Estimation of Total Organic Carbon in Source Rocks by Using Back-propagation Artificial Neural Network and Passay Method-A Case Study

Document Type : Research Paper

Authors

1 Department of Petroleum Engineering, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran

2 Petroleum Engineering Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran

Abstract

The purpose of this study is to calculate Total Organic Carbon (TOC) values of the Iranian field using a combination of sonic and resistivity logs (Passay method) and neural networks method in the conditions, where the core analysis or well-log measurement does not exist. We compared the resultant TOC with the ones obtained from the geochemical analysis. To correlate between the total organic carbon data and petrophysical log, which are available after logging, Multilayer Perceptron Artificial Neural Network is used. After analyzing 100 cutting samples by using rock -Eval pyrolysis, geochemical parameters have achieved.
By using the multi-layer perceptron with Levenberg–Marquardt training algorithm, the TOC with correlation coefficient 0.88 and MSE 1.443 have been provided in the intervals without analyzed samples. Finally, the TOC was estimated by using separation of resistivity and the sonic log, although, with the favorable results in some other fields, the estimation had a correlation coefficient of 51% in this field. Comparing the performance of the multi-layer perceptron with Levenberg–Marquardt training algorithm (with an accuracy of 88%) and results of the Passay method (with an accuracy of 51%) indicated that the neural network is more accurate and has better consistency compared with the empirical formula.

Keywords

References

  1. Welte DH, Tissot PB. Petroleum formation and occurrence. Springer-verlag; 1984Serra, O. 1983. "Fundamentals of well-log interpretation."
  2. Hunt JM. Petroleum Geochemistry and Geology, WH Freeman and Company, p. 541.
  3. Alqahtani A, Tutuncu A. Quantification of total organic carbon content in shale source rocks: An eagle ford case study. InSPE/AAPG/SEG Unconventional Resources Technology Conference 2014 Aug 25. OnePetro.
  4. Schmoker JW. Determination of organic content of Appalachian Devonian shales from formation-density logs: Geologic notes. AAPG Bulletin. 1979 Sep 1;63(9):1504-9.
  5. Schmoker JW, Hester TC. Organic carbon in Bakken formation, United States portion of Williston basin. AAPG bulletin. 1983 Dec 1;67(12):2165-74.
  6. Tabatabaei SM, Kadkhodaie-Ilkhchi A, Hosseini Z, Moghaddam AA. A hybrid stochastic-gradient optimization to estimating total organic carbon from petrophysical data: A case study from the Ahwaz oilfield, SW Iran. Journal of Petroleum Science and Engineering. 2015 Mar 1;127:35-43.
  7. Herron S, Letendre L, Dufour M. Source rock evaluation using geochemical information from wireline logs and cores. AAPG Bull.;(United States). 1988 Aug 1;72(CONF-8809346-).
  8. Azadi Moghaddam M, Kolahan F. Using combined artificial neural network and particle swarm optimization algorithm for modeling and optimization of electrical discharge machining process. Scientia Iranica. 2019.
  9. Rezazadeh Eidgahee D, Haddad A, Naderpour H. Evaluation of shear strength parameters of granulated waste rubber using artificial neural networks and group method of data handling. Scientia Iranica. 2019 Dec 1;26(6):3233-44.
  10. Erzin Y, Tuskan Y. The use of neural networks for predicting the factor of safety of soil against liquefaction. Scientia Iranica. 2019 Oct 1;26(5):2615-23.
  11. Yaghmaei-Sabegh S. Earthquake ground-motion duration estimation using general regression neural network. Scientia Iranica. 2018 Oct 1;25(5):2425-39.
  12. Saeedi Dehaghani AH, Sadeghnejad S, Soltaninejad M, Tajikmansori A. Estimation of Total Organic Carbon in Source Rocks by Using Back-propagation Artificial Neural Network and Passay Method-A Case Study. Journal of Chemical and Petroleum Engineering. 2021 Jul 13.
  13. Aliouane L, Ouadfeul SA, Djarfour N, Boudella A. Petrophysical parameters estimation from well-logs data using multilayer perceptron and radial basis function neural networks. InInternational Conference on Neural Information Processing 2012 Nov 12 (pp. 730-736). Springer, Berlin, Heidelberg.
  14. Schmoker JW. Organic content of Devonian shale in western Appalachian Basin. AAPG Bulletin. 1980 Dec 1;64(12):2156-65.
  15. Passey QR, Creaney S, Kulla JB, Moretti FJ, Stroud JD. A practical model for organic richness from porosity and resistivity logs. AAPG bulletin. 1990 Dec 1;74(12):1777-94.
  16. Peters KE. Guidelines for evaluating petroleum source rock using programmed pyrolysis. AAPG bulletin. 1986 Mar 1;70(3):318-29.
  17. Rosenblatt F. Principles of neurodynamics. perceptrons and the theory of brain mechanisms. Cornell Aeronautical Lab Inc Buffalo NY; 1961 Mar 15.
  18. Ouadfeul SA, Aliouane L. Lithofacies prediction from well log data using a multilayer perceptron (MLP) and Kohonen's self-organizing map (SOM)–a case study from the Algerian Sahara. Pattern Recognition in Physics. 2013 Jun 28;1(1):59-62.
  19. Ouadfeul SA, Aliouane L. Shale gas reservoirs characterization using neural network. Energy Procedia. 2014 Jan 1;59:16-21.
  20. Hagan MT, Menhaj MB. Training feedforward networks with the Marquardt algorithm. IEEE transactions on Neural Networks. 1994 Nov;5(6):989-93.
  21. Lafargue E, Marquis F, Pillot D. Rock-Eval 6 applications in hydrocarbon exploration, production, and soil contamination studies. Revue de l'institut français du pétrole. 1998 Jul 1;53(4):421-37.
  22. Peters KE, Cassa MR. Applied source rock geochemistry: Chapter 5: Part II. Essential elements.
  23. Saeedi Dehaghani AH, Sadeghnejad S, Soltaninejad M, Tajikmansori A. Estimation of Total Organic Carbon in Source Rocks by Using Back-propagation Artificial Neural Network and Passay Method-A Case Study. Journal of Chemical and Petroleum Engineering. 2021 Jul 13.
  24. Langford FF, Blanc-Valleron MM. Interpreting Rock-Eval pyrolysis data using graphs of pyrolizable hydrocarbons vs. total organic carbon. AAPG bulletin. 1990 Jun 1;74(6):799-804.
Journal of Chemical and Petroleum Engineering
Volume 55, Issue 2
December 2021
Pages 277-292

Files

History

  • Receive Date: 31 December 2019
  • Revise Date: 17 May 2021
  • Accept Date: 18 May 2021

Share

How to cite

Statistics