Advertisement

Abstract

As older producing fields have matured or have been intensively developed, and newly discovered fields have entered a development phase, the search for energy resources becomes more and more intensive and extensive. Meantime, optimally producing hydrocarbon from a field requires an accurate description of the reservoir, which, in turn, requires an integrated reservoir characterization and modeling using all relevant data. For this reason, reservoir modeling has seen significant leaps in the recent decades. It has evolved from fragmentary pieces into a coherent discipline for geoscience applications, from university research topics to value-added oilfield developments, from 2D mapping of reservoir properties to 3D digital representations of subsurface formations, and from solving isolated problems by individual disciplines to integrated multidisciplinary reservoir characterization. However, the exposure of quantitative geosciences in the literature has been uneven, and significant gaps exist between descriptive geosciences and quantitative geosciences for natural resource evaluations. This book attempts to fill some of these gaps by presenting quantitative methods for geoscience applications and through an integrative treatment of descriptive and quantitative geosciences.

References

  1. Bloch, A. (1991). The complete Murphy’s law: A definitive collection (Rev. ed.). Los Angeles: Price Stern Sloan.Google Scholar
  2. Brillinger, D. R., Fernholz, L. T., & Morgenthaler, S. (Eds.). (1997). The practice of data analysis: Essays in Honor of John W. Tukey. Princeton: Princeton University Press.zbMATHGoogle Scholar
  3. Cao, R., Ma, Y. Z., & Gomez, E. (2014). Geostatistical applications in petroleum reservoir modeling. SAIMM, 114, 625–629.Google Scholar
  4. Deutsch, C. V., & Journel, A. G. (1992). Geostatistical software library and user’s guide (p. 340p). Oxford: Oxford University Press.Google Scholar
  5. Huber, P. J. (2011). Data analysis: What can be learned from the past 50 years. Hoboken: Wiley.CrossRefGoogle Scholar
  6. Jacobs, T. (2018). Find out what Google and oil and gas companies are searching for in Big Data at 2018 ATCE. JPT, 70(9).Google Scholar
  7. Journel, A. G., & Huijbregts, C. J. (1978). Mining geostatistics. New York: Academic.Google Scholar
  8. Keynes, J. M. (1973). A treatise on probability (4th ed.). New York: St Martin’s Press.CrossRefGoogle Scholar
  9. Krige, D. G. (1951). A statistical approach to some basic mine valuation problems in the Witwatersrand. Journal of the Chemical, Metallurgical and Mining Society of South Africa, 52, 119–139.Google Scholar
  10. Ma, Y. Z. (2010). Error types in reservoir characterization and management. Journal of Petroleum Science and Engineering, 72(3–4), 290–301.  https://doi.org/10.1016/j.petrol.2010.03.030.CrossRefGoogle Scholar
  11. Ma, Y. Z., & La Pointe, P. (2011). Uncertainty analysis and reservoir modeling (AAPG Memoir 96). Tulsa: American Association of Petroleum Geologists.Google Scholar
  12. Ma, Y. Z., Seto, A., & Gomez, E. (2008). Frequentist meets spatialist: A marriage made in reservoir characterization and modeling. SPE 115836, SPE ATCE, Denver, CO, USA.Google Scholar
  13. Massonnat, G. J. (2000). Can we sample the complete geological uncertainty space in reservoir-modeling uncertainty estimates? SPE Journal, 5(1), 46–59.CrossRefGoogle Scholar
  14. Matheron, G. (1989). Estimating and choosing – An essay on probability in practice. Berlin: Springer.CrossRefGoogle Scholar
  15. Meng, X. L. (2014). A trio of inference problems that could win you a Nobel prize in statistics (if you help fund it). In X. Lin, C. Genest, D. L. Banks, G. Molenberghs, D. W. Scott, & J.-L. Wang (Eds.), Past, present, and future of statistical science (pp. 537–562). Boca Raton: CRC Press.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Y. Z. Ma
    • 1
  1. 1.SchlumbergerDenverUSA

Personalised recommendations