Comparison of Estimation Sonic Shear Wave Time Using Empirical Correlations and Artificial Neural Network
Keywords:Empirical correlations, artificial neural network, sonic shear wave, wellbore deviation, azimuth and inclination
Wellbore instability and sand production onset modeling are very affected by Sonic Shear Wave Time (SSW). In any field, SSW is not available for all wells due to the high cost of measuring. Many authors developed empirical correlations using information from selected worldwide fields for SSW prediction. Recently, researchers have used different Artificial Intelligence methods for estimating SSW. Three existing empirical correlations of Carroll, Freund, and Brocher are used to estimate SSW in this paper, while a fourth new empirical correlation is established. For comparing with the empirical correlation results, another study's Artificial Neural Network (ANN) was used. The same data that was adopted by the ANN study was used here where it is comprised of 1922 measured points of SSW and the other nine parameters of Gamma Ray, Compressional Sonic, Caliper, Neutron Log, Density Log, Deep Resistivity, Azimuth Angle, Inclination Angle, and True Vertical Depth from one Iraqi directional well. Three existing empirical correlations are based only on Compressional Sonic Wave Time (CSW) for predicting SSW. In the same way of developing previous correlations, a fourth empirical correlation was developed by using all measured data points of SSW and CSW. A comparison demonstrated that utilizing ANN was better for SSW predicting with a higher R2 equal to 0.966 and lower other statistical coefficients than utilizing four empirical correlations, where correlations of Carroll, Freund, Brocher, and developed fourth had R2equal to 0.7826, 0.7636, 0.6764, and 0.8016, respectively, with other statistical parameters that show the new developed correlation best than the other three existing. The use of ANN or new developed correlation in future SSW calculations is relevant to decision makers due to a number of limitations and target SSW accuracy.
H. H. Alkinani, A. T. Al-Hameedi, S. Dunn-Norman, R. E. Flori and 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.
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," Vol. 45, Rock Mechanics and Rock Engineering, 2012, pp. 45-63. doi 10.1007/s00603-011-0181-2.
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. https://doi.org/10.46717/igj.53.1a.R3.2020.01.30.
R. K. Abdul Majeed and A. A. Alhaleem, "An Accurate Estimation of Shear Wave Velocity Using Well Logging Data for Khasib Carbonate Reservoir - Amara Oil Field," Vol. 26, No.6, Journal of Engineering, 2022.
R. H. Allawi and M. S. Al-Jawad, "An Empirical Correlations to Predict Shear Wave Velocity at Southern Iraq Oilfield," No. 34 part 1, Journal of Petroleum Research and Studies, 2022, pp. 1-14.
M. A. Kassab and A. Weller, "Study on P-wave and S-wave velocity in dry and wet sandstones of Tushka region, Egypt," Vol. 24, Egyptian Journal of Petroleum, 2015, pp. 1-11.
Q. A. Abdul-Aziz and H. A. Abdul-Hussein, "Development a Statistical Relationship between Compressional Wave Velocity and Petrophysical Properties from Logs Data for JERIBE Formation ASMARI Reservoir in FAUQI Oil Field," Vol. 22, No. 3, Iraqi Journal of Chemical and Petroleum Engineering, 2022, pp. 1-9.
G. R. Pickett, "Acoustic Character Logs and Their Applications in Formation Evaluation," Vol. 15, No. 6, J Pet Technol, SPE-452-PA, 1963, pp. 659-667.
R. D. Carroll, "The determination of the acoustic parameters of volcanic rocks from compressional velocity measurements," Vol. 6, Int. J. Rock Mech, 1969, pp. 557-579.
J. P. Castagna, M. L. Batzle, and R. L. Eastwood, "Relationships between compressional-wave and shear-wave velocities in clastic silicate rocks," Vol. 50, No.4, Geophysics, 1985.
D. Freund, "Ultrasonic compressional and shear velocities in dry clastic rocks as a function of porosity, clay content, and confining pressure," Vol. 108, Geophys. J. Inr. 1992. pp. 125-135.
H. Eskandari, M. R. Rezaee and M. Mohammadina, "Application of multiple regression and artificial neural network techniques to predict shear wave velocity from wireline log data for carbonate reservoir, South-West Iran," Vol. 42, CSEG Recorder, 2004, pp. 40- 48.
T. M. Brocher, "Empirical Relations between Elastic Wave speeds and Density in the Earth’s Crust," Vol. 95, No. 6, Bulletin of the Seismological Society of America, 2005, pp. 2081–2092.
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)," Vol. 26, Marine and Petroleum Geology, 2009, pp. 430–444.
W. M. Al-Kattan, "Prediction of Shear Wave velocity for carbonate rocks," Vol. 16, No. 4, Iraqi Journal of Chemical and Petroleum Engineering. 2015. pp. 45-49.
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," Vol. 55, Journal of Petroleum Science and Engineering, 2007, pp. 201-212.
K. Tabari, O. Tabari and M. Tabari, "A Fast Method for Estimating Shear Wave Velocity by Using Neural Network," Vol. 5, No. 11, Australian Journal of Basic and Applied Sciences, 2011. pp. 1429-1434.
F. A. Hadi and R. Nygaard, "Shear Wave Prediction in Carbonate Reservoirs: Can Artificial Neural Network Outperform Regression Analysis," Paper presented at the 52nd US Rock Mechanics / Geomechanics Symposium held in Seattle, Washington, USA, 2018.
A. Al Ghaithi and M. Prasad, "Machine learning with Artificial Neural Networks for shear log predictions in the Volve field Norwegian North Sea," Society of Exploration Geophysicists. SEG International Exposition and 90th Annual Meeting, 2020.
J. M. Al Said Naji, G. H. Abdul-Majeed, and A. K. Alhuraishawy, " Prediction sonic shear wave by artificial neural network," Vol.55 (2E), pp.152-164, Iraqi geological journal, 2022.
Q. A. Abdul-Aziz and H. A. Abdul-Hussein, "Integration of Geomechanical and Petrophysical properties for estimating rate of penetration in Fauqi oil field Southern Iraqi," Doctorate dissertation, University of Baghdad, Collage of Engineering, Iraq, 2021.
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.
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.
Z. Tariq, S. Elkatatny, M. Mahmoud and A. Abdulraheem, "A New Artificial Intelligence Based Empirical Correlation to Predict Sonic Travel Time," Paper presented at the International Petroleum Technology Conference, IPTC-19005-MS, 2016.
S. Maleki, A. Moradzadeh,R. G. Riabi, R. Gholami and F. Sadeghzadeh, "Prediction of shear wave velocity using empirical correlations and artificial intelligence methods," Vol. 3, NRIAG Journal of Astronomy and Geophysics, 2014, pp. 70-81.
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)," Vol. 4, Open Journal of Geology, 2014, pp. 303-313.
E. Jorjani, C. S. Chehreh and S. H. Mesroghli, "Application of artificial neural networks to predict chemical desulfurization of Tabas coal," Vol. 87, Fuel, 2008, pp. 2727–3273.
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," Vol. 150. International Journal of Multiphase Flow, 2022.
M. A. Razavi, A. Mortazavi and M.Mousavi, "Dynamic modeling of milk ultrafiltration by artificial neural network," Vol. 120, J. Membrane. 2003, pp. 47-58.
Received on 25/06/2022
Accepted on 19/07/2022
Published on 30/12/2022
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