1887
Volume 68, Issue 5
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Tight oil siltstones are rocks with complex structure at pore scale and are characterized by low porosity and low permeability at macroscale. The production of tight oil siltstone reservoirs can be increased by hydraulic fracturing. For optimal fracking results, it is desirable to map the ability to fracture based on seismic data prior to fracturing. Brittleness is currently thought to be a key parameter for evaluating the ability to fracture. To link seismic information to the brittleness distribution, a rock physics model is required. Currently, there exists no commonly accepted rock physics model for tight oil siltstones. Based on the observed correlation between porosity and mineral composition and known microstructure of tight oil siltstone in Daqing oilfield of Songliao basin, we develop a rock physics model by combining the Voigt–Reuss–Hill average, self‐consistent approximation and differential effective medium theory. This rock physics model allows us to explore the dependence of the brittleness on porosity, mineral composition, microcrack volume fraction and microcrack aspect ratio. The results show that, as quartz content increases and feldspar content decreases, Young's modulus tends to increase and Poisson ratio decreases. This is taken as a signature of higher brittleness. Using well log data and seismic inversion results, we demonstrate the versatility of the rock physics template for brittleness prediction.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.12938
2020-02-24
2024-04-27
Loading full text...

Full text loading...

References

  1. AiC., ZhangJ., LiY., ZengJ., YangX. and WangJ.2016. Estimation criteria for rock brittleness based on energy analysis during the rupturing process. Rock Mechanics and Rock Engineering49, 4681–4698.
    [Google Scholar]
  2. AltindagR.2010. Assessment of some brittleness indexes in rock‐drilling efficiency. Rock Mechanics and Rock Engineering43, 361–370.
    [Google Scholar]
  3. BaJ., MaR., CarcioneJ.M. and PicottiS.2019. Ultrasonic wave attenuation dependence on saturation in tight oil siltstones. Journal of Petroleum Science and Engineering179, 1114–1122.
    [Google Scholar]
  4. BaJ., XuW., FuL., CarcioneJ.M. and ZhangL.2017. Rock anelasticity due to patchy‐saturation and fabric heterogeneity: A double double‐porosity model of wave propagation. Journal of Geophysical Research‐Solid Earth122, 1949–1976.
    [Google Scholar]
  5. BaJ., YanX., ChenZ., XuG. and SunW.2013. Rock physics model and gas saturation inversion for heterogeneous gas reservoirs. Chinese Journal of Geophysics56, 1696–1706.
    [Google Scholar]
  6. BerrymanJ.G.1980. Long wavelength propagation in composite elastic media II. Ellipsoidal inclusions. Acoustical Society of America Journal68, 1820–1831.
    [Google Scholar]
  7. BerrymanJ.G.1992. Single‐scattering approximations for coefficients in Biot's equations of poroelasticity. Acoustical Society of America Journal91, 551–571.
    [Google Scholar]
  8. BerrymanJ.G.1998. Long‐wavelength propagation in composite elastic media I. Spherical inclusions. Journal of the Acoustical Society of America68, 1809–1819.
    [Google Scholar]
  9. BoruahA. and GanapathiS.2015. Microstructure and pore system analysis of Barren Measures shale of Raniganj field, India. Journal of Natural Gas Science and Engineering26, 427–437.
    [Google Scholar]
  10. ChengW., BaJ., FuL. and LebedevM.2019. Wave‐velocity dispersion and rock microstructure. Journal of Petroleum Science and Engineering183, 106466.
    [Google Scholar]
  11. DengJ., WangH., ZhouH., LiuZ. and WangX.2015. Microtexture seismic rock physical properties and modeling of Longmaxi Formation Shale. Chinese Journal of Geophysics58, 2123–2136.
    [Google Scholar]
  12. DongN., HuoZ., SunZ., LiuZ. and SunY.2014. An investigation of a new rock physics model for shale. Chinese Journal of Geophysics57, 1990–1998.
    [Google Scholar]
  13. EconomidesM.J. and MartinT.2012. Modern Fracturing: Enhancing Natural Gas Production. Petroleum Industry Press.
    [Google Scholar]
  14. GuoZ., LiX., LiuC., FengX. and ShenY.2013. A shale rock physics model for analysis of brittleness index, mineralogy and porosity in the Barnett Shale. Journal of Geophysics and Engineering10, 025006.
    [Google Scholar]
  15. HerwangerJ.V., BottrillA.D. and MildrenS.D.2015. Uses and Abuses of the Brittleness Index with Applications to Hydraulic Stimulation. Unconventional Resources Technology Conference.
  16. HillR.1952. The Elastic Behaviour of a Crystalline Aggregate. Proceedings of the Physical Society65, 349–354.
    [Google Scholar]
  17. HuangW., HersiO.S., LuS. and DengS.2017. Quantitative modelling of hydrocarbon expulsion and quality grading of tight oil lacustrine source rocks: Case study of Qingshankou 1member, central depression, Southern Songliao Basin China. Marine and Petroleum Geology84, 34–48.
    [Google Scholar]
  18. HuangX., HuangJ., LiZ., YangQ., SunQ. and CuiW.2015. Brittleness index and seismic rock physics model for anisotropic tight‐oil sandstone reservoirs. Applied Geophysics12, 11–22.
    [Google Scholar]
  19. HuckaV. and DasB.1974. Brittleness determination of rocks by different methods. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts11, 389–392.
    [Google Scholar]
  20. JiaC., ZouC., LiJ., LiD. and ZhengM.2012. Assessment criteria, main type, basic features and resource prospects of the tight oil in China. Acta Petrolel Sinica33, 343–350.
    [Google Scholar]
  21. KahramanS. and AltindagR.2004. A brittleness index to estimate fracture toughness. International Journal of Rock Mechanics and Mining Sciences41, 343–348.
    [Google Scholar]
  22. LaiJ., WangG., FanZ., ChenJ., WangS., ZhouZ., et al. 2016. Research progress in brittleness index evaluation methods with logging data in unconventional oil and gas reservoirs. Petroleum Science Bulletin330–341.
    [Google Scholar]
  23. LaiJ., WangG., HuangL., LiW., RanY. and WangD.2015. Brittleness index estimation in a tight shaly sandstone reservoir using well logs. Journal of Natural Gas Science and Engineering27, 1536–1545.
    [Google Scholar]
  24. LinG. and ChenF.1998. CMS‐300 automatic core determination. Petroleum Instruments6, 36–38+56.
    [Google Scholar]
  25. LuY., ZengL., XieQ., JinY., MofazzalH.M. and SaeediA.2019. Analytical modelling of wettability alteration‐induced micro‐fractures during hydraulic fracturing in tight oil reservoirs. Fuel249, 434–440.
    [Google Scholar]
  26. MavkoG., MukerjiT. and DvorkinJ.2009. The Rock Physics Handbook: Tools for Seismic Analysis of Porous Media. Cambridge University Press.
    [Google Scholar]
  27. MengF., ZhouH., ZhangC., XuR. and LuJ.2015. Evaluation methodology of brittleness of rock based on post‐peak stress–strain curves. Rock Mechanics and Rock Engineering48, 1787–1805.
    [Google Scholar]
  28. Nicolás‐LópezR. and Valdiviezo‐MijangosO.C.2016. Rock physics templates for integrated analysis of shales considering their mineralogy, organic matter and pore fluids. Journal of Petroleum Science and Engineering137, 33–41.
    [Google Scholar]
  29. NorrisA.N., ShengP. and CallegariA.J.1985. Effective‐medium theories for two‐phase dielectric media. Journal of Applied Physics57, 1990–1996.
    [Google Scholar]
  30. OrtegaJ.A., UlmF.J. and AbousleimanY.2009. The nanogranular acoustic signature of shale. Geophysics74, D65–D84.
    [Google Scholar]
  31. PangM., BaJ., CarcioneJ.M., PicottiS., ZhouJ. and JiangR.2019. Estimation of porosity and fluid saturation in carbonates from rock‐physics templates based on seismic Q. Geophysics84, M25–M36.
    [Google Scholar]
  32. PerezM., CloseD., GoodwayB. and PurdueG.2011. Developing templates for integrating quantitative geophysics and hydraulic fracture completions data: part I—principles and theory. 81st SEG Meeting Expanded Abstracts, pp. 1794–1798.
  33. QianK., HeY., ChenY., LiuX. and LiX.2017. Prediction of brittleness based on anisotropic rock physics model for kerogen‐rich shale. Applied Geophysics14, 463–480.
    [Google Scholar]
  34. Rahimzadeh KiviI., AmeriM. and MolladavoodiH.2018. Shale brittleness evaluation based on energy balance analysis of stress‐strain curves. Journal of Petroleum Science and Engineering167, 1–19.
    [Google Scholar]
  35. ReussA.1929. Calculation of the flow limits of mixed crystals on the basis of the plasticity of monocrystals. Zeitschrift für Angewandte Mathematik9, 49–58.
    [Google Scholar]
  36. RickmanR., MullenM.J., PetreJ.E., GrieserW.V. and KundertD.2008. A practical use of shale petrophysics for stimulation design optimization: All shale plays are not clones of the Barnett shale. SPE Annual Technical Conference and Exhibition, Society of Petroleum Engineers, Denver, Colorado, USA, 21–24.
  37. SenaA., CastilloG., ChesserK., VoiseyS., EstradaJ. and CarcuzJ.2011. Seismic reservoir characterization in resource shale plays: Stress analysis and sweet spot discrimination. The Leading Edge30, 758–764.
    [Google Scholar]
  38. TanW., BaJ., GuoM., LiH., ZhangL., YuT., et al. 2018. Brittleness characteristics of tight oil siltstones. Applied Geophysics15, 175–187.
    [Google Scholar]
  39. VahidH. and PeterK.2003. Brittleness of rock and stability assessment in hard rock tunneling. Tunnelling and Underground Space Technology18, 35–48.
    [Google Scholar]
  40. VoigtW.1928. Lehrbuch der Kristallphysik, p. 739. Teubner, Leipzig
    [Google Scholar]
  41. WangM., WilkinsR.W.T., SongG., ZhangL., XuX. and LiZ.2015. Geochemical and geological characteristics of the Es3L lacustrine shale in the Bonan sag, Bohai Bay Basin, China. International Journal of Coal Geology138, 16–29.
    [Google Scholar]
  42. XiaY., LiL., TangC., MaS., LiM. and BaoC.2016. Rock brittleness evaluation based on stress dropping rate after peak stress and energy ratio. Chinese Journal of Rock Mechanics and Engineering35, 1141–1154.
    [Google Scholar]
  43. XuS. and WhiteR.E.1995. A new velocity for clay‐sand mixtures. Geophysical Prospecting43, 91–118.
    [Google Scholar]
  44. YagizS.2009. Assessment of brittleness using rock strength and density with punch penetration test. Tunnelling and Underground Space Technology24, 66–74.
    [Google Scholar]
  45. YangZ., HeT. and ZouC.2017. Shales in the Qiongzhusi and Wufeng‐Longmaxi Formations: a rock‐physics model and analysis of the effective pore aspect ratio. Applied Geophysics14, 325–336.
    [Google Scholar]
  46. YangK., XiaoJ., WangY. and NingX.2017. A study on Qingshankou Formation's tight oil characteristics and accumulation mode in the northern Songliao Basin. Acta Sedimentologica Sinica35, 600–610.
    [Google Scholar]
  47. ZhangL., BaJ., FuL., CarcioneJ.M. and CaoC.2019. Estimation of pore microstructure by using the static and dynamic moduli. International Journal of Rock Mechanics and Mining Sciences113, 24–30.
    [Google Scholar]
  48. ZhenJ. and LiuY.2011. Review over physical model of fractured rock medium. Progress in Geophysics (in Chinese)26, 1708–1716.
    [Google Scholar]
  49. ZimmermanR.W.1991. Compressibility of sandstones. Developments in Petroleum Science29, 173.
    [Google Scholar]
  50. ZouC., ZhuR., WuS., YangZ., TaoS., YuanX., et al. 2012. Types, characteristics, genesis and prospects of conventional and unconventional hydrocarbon accumulations: taking tight oil and tight gas in China as an instance. Acta Petrolel Sinica33, 173–187.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.12938
Loading
/content/journals/10.1111/1365-2478.12938
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Parameter estimation; Rock physics; Seismics

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error