Poro-Acoustic Impedance as a New Seismic Inversion Attribute for Reservoir Characterization

نوع مقاله : مقاله پژوهشی

نویسندگان

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

چکیده

Porosity is one of the most important petrophysical parameters, studied in the subject of reservoir characterization. Determining porosity and how it changes in hydrocarbon reservoirs is an important issue that has been addressed in various researches. In this research, Poro-Acoustic Impedance (PAI) is introduced as an extended form of Acoustic Impedance (AI). The difference between PAI and AI is related porosity that is directly involved in the PAI. The inclusion of porosity data in the PAI formula made porosity effective in forward modeling and inversion of seismic data. The use of PAI in the forward modeling of synthetic models increases the contrast between the subsurface layers, and the contrast increases twice as compared to the AI. Band Limited Recursive Inversion (BLRI) algorithm is used for inversion of synthetic seismograms and model-based algorithm is used for real seismic data inversion. For real data, due to the existence of well data, seismic horizons and geological information, using the basic model method for inversion is more accurate. The main difference between inversion using PAI and AI is that changes in porosity can be seen directly in the results of PAI inversion. The correlation of porosity with PAI and AI is -0.93 and -0.85, respectively, which shows that porosity has a stronger relationship with PAI. The use of PAI can be a quick and simple solution to understand porosity changes in hydrocarbon reservoirs and increase the accuracy of porosity determination in reservoirs to a great extent.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Poro-Acoustic Impedance as a New Seismic Inversion Attribute for Reservoir Characterization

نویسندگان [English]

  • Saeed Aftab
  • Ahsan Leisi
  • Navid Shad Manaman
Department of Mining Exploration, Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran.
چکیده [English]

Porosity is one of the most important petrophysical parameters, studied in the subject of reservoir characterization. Determining porosity and how it changes in hydrocarbon reservoirs is an important issue that has been addressed in various researches. In this research, Poro-Acoustic Impedance (PAI) is introduced as an extended form of Acoustic Impedance (AI). The difference between PAI and AI is related porosity that is directly involved in the PAI. The inclusion of porosity data in the PAI formula made porosity effective in forward modeling and inversion of seismic data. The use of PAI in the forward modeling of synthetic models increases the contrast between the subsurface layers, and the contrast increases twice as compared to the AI. Band Limited Recursive Inversion (BLRI) algorithm is used for inversion of synthetic seismograms and model-based algorithm is used for real seismic data inversion. For real data, due to the existence of well data, seismic horizons and geological information, using the basic model method for inversion is more accurate. The main difference between inversion using PAI and AI is that changes in porosity can be seen directly in the results of PAI inversion. The correlation of porosity with PAI and AI is -0.93 and -0.85, respectively, which shows that porosity has a stronger relationship with PAI. The use of PAI can be a quick and simple solution to understand porosity changes in hydrocarbon reservoirs and increase the accuracy of porosity determination in reservoirs to a great extent.

کلیدواژه‌ها [English]

  • Poro-Acoustic Impedance
  • Reservoir Characterization
  • Seismic Inversion
  • Seismic Attributes
  • Inversion
  • Forward Modeling

مراجع

Ali, A., Younas, M., Ullah, M., Hussain, M., Toqeer, M., Samuel, A., & Khan, A. (2019). Characterization of secondary reservoir potential via seismic inversion and attribute analysis : A case study. Journal of Petroleum Science and Engineering, 178(September 2018), 272–293. https://doi.org/10.1016/j.petrol.2019.03.039.
Anifowose, F., Adeniye, S., Abdulraheem, A., & Al-shuhail, A. (2016). Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models. Journal of Petroleum Science and Engineering, 145, 230–237. https://doi.org/10.1016/j.petrol.2016.05.019.
Azevedo, L., Narciso, J., Nunes, R., & Soares, A. (2020). Geostatistical Seismic Inversion with Self-Updating of Local Probability Distributions. Mathematical Geosciences. https://doi.org/10.1007/s11004-020-09896-9.
Farfour, M., Yoon, W. J., & Kim, J. (2015). Seismic attributes and AI inversion in interpretation of complex hydrocarbon reservoirs. Journal of Applied Geophysics, 114, 68–80. https://doi.org/10.1016/j.jappgeo.2015.01.008.
Ghadami, N., Rasaei, M. R., Hejri, S., Sajedian, A., & Afsari, K. (2015). Consistent porosity – permeability modeling , reservoir rock typing and hydraulic fl ow unitization in a giant carbonate reservoir. Journal of Petroleum Science and Engineering, 131, 58–69. https://doi.org/10.1016/j.petrol.2015.04.017.
Gholami, A., & Reza, H. (2017). Estimation of porosity from seismic attributes using a committee model with bat-inspired optimization algorithm. Journal of Petroleum Science and Engineering, 152(May 2016), 238–249. https://doi.org/10.1016/j.petrol.2017.03.013.
Grana, D., Fjeldstad, T., & Omre, H. (2017). Bayesian Gaussian Mixture Linear Inversion for Geophysical Inverse Problems. Mathematical Geosciences. https://doi.org/10.1007/s11004-016-9671-9.
Kadkhodaie-ilkhchi, R., Moussavi-harami, R., & Rezaee, R. (2014). Seismic inversion and attributes analysis for porosity evaluation of the tight gas sandstones of the Whicher Range fi eld in the Perth Basin , Western Australia. Journal of Natural Gas Science and Engineering, 21, 1073–1083. https://doi.org/10.1016/j.jngse.2014.10.027.
Kassab, M. A., & Weller, A. (2011). Porosity estimation from compressional wave velocity : A study based on Egyptian sandstone formations. Journal of Petroleum Science and Engineering, 78(2), 310–315. https://doi.org/10.1016/j.petrol.2011.06.011.
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, 211, 104971. https://doi.org/10.1016/j.jappgeo.2023.104971.
Khoshdel, H., & Riahi, M. A. (2011). Multi attribute transform and neural network in porosity estimation of an offshore oil field — A case study. Journal of Petroleum Science and Engineering, 78(3–4), 740–747. https://doi.org/10.1016/j.petrol.2011.08.016
Leisi, A., & Falahat, R. (2021). Investigation of Some Porosity Estimation Methods Using Seismic Data in One of the South Iranian Oil Fields. Petroleum Research, 31(4). https://doi.org/10.22078/pr.2021.4438.3007)
Leisi, A., Kheirollahi, H., & Shadmanaman, N. (2022). Investigation and comparison of conventional methods for estimating shear wave velocity from well logging data in one of the sandstone reservoirs in southern Iran. Iranian Gournal of Geophysics. https://doi.org/https://doi.org/10.30499/IJG.2022.320098.1385.
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. https://doi.org/https://doi.org/10.1007/s12145-022-00902-8.
Liang, J., Wang, H., Blum, M. J., & Ji, X. (2019). Demarcation and correlation of stratigraphic sequationuences using wavelet and Hilbert-Huang transforms : A case study from Niger Delta Basin. Journal of Petroleum Science and Engineering, 182(August), 106329. https://doi.org/10.1016/j.petrol.2019.106329.
Lindberg, D. V., & Grana, D. (2015). Petro-Elastic Log-Facies Classification Using the Expectation – Maximization Algorithm and Hidden Markov Models. Mathematical Geosciences, 47(6), 719–752. https://doi.org/10.1007/s11004-015-9604-z.
Maurya, S. P., Singh, N. P., & Singh, K. H. (2020). Seismic Inversion Methods : A Practical Approach. springer.
Onajite, E. (2021). applied techniques to integrated oil and gas reservoir characterization. Candice Janco, Elsevier.
Shahbazi, A., Soleimani M. M., Thiruchelvam, V., Fei, K. T., & Babasafari, A. A. (2020). Integration of knowledge-based seismic inversion and sedimentological investigations for heterogeneous reservoir. Journal of Asian Earth Sciences, 202, 104541. https://doi.org/10.1016/j.jseaes.2020.104541.
Soleimani, M., Jodeiri, S. B., & Rafiei, M. (2016), Integrated petrophysical modeling for a strongly heterogeneous and fractured reservoir, Sarvak Formation, SW Iran. Natural Resources Research, 26(1), 75-88.
Silva, G. M., Souza, V. De, Davolio, A., Schiozer, D. J., & Petr, P. (2020). Improving fluid modeling representation for seismic data assimilation in compositional reservoir simulation. Journal of Petroleum Science and Engineering, 194(June), 107446. https://doi.org/10.1016/j.petrol.2020.107446.
Simm, R., & Bacon, M. (2014). Seismic Amplitude. Cambridge university press.
Soares, A. (2021). Geostatistical Seismic Inversion : One Nugget from the Tróia Conference. Mathematical Geosciences, 53(2), 211–226. https://doi.org/10.1007/s11004-020-09910-0.
Wei, W., Zhu, X., Meng, Y., Xiao, L., Xue, M., & Wang, J. (2016). Porosity model and its application in tight gas sandstone reservoir in the southern part of West Depression , Liaohe Basin , China. Journal of Petroleum Science and Engineering, 141, 24–37. https://doi.org/10.1016/j.petrol.2016.01.010.
Yasin, Q., Mohyuddin, G., Khalid, P., Baklouti, S., & Du, Q. (2021). Application of machine learning tool to predict the porosity of clastic depositional system , Indus Basin , Pakistan. Journal of Petroleum Science and Engineering, 197(September 2020), 107975. https://doi.org/10.1016/j.petrol.2020.107975.
فیزیک زمین و فضا
دوره 49، شماره 4
تاریخ انتشار الکترونیکی: 1 اسفند 1402
اسفند 1402
صفحه 11-25

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