Journal of Oil, Gas and Petrochemical Technology

Journal of Oil, Gas and Petrochemical Technology

Comparison of Empirical and Rock Physics Models in Estimating Shear Wave Velocity

Document Type : Research Paper

Authors
Ali RANJBAR 1
Seyed Alireza Kamani 2
1 Faculty of Petroleum, Gas and Petrochemical Engineering, Persian Gulf University, Iran
2 Petroleum University of Technology, Ahwaz, Iran
10.22034/jogpt.2024.408406.1122
Abstract
The measurement of shear wave velocity (Vs) using the dipole sonic imager (DSI) logging tool is regarded as a crucial physical parameter for rocks. However, not all wells have access to this data, making it essential to accurately and reliably estimate this parameter with minimal uncertainty to determine reservoir characteristics effectively. The Vs estimation approach in this study includes empirical methods and two rock physic models. In empirical methods, empirical correlations reported in studies have been used. In the second approach, two rock physics models, Gaussmann and Xu-Payne, which are more complicated than the experimental models, have been chosen to determine the characteristics of the Vs. The main innovation of this paper is the comparison of all the mentioned methods in Vs estimation. Correlation coefficient (R2) and Average Relative Error (ARE) were chosen as statistical comparison criteria. Based on the final findings, the Greenberg and Castagna method, incorporating Gaussmann fluid replacement theory, exhibited consistent performance and improved estimation accuracy of Vs with R2 and ARE values of 0.9067 and 3.2292, respectively. The suggested approach has the potential to be employed in various other oil and gas exploration fields and can provide accurate Vs estimates.
Keywords
Shear wave
Rock
Physics
Velocity
Gaussmann
  1. Ranjbar, A., Hassani, H., Shahriar, K., 3D geomechanical modeling and estimating the compaction and subsidence of Fahlian reservoir formation (X-field in SW of Iran). Arabian Journal of Geosciences, 2017. 10: p. 1-12.
  2. Ebrahimi, A., Izadpanahi, A., Ebrahimi, P., Ranjbar, A., Estimation of Vs in an Iranian oil reservoir using machine learning methods. Journal of Petroleum Science and Engineering, 2022. 209: p. 109841.
  3. Ranjbar, A., Kamani, S. A., Ghaderi, A, Petrophysical Evaluation of Fahliyan Reservoir Formation.
  4. Ahmadi, Y., M. Mansouri, and E. Jafarbeigi, Improving simultaneous water alternative associate gas tests in the presence of newly synthesized γ-Al2O3/ZnO/Urea nano-composites: An experimental core flooding tests. ACS omega, 2022. 8(1): p. 1443-1452.
  5. Mansouri, M. and Y. Ahmadi, Applications of zeolite-zirconia-copper nanocomposites as a new asphaltene inhibitor for improving permeability reduction during CO2 flooding. Scientific Reports, 2022. 12(1): p. 6209.
  6. Ahmadi, Y., Improving fluid flow through low permeability reservoir in the presence of nanoparticles: An experimental core flooding WAG tests. Iranian Journal of Oil and Gas Science and Technology, 2021.
  7. Jafarbeigi, E., Ahmadi, Y., Mansouri, M., Ayatollahi, S., Experimental core flooding investigation of new ZnO− γAl2O3 nanocomposites for enhanced oil recovery in carbonate reservoirs. ACS omega, 2022. 7(43): p. 39107-39121.
  8. Mansouri, M., Y. Ahmadi, and E. Jafarbeigi, Introducing a new method of using nanocomposites for preventing asphaltene aggregation during real static and dynamic natural depletion tests. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2022. 44(3): p. 7499-7513.
  9. Mansouri, M., Parhiz, M., Bayati, B., Ahmadi, Y., Preparation of nickel oxide supported zeolite catalyst (NiO/na-ZSM-5) for asphaltene adsorption: A kinetic and thermodynamic study. Iranian Journal of Oil and Gas Science and Technology, 2021. 10(2): p. 63-89.
  10. Du, Q., Yasin, Q., Ismail, A., Sohail, G. M., Combining classification and regression for improving Vs estimation from well logs data. Journal of Petroleum Science and Engineering, 2019. 182: p. 106260.
  11. Pickett, G.R., Acoustic character logs and their applications in formation evaluation. Journal of Petroleum technology, 1963. 15(06): p. 659-667.
  12. Carroll, R.D. The determination of the acoustic parameters of volcanic rocks from compressional velocity measurements. in International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts. 1969. Elsevier.
  13. Eskandary, H., Rezaei, MR., Javaherian, A., Mohammadnia, M., Vs estimation utilizing wireline logs for a carbonate reservoir, south-west Iran. 2003.
  14. Brocher, T.M., Empirical relations between elastic wavespeeds and density in the Earth's crust. Bulletin of the seismological Society of America, 2005. 95(6): p. 2081-2092.
  15. Plumb, R., Edwards, S., Pidcock, G., Lee, D., Stacey, B. The mechanical earth model concept and its application to high-risk well construction projects. in IADC/SPE Drilling Conference. 2000. OnePetro.
  16. Ranjbar, A., Hassani, H., Shahriar, K., AmeriShahrabi, MJ., Thermo-hydro-mechanical modeling of fault discontinuities using zero-thickness interface element. Journal of Rock Mechanics and Geotechnical Engineering, 2020. 12(1): p. 74-88.
  17. Ranjbar, A., A. Izadpanahi, and A. Ebrahimi, Numerical analysis of geomechanical behavior of fractures and faults in a deformable porous medium. Journal of Petroleum Exploration and Production Technology, 2022. 12(11): p. 2955-2966.
  18. Ghazi, A., Moghadas, NH., Sadeghi, H., Ghafoori, M., Lashkaripur, GR., Empirical relationships of Vs, SPT-N value and vertical effective stress for different soils in Mashhad, Iran. Annals of Geophysics, 2015. 58(3): p. S0325-S0325.
  19. Miller, S.L. and R.R. Stewart, Effects of lithology, porosity and shaliness on P-and S-wave velocities from sonic logs. Canadian Journal of Exploration Geophysics, 1990. 26(1-2): p. 94-103.
  20. Wantland, D., Laroque, GE., Bollo, MF., Dickey, DD., Goodman, RE., Geophysical measurements of rock properties in situ. 1964.
  21. Xu, S. and R.E. White, A physical model for shear‐wave velocity prediction1. Geophysical prospecting, 1996. 44(4): p. 687-717.
  22. Kuster, G.T. and M.N. Toksöz, Velocity and attenuation of seismic waves in two-phase media: Part I. Theoretical formulations. Geophysics, 1974. 39(5): p. 587-606.
  23. Xu, S. and M.A. Payne, Modeling elastic properties in carbonate rocks. The Leading Edge, 2009. 28(1): p. 66-74.
  24. Ruiz, F. and I. Azizov, Fluid substitution in tight shale using the soft-porosity model, in SEG Technical Program Expanded Abstracts 2011. 2011, Society of Exploration Geophysicists. p. 2272-2276.
  25. Sohail, G.M. and C.D. Hawkes, An evaluation of empirical and rock physics models to estimate Vs in a potential shale gas reservoir using wireline logs. Journal of petroleum science and engineering, 2020. 185: p. 106666.
  26. Ameen, M., Smart, BGD., Somerville, J., Hammilton, S., Naji, NA., Predicting rock mechanical properties of carbonates from wireline logs (A case study: Arab-D reservoir, Ghawar field, Saudi Arabia). Marine and Petroleum Geology, 2009. 26(4): p. 430-444.
  27. Rasouli, V., Z.J. Pallikathekathil, and E. Mawuli, The influence of perturbed stresses near faults on drilling strategy: a case study in Blacktip field, North Australia. Journal of Petroleum Science and Engineering, 2011. 76(1-2): p. 37-50.
  28. Yasin, Q., Du, Q., Sohail, GM., Ismail, A., Fracturing index-based brittleness prediction from geophysical logging data: application to Longmaxi shale. Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 2018. 4(4): p. 301-325.
  29. Azadpour, M., Saberi, MR., Javaherian, A., Shabani, M., Rock physics model-based prediction of Vs utilizing machine learning technique for a carbonate reservoir, southwest Iran. Journal of Petroleum Science and Engineering, 2020. 195: p. 107864.
  30. Zhang, B., Jin, S., Liu, C., Guo, Z., Liu, X., Prediction of Vs based on a statistical rock-physics model and Bayesian theory. Journal of Petroleum Science and Engineering, 2020. 195: p. 107710.
  31. Feng, G., Zeng, H., Xu, X., Tang, G., Wang, Y., Vs prediction based on deep neural network and theoretical rock physics modeling. Frontiers in Earth Science, 2023. 10: p. 1025635.
  32. Du, Q., Q. Yasin, and A. Ismail. A comparative analysis of artificial neural network and rock physics for the estimation of Vs in a highly heterogeneous reservoir. in SEG International Exposition and Annual Meeting. 2018. SEG.
  33. Vukadin, D., Shear-wave velocity estimation based on rock physics modelling of a limestone gas reservoir in the Pannonian Basin. Petroleum Geoscience, 2023. 29(2): p. petgeo2022-070.
  34. Seifi, H., B. Tokhmechi, and A. Moradzadeh, Improved estimation of shear-wave velocity by ordered weighted averaging of rock physics models in a carbonate reservoir. Natural Resources Research, 2020. 29: p. 2599-2617.
  35. Maleki, S., Moradzadeh, A., Riabi, R., Gholami, R., Sadeghzadeh, F., Prediction of Vs using empirical correlations and artificial intelligence methods. NRIAG Journal of Astronomy and Geophysics, 2014. 3(1): p. 70-81.
  36. Tabari, K., O. Tabari, and M. Tabari, A fast method for estimating Vs by using neural network. Australian Journal of Basic and Applied Sciences, 2011. 5(11): p. 1429-1434.
  37. Asoodeh, M. and P. Bagheripour, ACE stimulated neural network for Vs determination from well logs. Journal of Applied Geophysics, 2014. 107: p. 102-107.
  38. Han, D.-h., A. Nur, and D. Morgan, Effects of porosity and clay content on wave velocities in sandstones. Geophysics, 1986. 51(11): p. 2093-2107.
  39. Qiao, H., B. Zhang, and C. Liu, Research on Prediction Method of Volcanic Rock Vs Based on Improved Xu–White Model. Energies, 2022. 15(10): p. 3611.
  40. Oloruntobi, O. and S. Butt, The shear-wave velocity prediction for sedimentary rocks. Journal of Natural Gas Science and Engineering, 2020. 76: p. 103084.
  41. Liu, Y., Z. Chen, and K. Hu. Shear velocity prediction and its rock mechanic implications. in Conf. GeoConvention. 2012.
  42. Vernik, L., Seismic petrophysics in quantitative interpretation. 2016: Society of Exploration Geophysicists.
  43. Gassmann, F., Uber die elastizitat poroser medien. Vierteljahrsschrift der Naturforschenden Gesellschaft in Zurich, 1951. 96: p. 1-23.
  44. Krief, M., Garat, J., Stellingwerff, J., Ventre, J, A petrophysical interpretation using the velocities of P and S waves (full-waveform sonic). The Log Analyst, 1990. 31(6)
  45. Nur, A., Critical porosity and the seismic velocities in rocks. EOS Transactions AGU, 1992. 73: p. 66.
  46. Pride, S.R., J.G. Berryman, and J.M. Harris, Seismic attenuation due to wave‐induced flow. Journal of Geophysical Research: Solid Earth, 2004. 109(B1).
  47. Voigt, W., Lehrbuch der Kristallphysik (MIT Ausschlußder Kristalloptik). BG Teubner-Verlag, Leipzig, 1910.
  48. Reuß, A., Berechnung der fließgrenze von mischkristallen auf grund der plastizitätsbedingung für einkristalle. ZAMM‐Journal of Applied Mathematics and Mechanics/Zeitschrift für Angewandte Mathematik und Mechanik, 1929. 9(1): p. 49-58.
  49. Hill, R. Procedures of the Physical. in Society (London) A. 1952.
  50. Berryman, J.G., Long‐wavelength propagation in composite elastic media II. Ellipsoidal inclusions. The Journal of the Acoustical Society of America, 1980. 68(6): p. 1820-1831.
  51. Zhang, Y., Zhong, H., Wu, Z., Zhou, H., Ma, Q., Improvement of petrophysical workflow for Vs prediction based on machine learning methods for complex carbonate reservoirs. Journal of Petroleum Science and Engineering, 2020. 192: p. 107234.
  52. de Oliveira Dias, J., Rock Physics Modeling Evaluation for Carbonate Reservoirs. 2017, PUC-Rio.
  53. Armstrong, R.T., D. Wildenschild, and B.K. Bay, The effect of pore morphology on microbial enhanced oil recovery. Journal of Petroleum Science and Engineering, 2015. 130: p. 16-25.
Journal of Oil, Gas and Petrochemical Technology
Volume 10, Issue 2
August 2023
Pages 95-109