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Computational challenges in the search for and production of hydrocarbons

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Scientific Modeling and Simulation SMNS

Abstract

The exploration and production of oil and natural gas facing unprecedented demands for a secure energy supply worldwide is continuing a long trend to develop and adopt new technologies to help meet this challenge. For many oilfield technologies mathematical modeling and simulation have played a truly enabling role throughout their development and eventually their commercial adoption. Looking ahead, the vision of data-driven “intelligent” oilfields designed and operated using simulations to reach higher recovery factors is becoming a reality. Very little of this vision would be possible let alone make sense without the capability to move information across several simulation domains. We will examine several successes of modeling and simulation as well as current limitations which need to be addressed by new developments in theory, modeling, algorithms and computer hardware. Finally, we will mention several fundamental issues affecting oil recovery for which increased understanding is needed from new experimental methods coupled to simulation.

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References

  1. Abubakar A., van den Berg P.M., Habashy T.M.: An integral equation approach for 2.5-dimensional forward and inverse electromagnetic scattering. Geophys. J. Int. 165, 744–762 (2006)

    Article  ADS  Google Scholar 

  2. Akubakar, A., Habashy, T.M., Drushkin, V.L., Knizhnerman, L., Alumbaugh, D.: Two-and-half dimensional forward and inverse modelling for interpretation of low-frequency electromagnetic measurements. to be published in Geophysics (2008)

  3. Altman, R., Ferris, P., Filardi, F.: Latest generation horizontal well placement technology helps maximize production in deep water turbidite reservoirs. In: SPE 108693. Veracruz (2007)

  4. Auzerais F. et al.: Transport in sandstone: a study based on three dimensional microtomography. Geophys. Lett. 23, 705–708 (1996)

    Article  ADS  Google Scholar 

  5. Bear J.: Dynamics of Fluids in Porous Media. Dover, Mineola (1972)

    Google Scholar 

  6. Blair S.A., Berge P.A., Berryman J.G.: Using two point correlation functions to characterize microgeometry and estimate permeabilities of sandstones and porous glass. J. Gepphys. Res. 101, 20359–20375 (1996)

    Article  ADS  Google Scholar 

  7. Blunt M.J.: Flow in porous media-pore-network models and multiphase flow. Curr. Opin. Colloid Interface Sci. 6, 197–207 (2001)

    Article  CAS  Google Scholar 

  8. Boutte, D.: Seismic-to-simulation arrives. Hart’s E&P J (July 2007)

  9. Branco V., Mansoori G.A., Xavier L., Park S.J., Manafi H.: Asphaltene flocculation and collapse from petroleum fluids. J. Petrol. Sci. Eng. 32, 217–230 (2001)

    Article  CAS  Google Scholar 

  10. Burstedde, C., Ghattas, O.: Algorithmic strategies for full waveform inversion: 1D experiments. In: Society of Exploration Geophysicists, San Antonio, 1913–1917 (2007)

  11. Chen Z., Appenzeller J., Knoch J., Lin Y.-M., Avouris P.: The role of metal-nanotube contact in the performance of carbon nanotube field-effect transistors. Nano Lett. 5, 1497–1502 (2005)

    Article  PubMed  CAS  Google Scholar 

  12. Colombo D., De Stefano M.: Geophysical modeling via simultaneous joint inversion of seismic, gravity, and electromagnetic data: application to prestack depth imaging. Leading Edge 26, 326–331 (2007)

    Article  Google Scholar 

  13. Datta S.: Quantum Transport: Atom to Transister. Cambridge University Press, Cambridge (2005)

    Google Scholar 

  14. Eidesmo T. et al.: Sea bed logging. A new method for remote and direct identification of hydrocarbon-filled layers in deepwater areas. First Break 20, 144–152 (2002)

    Google Scholar 

  15. Ellis, D.V., Singer, J.M.: Well Logging for Earth Scientists. Springer Verlag (2007)

  16. Frankland, S.J.V., Harik, V.M.: Simulation of carbon nanotube pull-out when bonded to a polymer matrix. In: Materials Research Society Proceedings, vol. 740, paper I12.1, 1-6 (2003)

  17. Friedrich J.T., DiGiovanni A.A., Noble D.R.: Predicting macroscopic transport properties using microscopic image data. J. Geophys. Res. 111, 1–14 (2006)

    Google Scholar 

  18. Glotzer S., Paul W.: Molecular and mesoscale simulation methods for polymer materials. Annu. Rev. Mater. Res. 32, 401–436 (2002)

    Article  CAS  Google Scholar 

  19. Griebel M., Hamaekers J.: Molecular dynamics simulations of the elastic moduli of polymer carbon nanotube composites. Comput. Methods Appl. Mech. Eng. 193, 1773–1788 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  20. Habashy T.M., Abuakar A.: A general framework for constraint minimization for inversion of electromagnetic measurements. Progr. Electromagn. Res. 46, 265–312 (2004)

    Article  Google Scholar 

  21. Kang Q., Lichtner P.C., Zhang D.: Lattice-Boltzmann pore-scale model for multicomponent reactive transport in porous media. J. Geophys. Res. 111, B05203 (2006)

    Article  Google Scholar 

  22. Keehm Y., Nur A.: Permeability from thin sections: 3D reconstruction and lattice-Boltzmann simulation. Geophys. Res. Lett. 31, 1–4 (2004)

    Article  Google Scholar 

  23. Khairul A., Lake R.K.: Leakage and performance of zero-Schottky-barrier carbon nanotube transistors. J. Appl. Phys. 98, 064307 (2005)

    Article  Google Scholar 

  24. Kleinberg R., Clark B.: Physics in oil exploration. Phys. Today 48, 48–53 (2002)

    Google Scholar 

  25. Lake R., Klimeck G., Bowen R.C., Jovanovic D.: Single and multiband modeling of quantum electron transport through layered semiconductor devices. J. Appl. Phys. 81, 7845–7869 (1997)

    Article  ADS  CAS  Google Scholar 

  26. Lasaga A.C., Luttge A.: Variation of crystal dissolution rate based on a dissolution stepwave model. Science 291, 2400–2404 (2001)

    Article  PubMed  ADS  CAS  Google Scholar 

  27. Li, Q., et al.: New directional electromagnetic tool for proactive geosteering and accurate formation evaluation while drilling. In: SPWLA 46th Annual Logging Symposium. New Orleans (2005)

  28. Liu Y.J.: Large scale modeling of carbon nanotube composites by a fast multipole boundary element method. Comput. Mater. Sci. 34, 173–187 (2005)

    Article  ADS  Google Scholar 

  29. Luthi, S.M.: Geological Well Logs: Their Uses in Reservoir Modeling. Springer Verlag (2001)

  30. Luttge A., Winkler U., Lasaga A.C.: Interferometric study of the dolomite dissolution: a new conceptual model for mineral dissolution. Geochim. Cosmochim. Acta 67, 1099–1116 (2003)

    Article  ADS  CAS  Google Scholar 

  31. Martys N., Chen H.: Simulations of multicomponent fluids in complex three dimensional geometries by the lattice Boltzmann method. Phys. Rev. E 53, 743–750 (1996)

    Article  ADS  CAS  Google Scholar 

  32. Mendoza, A., Preeg, W., Torres-Verdin, C., Alpak, C.: Monte Carlo modeling of nuclear measurements in vertical and horizontal wells in the presence of mud-filtrate invasion and salt mixing. In: Society of Petrophysicists and Well Log Analysts 46th Annual Logging Symposium, New Orleans (2005)

  33. Mullins O., Sheu E.Y., Hammami A., Marshall A.G. (eds.): Asphaltenes, Heavy oils, and Petroleomics. Springer Science, New York (2007)

    Google Scholar 

  34. Odegard G.M., Gates T.S., Wise K.E., Park C., Siochi E.J.: Constitutive modeling of carbon nanotube re-inforced polymer composites. Compos. Sci. Technol. 63, 1671–1687 (2003)

    Article  CAS  Google Scholar 

  35. Oden, J.T., et al.: Revolutionizing engineering science through simulation. Report of the National Science Foundation Blue Ribbon Panel on Simulation-Based Engineering Science, National Science Foundation (2006)

  36. Osdal B. et al.: Mapping the fluid front and pressure buildup using 4D data on Norne field. Leading Edge 9, 1134–1141 (2006)

    Article  Google Scholar 

  37. Pickering, S.: Q-reservoir: advanced seismic technology for reservoir solutions. Oil Gas North Africa Mag. Jan–Feb 2003, 26–30 (2003)

  38. Pratt R.G., Shin C., Hicks G.J.: Gauss–Newton and full Newton methods in frequency-space seismic waveform inversion. Geophys. J. Int. 133, 341–362 (1998)

    Article  ADS  Google Scholar 

  39. Rothman, D.H., Zaleskki, S.: Lattice-Gas Cellular Automata. Cambridge University Press (1997)

  40. Ryu, S.: Personnal Communication (2008)

  41. Sham T.K., Rivers M.L.: A brief overview of synchrontron radiation in low temperature geochemistry and environmental science. Rev. Mineral. Geochem. 49, 117–147 (2002)

    Article  CAS  Google Scholar 

  42. Sinha M.C., MacGregor L.M.: Use of marine controlled-source electromagnetic sounding for sub-basalt exploration. Geophys. Prospecting 48, 1091–1106 (2000)

    Article  Google Scholar 

  43. Sirgue L., Pratt R.G.: Efficient waveform inversion and imaging: a strategy for selecting temporal frequencies. Geophysics 69, 231–248 (2004)

    Article  ADS  Google Scholar 

  44. Steefel C.I., MacQuarrie K.T.B.: Approaches to modeling reactive transport in porous media. Rev. Minerol. 34, 83–125 (1996)

    CAS  Google Scholar 

  45. Tarantola A.: Inverse Problem Theory. Elsevier Science B.V., Amsterdam (1987)

    MATH  Google Scholar 

  46. Top500. http://www.top500.org/.

  47. Tweedie C.A., Anderson D.G., Langer R., Van Vliet K.J.: Combinatorial materials mechanics: high-throughput polymer synthesis and nanomechanical screening. Adv. Mater. 17, 2599–2604 (2005)

    Article  CAS  Google Scholar 

  48. Valvatne P.H., Blunt M.J.: Predictive pore scale modeling of two phase flow in mixed wet media. Water Resour. Res. 40, 1–21 (2004)

    Article  Google Scholar 

  49. Vander Veen J.F., Reichert H.: Structural ordering at the solid–liquid interface. Mater. Res. Soc. Bull. 29, 958–962 (2004)

    Google Scholar 

  50. Venugopal R., Ren Z., Datta S., Lundstrom M.S., Jovanovic D.: Simulating quantum transport in nanoscale MOSFETs: real vs mode space approaches. J. Appl. Phys. 92, 3730–3739 (2002)

    Article  ADS  CAS  Google Scholar 

  51. Voter A.F., Montalenti F., Germann T.C.: Extending the time scale in atomistic simulation of materials. Annu. Rev. Mater. Res. 32, 321–346 (2002)

    Article  CAS  Google Scholar 

  52. Wong E.W., Sheehan P.E., Lieber C.: Nanobeam mechanics: elasticity, strength and toughness of nanorods and nanotubes. Science 277, 1971–1975 (1997)

    Article  CAS  Google Scholar 

Download references

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  1. Schlumberger-Doll Research, One Hampshire St, Cambridge, MA, 02141, USA

    John Ullo

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Ullo, J. Computational challenges in the search for and production of hydrocarbons. Sci Model Simul 15, 313–337 (2008). https://doi.org/10.1007/s10820-008-9095-z

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