). Oxford: Oxford University Press.Google Scholar
  8. Ghosh, B., & Lowe, D. R. (1993). The architecture of deep-water channel complexes, Cretaceous Venado Sandstone Member, Sacramento Valley, California. In S. A. Graham & D. R. Lowe (Eds.), Advances in the sedimentary geology of the Great Valley Group, Sacramento Valley, California (pp. 51–65). Los Angeles: SPEM Pacific Section.Google Scholar
  9. Graham, G. H., Jackson, M. D., & Hampson, G. J. (2015). Three-dimensional modeling of clinoforms in shallow-marine reservoirs: Part 2. Impact on fluid flow and hydrocarbon recovery in fluvial-dominated deltaic reservoirs. AAPG Bulletin, 99(6), 1049–1080.CrossRefGoogle Scholar
  10. Haldorsen, H. H., & Chang, D. W. (1986). Notes on stochastic shales: From outcrop to simulation model. In L. W. Lake & H. B. Caroll (Eds.), Reservoir characterization (pp. 445–485). London: Academic Press.CrossRefGoogle Scholar
  11. Haldorsen, H. H., & Lake, L. W. (1984). A new approach to shale management in field-scale models. Society of Petroleum Engineers Journal, 24(4), 447–457.CrossRefGoogle Scholar
  12. Hassanpour, M. M., Pyrcz, M. J., & Deutsch, C. V. (2013). Improved geostatistical models of inclined heterolithic strata for McMurray Formation, Alberta, Canada. AAPG Bulletin, 97(7), 1209–1224.CrossRefGoogle Scholar
  13. Heller, P. L., Paola, C., Hwang, I.-G., John, B., & Steel, R. (2001). Geomorphology and sequence stratigraphy due to slow and rapid base-level changes in an experimental subsiding basin (XES 96-1). AAPG Bulletin, 85, 817–838.Google Scholar
  14. Hodgson, D. M., Flint, S. S., Hodgetts, D., Drinkwater, N. J., Johannessen, E. P., & Luthi, S. M. (2006). Stratigraphic evolution of fine-grained submarine fan systems, Tanqua depocenter, Karoo Basin, South Africa. Journal of Sedimentary Research, 76(1), 20–40.CrossRefGoogle Scholar
  15. Hovadik, J. M., & Larue, D. K. (2010). Stratigraphic and structural connectivity. Geological Society, 347(1), 219–242.CrossRefGoogle Scholar
  16. Koo, W., Olariu, C., Steel, R. J., Olariu, M. I., Carvajal, C. R., & Kim, W. (2016). Coupling between shelf-edge architecture and submarine-fan growth style in a supply-dominated margin. Journal of Sedimentary Research, 86, 613–628.CrossRefGoogle Scholar
  17. Kostrewa, R. (2004). Internal architecture, geometry and reservoir characterisation of depositional lobes in outcrop and subsurfaceExamples from S-Turkey and the North Sea. Ph.D. thesis, Universität Tübingen.Google Scholar
  18. Mariethoz, G., & Caers, J. (2014). Multiple-point geostatistics: Stochastic modeling with training images. Chichester: Wiley.CrossRefGoogle Scholar
  19. Michael, H. A., Li, H., Boucher, A., Sun, T., Caers, J., & Gorelick, S. M. (2010). Combining geologic-process models and geostatistics for conditional simulation of 3-D subsurface heterogeneity. Water Resources Research, 46(5), 1–20.CrossRefGoogle Scholar
  20. Mutti, E., & Normark, W. R. (1987). Comparing examples of modern and ancient turbidite systems: Problems and concepts. In J. K. Leggett & G. G. Zuffa (Eds.), Marine clastic sedimentology (pp. 1–38). Dordrecht: Springer.Google Scholar
  21. Paola, C., Mullin, J., Ellis, C., Mohrig, D. C., Swenson, J. B., Parker, G., et al. (2001). Experimental stratigraphy. GSA Today, 11(7), 4–9.CrossRefGoogle Scholar
  22. Patterson, P. E., Jones, T. A., Donofrio, C. J., Donovan, A. D., & Ottmann, J. D. (2002). Geological modelling of external and internal reservoir architecture of fluvial depositional systems. In M. Armstrong, C. Bettini, N. Champigny, A. Galli, & A. Remacre (Eds.), Geostatistics Rio 2000 (pp. 41–52). Dordrecht: Springer.CrossRefGoogle Scholar
  23. Prélat, A., Hodgson, D., & Flint, S. (2009). Evolution, architecture and hierarchy of distributary deep-water deposits: A high-resolution outcrop investigation of submarine lobe deposits from the Permian Karoo Basin, South Africa. Sedimentology, 56, 2132–2154.CrossRefGoogle Scholar
  24. Pyrcz, M. J. (2004). Integration of geologic information into geostatistical models. Ph.D. Thesis, University of Alberta.Google Scholar
  25. Pyrcz, M. J., Boisvert, J. B., & Deutsch, C. V. (2009). ALLUVSIM: A program for event-based stochastic modeling of fluvial depositional systems. Computers & Geosciences, 35, 1671–1685.CrossRefGoogle Scholar
  26. Pyrcz, M. J., Catuneanu, O., & Deutsch, C. V. (2005). Stochastic surface-based modeling of turbidite lobes. AAPG Bulletin, 89, 177–191.CrossRefGoogle Scholar
  27. Pyrcz, M. J., & Deutsch, C. V. (2014). Geostatistical reservoir modeling (2nd ed., p. 433). New York: Oxford University Press.Google Scholar
  28. Pyrcz, M. J., McHargue, T., Clark, J., Sullivan, M., & Strebelle, S. (2012). Event-based geostatistical modeling: Description and applications. Quantitative Geology and Geostatistics, 17, 27–38.CrossRefGoogle Scholar
  29. Pyrcz, M. J., Sech, R. P., Covault, J. A., Willis, B. J., Sylvester, Z., & Sun, T. (2015). Stratigraphic rule-based reservoir modeling. Bulletin of Canadian Petroleum Geology, 63(4), 287–303.CrossRefGoogle Scholar
  30. Pyrcz, M. J., & Strebelle, S. (2006). Event-based geostatistical modeling: Application to deep-water systems. In R. M. Slatt, N. C. Rosen, M. Bowman, J. Castagna, T. Good, R. Loucks, R. Latimer, R. Scheihing, & R. Smith (Eds.), Reservoir characterization: Integrating technology and business practices (pp. 893–922). Houston: SEPM Society for Sedimentary Geology.CrossRefGoogle Scholar
  31. Ringrose, P., & Bentley, M. (2015). Reservoir model design: A practitioner’s guide (p. 249). Dordrecht: Springer.Google Scholar
  32. Shanmugam, G., & Moiola, R. J. (1988). Submarine fans: Characteristics, models, classification, and Reservoir Potential. Earth-Science Reviews, 24, 383–428.CrossRefGoogle Scholar
  33. Stow, D. A. V., & Johansson, M. (2000). Deep-water massive sands: Nature, origin and hydrocarbon implications. Marine and Petroleum Geology, 17, 145–174.CrossRefGoogle Scholar
  34. Stoyan, D., Kendall, W. S., & Mecke, J. (1987). Stochastic geometry and its applications. New York: Wiley.Google Scholar
  35. Straub, K. M., Paola, C., Mohrig, D., Wolinsky, M. A., & George, T. (2009). Compensational stacking of channelized sedimentary deposits. Journal of Sedimentary Research, 79, 673–688.CrossRefGoogle Scholar
  36. Straub, K. M., & Pyles, D. R. (2012). Quantifying the hierarchical organization of compensation in submarine fans using surface statistics. Journal of Sedimentary Research, 82(11), 889–898.CrossRefGoogle Scholar
  37. Sullivan, M. D., Foreman, J. L., Jennette, D. C., Stern, D., Jensen, G. N., & Goulding, F. J. (2004). An integrated approach to characterization and modeling of deep-water reservoirs, Diana field, western Gulf of Mexico. In G. M. Grammer, P. M. Harris, & G. P. Eberli (Eds.), Integration of outcrop and modern analogs in reservoir modeling: AAPG Memoir, vol80 (pp. 215–234). Tulsa: AAPG.Google Scholar
  38. Sullivan, M., Jensen, G., Goulding, F., Jennette, D., Foreman, L., & Stern, D. (2000). Architectural analysis of deep-water outcrops: Implications for exploration and development of the Diana Sub-basin, western Gulf of Mexico. In P. Weimer (Ed.), Deep-water reservoirs of the world: Gulf Coast Section SEPM Foundation, 20th annual research conference (pp. 1010–1032). Houston: SEPM Society for Sedimentary Geology.CrossRefGoogle Scholar
  39. Wang, Y., Straub, K. M., & Hajek, E. A. (2011). Scale-dependent compensational stacking: An estimate of autogenic time scales in channelized sedimentary deposits. Geology, 39(9), 811–814.CrossRefGoogle Scholar
  40. Wen, R. (2005). SBED studio: An integrated workflow solution for multi-scale geo modelling. In 67th EAGE conference and exhibition, Madrid, Spain, 13–16 June 2005.Google Scholar
  41. Xie, Y., Cullick, A. S., & Deutsch, C. V. (2001). Surface-geometry and trend modeling for integration of stratigraphic data in reservoir models. SPE western regional meeting, Bakersfield, California, 26–30 March 2001.Google Scholar
  42. Zhang, X., Pyrcz, M. J., & Deutsch, C. V. (2009). Stochastic surface modeling of deepwater depositional systems for improved reservoir models. Journal of Petroleum Science and Engineering, 68(1–2), 118–134.CrossRefGoogle Scholar
  43. Zhang, J., Wu, S., Fan, T., Fan, H., Jian, L., Chen, C., et al. (2016). Research on the architecture of submarine-fan lobes in the Niger Delta Basin, offshore West Africa. Journal of Palaeogeography, 5(3), 185–204.CrossRefGoogle Scholar

Copyright information

© International Association for Mathematical Geosciences 2019

Authors and Affiliations

  1. 1.Hildebrand Department of Petroleum and Geosystems EngineeringThe University of Texas at AustinAustinUSA
  2. 2.Bureau of Economic Geology, Jackson School of GeosciencesThe University of Texas at AustinAustinUSA

Personalised recommendations