Document Type : Original Research Paper


1 Department of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Petroleum, Materials and Mining Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

3 Deputy Manager Geoscience Faculty, Research Institute of Petroleum Industry (RIPI), Tehran, Iran


In the seismic methods, estimation of the formation pressures is obtained by converting the seismic velocity to the pore pressure, and comparing it with the effective pressure during the well-test program. This work is a new challenge regarding the velocity study domain in an oil field in SW Iran. The reservoir generally consists of carbonate rocks, and contains no shale interbeds. Here, 23 well information, seismic data interpretation, compressional (Vp), and shear velocity (Vs) models are implemented. The models are determined from the combined geo-statistical methods, and the results obtained are compared with the fractal models. The final Vs cube is modeled in order to determine the formation fracture pressure using the exploratory well cores and dipole sonic imager (DSI) Vs logs with a correlation coefficient of 0.95 for the Vs data obtained from the porosity, lithology, and primary DSI data. The vertical seismic profiling (VSP) data introduce a maximum interval velocity of 2760-2900 m/s in the field related to the Gotnia formation. The final amounts ​​of seismic acoustic impedance inversion (AI) at the bottom of the field are mostly in the range of 8000-15000 [(m/s)*(g/cm3)], which can be related to the calcareous formations. Based on the Logratio matrix obtained from the fractal velocity-volume (Vp-V) model, the maximum overall accuracy (OA) in the dominant limestone intervals is 0.74. It indicates a high correlation of the Vp cube model obtained from the combination of sequential Gaussian simulation (SGS) and co-kriging models with AI. The uncertainty studies of Vp model in blind wells are about 50%, which is acceptable considering the large well numbers.


[1] Maddahi, A., Ghazi Nezhad, S., Ismailpour, S., and Heydari, M. (2014). Feasibility study of exploiting four-dimensional seismic studies of Sarvak reservoir in Azadegan field, Journal of Petroleum Research, No. 78, p. 126-117 (in Persian).
[2] Adim, A., Riahi, M., and Bagheri, M. (2018). Estimation of pore pressure by Eaton and Bowers methods using seismic and well survey data, Journal of Applied Geophysical Research, Volume 4, Number 2, p. 275-267, Digital Identification (DOI): 10.22044 / JRAG.2018.6360.1167 (in Persian).
[3] Poorsiami, H. (2013). Modeling the pore pressure of a hydrocarbon reservoir in southwestern Iran using well-logging data, Journal of Petroleum Research, Volume 23, Number 74, p. 86-72 (in Persian).
[4] Aghebati, R. (2008). Introduction of a field: Azadegan field development plan, scientific Journal of Oil and Gas exploration and production, number 51, November 2008 p. 8-6 (in Persian).
[5] Jindal N., Kumar Biswal A., and Hemant Singh K. (2016). Time-Depth Modeling in High Pore-Pressure Environment, Offshore East Coast of India,  AAPG 2016 Annual Convention and Exhibition, Calgary, Alberta, Canada, June 19-22, 2016.
[6] Haris A., Sitorus R.J., and Riyanto A. (2017). Pore pressure prediction using probabilistic neural network: case study of South Sumatra Basin, Southeast Asian Conference on Geophysics, IOP Conf. Series: Earth and Environmental Science1 26324 (526071879)0 012021.
[7] Amirzadeh, M., Kamali, M.R., and Nabi Bidehandi, M. (2013). Investigation of reservoir characteristics by performing seismic data conversion and seismic markers in Sarvak Formation in one of the oil fields in southwestern Iran, Journal of Petroleum Research, Vol. 23, No. 75, 2013, p. 29-20 (in Persian).
[8] Amiri Bakhtiar, M.S., Zargar, Gh, Riahi, M.A., and Ansari, H.R. (2017). Seismic inversion by spectral simulation in one of the oil fields in southwestern Iran, the third oil exploration geophysics seminar, Exploration Directorate of the National Iranian Oil Company, May 2017, pp. 74-70 (in Persian).
[9] Castagna J. P., Batzle M. L., and Eastwood R. L (1985) Relationships between compressional-wave and shear-wave velocities in clastic silicate rocks, GEOPHYSICS, VOL. 50, NO.4 (APRIL 1985); P. 571-581, 25 FIGURES, 2 TABLES.
[10] Castagna, J. P., Batzle, M.L. and Kan, (1993). Rock Physics:The link between rock properties and AVO response, In: Offset-dependet reflectivity - Theory and practice of AVO analysis, Castagna, J.P. and Backus, M. M., editors. Investigations in Geophysics no. 8, SEG, OK, 135-171.
[11] Fatahi, H., Askari, M., Majdi and Far, S. (2016). Estimation of shear wave velocity in one of the hydrocarbon reservoirs of southwest Iran using different well logs and a new intelligent combined method Journal of Advanced Applied Geology, Winter 95, No. 22. pp. 35-43 (in Persian).
[12] Bowers, G. (1995). Pore Pressure Estimation from Velocity Data: Accounting for Overpressure Mechanisms Besides Undercompaction, SPE Drilling & Completion, Vol. 10, No. 2, p. 89-95, 1995.
[13] Bowers G.L. (2002). Detecting high overpressure, The Leading edge, 21 (2) (2002), pp. 174-177.
 [14] Lantuejoul, C.h. (2002). Geostatistical Simulation Models and Algorithms, Springer-VerlagBerlin Heidelberg GmbH, ISBN: 978-3-662-04808-5, 256 p.
[15] Kelkar, M. and Perez, G. (2002). Applied Geostatistics for Reservoir Characterization, Society of Petroleum Engineers. ISBN: 978-1-55563-095-9, 264 p.
[16] Bohling, G. (2007). INTRODUCTION TO GEOSTATISTICS, Hydro-geophysics: Theory, Methods, and Modeling, Boise State University, Boise, Idaho.
[17] Armstrong, M., Galli, A., Beucher, H., LeLoc’h,G., Renard, D., Eschard, R., Doligez, B., and Geffroy, F. (2011). Pluri-gaussian Simulations in Geosciences, Springer-Verlag Berlin Heidelberg GmbH, ISBN: 978-3-662-12718-6, Pages: 149.
[18] Hassanpour, S., and Afzal, P. (2013). Application of concentration–number (C–N) multifractal modeling for geochemical anomaly separation in Haftcheshmeh porphyry system, NW Iran. Arab J Geosci 6, 957–970.
[19] Paravarzar, S., Maarefvand, P., Maghsoudi, and A. Afzal, P. (2014). Correlation between geological units and mineralized zones using fractal modeling in Zarshuran gold deposit (NW Iran). Arab J Geosci 8, 3845–3854.
[20] Soltani, F., Afzal, P., and Asghari, O. (2014). Delineation of alteration zones based on Sequential Gaussian Simulation and concentration–volume fractal modeling in the hypogene zone of Sungun copper deposit, NW Iran. Journal of Geochemical Exploration, 140, 64–76. doi: 10.1016/j.gexplo.2014.02.007 
[21] Carranza EJM (2011). Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values. J Geochem Explor 110(2):167–185.
[22] Mokhtari M, Zandifar H, and Abdollahie Fard I (1999). Seismic Interpretation of top Sarvak Formation in Azadegan-Nir Kabir Area (SW Iran), National Iranian Oil Company, Exploration Directorate, Published report in Farsi.
[23] Abdollahie Fard I (2006) Structural models for the South Khuzestan area based on reflection seismic data, Ph.D. Dissertation in Geology Tectonics, School of Earth Sciences, Shahid Beheshti University, Tehran, Iran
[24] Shahbazi, A., Soleimani Monfared, M., Thiruchelvam, V., Ka Fei, T., and Babasafari, A.A. (2020). Integration of knowledge-based seismic inversion and sedimentological investigations for heterogeneous reservoir. Journal of Asian Earth Sciences, 202, 104541.
[25] Rointan, A., Soleimani Monfared, M. and Aghajani, H. (2021). Improvement of seismic velocity model by selective removal of irrelevant velocity variations. Acta Geodaetica et Geophysica 56, 145–176.
[26] Soleimani, M. (2016). Seismic imaging by 3D partial CDS method in complex media. Journal of Petroleum Science and Engineering, 143.
[27] Shahbazi, A. Ghosh, D., Soleimani, M., and Gerami, A. (2016), Seismic imaging of complex structures with the CO-CDS stack method. Studia Geophysica et Geodaetica. 60, 662–678.