Intelligent Approach for Investigating Reservoir Heterogeneity Effect on Sonic Shear Wave


  • Jassim Mohammed Al Said Naji University of Baghdad, College of Engineering, Department of Petroleum
  • Ghassan H. Abdul-Majeed University of Baghdad
  • Alhuraishawy Ministry of Oil, Baghdad, Iraq



Reservoir heterogeneity, Zonation, Sonic shear wave, Artificial neural network, Empirical correlations


Heterogeneity refers to a not uniform distribution of reservoir properties. To overcome the problem of heterogeneity, most reservoir studies split the reservoir into different zones. In general, this disparity affects all log tools. Sonic shear wave time (SSW) is a critical metric in geomechanical modeling that is strongly influenced by reservoir heterogeneity and the kind of porous fluid composition. To detect the effect of reservoir heterogeneity on SSW prediction, an artificial neural network (ANN) was applied as an intelligent technique. One Iraqi vertical well that penetrated the Asmari reservoir was selected for this study. It contains 2462 SSW measured points as well as the following seven log parameters: Gamma Ray, Caliper, Density, Neutron, Compressional sonic, and True resistivity log over measured depth. Based on formation assessment and available well data, the Asmari reservoir was classified into six zones (with different lithology and different fluid content): A, B1, B2, B3, B4, and C. To investigate the effect of lithology on SSW, two runs of ANN had been conducted in this study.

Initially, we developed a single ANN for all 2462 measured points, while in the second, six ANNs were built, one for each zone. The optimum structure for all the developed ANNs was obtained with one hidden layer of 12 neurons (7-12-1). The statistical parameters used for comparison are average percent error (APE), absolute average percent error (AAPE), standard deviation (SD), mean square error (MSE), and correlation coefficient (R2). It was observed that these parameters are approximately close to each other for the developed seven ANNs. The R2 values of the seven ANNs are 0.98 for all zones, and 0.99, 0.99, 0.99, 0.99, 0.99 and 0.96 for each zone respectively. The insignificant differences of results can be explained by the fact that the log readings (i.e. inputs variables) are already reflected the effect of lithology. Therefore, we recommended using the ANN based on 2462 for predicting SSW to any lithology zone. A mathematical model for representing the suggested ANN to simplify the calculation.


S. Mohaghegh. "Virtual-Intelligence Applications in Petroleum Engineering: Part 1—Artificial Neural Networks," Society of Petroleum Engineers, SPE 58046, 2000.

H. Li and J. F. Reynold, "On Definition and Quantification of Heterogeneity," Olkos, vol. 73, No. 2, pp. 280-284 (5 pages), 1995.

W. Zhengquan, W. Qingcheng, Z. Yandong, and H. Li, "Quantification of spatial heterogeneity in old growth forests of Korean pine," Journal of Forestry Research, vol. 8, p. 65–73, 1997.

P. J. R. Fitch, M. A. Lovell, S. J. Davies, T. Pritchard and P. K. Harvey, "An integrated and quantitative approach to petrophysical heterogeneity," Marine and Petroleum Geology, vol. 63, p. 82-96, 2015.

T. Ertekin, J.H. Abou-Kassem, G.R. King, "Basic Applied Reservoir Simulation," Henry L. Doherty Memorial Fund of AIME, Society of Petroleum Engineers, Richardson, Texas, 2001.

Schlumberger team, "heterogeneity, " Energy glossary website, 2022.

J. Schon, Basic Well Logging and Formation Evaluating, 1st edition, eBook at Bookboon, 2015.

R.H. Allawi and M. S. Al-Jawad, " An Empirical Correlations to Predict Shear Wave Velocity at Southern Iraq Oilfield," Journal of Petroleum Research and Studies, no. 34 part 1, pp. 1-14, 2022.

W. M. Al-Kattan, " Prediction of Shear Wave velocity for carbonate rocks," Iraqi Journal of Chemical and Petroleum Engineering, vol.16, no.4, p. 45- 49, 2015.

F.A. Hadi and R. Nygaard, " Shear Wave Prediction in Carbonate Reservoirs: Can Artificial Neural Network Outperform Regression Analysis," ARMA 18–905. 2018.

M.R. Rezaee, A. K. Ilkhchi and A. Barabadi, " Prediction of shear wave velocity from petrophysical data utilizing intelligent systems: An example from a sandstone reservoir of Carnarvon Basin, Australia," Journal of Petroleum Science and Engineering, vol. 55, p. 201–212, 2007.

G.R. Pickett, "Acoustic Character Logs and Their Applications in Formation Evaluation," J Pet Technol, SPE-452-PA, vol. 15 (06), p.659–667 1963.

R.D. Carroll, "The determination of the acoustic parameters of volcanic rocks from compressional velocity measurements," Int. J. Rock Mech. Iin. Sci. vol. 6, pp. 557-579, 1969.

J. P. Castagna, M. L. Batzle and R. L. Eastwood, "Relationships between compressional-wave and shear-wave velocities in clastic silicate rocks," Geophysics, vol. 50, no.4, 1985.

M. L. Greenberg and J. P. Castagna, "Shear wave velocity estimation in porous rocks: Theoretical formulation, preliminary verification and applications," Geophysical Prospecting, vol. 40, p. 195-209, 1992.

D. Freund, "Ultrasonic compressional and shear velocities in dry clastic rocks as a function of porosity, clay content, and confining pressure," Geophys. J. Inr. Vol. 108, pp. 125-135. 1992.

T. M. Brocher, " Empirical Relations between Elastic Wave speeds and Density in the Earth’s Crust," Bulletin of the Seismological Society of America, vol. 95, no. 6, pp. 2081–2092, 2005. 10.1785/0120050077.

M. S. Ameen, B. G. D. Smart, J. M. Somerville, S. Hammilton and N. A. Naji, " Predicting rock mechanical properties of carbonates from wireline logs (A case study: Arab-D reservoir, Ghawar field, Saudi Arabia)," Marine and Petroleum Geology, vol. 26, p. 430–444, 2009.

J. M. Al Said Naji, G. H. Abdul-Majeed, and A. K. Alhuraishawy, " Comparison of Estimation Sonic Shear Wave Time Using Empirical Correlations and Artificial Neural Network," paper accepted by Iraqi Journal of Chemical and Petroleum Engineering, 2022.

M. A. Kassab and A. Weller, " Study on P-wave and S-wave velocity in dry and wet sandstones of Tushka region, Egypt," Egyptian Journal of Petroleum, vol. 24, p. 1-11, 2015.

K. Tabari, O. Tabari and M. Tabari, "A Fast Method for Estimating Shear Wave Velocity by Using Neural Network," Australian Journal of Basic and Applied Sciences, vol. 5, no. 11, p. 1429-1434. 2011.

A. Al Ghaithi and M. Prasad, "Machine learning with Artificial Neural Networks for shear log predictions in the Volve field Norwegian North Sea," SEG, International Exposition and 90th Annual Meeting, 2020.

H. H. Alkinani, A. T. Al-Hameedi, S. Dunn-Norman, R. E. Flori anf M. A. Al-Alwani, "Intelligent Data-Driven Analytics to Predict Shear Wave Velocity in Carbonate Formations: Comparison Between Recurrent and Conventional Neural Networks," Paper presented at the 53rd US Rock Mechanics/Geomechanics Symposium held in New York, NY, USA, 2019.

J. M. Al Said Naji, G. H. Abdul-Majeed, and A. K. Alhuraishawy, " Prediction sonic shear wave by artificial neural network," paper submitted to Iraqi geological journal, 2022.

W. I. Taher, M. S. Al Jawad and C. W. V. Kirk, "Reservoir study for Asmari reservoir/Fauqi field," Master thesis, University of Baghdad, Collage of Engineering, Iraq, 2011.

Q. A. Abdul-Aziz and H. A. Abdul-Hussain, "Integration of Geomechanical and Petrophysical properties for estimating rate of penetration in Fauqi oil field Southern Iraqi," Doctorate dissertation, University of Baghdad, Collage of Engineerin, Iraq, 2021.

J. E. Fox and T. S. Ahlbrandt, "Petroleum Geology and Total Petroleum Systems of the Widyan Basin and Interior Platform of Saudi Arabia and Iraq. U.S," Geological Survey Bulletin, 2002.

L. Wang, " Study on Reservoir Heterogeneity in Block S," IOP Conf. Series: Earth and Environmental Science, vol. 770, 2021.

F. S. Kadhim, A. Samsuri and Y. Al-Dunainawi, " ANN-Based Prediction of Cementation Factor in Carbonate Reservoir," SAI Intelligent Systems Conference, London, UK, pp 681-68. 2015.

M.C. Popescu, V.E. Balas, L.P. Popescu, "Multilayer perceptron and neural networks," WSEAS Transactions on Circuits and Systems, Vol.8, pp. 579-588, 2009.

G. H. Abdul-Majeed, F.S. Kadhim, F. H. Almahdawi, Y. Al-Dunainawi, A. Arabi and W. K. Al-Azzawi, "Application of artificial neural network to predict slug liquid holdup," International Journal of Multiphase Flow, Vol. 150, 2022.

E. Jorjani, C. S. Chehreh and S. H. Mesroghli, "Application of artificial neural networks to predict chemical desulfurization of Tabas coal," Fuel,Vol. 87, pp. 2727–3273. 2008.

M. A. Razavi, A. Mortazavi and M.Mousavi, "Dynamic modeling of milk ultrafiltration by artificial neural network," J. Membrane , Vol. 120, pp. 47-58, 2003.

M. Asoodeh and P. Bagheripour, "Prediction of Compressional, Shear, and Stoneley Wave Velocities from Conventional Well Log Data Using a Committee Machine with Intelligent Systems," Rock Mechanics and Rock Engineering, Vol. 45, pp. 45-63, 2012. doi 10.1007/s00603-011-0181-2.

S. Maleki, A. Moradzadeh,R. G. Riabi, R. Gholami and F. Sadeghzadeh, "Prediction of shear wave velocity using empirical correlations and artificial intelligence methods," NRIAG Journal of Astronomy and Geophysics, Vol. 3, , pp. 70-81. 2014.

H. Akhundi, M. Ghafoori and G. R. Lashkaripour, "Prediction of Shear Wave Velocity Using Artificial Neural Network Technique, Multiple Regression and Petrophysical Data: A Case Study in Asmari Reservoir (SW Iran)," Open Journal of Geology, Vol. 4, pp. 303-313, 2014.

R. K. Abdul Majeed and A. A. Alhaleem, "Estimation of shear wave velocity from wireline logs data for Amara oilfield, Mishrif formation," Vol. 53, No.1A, Iraqi Geological Journal, 2020.




How to Cite

Al Said Naji, J. M.; Abdul-Majeed, G. H. .; Alhuraishawy, A. K. Intelligent Approach for Investigating Reservoir Heterogeneity Effect on Sonic Shear Wave. Journal of Petroleum Research and Studies 2023, 13, 56-73.




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