Application of artificial neural networks for the prediction of carbonate lithofacies, based on well log data, Sarvak Formation, Marun oil field, SW Iran

Document Type : Research Paper


Department of Geology, Faculty of Science, Bu-Ali Sina University, Hamedan, Iran


Lithofacies identification can provide qualitative information about rocks. It can also explain rock textures which are important
components for hydrocarbon reservoir description Sarvak Formation is an important reservoir which is being studied in the Marun oil
field, in the Dezful embayment (Zagros basin). This study establishes quantitative relationships between digital well logs data and
routine petrographic data, obtained from thin sections description. Attempts were made to predict lithofacies in 13 wells, all drilled in
the Marun oil field. Seven well logs, namely, Gamma Ray (SGR and CGR), Deep Resistivity (RD), Formation Density (RHOB),
Neutron Porosity (PHIN), Sonic log (DT), and photoelectric factor (PEF) as input data and thin section/core-derived lithofacies were
used as target data in the ANN (artificial neural network) to predict lithofacies. The results show a strong correlation between the given
data and those obtained from ANN (R²= 95%). The performance of the model has been measured by the Mean Squared Error function
which doesn't exceed 0.303. Hence, neural network techniques are recommended for those reservoirs in which facies geometry and
distribution are key factors controlling the heterogeneity and distribution of rock properties. Undoubtedly, this approach can reduce
uncertainty and save plenty of time and cost for the oil industry.


Article Title [Persian]

کاربرد شبکه عصبی برای پیش بینی رخساره های سنگی سازند کربناته بر پایه داده های چاه نگاری، میدان نفتی مارون، جنوب غرب ایران.

Authors [Persian]

  • حسن محسنی
  • موسی اسفندیاری
  • الهام حبیبی اصل

Keywords [Persian]

  • سازند سروک
  • شبکه عصبی
  • ویژگی مخزنی
  • رخساره سنگی
  • حوضه زاگرس
Alavi, M., 2004. Regional stratigraphy of the Zagros fold-thrust belt of Iran and its proforeland evolution. American
Journal of Science. 304: 1–20.
Aminian, K., Ameri, S., 2005. Application of artificial neural networks for reservoir characterization with limited data.
Journal of Petroleum Science and Engineering. 49: 212- 222.
Beiranvand, B., Ahmadi, A., Sharafodin, M., 2007.Mapping and classifying flow units in the upper part of the mid-
Cretaceous Sarvak formation (western Dezful Embayment, SW Iran) based on a determination of reservoir types. Journal
of Petroleum Geology. 30: 357–373.
Bohling, G.C., Dubois, M.K., 2003. An integrated application of neural network and Markov chain techniques to
prediction of lithofacies from well logs. Kansas Geological Survey Open-file Report: 50, 6 pp
Bordenave, M.L., 2002. Gas prospective areas in the Zagros domain of Iran and in the Gulf Iranian Waters. AAPG Annual
Meeting, March 10-13, Houston, Texas.
Bordenave, M.L., 2002. Gas prospective areas in the Zagros domain of Iran and in the Gulf Iranian Waters. AAPG Annual
Meeting, March 10-13, Houston, Texas.
Cacini, G., Gillespie, P.A., Verges, J., Romaire, I., Fernndez, N., Casciello, E., Saura, E., Mehl, C., Homke, S., Embry, J.
C., Aghajari, L., Hunt, D. W., 2011. Sub-seismic fractures in foreland fold and thrust belts: insight from the Lurestan
Province, Zagros Mountains, Iran. Petroleum Geoscience. 17(3): 263-282.
Dunham, R.J. 1962. Classification of carbonate rocks according to depositional texture. In: Ham, W.E. (Ed.),
Classification of Carbonate Rocks, American Association of Petroleum Geologists Memoir 1. American Association of
Petroleum Geologists, Tulsa, Oklahoma. 108-121 pp.
Asadi Mehmandosti, E., Adabi, M. H., Woods, A. D., 2013. Microfacies and geochemistry of the Middle Cretaceous
Sarvak Formation in Zagros Basin, Izeh Zone, SW Iran, Sedimentary Geology, 293L 9-20.
El-Sebakhy, E. A., Asparouhov, O., Abdulraheem, A. A., Al-Majed, A. A., Wu, D., Latinski, K., Raharja, I., 2012.
Functional networks as a new data mining predictive paradigm to predict permeability in a carbonate reservoir. Expert
Systems with Applications. 39: 10359-10375.
Farzadi, P., Hesthmer, J., 2007, Diagnosis of the upper Cretaceous palaeokarst and turbidite systems from the Iranian
Persian Gulf using volume-based multiple seismic attribute analysis and pattern recognition. Petrol Geosci. 13: 227-240.
Fausett, L.V., 1994. Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice Hall Inc,
New Jersey. 461 p.
Fung,C.C., Wong, K.W., Eren, H., 1997. Modular artificial neural network for prediction of petrophysical properties from
well log data, IEEE Trans. on Instrumentation and Measurement. 46 (6).
Ghabeishavi, A., Vaziri-Moghaddam, H., Taheri, A., Taati, F., 2010. Microfacies and depositional environment of the
Cenomanian of the Bangestan anticline, SW Iran. J Asian Earth Sci. 37: 275-285.
Hajikazemi, E., Al-Aasm, IS., Coniglio, M. 2010. Subaerial exposure and meteoric diagenesis of the Cenomanian-
Turonian upper Sarvak formation, southwestern Iran. In: LETURMY, P. and ROBIN, C. (Eds), Tectonic and
Stratigraphic Evolution of Zagros and Makran during the Meso-Cenozoic. Geol. Soc. Lond. Special Publication. 330:
James, G.A., Wynd, J.G., 1965. Stratigraphic nomenclature of Iranian oil consortium, agreement area. American Association of Petroleum Geologists Bulletin. 49: 2182-2245.
Lee, S.H., Kharghoria, A., Datta-Gupta, A., 2000. Electrofacies characterization and permeability predictions in complex
reservoirs. Society of Petroleum Engineers, Reservoir Evaluation and Engineering. 237-248.
Ligtenberg, L.H., Wansink, A.G., 2001. Neural network prediction of permeability in the El Graia Formation, Ashtart
Oilfield, offshore Tunisia. Journal of Petroleum Geology. 24 (4): 389-404.
Lim, J. S., 2003, Reservoir permeability determination using artificial neural network. J. Korean Soc. Geosyst. Eng. 40:
Marmoa, R., Amodiob, S., Tagliaferrid, R., Ferrerib, V., Longo, G., 2005. Textural identification of carbonate rocks by
image processing and neural network: Methodology proposal and examples. Computers & Geosciences. 31: 649-659.
Mathisen, T., Lee, S.H., Datta-Gupta, A., 2003. Improved permeability estimates in carbonate reservoirs using
electrofacies characterization: a case study of the North Robertson Unit, West Texas. Society of Petroleum Engineers,
Reservoir Evaluation and Engineering. 176-184.
Motiei, H., 1993. Treatise on the geology of Iran: Stratigraphy of Zagros. Geological Survey of Iran, Tehran, 497p (in
Negi, J. K., Verma, C. P., Kumar, A., Prasad, U. S., Lal, C., 2006, Predicting Lithofacies Using Artificial Neural Network
and Log-Core Correlations, 6th International Conference and Exposition on Petroleum Geophysics, Kolkata. 809-811.
Nikravesh, M., Aminzadeh, F., 2001, Past, present and future intelligent reservoir characterization trends (editors’ view
points): Journal of Petroleum Science and Engineering. 31: 67-79.
Nikravesh, M., Aminzadeh, F., Zadeh, L.A., 2003, Soft computing and intelligent data analysis in oil exploration:
Developments in petroleum sciences 51.
Ouenes, A., 2000. Practical application of fuzzy logic and neural networks to fractured reservoir characterization.
Computers & Geosciences 26, 953-962.
Qi, L., Carr, T.R., Goldstein, R.H., 2007. Geostatistical three-dimensional modeling of oolite shoals, St. Louis Limestone,
southwest Kansas. AAPG Bulletin. 91 (1): 69-96.
Rahimpour-Bonab, H., Mehrabi, H., Enayatibidgoli, A.H. and Omidvar, M., 2012. Coupled imprints of tropical climate
and recurring emersions on reservoir evolution of a mid-Cretaceous carbonate ramp, Zagros Basin, SW Iran. Cretaceous
Research, 37:15-34.
Razin, P., Taati, F., and van Buchem, F. S. P., 2010. Sequence stratigraphy of Cenomanian_Turonian carbonate platform
margins (Sarvak Formation) in the High Zagros, SW Iran: an outcrop reference model for the Arabian Plate. Geological
Society, London, Special Publications. 329: 187-218.
Saggaf, M.M., Nebrija, E.L., 2003a. A fuzzy logic approach for the estimation of facies from wire-line logs. American
Association of Petroleum Geologists Bulletin. 87 (7): 1233-1240.
Saggaf, M.M., Nebrija, E.L., 2003b. Estimation of missing logs by regularized neural networks. American Association of
Petroleum Geologists Bulletin. 87 (8): 1377-1389.
Sepehr, M. and Cosgrove, J.W., 2005. Role of the Kazerun fault zone in the formation and deformation of the Zagros fold
thrust belt, Iran. Tectonics, 24.
Trappe, H., Hellmich, C., 2000. Using neural networks to predict porosity thickness from 3D seismic. First Break 18 (9):
Vermaa, A. K., Cheadlea, B. A., Routrayc, A., Mohantyb ,W. K., Mansinhaa, L., 2012. Porosity and permeability
estimation using Neural Network approach from well log data, GeoConvention:vision.
Wang, G., Carr, T. R., 2012. Methodology of organic-rich shale lithofacies identification and prediction: case study from
Marcellus Shale in the Appalachian basin. Computers & Geosciences. 49:151-163.
Wong, P.M., Henderson, D.J., Brooks, L.J., 1998. Permeability determination using neural networks in the Ravva Field,
Offshore India. Society of Petroleum Engineers, Reservoir Evaluation and Engineering, 99-104.
Zee Ma, Y., 2011, Lithofacies Clustering Using Principal Component Analysis and Neural Network: Applications to
Wireline Logs, Math Geosci. 43: 401-419.
Ziegler M (2001) Late Permian to Holocene paleofacies evolution of the Arabian Plate and its hydrocarbon occurrences.
Geoarabia. 6: 445-504.