Oil Reservoir Permeability Estimation from Well Logging Data Using Statistical Methods (A Case Study: South Pars Oil Reservoir)

Akbar Esmailzadeh, Sina Ahmadi, Reza Rooki, Reza Mikaeil


Permeability is a key parameter that affects fluids flow in reservoir and its accurate determination is a significant task. Permeability usually is measured using practical approaches such as either core analysis or well test which both are time and cost consuming. For these reasons applying well logging data in order to obtaining petrophysical properties of oil reservoir such as permeability and porosity is common. Most of petrophysical parameters generally have relationship with one of well logged data. But reservoir permeability does not show clear and meaningful correlation with any of logged data. Sonic log, density log, neutron log, resistivity log, photo electric factor log and gamma log, are the logs which effect on permeability. It is clear that all of above logs do not effect on permeability with same degree. Hence determination of which log or logs have more effect on permeability is essential task. In order to obtaining mathematical relationship between permeability and affected log data, fitting statistical nonlinear models on measured geophysical data logs as input data and measured vertical and horizontal permeability data as output, was studied. Results indicate that sonic log, density log, neutron log and resistivity log have most effect on permeability, so nonlinear relationships between these logs and permeability was done.


Permeability; Oil Reservoir; South Pars; Well Logging; Statistical Nonlinear Models; SPSS.


Kumar, N., N. Hughes, and M. Scott. "Using well logs to infer permeability." Center for Applied Petrophysical Studies, Texas Tech University (2000).

Bhatt, Alpana. "Reservoir Properties from Well Logs using neural Networks." PhD diss., Norwegian University of Science and Technology, Trondheim, Norway, 2002.

Russell, Brian Henderson. The application of multivariate statistics and neural networks to the prediction of reservoir parameters using seismic attributes. 2004.

Moradzadeh, A., Ghavami, R., Well logging for engineer. Shahrood university press, 2001.

Wang, Kejun, Bo He, and Ruolei Chen. "Predicting parameters of nature oil reservoir using general regression neural network." In Mechatronics and Automation, 2007. ICMA 2007. International Conference on, pp. 822-826. IEEE, 2007.

Artun, Emre, Shahab D. Mohaghegh, Jaime Toro, Tom Wilson, and Alejandro Sanchez. "Reservoir characterization using intelligent seismic inversion." In SPE Eastern Regional Meeting. Society of Petroleum Engineers, 2005.

Jeirani, Z., and A. Mohebbi. "Estimating the initial pressure, permeability and skin factor of oil reservoirs using artificial neural networks." Journal of petroleum science and engineering 50, no. 1 (2006): 11-20.

Hamada, G. M., and M. A. Elshafei. "Neural network prediction of porosity and permeability of heterogeneous gas sand reservoirs using NMR and conventional logs." Nafta 61, no. 10 (2010): 451-460.

Olatunji, Sunday Olusanya, Ali Selamat, and Abdulazeez Abdulraheem. "Modeling the permeability of carbonate reservoir using type-2 fuzzy logic systems." Computers in industry 62, no. 2 (2011): 147-163.

Joonaki, Edris, Shima Ghanaatian, and Ghassem Zargar. "An intelligence approach for porosity and permeability prediction of oil reservoirs using seismic data." International Journal of Computer Applications 80, no. 8 (2013).

Helmy, Tarek, and Anifowose Fatai. "Hybrid computational intelligence models for porosity and permeability prediction of petroleum reservoirs." International Journal of Computational Intelligence and Applications 9, no. 04 (2010): 313-337.

Weston, Jason, Alex Gammerman, M. Stitson, Vladimir Vapnik, Volodya Vovk, and Chris Watkins. "Support vector density estimation." Advances in Kernel Methods—Support Vector Learning (1999): 293-306.

Vapnik, V. "The nature of statistical learning theory Springer New York Google Scholar." (1995).

Behzad, M., Asghari, K., Eazi, M., & Palhang, M. (2009). Generalization performance of support vector machines and neural networks in runoff modeling. Expert Systems with applications, 36(4), 7624-7629.

Behzad, Mohsen, Keyvan Asghari, Morteza Eazi, and Maziar Palhang. "Generalization performance of support vector machines and neural networks in runoff modeling." Expert Systems with applications 36, no. 4 (2009): 7624-7629.

Alfaaouri, S., M. A. Riahi, N. Alizadeh, and M. Rezaei. "Permeability prediction in an oil reservoir and construction of 3D geological model by stochastic approaches." (2009).

Kaydani, Hossein, Ali Mohebbi, and Ali Baghaie. "Permeability prediction based on reservoir zonation by a hybrid neural genetic algorithm in one of the Iranian heterogeneous oil reservoirs." Journal of Petroleum Science and Engineering 78, no. 2 (2011): 497-504.

Baziar, Sadegh, Mohammad Mobin Gafoori, Mohaimenian Pour, Seyed Mehdi, Majid Nabi Bidhendi, and Reza Hajiani. "Toward a Thorough Approach to Predicting Klinkenberg Permeability in a Tight Gas Reservoir: A Comparative Study." Iranian Journal of Oil & Gas Science and Technology 4, no. 3 (2015): 18-36.

Rafik, Baouche, and Baddari Kamel. "Prediction of permeability and porosity from well log data using the nonparametric regression with multivariate analysis and neural network, Hassi R’Mel Field, Algeria." Egyptian Journal of Petroleum 26, no. 3 (2017): 763-778.

Ali Ahmadi, Mohammad, Sohrab Zendehboudi, Ali Lohi, Ali Elkamel, and Ioannis Chatzis. "Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization." Geophysical Prospecting 61, no. 3 (2013): 582-598.

Mohebbi, A., R. Kamalpour, K. Keyvanloo, and A. Sarrafi. "The prediction of permeability from well logging data based on reservoir zoning, using artificial neural networks in one of an Iranian heterogeneous oil reservoir." Petroleum Science and Technology 30, no. 19 (2012): 1998-2007.

Kaydani, Hossein, Ali Mohebbi, and Mehdi Eftekhari. "Permeability estimation in heterogeneous oil reservoirs by multi-gene genetic programming algorithm." Journal of Petroleum Science and Engineering 123 (2014): 201-206.

Almeida, Paula, and Abel Carrasquilla. "Integrating Geological Attributes with a Multiple Linear Regression of Geophysical Well Logs to Estimate the Permeability of Carbonate Reservoirs in Campos Basin, Southeastern Brazil." Analyst 9, no. 4 (2016): 8-17.

Yan, W., J. Sun, K. Liu, L. Cui, and H. Dong. "Fractured Carbonate Reservoir Permeability Estimation by Microresistivity Imaging Logging." In 79th EAGE Conference and Exhibition 2017. 2017.

Ge, Xinmin, Yiren Fan, Jianyu Liu, Li Zhang, Yujiao Han, and Donghui Xing. "An improved method for permeability estimation of the bioclastic limestone reservoir based on NMR data." Journal of Magnetic Resonance 283 (2017): 96-109.

Carman, Philip Crosbie. Flow of gases through porous media. Academic press, 1956.

Carman, Philip Crosbie. "Fluid flow through granular beds." Transactions-Institution of Chemical Engineeres 15 (1937): 150-166.

Tiab, Djebbar, and Erle C. Donaldson. Petrophysics: theory and practice of measuring reservoir rock and fluid transport properties. Gulf professional publishing, 2015.

National Iranian Oil Company (NIOC), http://www.nioc.ir

Mehrabi, Hamzeh, Maryam Mansouri, Hossain Rahimpour-Bonab, Vahid Tavakoli, and Maryam Hassanzadeh. "Chemical compaction features as potential barriers in the Permian-Triassic reservoirs of Southern Iran." Journal of Petroleum Science and Engineering 145 (2016): 95-113.

Seber, George AF, and Alan J. Lee. Linear regression analysis. Vol. 936. John Wiley & Sons, 2012.

Bates, D. M., & Watts, D. G., Nonlinear regression analysis and its applications (Vol. 2). 1988, New York: Wiley.

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DOI: 10.28991/cej-030918


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