Skip to main content

Advertisement

SpringerLink
Log in

Cell size optimization for fracture measure estimation in multi-scale studies within oil wells

  • Original Article
  • Published:
Carbonates and Evaporites Aims and scope Submit manuscript

Abstract

Fracture measure (FM) is a newly developed concept that shows the effectiveness of fractures in each horizon fuzzily. Different cell sizes could be used in fracture modeling, including FM calculations. The optimization of cell size should be carried out due to size of the target of study and other logging parameters. This work aims to investigate the limitations of using FM in different cell sizes. To obtain this purpose, initially, six different grids including 2.5, 5, 7, 15, 30, and 60 cm around the well axis are designed, and then the FM is calculated in each grid size by using an analytical relationship. Afterwards, four controlling factors are devised to find the most optimum cell size. The controlling factors are Log Response Uncertainty (LRU), Mean Squared Error (MSE), Fracture Length Effect (FLE), and Clearness of Fracture Measure (CFM), which are fused by using Sugeno integral method. The results show that the cell size of 30 cm is the most optimum size for FM estimation in the two wells under study. Finally, estimated FM in each cell size was fused again by the operator of Sugeno. Fused FM not only has the trend of calculated FM in the cell size of 30 cm, but also reconstructed the peaks of high frequency, which are not detectable in the calculated FM in the cell size of 30 cm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Genter A, Castaing C, Dezayes C, Tenzer H, Traineau H, Villemin T (1997) Comparative analysis of direct (core) and indirect (borehole imaging tools) collection of fracture data in the Hot Dry Rock Soultz reservoir (France). J Geophys Res Solid Earth 102(B7):15419–15431

    Article  Google Scholar 

  • Ja’fari A, Kadkhodaie-Ilkhchi A, Sharghi Y, Ghaedi M (2013) Integration of adaptive neuro-fuzzy inference system, neural networks and geostatistical methods for fracture density modeling. Oil Gas Sci Technol Rev IFP Energies Nouv 69(7):1143–1154. doi:10.2516/ogst/2012055

    Article  Google Scholar 

  • Ja’Fari A, Kadkhodaie-Ilkhchi A, Sharghi Y, Ghanavati K (2012) Fracture density estimation from petrophysical log data using the adaptive neuro-fuzzy inference system. J Geophys Eng 9(1):105–114

    Article  Google Scholar 

  • Jambayev AS (2013) Discrete fracture network modeling for a carbonate reservoir. Colorado School of Mines, Colorado

    Google Scholar 

  • Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic, theory and applications, 4. Prentice Hall New Jersey, New Jersey

    Google Scholar 

  • Krause F, Collins H, Nelson D, Machemer S, French P (1987) Multiscale anatomy of a reservoir: geological characterization of Pembina-Cardium pool, west-central Alberta, Canada. AAPG Bull 71(10):1233–1260

    Google Scholar 

  • Kuncheva LL (2004) Combining pattern classifiers, methods and algorithms. A Wiley-Interscience publication, Hoboken, p 350

    Book  Google Scholar 

  • Lacombe O, Bellahsen N, Mouthereau F (2011) Fracture patterns in the Zagros Simply Folded Belt (Fars, Iran): constraints on early collisional tectonic history and role of basement faults. Geol Mag 148(5–6):940–963

    Article  Google Scholar 

  • Laubach SE (1997) A method to detect natural fracture strike in sandstones. AAPG Bull 81(4):604–623

    Google Scholar 

  • Mabee SB, Hardcastle KC, Wise DU (1994) A method of collecting and analyzing lineaments for regional-scale fractured-bedrock aquifer studies. Ground Water 32(6):884–894

    Article  Google Scholar 

  • MacKnight RB, Silver E, Kennedy-Bowdoin T, Pickles WL, Waibel A (2004) Remote sensing analysis of structure and geothermal potential of the Humboldt Block, Nevada, geoscience and remote sensing symposium, 2004. IGARSS’04. Proceedings. 2004 IEEE International. IEEE

  • Majer EL, Peterson JE, Daley T, Kaelin B, Myer L, Queen J, D’Onfro P, Rizer W (1997) Fracture detection using crosswell and single well surveys. Geophysics 62(2):495–504

    Article  Google Scholar 

  • Masoudi P, Tokhmechi B, Memarian H, Ghiasi-Nasab M (2013) Scale-based categorization of geoscience projects in Iran. In: 1st national conference on exploration engineering of underground resources. University of Shahrood, Shahrood

  • Masoudi P, Tokhmechi B, Memarian H, Ghiasi-nasab M (2014) Development of scale-based categorization of geoscience projects in Iran, GSI32, Geological Survey of Iran

  • Masoudi P, Asgarinezhad Y, Tokhmechi B (2015) Feature selection for reservoir characterisation by Bayesian network. Arab J Geosci 8(5):3031–3043

    Article  Google Scholar 

  • Mazaheri A, Memarian H, Tokhmechi B, Araabi BN (2015) Developing fracture measure as an index of fracture impact on well-logs. Energy Explor Exploit 33(4):555–574

    Article  Google Scholar 

  • Motiei H (1993) Geology of Iran: stratigraphy of Zagros. Geological Survey of Iran Publication, Tehran, p 583

    Google Scholar 

  • Ortega OJ, Marrett RA, Laubach SE (2006) A scale-independent approach to fracture intensity and average spacing measurement. AAPG Bull 90(2):193–208

    Article  Google Scholar 

  • Ranjbaran M, Fayazi F, Al-Aasm I (2007) Sedimentology, depositional environment and sequence stratigraphy of the Asmari formation (Oligocene-Lower Miocene), Gachsaran Area; SW Iran. Carbonates Evaporites 22(2):135–148

    Article  Google Scholar 

  • Roehl PO, Choquette PW (1985) Carbonate petroleum reservoirs. Springer Science and Business Media, Berlin

    Book  Google Scholar 

  • Sabins FF (1999) Remote sensing for mineral exploration. Ore Geol Rev 14(3–4):157–183

    Article  Google Scholar 

  • Sheremetov L, Cosultchi A, Martínez-Muñoz J, Gonzalez-Sánchez A, Jiménez-Aquino MA (2014) Data-driven forecasting of naturally fractured Reservoirs based on nonlinear autoregressive neural Networks with exogenous input. J Petrol Sci Eng 123:106–119

    Article  Google Scholar 

  • Sisler JJ (1971) Study of Asmari Core Material FromWellsParsi 15 and 16. Parsi Field, Iran, OSCO

    Google Scholar 

  • Stephenson BJ, Koopman A, Hillgartner H, McQuillan H, Bourne S, Noad JJ, Rawnsley K (2007) Structural and stratigraphic controls on fold-related fracturing in the Zagros Mountains, Iran: implications for reservoir development. Geol Soc Lond Spec Publ 270(1):1–21

    Article  Google Scholar 

  • Tokhmchi B, Memarian H, Rezaee MR (2010) Estimation of the fracture density in fractured zones using petrophysical logs. J Petrol Sci Eng 72(1–2):206–213

    Article  Google Scholar 

  • Tokhmechi B, Memarian H, Noubari H, Moshiri B (2009a) A novel approach proposed for fractured zone detection using petrophysical logs. J Geophys Eng 6(4):365

    Article  Google Scholar 

  • Tokhmechi B, Memarian H, Rasouli V, Noubari HA, Moshiri B (2009b) Fracture detection from water saturation log data using a Fourier–wavelet approach. J Petrol Sci Eng 69(1–2):129–138

    Article  Google Scholar 

  • Wong PM (2003) A novel technique for modeling fracture intensity: a case study from the Pinedale anticline in Wyoming. AAPG Bull 87(11):1717–1727

    Article  Google Scholar 

  • Zerrouki AA, Aïfa T, Baddari K (2014) Prediction of natural fracture porosity from well log data by means of fuzzy ranking and an artificial neural network in Hassi Messaoud oil field, Algeria. J Petrol Sci Eng 115:78–89

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to declare their sincere thanks to the National Iranian South Oil Company (NISOC) and Mrs. Mohammadian and Mr. Kordavani of the NISOC for their support to provide the information and their technical input regarding this work.

Author information

Authors and Affiliations

  1. School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Atie Mazaheri & Hossein Memarian

  2. Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, ON, Canada

    Hossein Memarian

  3. School of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran

    Behzad Tokhmechi

  4. School of Electrical and Computer Engineering, Control and Intelligent Processing Center of Excellence, College of Engineering, University of Tehran, Tehran, Iran

    Babak Nadjar Araabi

  5. School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics, Tehran, Iran

    Babak Nadjar Araabi

Authors
  1. Atie Mazaheri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hossein Memarian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Behzad Tokhmechi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Babak Nadjar Araabi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hossein Memarian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mazaheri, A., Memarian, H., Tokhmechi, B. et al. Cell size optimization for fracture measure estimation in multi-scale studies within oil wells. Carbonates Evaporites 34, 261–272 (2019). https://doi.org/10.1007/s13146-017-0378-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13146-017-0378-x

Keywords

Search

Navigation