Researches in Earth Sciences

Researches in Earth Sciences

Estimation and modeling of the TOC using hybrid neural network and geostatistical approaches in the one of the Iranian fields

Document Type : علمی -پژوهشی

Authors
Ebrahim Sefidari 1
Seyed Mohammad Zamanzadeh 2
Ali Dashti 3
Mohammad Hasan Tavakol 4
Sepideh Yasmi 5
1 Ph.D Student in Geology, Faculty of Geology, University of Tehran
2 Assistant Professor, Faculty of geography, University of Tehran
3 M.Sc in geology, Faculty of Geology, University of Tehran
4 Ph.D Student, Geology Research Group, Research Institute of Applied Sciences, ACECR
5 M.Sc in geology, Geology Research Group, Research Institute of Applied Sciences, ACECR
10.29252/esrj.9.3.94
Abstract
The amount of the Total Organic Carbon (TOC) is one of the most important parameters in geochemical evaluation of hydrocarbon source rocks and subsequent petroleum system modeling. We proposed a three- step approach in predicting and modeling TOC content from well log data. Initially, TOC evaluated for 92 core and cutting samples by Rock-Eval pyrolysis method. In the next step the TOC were predicted using intelligent neural network with back propagation algorithm from well log data. Correlation coefficient between the network output and target data in the training, validation and testing steps for the optimized model is 0.9, 0.88 and 0.91 respectively which indicate the satisfactory approach in predicting TOC. Finally geostatistical methods were used to 3D modeling of this parameter in the field study. The proposed methodology is illustrated using a case study from the world's largest non-associated gas reservoir, the South Pars Gas Field, the Persian Gulf basi
Keywords
Pyrolysis
Geostatistics
Neural Network
Total Organic Carbon
  1. -Allison, L.E., 1960. Wet-combustion apparatus and procedure for organic and inorganic carbon in soil: Soil Science Society of America Journal, v. 24, p. 36-40.
  2. -Al-Qahtani, F.A., 2000. Porosity distribution prediction using Artificial Neural Networks, Msc. Thesis, Morgantown Virginia University.
  3. -Anderson, J.K., 1996. Limitations of seismic inversion for porosity and pore fluid: Lessons from chalk reservoir characterization exploration: SEG Annual Meeting. Society of Exploration Geophysicists, p. 309-312.
  4. -Bhatt, A. and Helle, B.H., 1999. Porosity, permeability and TOC prediction from well logs using a neural network approach, EAGE, Helsink June 1999, p. 7-11.
  5. -Chen, Q. and Sidney, S., 1997. Seismic attribute technology for reservoir forecasting and monitoring: The Leading Edge, v. 16(5), p. 445-456.
  6. -Chi, C.Y., Mendel, J.M. and Hampson, D., 1984. A computationally fast approach to maximum-likelihood deconvolution: Geophysics, v. 49(05), p. 550-565.
  7. -Deutsch, C.V., 2001. Geostatistical reservoir modeling, Oxford University Press, New York.
  8. -Fegh, A., Riahi, M.A. and Norouzi, G.H., 2013. Permeability prediction and construction of 3D geological model: application of neural networks and stochastic approaches in an Iranian gas reservoir, Neural Computing and Applications, v. 23(6), p. 1763-1770.
  9. -Hunt, J.M., 1979. Petroleum geochemistry and geology, 1st ed. San Francisco: Freeman, 766 p.
  10. -Hunt, J.M., 1996. Petroleum geochemistry and geology, 2nd edition. W. H. Freeman and Company, 743 p.
  11. -Isaaks, E.H. and Srivastava, R.M., 1989. An introduction to Applied Geostatistics, Oxford, 345 p.
  12. -Kadkhodaie-Ilkhchi, A., Rahimpour-Bonab, H. and Rezaee, M.R., 2009. A committee machine with intelligent systems for estimation of total organic carbon content from petrophysical data: Computers and Geosciences, v. 35, p. 457-474.
  13. -Kamali, M.R. and Mirshady, A.A., 2004.Total organic carbon content determined from well logs using Δ log R and neuro-fuzzy techniques, Journsl of Petroleum Scence and Engineering, v. 45, p. 141-148.
  14. -Kelkar, M., Perez, G. and Chopra, A., 2002. Applied geostatistics for reservoir characterization, Texas, Society of Petroleum Engineers (SPE).
  15. -Monjezi, M., Rajabalizadeh, M.K. and Ataei, M.A., 2011. comparative study between sequential Gaussian simulation and kriging method grade modeling in open-pit mining, Arabian Journal of Geosciences, doi: 10.1007/s12517 011-0293-8.
  16. -Peters, K.E., 1986. Guidelines for evaluating petroleum source rock using programmed pyrolysis: American Association of Petroleum Geologists Bulletin, v. 70, p. 318-329.
  17. -Peyravi, M., Kamali, M.R. and Kalani, M., 2010. Depositional environments and sequence stratigraphy of the Early Triassic Kangan Formation in the northern part of the Persian Gulf: implications for reservoir characteristics, Journal of Petroleum Geology, v. 33(4), p. 371-386.
  18. -Rahimpour‐Bonab, H., Asadi‐Eskandar, A. and Sonei, R., 2009. Effects of the Permian–Triassic boundary on reservoir characteristics of the South Pars gas field, Persian Gulf, Geological journal, v. 44(3), p. 341-364.
  19. -Russell, B.H., Lines, L.R. and Hampson, D.P., 2003. Application of the radial basis function neural networks to the prediction of log properties from seismic data: Exploration Geophysics, v. 34, p. 15-23.
  20. -Schmoker, J.W., 1981. Determination of organic-matter content of Appalachian Devonian shales from gamma-ray logs: American Association of Petroleum Geologists Bulletin, v. 65, p. 2165-2174
  21. -Sfidari, E., Kadkhodaie-Ilkhchi, A. and Najjari, S., 2012. Comparison of intelligent and statistical clustering approaches to predicting total organic carbon using intelligent systems: Journal of Petroleum Science and Engineering, v. 86, p. 190-205.
  22. -Sfidari, E., Kadkhodaie, A., Rahimpour-Bonab, H. and Soltani, B., 2014. A hybrid approach for litho-facies characterization in the framework of sequence stratigraphy: a case study from the South Pars gas field, the Persian Gulf basin: Journal of Petroleum Science and Engineering, v. 121, p. 87-102.