1887
Volume 14, Issue 3
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

ABSTRACT

A neural network approach to porosity prediction below the well bottom from either the electrical resistivity logs or electromagnetic resistivity profiles is developed using the data from Soultz‐sous‐Forets area (France). It is shown that the neural network approach enables the porosity to be predicted at the depths below the bottom of the borehole from the resistivity well‐logging data and by resistivity profiles determined by inversion of the electromagnetic sounding data collected in the vicinity of the well. The results indicate that the forecasts based on the electrical logging data are more accurate (average relative errors being equal to 3%–5%) when the target depths do not exceed the double length of the well log used for calibration. In the absence of the resistivity logs at target depths or when the target to well depth ratio is large enough, the forecasts based on the electromagnetic resistivity data are more preferable.

Loading

Article metrics loading...

/content/journals/10.3997/1873-0604.2016019
2016-03-01
2024-04-25
Loading full text...

Full text loading...

References

  1. ArchieG.E.1942. The electrical resistivity log as an aid in determining some reservoir characteristics. Transactions of the American Institute of Mining and Metallurgical Engineers146, 54–62.
    [Google Scholar]
  2. BhattA. and HelleH.B.2002. Committee neural networks for porosity and permeability prediction from well logs. Geophysical Prospecting50, 645–660.
    [Google Scholar]
  3. Dell’AversanaP., BernasconiG., MiottiF. and RovettaD.2011. Joint inversion of rock properties from sonic, resistivity and density well‐ log measurements. Geophysical Prospecting59, 1144–1154.
    [Google Scholar]
  4. DezayesC., GenterA. and HooijkaasG.2005. Deep‐seated geology and fracture system of the EGS Soultz reservoir (France) based on recent 5km depth boreholes. World Geothermal Congress, Antalya, Turkey, Expanded Abstracts.
    [Google Scholar]
  5. DolbergD.M., HelgesenJ., HanssenT.H., MagnusI., SaigalG., and PedersenB.K.2000. Porosity prediction from seismic inversion, Lavrans Field, Halten Terrace, Norway. The Leading Edge4, 392–399.
    [Google Scholar]
  6. GeiermannJ. and SchillE.2010. 2‐D Magnetotellurics at the geothermal site at Soultz‐sous‐Forets: Resistivity distribution to about 3000 m depth. Comptes Rendus Geoscience342, 493–501.
    [Google Scholar]
  7. GuntoroT., PutriI. and BahriA.S.2013. Petrophysical relationship to predict synthetic porosity log. AAPG Annual Convention and Exhibition, Pittsburgh, USA, Expanded Abstracts.
    [Google Scholar]
  8. HaykinS.1999. Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall.
    [Google Scholar]
  9. HossainZ. and CohenA.J.2012. Relationship among porosity, permeability, electrical and elastic properties. 82nd SEG meeting, Las Vegas, USA, Expanded Abstracts.
    [Google Scholar]
  10. KalkomeyC.T.1997. Potential risks when using seismic attributes as predictors of reservoir properties. The Leading Edge3, 247–251.
    [Google Scholar]
  11. LedesertB.1993. Fracturation et paleocirculations hydrothermales. Application au granite de Soultz‐sous‐Forêts. Ph.D. thesis, Universite de Poitiers, France.
    [Google Scholar]
  12. MiottiE., BernasconiG. and RovettaD.2009a. Joint inversion of rock properties: A case study. 71st EAGE meeting, Amsterdam, the Netherlands, Expanded Abstracts, 2013.
    [Google Scholar]
  13. MiottiE., RovettaD. and BernasconiG.2009b. Joint inversion of well‐log data. 79th SEG meeting, Houston, Texas, USA, Expanded Abstracts, 2218–2222.
    [Google Scholar]
  14. PanR. and MaX.1997. An approach to reserve estimation enhanced with 3‐D seismic data. Renewable Resources6(4), 251–255.
    [Google Scholar]
  15. RosenerM.2007. Etude petrophysique et modelisation des transferts thermiques entre roche et fluide dans le contexte geothermique de Soultz‐sous‐Forets. Ph.D. dissertation, Universite Louis Pasteur Strasbourg, France.
    [Google Scholar]
  16. SinghS., KanliA.I. and SevgenS.2016a. A general approach for porosity estimation using artificial neural network method, a case study from Kansas gas field. Studia Geophysica et Geodaetica60, 130–140.
    [Google Scholar]
  17. SinghS., KanliA.I. and SevgenS.2016b. Estimating shear wave velocities in oil fields: a neural network approach. Geosciences Journal20(2), 221–228.
    [Google Scholar]
  18. SpichakV.V.2011. Application of ANN based techniques in EM induction studies. In: The Earth’s Magnetic Interior, IAGA Special Sopron Book Series, 1, pp. 19–30. Springer.
    [Google Scholar]
  19. SpichakV.V., GeiermannJ., ZakharovaO., CalcagnoP., GenterA. and SchillE.2015. Estimating deep temperatures in the Soultz‐sous‐Forets geothermal area (France) from magnetotelluric data. Near Surface Geophysics13(4), 397–408.
    [Google Scholar]
  20. SpichakV.V. and GoidinaA.2016. Neural network estimate of seismic velocities and resistivity of rocks from electromagnetic and seismic sounding data. Izvestiya, Physics of the Solid Earth3, 1–12.
    [Google Scholar]
  21. SurmaF.2003. Determination de la porosite des zones endommagees autour des failles et rle de l’etat du materiau sur les proprietes d’echange fluides‐roches: mineralogie, structures de porosite, carac‐teristiques mecaniques. Ph.D. dissertation, Universite Louis Pasteur Strasbourg, France.
    [Google Scholar]
  22. VernouxJ.F., GenterA., RazinP. and VinchonC.1995. Geological and petrophysical parameters of a deep fractured sandstone formation as applied to geothermal exploitation, EPS‐1 borehole, Soultz‐sous‐Forets, France. In: Rapport BRGM R 38622, pp. 70.
    [Google Scholar]
  23. VidalJ., GenterA. and SchmittbuhlJ.2015. How do permeable fractures in the Triassic sediments of Northern Alsace characterize the top of hydrothermal convective cells? Evidence from Soultz geothermal boreholes (France). Geothermal Energy3(8).
    [Google Scholar]
  24. VuatazF.‐D., BrachM., CriaudA. and FouillacC.1990. Geochemical monitoring of drilling fluids: a powerful tool to forecast and detect formation waters. SPE Formation Evaluation5(2), 177–184.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.3997/1873-0604.2016019
Loading
/content/journals/10.3997/1873-0604.2016019
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