1887
  1. Home
  2. A-Z Publications
  3. Petroleum Geoscience
  4. Volume 29, Issue 4
  5. Article
Volume 29, Issue 4
  • ISSN: 1354-0793
  • E-ISSN:

Abstract

This research focuses on how ‘static’ properties of fracture networks can be studied by considering ‘dynamic’ flow simulation, while static properties such as clustering, connectivity, variation in aperture and, of course, anisotropy of fracture networks can be quantified using different geostatistical/data analysis techniques. The flow responses through such networks can be simulated to check if flow simulation can be used as a tool for evaluating its geometry. In order to achieve this, outcrop analogues of fractured reservoirs are converted into permeability structured grids implementing the fracture continuum (FC) concept. These FC models are flow simulated in a streamline simulator, TRACE3D. Results of the first experiment show that rather than the ‘fractal dimension’, the ‘lacunarity parameter’, which quantifies scale-dependent clustering of fractures, is a unique identifier of network geometry and acts as a proxy for fracture connectivity and an indicator of flow behaviour. The FC model further accommodates variability in fracture apertures and, thus, in a second experiment a set of models with a hierarchical aperture distribution was built and tested for their time-of-flight (TOF) and recovery curves, which showed that smaller fractures with narrow apertures do not significantly contribute to flow. In a third experiment considering anisotropy, it was observed that tightly clustered fractures along preferential directions can be identified from anisotropy in flow patterns. The results from these three experiments show that flow patterns in fracture networks can indicate the overall scale-dependent clustering, the anisotropy that arises from such clustering and that narrower fractures do not significantly alter the overall flow behaviour.

This article is part of the Digitally enabled geoscience workflows: unlocking the power of our data collection available at: https://www.lyellcollection.org/topic/collections/digitally-enabled-geoscience-workflows

© 2023 The Author(s). Published by The Geological Society of London for GSL and EAGE. All rights reserved
Loading

Article metrics loading...

/content/journals/10.1144/petgeo2023-032
2023-10-18
2025-06-20

  • From This Site

    /content/journals/10.1144/petgeo2023-032
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Allain, C. and Cloitre, M.1991. Characterizing the lacunarity of random and deterministic fractal sets. Physical Review A, 44, 3552–3558, https://doi.org/10.1103/PhysRevA.44.3552
     [Google Scholar]
  2. Andrianov, N. and Nick, H.M.2019. Modeling of water flood efficiency using outcrop-based fractured models. Journal of Petroleum Science and Engineering, 183, 106350, https://doi.org/10.1016/j.petrol.2019.106350
     [Google Scholar]
  3. Barton, C.C. and Heish, P.A.1989. Physical and Hydrologic-Flow Properties of Fractures. Field Trip Guidebook, T385. American Geophysical Union, Washington, DC.
     [Google Scholar]
  4. Belayneh, M., Geiger, S. and Matthäi, S.K.2006. Numerical simulation of water injection into layered fractured carbonate reservoir analogs. AAPG Bulletin, 90, 1473–1493, https://doi.org/10.1306/05090605153
     [Google Scholar]
  5. Berkowitz, B.1995. Analysis of fracture network connectivity using percolation theory. Mathematical Geology, 27, 467–483, https://doi.org/10.1007/BF02084422
     [Google Scholar]
  6. Berkowitz, B. and Hadad, A.1997. Fractal and multifractal measures of natural and synthetic fracture networks. Journal of Geophysical Research: Solid Earth, 102, 12 205–12 218, https://doi.org/10.1029/97JB00304
     [Google Scholar]
  7. Bisdom, K., Gauthier, B.D.M., Bertotti, G. and Hardebol, N.J.2014. Calibrating discrete fracture network models with a carbonate three-dimensional outcrop fracture network: Implications for naturally fractured reservoir modeling. AAPG Bulletin, 98, 1351–1376, https://doi.org/10.1306/02031413060
     [Google Scholar]
  8. Bonnet, E., Bour, O., Odling, N.E., Davy, P., Main, I., Cowie, P. and Berkowitz, B.2001. Scaling of fracture system in geological media. Reviews of Geophysics, 39, 347–383, https://doi.org/10.1029/1999RG000074
     [Google Scholar]
  9. Bour, O. and Davy, P.1997. Connectivity of random fault networks following a power law fault length distribution. Water Resources Research, 33, 1567–1583, https://doi.org/10.1029/96WR00433
     [Google Scholar]
  10. Bour, O., Davy, P., Darcel, C. and Odling, N.E.2002. A statistical scaling model for fracture network geometry, with validation on a multiscale mapping of a joint network (Hornelen Basin, Norway). Journal of Geophysical Research: Solid Earth, 107, ETG 4-1–ETG 4-12, https://doi.org/10.1029/2001JB000176
     [Google Scholar]
  11. Cappa, F., Guglielmi, Y., Rutqvist, J., Tsang, C.F. and Thoraval, A.2008. Estimation of fracture flow parameters through numerical analysis of hydromechanical pressure pulses. Water Resources Research, 44, W11408, https://doi.org/10.1029/2008WR007015
     [Google Scholar]
  12. Chen, H., Oniashi, T., Olalotiti-Lawal, F. and Datta-Gupta, A.2018. Streamline tracing and applications in naturally fractured reservoirs using embedded discrete fracture models. Paper SPE-191475-MS presented at theSPE Annual Technical Conference and Exhibition, September 24–26, 2018, Dallas, Texas, USA, https://doi.org/10.2118/191475-MS
     [Google Scholar]
  13. Darcel, C., Bour, O., Davy, P. and de Dreuzy, J.R.2003. Connectivity properties of two-dimensional fracture networks with stochastic fractal correlation. Water Resources Research, 39, 1272, https://doi.org/10.1029/2002WR001628
     [Google Scholar]
  14. Datta-Gupta, A. and King, M.J.2007. Streamline Simulation: Theory and Practice. Textbook Series, 11. Society of Petroleum Engineers, Richardson, TX.
     [Google Scholar]
  15. de Dreuzy, J.R., Davy, P. and Bour, O.2002. Hydraulic properties of two-dimensional random fracture networks following power law distributions of length and aperture. Water Resources Research, 38, 12-1–12-9, https://doi.org/10.1029/2001WR001009
     [Google Scholar]
  16. Gillespie, P.A., Johnston, J.D., Loriga, M.A., McCaffrey, K.J.W., Walsh, J.J. and Watterson, J.1999. Influence of layering on vein systematics in line samples. Geological Society, London, Special Publications, 155, 35–56, https://doi.org/10.1144/GSL.SP.1999.155.01.05
     [Google Scholar]
  17. Healy, D., Rizzo, R.E. et al.2017. FracPaQ: A MATLAB™ toolbox for the quantification of fracture patterns. Journal of Structural Geology, 95, 1–16, https://doi.org/10.1016/j.jsg.2016.12.003
     [Google Scholar]
  18. Kruhl, J.H.2013. Fractal-geometry techniques in the quantification of complex rock structures: A special view on scaling regimes, inhomogeneity and anisotropy. Journal of Structural Geology, 46, 2–21, https://doi.org/10.1016/j.jsg.2012.10.002
     [Google Scholar]
  19. Langevin, C.D.2003. Stochastic ground water flow simulation with a fracture zone continuum model. Ground Water, 41, 587–601, https://doi.org/10.1111/j.1745-6584.2003.tb02397.x
     [Google Scholar]
  20. Matthai, S.K. and Belayneh, M.2004. Fluid flow partitioning between fractures and a permeable rock matrix. Geophysical Research Letters, 31, L07602, https://doi.org/10.1029/2003GL019027
     [Google Scholar]
  21. National Research Council1996. Rock Fractures and Fluid Flow: Contemporary Understanding and Applications. The National Academies Press, Washington, DC.
     [Google Scholar]
  22. Nelson, R.A.2001. Geologic Analysis of Naturally Fractured Reservoirs. Gulf Professional Publishing, Houston, TX.
     [Google Scholar]
  23. Neuman, S.P.2005. Trends, prospects and challenges in quantifying flow and transport through fractured rocks. Hydrogeology Journal, 13, 124–147, https://doi.org/10.1007/s10040-004-0397-2
     [Google Scholar]
  24. Odling, N.E.1992. Network properties of a two-dimensional natural fracture pattern. Pure and Applied Geophysics, 138, 94–114, https://doi.org/10.1007/BF00876716
     [Google Scholar]
  25. Odling, N.E.1997. Scaling and connectivity in joint systems in sandstones from western Norway. Journal of Structural Geology, 27, 1257–1271, https://doi.org/10.1016/S0191-8141(97)00041-2
     [Google Scholar]
  26. Odling, N.E. and Roden, J.E.1997. Contaminant transport in fractured rocks with significant matrix permeability, using natural fracture geometries. Journal of Contaminant Hydrology, 27, 263–283, https://doi.org/10.1016/S0169-7722(96)00096-4
     [Google Scholar]
  27. Plotnick, R.E., Gardner, R.H. and O'Neill, R.V.1993. Lacunarity indices as measures of landscape texture. Landscape Ecology, 8, 201–211, https://doi.org/10.1007/BF00125351
     [Google Scholar]
  28. Plotnick, R.E., Gardner, R.H., Hargrove, W.W., Prestegaard, K. and Perlmutter, M.1996. Lacunarity analysis: a general technique for the analysis of spatial patterns. Physical Review E, 53, 5461–5468, https://doi.org/10.1103/PhysRevE.53.5461
     [Google Scholar]
  29. Reeves, D.M., Benson, D.A. and Meerschaert, M.M.2008. Transport of conservative solutes in simulated fracture networks: 1. Synthetic data generation. Water Resources Research, 44, W05404, https://doi.org/10.1029/2007WR006179
     [Google Scholar]
  30. Robinson, P.1983. Connectivity of fracture systems: a percolation theory approach. Journal of Physics A: Mathematical and General, 16, 605, https://doi.org/10.1088/0305-4470/16/3/020
     [Google Scholar]
  31. Rohrbaugh, M.B., Dunne, W.M. and Mauldon, M.2002. Estimating fracture trace intensity, density and mean length using circular scanlines and windows. AAPG Bulletin, 86, 2089–2104, https://doi.org/10.1306/61EEDE0E-173E-11D7-8645000102C1865D
     [Google Scholar]
  32. Roy, A. and Perfect, E.2013. Anisotropy in fracture clustering: a lacunarity study. Geological Society of America Abstracts with Programs, 45, 75, https://gsa.confex.com/gsa/2013SC/webprogram/Paper217475.html
     [Google Scholar]
  33. Roy, A., Perfect, E., Dunne, W.M. and McKay, L.D.2007. Fractal characterization of fracture networks: an improved box-counting technique. Journal of Geophysical Research: Solid Earth, 112, B12201, https://doi.org/10.1029/2006JB004582
     [Google Scholar]
  34. Roy, A., Perfect, E., Dunne, W.M., Odling, N.E. and Kim, J.2010. Lacunarity analysis of fracture networks: evidence for scale-dependent clustering. Journal of Structural Geology, 32, 1444–1449, https://doi.org/10.1016/j.jsg.2010.08.010
     [Google Scholar]
  35. Roy, A., Perfect, E., Kumar, J. and Mills, R.T.2012. Does anisotropy in fracture clustering translate into anisotropy in intrinsic permeability. Search and Discovery Article #40959, AAPG Annual Convention and Exhibition, April 22–25, 2012, Long Beach, California, USA.
     [Google Scholar]
  36. Roy, A., Perfect, E., Dunne, W.M. and McKay, L.D.2014. A technique for revealing scale-dependent patterns in fracture spacing data. Journal of Geophysical Research: Solid Earth, 119, 5979–5986, https://doi.org/10.1002/2013JB010647
     [Google Scholar]
  37. Sahu, A.K.2022. Characterization of Fracture Networks from Outrop Analogs: A Flow Simulation Approach. PhD thesis, Indian Institute of Technology, Kharagpur, India.
     [Google Scholar]
  38. Sahu, A.K. and Roy, A.2020. Clustering, connectivity and flow responses of deterministic fractal-fracture networks. Advances in Geosciences, 54, 149–156, https://doi.org/10.5194/adgeo-54-149-2020
     [Google Scholar]
  39. Sahu, A.K. and Roy, A.2021. Clustering, connectivity and flow in naturally fractured reservoir analogs. Paper SPE-206009-MS presented at theSPE Annual Technical Conference and Exhibition, September 21–23, 2021, Dubai, UAE, https://doi.org/10.2118/206009-MS
     [Google Scholar]
  40. Sarkar, S., Toksoz, M.N. and Burns, D.R.2004. Fluid Flow Modeling in Fractures. Corpus ID 14317982. Massachusetts Institute of Technology. Earth Resources Laboratory, Cambridge, MA.
     [Google Scholar]
  41. Snow, D.T.1969. Anisotropic permeability of fractured media. Water Resource Research, 5, 1273–1289, https://doi.org/465 10.1029/WR005i006p01273
     [Google Scholar]
  42. Somogyvari, M., Jalali, M., Parras, S.J. and Bayer, P.2017. Synthetic fracture network characterization with transdimensional inversion. Water Resources Research, 53, 5104–5123, https://doi.org/10.1002/2016WR020293
     [Google Scholar]
  43. Stauffer, D. and Aharony, A.1992. Introduction to Percolation Theory. 2nd edn. Taylor & Francis, London.
     [Google Scholar]
  44. Svensson, U.2001. A continuum representation of fracture networks. Part II: application to the Äspo Hard Rock Laboratory. Journal of Hydrology, 250, 187–205, https://doi.org/10.1016/S0022-1694(01)00436-X
     [Google Scholar]
  45. Zimmerman, R.W. and Bodvarsson, G.S.1996. Hydraulic conductivity of rock fracture. Transport in Porous Media, 23, 1–30, https://doi.org/10.1007/BF00145263
     [Google Scholar]
/content/journals/10.1144/petgeo2023-032
Loading
Characterizing fractured reservoirs by integrating outcrop analogue studies with flow simulations
Petroleum Geoscience 29, petgeo2023-032 (2023); https://doi.org/10.1144/petgeo2023-032
/content/journals/10.1144/petgeo2023-032
/content/journals/10.1144/petgeo2023-032
Loading

Data & Media loading...

  • Article Type: Research Article

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