1887
  1. Home
  2. Conferences
  3. Conference Proceedings
  4. Third EAGE Integrated Reservoir Modelling Conference
  5. Conference paper

Abstract

Summary

Fracture prediction at borehole and distribution in spatiality are crucial for evaluating and developing a fractured reservoir. It is difficult to characterise fractures properly because the extremely heterogeneity and complexity of them.This paper presents an integrated approach for fracture prediction and fracture modelling for a naturally fractured buried-hill carbonate reservoir in Jingbei Oilfield. The work we have done is providing an enhanced workflow combining three dimensional seismic data volume, conventional well logs and core data. In order to characterize the fractures comprehensively, we divided the fracture into two types that are large-scale fracture (LSF) and small-scale fracture (SSF).The ant tracking attribute is used to detect the large-scale fractures.Small-scale fractures are firstly observed from the core. By analyzing the core data, then the fracture density and dip angle are evaluated. Additionally, we predict the spatial distribution of the fracture density comparing the core data and well logging data. In this prediction process, artificial neural network algorithm is used to create a numerical model.Different methods are used to build the Discrete Fracture Network (DFN) model for different types of fractures deterministically stochastically.The result shows that the workflow of fracture prediction and modeling is effective for the naturally fractured buried-hill carbonate reservoir.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201602395
2016-12-05
2024-04-25

  • From This Site

    /content/papers/10.3997/2214-4609.201602395
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Al-Anazi, A. and Babadagli, T.
    [2010] Automatic fracture density update using smart well data and artificial neural networks. Computer & Geosciences, 36, 335–347.
     [Google Scholar]
  2. Basir, H.M. Javaherian, A. and Yaraki, M.T.
    [2013] Multi-attribute ant-tracking and neural network for fault detection: a case study of an Iranian oilfield. Journal of Geophysics and Eengineering, 10, 1–10.
     [Google Scholar]
  3. La Pointe, P.
    [2004] A workflow for building and validation static reservoir models for fractured reservoirs for direct implementation into Dynamic Simulators. AAPG Annual meeting, Dallas, article #90026, 18–21.
     [Google Scholar]
  4. Lefranc, M. Farag, S. Souche, L. and Dubois, A.
    [2012] Fractured basement reservoir characterization for fracture distribution, porosity and permeability prediction. AAPG International Conference and Exhibition, Singapore, September 16–19, article #41106.
     [Google Scholar]
  5. Ngeri, A.P. Tamunobereton-ari, I. and Amakiri, A.R.C.
    , [2015] Ant-tracker attributes: an effective approach to enhancing fault identification and interpretation. IOSR Journal of VLSI and Signal Processing, 5(6), 67–73.
     [Google Scholar]
  6. Nelson, R.A.
    [2001] Geologic Analysis of Naturally Fractured Reservoirs, 2nd ed. Gulf Professional Publishing, Huston, Texas, USA.
     [Google Scholar]
  7. Pedersen, S.I. Skov, T. Hetlelid, A. Fayemendy, P. Randen, T. and Sonneland, L.
    [2003] New paradigm of fault interpretation. SEG Expanded Abstracts, 350–353
     [Google Scholar]
  8. Petrel
    [2014] Petrel Help Center. Schlumberger.
     [Google Scholar]
  9. Rijks, E.J.H. and Jauffred, J.C.E.M.
    [1991] Attribute extraction: an important application in any detailed 3-D interpretation study. Leading Edge, 10, 11–9.
     [Google Scholar]
  10. Schlumberger
    [2013]. High-definition formation microimager. www.slb.com/fmi-hd.
     [Google Scholar]
  11. Silva, C.C. Marcolino, C.S. and Lima, F.D.
    [2005] Automatic fault extraction using ant tracking algorithm in the Marlim South Filed, Campos Basin. 9th International Congress of the Brazilian Geophysical Society, Extended Abstracts, 857–860.
     [Google Scholar]
  12. Tokhmchi, B. Memarian, H. and Rezaee, M.R.
    [2010] Estimation of the fracture density in fractured zones using petrophysical logs. Journal of Petroleum Science and Engineering, 72, 206–213.
     [Google Scholar]
  13. Weatherford
    [2013] Compact Microimager. http://www.weatherford.com/doc/wft268562.
     [Google Scholar]
  14. Zazoun, R.S.
    [2013] Fracture density estimation from core and conventional well logs data using artificial neural networks: The Cambro-Ordovician reservoir of Mesdar oil field, Algeria. Journal of African Earth Sciences, 83, 55–73.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201602395
Loading
A Novel Workflow in Discrete Fracture Network Modelling: A Case Study in Jingbei Oilfiled, Northeastern China
Conference Proceedings 2016, 1 (2016); https://doi.org/10.3997/2214-4609.201602395
/content/papers/10.3997/2214-4609.201602395
/content/papers/10.3997/2214-4609.201602395
Loading

Data & Media loading...

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