Shear wave velocity estimation using seismic attributes in one of the sandstone reservoirs of southern Iran

Document Type : Research Article


Department of Mining Exploration, Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran.


Shear wave velocity is a key factor to estimate the elastic and petrophysical parameters of the hydrocarbon reservoir. However, shear wave velocity is rarely logged at wells due to the imposition of high costs. Therefore, it is usually attempted to estimate this parameter by different methods from the available and related data. Describing the elastic parameters of reservoir rock, including shear modulus, bulk modulus and Poisson's ratio, requires the measurement of density and compressional and shear wave velocities of the reservoir formations. Direct measurement of the shear wave velocity is done by drilling cores and DSI (Dipole Shear Sonic imager) tools, which are unfortunately very time-consuming and expensive. In this study, a practical method for estimating shear wave velocity in a sandstone oil reservoir is presented. In the studied reservoir, from seven existing wells, the shear wave velocity has been measured by DSI tools in only one of them (well #7). The shear wave velocity log in the location of the other wells was estimated using a petrophysical equation, defined for the location of well #7. The correlation of other logs (i.e. acoustic, density, porosity, resistivity, gamma ray, dolomite volume, quartz volume, and water saturation logs) with the shear wave velocity was investigated in well #7. We found that the compressional wave velocity, density, porosity, dolomite volume and quartz volume logs were more correlated with the shear wave velocity log in well #7. Thus, these logs were selected as input for estimating shear wave velocity log and the experimental equation using the multivariable linear regression method was calculated. The estimated shear wave velocity log using the obtained relationship has a 90% correlation with the measured shear wave velocity log in well #7. Using this petrophysical relationship, the shear wave velocity were estimated in the other wells (blind wells). The main goal in this study, was to produce the volume of the shear wave velocity information at the sandstone reservoir. To obtain 3D volume of shear wave velocity distribution in the reservoir, the seismic and well data are integrated. To achieve this goal, the model-based seismic inversion technique has been performed to obtain the acoustic impedance volume for the sandstone reservoir. The calculated acoustic impedance volume using model-based algorithm has an average of 99% correlation and 15% error with the real acoustic impedance log. The results of the seismic inversion were fed into the cross validation method to derive the optimal number of seismic attributes relevant to shear wave velocity information. The cross validation method shows that the attributes of the filter 20/25-30/45, the cosine instantaneous phase, the acoustic impedance and the instantaneous frequency have the reasonable correlation with the shear wave velocity information respectively, and are selected as the input attributes for the estimation of shear wave velocity volume in the sandstone reservoir. Our results show a good agreement between the real shear velosity log and the predicted shear velocity from the seismic attributes in the place of well #7. The obtained shear wave velocity volume accompanied by the compressional wave velocity information can be used to infer more robust petrophysical parameters in the reservoir.


Main Subjects

لیثی، ا. و فلاحت، ر. (1400). بررسی و مقایسه روش‌های مرسوم تخمین تخلخل با استفاده از داده‌های لرزه‌نگاری در یکی از میادین نفتی خلیج فارس. مجله پژوهش نفت، 31(4)، 88-97.
لیثی، ا.؛ خیرالهی، ح. و شاد منامن، ن. (1401). بررسی و مقایسه روش‌های مرسوم تخمین سرعت موج برشی از روی داده‌های چاه‌پیمایی در یکی از مخازن ماسه‌سنگی جنوب ایران. مجله ژئوفیزیک ایران، 16(3)، 23-35.
Abdolahi, A., Chehrazi, A., Kadkhodaie, A., & Babasafari, A.A. )2022.( Seismic inversion as a reliable technique to anticipating of porosity and facies delineation, a case study on Asmari Formation in Hendijan field, southwest part of Iran. Journal of Petroleum Exploration and Production Technology, 12, 3091–3104.
Akhundi, H., Ghafoori, M., & Lashkaripour, G.R. )2014.( Prediction of Shear Wave Velocity Using Artificial Neural Network Technique, Multiple Regression and Petrophysical Data: A Case Study in Asmari Reservoir (SW Iran). Open J. Geol., 04, 303–313.
Anemangely, M., Ramezanzadeh, A., Amiri, H., & Hoseinpour, S.A. )2019.( Machine learning technique for the prediction of shear wave velocity using petrophysical logs. J. Pet. Sci. Eng., 174, 306–327.
Brocher, T.M. )2005.( Empirical relations between elastic wavespeeds and density in the Earth’s crust. Bull. Seismol. Soc. Am., 95, 2081–2092.
Brown, A.R. )2001.( Understanding seismic attributes. Geophysics, 66, 47-48.
Castagna, J.P., Batzle, M. L., & Eastwood, R.L. )1985.( Relationships between compressional-wave and shear-wave velocities in clastic silicate rocks. Geophysics, 50, 571-581.
Das, B., & Chatterjee, R. )2016.( Porosity mapping from inversion of post-stack seismic data. Georesursy, 18, 306-313.
Das, B., Chatterjee, R., Singha, D. K., & Kumar, R. )2017.( Post-stack seismic inversion and attribute analysis in shallow offshore of Krishna-Godavari basin, India. Journal of the Geological Society of India, 90, 32-40.
Du, Q., Yasin, Q., Ismail, A., & Sohail, G.M. )2019). Combining classification and regression for improving shear wave velocity estimation from well logs data. J. Pet. Sci. Eng., 182, 106260.
Ebrahimi, A., Izadpanahi, A., Ebrahimi, P., & Ranjbar, A. )2022). Estimation of shear wave velocity in an Iranian oil reservoir using machine learning methods. J. Pet. Sci. Eng., 209, 109841.
Eskandari, H., Rezaee, M. R., & Mohammadnia, M. )2004.( Application of Multiple Regression and Artificial Neural Network Techniques to Predict Shear Wave Velocity from Wireline Log Data for a Carbonate Reservoir, South-West Iran. CSEG Recorder, 29, 42-48.
Greenberg, M.L., & Castagna J. P. )1992.( Shear wave velocity estimation in porous rocks: theoretical formulation, prelimining verification and applications. Geophys Prospect., 40, 195–209.
Gogoi, T., & Chatterjee, R. )2019.( Estimation of petrophysical parameters using seismic inversion and neural network modeling in Upper Assam basin, India. Geoscience Frontiers, 10, 1113-1124.
Habimana, J., Labiouse, V., & Descoeudres, F. (2002.( Geomechanical characterisation of cataclastic rocks: experience. International Journal of Rock Mechanics & Mining Sciences, 6, 677–693.
Hampson, D.P., Schuelke, J. S., & Quirein, J. A. )2001.( Use of multi-attribute transforms to predict log properties from seismic data. Geophysics, 66, 220-236.
Hampson, D.P. )2007.( CGGVeritas Hampson-Russell Software CE8 version References Manuals, Hampson-Russell Software Services Ltd, Canada.
Han, D. )1989.( Empirical relationships among seismic velocity, effective pressure, porosity and clay content in sandstone. Geophysics, 54, 82–89.
Kheirollahi, H., Shad Manaman, N., & Leisi, A. )2023.( Robust Estimation of Shear Wave Velocity in a Carbonate Oil Reservoir from Conventional Well Logging Data Using Machine Learning Algorithms. Journal of Applied Geophysics,
Leisi, A., & Saberi, M.R. )2022.( Petrophysical parameters estimation of a reservoir using integration of wells and seismic data: a sandstone case study. Earth Science Informatics, 1-16,
Leite, E. P., & Vidal, A. C. )2011.( 3D Porosity predication from seismic inversion and neural networks. Computers & Geosciences, 37, 1174-1180.
Lim, J. S. )2005.( Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea. J. Petrol. Sci. Eng., 49, 182-192.
Mavko, G., Mukerji, T., & Dvorkin, J. )2020(. The Rock Physics Handbook. CUP.
Mehrad, M., Ramezanzadeh, A., Bajolvand, M., & Reza Hajsaeedi, M. )2022.( Estimating shear wave velocity in carbonate reservoirs from petrophysical logs using intelligent algorithms. J. Pet. Sci. Eng., 212, 110254.
Mehrgini, B., Izadi, H., & Memarian, H. )2017.( Shear wave velocity prediction using Elman artificial neural network. Carbonates and Evaporites, 34, 1281–1291.
Moatazedian, I., Rahimpour Bonab, H., Kadkhodaie-Ilkhchi, A., & Rajoli, M.R. )2011.( Prediction of shear and compressional wave velocities from petrophysical data utilizing genetic algorithms technique: a case study in Hendijan and Abuzar fields located in Persian Gulf. J. Geopersia, 1, 1-17.
Murphy, W., Reischer, A., & Hsu, K. (1993). Modulus decomposition of compressional and shear velocities in sand bodies, Geophysics, 58, 227–239.
Nourafkan, A., & Kadkhodaie-Ilkhchi, A. )2015.( Shear wave velocity estimation from conventional well log data by using a Hybrid ant colony-fuzzy inference system, a case study from Cheshmeh-Khosh oilfield. Journal of Petroleum Science and Engineering, 127, 459-468.
Oldenburg, D., Scheur, T., & Levy, S. )1983.( Recovery of the acoustic impedance from reflection seismogram. Geophysics, 48, 1318-1337.
Oloruntobi, O., Onalo, D., Adedigba, S., James, L., Chunduru, R., & Butt, S. )2019.( Data-driven shear wave velocity prediction model for siliciclastic rocks. J. Pet. Sci. Eng., 183, 106293.
Parvizi, S., Kharrat, R., Asef, M.R., Jahangiry, B., & Hashemi, A. )2015.( Prediction of the Shear Wave Velocity from Compressional Wave Velocity for Gachsaran Formation. Acta Geophys., 63, 1231–1243.
Pickett, G.R. )1963.( Acoustic character logs and their application information evaluation. J. Pet. Technol., 15, 650–667.
Russell, B., Hampson, D.P., & Lines, L.R. )2003.( Application of the radial basis function neural network to the prediction of log properties from seismic attributes—A channel sand case study, in SEG Technical Program, Expanded Abstracts. Society of Exploration Geophysicists, 454-457.
Russell, B. )2004.( The application of multivariate statistics and neural networks to the prediction of reservoir parameters using seismic attributes, Ph.D. Dissertation, University of Calgary, Alberta, 392 pp.
Serra, O., & Serra, L. )2004(. Well Logging-Data Acquisition and Applications, Editions Technip.
Shiri, S., & Falahat, R. )2019.( Carbonate rock physics modelling and 4D seismic feasibility study. J. Appl. Geophys., 172, 103855.
Soleimani, B., Bahadori, A., & Meng, F. )2013.( Microbiostratigraphy, microfacies and sequence stratigraphy of upper cretaceous and paleogene sediments, Hendijan oilfield, Northwest of Persian Gulf, Iran. Natural Science, 5, 1165-1176.
Wang, P., & Peng, S. )2019.( On a new method of estimating shear wave velocity from conventional well logs. J. Pet. Sci. Eng., 180, 105–123.
Wang, J., Cao, J., & Yuan, S. )2020.( Shear wave velocity prediction based on adaptive particle swarm optimization optimized recurrent neural network. J. Pet. Sci. Eng., 194, 107466.
Xu, S., & White, R. E. )1995.( A new velocity model for clay-sand mixtures 1. Geophys. Prospect. 43, 91–118.
Xu, S., & Payne, M.A. )2009.( modeling elastic properties in carbonate rocks. Lead. Edge, 28, 66–74.
Yongzhong, X.U., Tongjun, C., Shizhong, C., Weichuan, H., & Gang, W. )2010.( Comparison between several seismic inversion methods and their application in mountainous coal fields of western China. Mining Science and Technology, 20, 585-590.