1887
Volume 53, Issue 3
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533

Abstract

In geothermal exploration, the subsurface temperature distribution is an important parameter since it is closely related to the existing geothermal system. However, subsurface temperature information can only be obtained from boreholes, requiring a considerable cost and a limited number. A new geothermometry technique based on the relationship between temperature and resistivity has been proposed to overcome this difficulty. In order to enhance the reliability of this method, the subsurface heat condition in a different geological and geographical location was reconstructed. The temperature up to some depth beneath the surface could be estimated incorporating the back-propagation neural network using resistivity determined by high-resolution audio-magnetotelluric soundings. The temperature profiles resulting from the neural network fit with those observed from nearby boreholes, providing a satisfying evaluation result of the model’s predictive powers. Joint interpretation was then carried out following the one-dimensional resistivity inversion result and temperature cross-section. Both resistivity and temperature anomalies show an excellent agreement, so that subsurface anomaly such as altered layers, reservoir zone, and some possible faults was detected. With an appropriate training strategy, this technique can help to significantly reduce the costs at estimating the subsurface temperature.

© 2021 Australian Society of Exploration Geophysicists
Loading

Article metrics loading...

/content/journals/10.1080/08123985.2021.1949945
2022-05-04
2026-01-13

  • From This Site

    /content/journals/10.1080/08123985.2021.1949945
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Akpan, A.E., M.Narayanan, and T.Harinarayana. 2014. Estimation of subsurface temperatures in the Tattapani geothermal field, central India, from limited volume of magnetotelluric data and borehole thermograms using a constructive back-propagation neural network. Earth Interactions18. doi:10.1175/2013EI000539.1.
     https://doi.org/10.1175/2013EI000539.1 [Google Scholar]
  2. Artemieva, I.M.2006. Global 1° × 1° thermal model TC1 for the continental lithosphere: implications for lithosphere secular evolution. Tectonophysics416: 245–77. doi:10.1016/j.tecto.2005.11.022.
     https://doi.org/10.1016/j.tecto.2005.11.022 [Google Scholar]
  3. Azcona, D., and K.Casey. 2015. Micro-analytics for student performance prediction, leveraging fine-grained learning analysis to predict performance. International Journal of Computer Science and Software Engineering4: 218–23.
     [Google Scholar]
  4. Bajpai, S., K.Jain, and N.Jain. 2011. Artificial neural network. International Journal of Soft Computing and Engineering1: 27–31.
     [Google Scholar]
  5. Bassam, A., E.Santoyo, J.Andaverde, J.A.Hernández, and O.M.Espinoza-Ojeda. 2010. Estimation of static formation temperatures in geothermal wells by using an artificial neural network approach. Computers & Geosciences36: 1191–9. doi:10.1016/j.cageo.2010.01.006.
     https://doi.org/10.1016/j.cageo.2010.01.006 [Google Scholar]
  6. Benvenuto, N., and F.Piazza. 1992. The back-propagation algorithm. IEEE Transactions on Signal Processing40: 967–9. doi:10.1109/78.127967.
     https://doi.org/10.1109/78.127967 [Google Scholar]
  7. Caldwell, T.G., H.M.Bibby, and C.Brown. 2004. The magnetotelluric phase tensor. Geophysical Journal International158: 457–69. doi:10.1111/j.1365‑246X.2004.02281.x.
     https://doi.org/10.1111/j.1365-246X.2004.02281.x [Google Scholar]
  8. Constable, S.C.1987. Occam's inversion: A practical algorithm for generating smooth models from electromagnetic sounding data. Geophysics52: 289. doi:10.1190/1.1442303.
     https://doi.org/10.1190/1.1442303 [Google Scholar]
  9. Corchado, J.M., and C.Fyfe. 1999. Unsupervised neural method for temperature forecasting. Artificial Intelligence in Engineering13: 351–7. doi:10.1016/S0954‑1810(99)00007‑2.
     https://doi.org/10.1016/S0954-1810(99)00007-2 [Google Scholar]
  10. D'amore, F., and C.Panichi. 1985. Geochemistry in geothermal exploration. International Journal of Energy Research9: 277–98. doi:10.1002/er.4440090307.
     https://doi.org/10.1002/er.4440090307 [Google Scholar]
  11. de Groot-Hedlin, C.1990. Occam’s inversion to generate smooth, two-dimensional models from magnetotelluric data. Geophysics55: 1613. doi:10.1190/1.1442813.
     https://doi.org/10.1190/1.1442813 [Google Scholar]
  12. Fushiki, T., 2011, Estimation of prediction error by using K-fold cross-validation.Statistics and Computing, 21, 137–46. doi:10.1007/s11222‑009‑9153‑8
     https://doi.org/10.1007/s11222-009-9153-8 [Google Scholar]
  13. García, X., and A.G.Jones. 2005. A new methodology for the acquisition and processing of audio-magnetotelluric (AMT) data in the AMT dead band. Geophysics70: G119–G126. doi:10.1190/1.2073889.
     https://doi.org/10.1190/1.2073889 [Google Scholar]
  14. Goko, K.2000. Structure and hydrology of the origi field, West Kirishima geothermal area, Kyushu, Japan. Geothermics29: 127–49. doi:10.1016/S0375‑6505(99)00055‑3.
     https://doi.org/10.1016/S0375-6505(99)00055-3 [Google Scholar]
  15. Groom, R.W., and R.C.Bailey. 1989. Decomposition of magnetotelluric impedance tensors in the presence of local three-dimensional galvanic distortion. Journal of Geophysical Research94: 1913–25. doi:10.1029/JB094iB02p01913.
     https://doi.org/10.1029/JB094iB02p01913 [Google Scholar]
  16. Guo, Q., and Y.Wang. 2012. Geochemistry of hot springs in the Tengchong hydrothermal areas, Southwestern China. Journal of Volcanology and Geothermal Research215-216: 61–73. doi:10.1016/j.jvolgeores.2011.12.003.
     https://doi.org/10.1016/j.jvolgeores.2011.12.003 [Google Scholar]
  17. Gupta, H., and S.Roy. 2007. Geothermal Energy: a alternative resource for the 21st century. Elsevier. doi:10.1016/B978‑0‑444‑52875‑9.X5000‑X.
     https://doi.org/10.1016/B978-0-444-52875-9.X5000-X
  18. Kalogirou, S.A., G.A.Florides, P.D.Pouloupatis, I.Panayides, J.Joseph-Stylianou, and Z.Zomeni. 2012. Artificial neural networks for the generation of geothermal maps of ground temperature at various depths by considering land configuration. Energy48: 233–40. doi:10.1016/j.energy.2012.06.045.
     https://doi.org/10.1016/j.energy.2012.06.045 [Google Scholar]
  19. Key, K.2009. 1D inversion of multicomponent, multifrequency marine CSEM data: Methodology and synthetic studies for resolving thin resistive layers. Geophysics74: F9–F20. doi:10.1190/1.3058434.
     https://doi.org/10.1190/1.3058434 [Google Scholar]
  20. Kharaka, Y.K., and R.H.Mariner. 1989. Chemical geothermometers and their application to formation waters from Sedimentary basins. In Thermal history of Sedimentary basins, eds. N. D.Naeser, and T. H.McCulloh. New York: Springer. doi:10.1007/978‑1‑4612‑3492‑0_6.
     https://doi.org/10.1007/978-1-4612-3492-0_6 [Google Scholar]
  21. Kirkby, A., F.Zhang, and J.R.Peacock. 2019. The MTPy software package for magnetotelluric data analysis and visualization. Journal of Open Source Software4 no. 37: 1358. doi:10.21105/joss.01358.
     https://doi.org/10.21105/joss.01358 [Google Scholar]
  22. Koike, K., S.Matsuda, and B.Gu. 2001. Evaluation of interpolation accuracy of neural kriging with application to temperature-distribution analysis. Mathematical Geology33: 421–48. doi:10.1023/A:1011084812324.
     https://doi.org/10.1023/A:1011084812324 [Google Scholar]
  23. Kummerow, J., and S.Rabb. 2015a. Temperature dependence of electrical resistivity – part 1: experimental investigations of hydrothermal fluids. Energy Procedia76: 240–6. doi:10.1016/j.egypro.2015.07.854.
     https://doi.org/10.1016/j.egypro.2015.07.854 [Google Scholar]
  24. Kummerow, J., and S.Rabb. 2015b. Temperature dependence of electrical resistivity – part 2: experimental set-up to study fluid saturated rocks. Energy Procedia76: 247–55. doi:10.1016/j.egypro.2015.07.855.
     https://doi.org/10.1016/j.egypro.2015.07.855 [Google Scholar]
  25. LeCun, Y.A., L.Bottou, G.B.Orr, and K.R.Müller. 2012. Efficient BackProp. In Neural networks: tricks of the trade, lecture notes in Computer Science vol. 7700, eds. G.Montavon, G. B.Orr, and K. R.Müller. Berlin, Heidelberg: Springer. doi:10.1007/978‑3‑642‑35289‑8_3.
     https://doi.org/10.1007/978-3-642-35289-8_3 [Google Scholar]
  26. Limberger, J., P.Calcagno, A.Manzella, E.Trumpy, T.Boxem, M.P.D.Pluymaekers, and J.D.van Wees. 2014. Assessing the prospective resource base for enhanced geothermal systems in Europe. Geothermal Energy Science2 no. 1: 55–71. doi:10.5194/gtes‑2‑55‑2014.
     https://doi.org/10.5194/gtes-2-55-2014 [Google Scholar]
  27. Llera, F.J., M.Sate, K.Nakatsuka, and H.Yokoyama. 1990. Temperature dependence of the electrical resistivity of water-saturated rocks. Geophysics55: 576–85. doi:10.1190/1.1442869.
     https://doi.org/10.1190/1.1442869 [Google Scholar]
  28. Maltarollo, V.G., K.M.Honorio, and A.B.F.da Silva. 2013. Application of artificial neural networks in chemical problem. In Artificial neural network – architecture and applications, Ch.10, ed. K.Suzuki. IntechOpen. doi:10.5772/51275.
     https://doi.org/10.5772/51275 [Google Scholar]
  29. Maryadi, M., and H.Mizunaga. 2016. Correlation analysis between audio-magnetotelluric and borehole thermograms data for developing electromagnetic geothermometry. Proceedings of International symposium on Earth Science and technology 2016, fukuoka, Japan, paper No.97.
  30. Nielsen, M.A.2015. Neural networks and deep learning. ct: Determination Press. http://neuralnetworksanddeeplearning.com/.
  31. Pribnow, D., and R.Schellschmidt. 2000. Thermal tracking of upper crustal fluid flow in the rhine graben. Geophysical Research Letters27: 1957–60. doi:10.1029/2000GL008494.
     https://doi.org/10.1029/2000GL008494 [Google Scholar]
  32. Rojas, R.1996. Fast learning algorithms. In Neural networks, ed. Berlin, Heidelberg: Springer. doi:10.1007/978‑3‑642‑61068‑4_8.
     https://doi.org/10.1007/978-3-642-61068-4_8 [Google Scholar]
  33. Ruhaak, W., K.Bar, and I.Sass. 2014. Combining numerical modeling with geostatistical interpolation for an improved reservoir exploration. Energy Procedia59: 315–22. doi:10.1016/j.egypro.2014.10.383.
     https://doi.org/10.1016/j.egypro.2014.10.383 [Google Scholar]
  34. Spichak, V.2011. Application of ANN based techniques in EM induction studies. In The Earth's magnetic interior: IAGA special sopron book series, 1, eds. E.Petrovsky, E.Herrero-Bervera, T.Harinarayana, and DIvers, 19–30. Dordrecht: Springer.
     [Google Scholar]
  35. Spichak, V., and I.Popova. 2000. Artificial neural network inversion of magnetotelluric data in terms of three-dimensional earth macroparameters. Geophysical Journal International142: 15–26. doi:10.1046/j.1365‑246X.2000.00065.x.
     https://doi.org/10.1046/j.1365-246X.2000.00065.x [Google Scholar]
  36. Spichak, V.V., and O.K.Zakharova. 2009. The application of an indirect electromagnetic geothermometer to temperature extrapolation in depth. Geophysical Prospecting57: 653–64. doi:10.1111/j.1365‑2478.2008.00778.x.
     https://doi.org/10.1111/j.1365-2478.2008.00778.x [Google Scholar]
  37. Spichak, V.V., and O.K.Zakharova. 2015. Electromagnetic geothermometry. Amsterdam: Elsevier. doi:10.1016/C2014‑0‑01908‑9.
     https://doi.org/10.1016/C2014-0-01908-9
  38. Spichak, V.V., O.K.Zakharova, and A.K.Rybin. 2011. Methodology of the indirect temperature estimation basing on magnetotelluric data: northern tien shan case study. Journal of Applied Geophysics73: 164–73. doi:10.1016/j.jappgeo.2010.12.007.
     https://doi.org/10.1016/j.jappgeo.2010.12.007 [Google Scholar]
  39. Steingrimsson, B.2013. Geothermal Well Logging: Temperature and Pressure Logs, in: Short Course V on Conceptual Modeling of Geothermal System by UNU-GTP and LaGeo, El Salvador. Santa Tecla, 1–16.
  40. Strangway, D.W., and A.Koziar. 1979. Audio-frequency magnetotelluric sounding - a case history at the cavendish geophysical test range. Geophysics44: 1429–46. doi:10.1190/1.1441016.
     https://doi.org/10.1190/1.1441016 [Google Scholar]
  41. Swift, C.M.1967. A magnetotelluric investigation of an electrical conductivity anomaly in the southwestern United States: Ph.D. Dissertation, 211.
  42. Ussher, G., C.Harvey, R.Johnstone, and E.Anderson. 2000. Understanding the resistivities observed in geothermal systems, in: World Geothermal Congress 2000. Kyushu-Tohoku, Japan, 1915–1920.
  43. Weiner, L., P.Chiotti, and H.A.Wilhelm. 1952. Temperature dependence of electrical resistivity of metals. Ames Laboratory ISC Technical Reports 58. https://lib.dr.iastate.edu/ameslab_iscreports/58.
  44. Yang, B., A.Zhang, S.Zhang, Y.Liu, S.Zhang, Y.Li, Y.Xu, and Q.Wang. 2016. Three-dimensional audio-frequency magnetotelluric imaging of akebasitao granitic intrusions in western junggar, NW China. Journal of Applied Geophysics135: 288–96. doi:10.1016/j.jappgeo.2016.10.010.
     https://doi.org/10.1016/j.jappgeo.2016.10.010 [Google Scholar]
  45. Yokoyama, H., K.Nakatsuka, M.Abe, and K.Watanabe. 1983. Temperature dependency of electrical resistivity of water saturated rocks and the possibility of underground temperature estimation. Journal of the Geothermal Research Society of Japan5: 103–20. doi:10.11367/grsj1979.5.103.
     https://doi.org/10.11367/grsj1979.5.103 [Google Scholar]
  46. Zakharova, O.K., V.V.Spichak, A.K.Rybin, V.Y.Batalev, and A.G.Goidina. 2007. Estimation of the correlation between magnetotelluric and geothermal data in the Bishkek geodynamic Research area. Physics of the Solid Earth43: 297–303. doi:10.1134/S1069351307040064.
     https://doi.org/10.1134/S1069351307040064 [Google Scholar]
/content/journals/10.1080/08123985.2021.1949945
Loading
Subsurface temperature estimation in a geothermal field based on audio-frequency magnetotelluric data
Exploration Geophysics 53, 275 (2022); https://doi.org/10.1080/08123985.2021.1949945
/content/journals/10.1080/08123985.2021.1949945
/content/journals/10.1080/08123985.2021.1949945
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): artificial neural network; audio-magnetotelluric; Geothermal; resistivity; temperature

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