Skip to main content

Advertisement

SpringerLink
Log in

Reservoir petrophysical Index (RPI) as a robust tool for reservoir quality assessment

  • RESEARCH
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Investigating the quality of hydrocarbon reservoirs, especially carbonate reservoirs, is a very critical issue in the economic evaluation of the reservoirs. Therefore, we have tried to present a robust and practical formula for evaluating reservoirs quality. The novel Reservoir Petrophysical Index (RPI) based on well-logging data integration is presented in this research. Density, P-wave velocity, gamma ray, resistivity and effective porosity are inputs of the RPI. The RPI aims to distinguish the reservoir and non-reservoir units. The reservoir quality can effectively be evaluated by RPI. To study the performance of the RPI in a practical case, the RPI was calculated for the Iranian offshore oilfield data. The Iranian offshore oilfield is located in the Persian Gulf and on the east of the giant South Pars field. We selected three wells for evaluation of the RPI. The RPI was calculated for all wells, and we concluded that the data integration causes the discrimination of the different zones more accurately. The RPI's sensitivity to reservoir quality factors was evaluated, and we concluded that the various factors affect the RPI results. It was shown that the RPI sensitivity to the porosity and permeability is high. Thus, in non-reservoir zones where porosity and permeability are extremely low, RPI shows a sharp decrease, and the index increases with the increase of porosity and permeability in oil-saturated zones. Also, the effect of the saturation revealed in RPI results and the oil saturation affect the RPI. As a result, the rocks and fluids effects were considered. As the prototype reservoir to examine the proposed methodology, the Surmeh Formation in one of oilfields of Persian Gulf was successfully divided into six rock types based on the newly calculated RPI and Self Organizing Maps (SOM). Reservoir quality decreases from rock types 1 to 6. The use of RPI facilitates the interpretation of well-logging data in an easy, robust and quick way to address the heterogeneity of carbonate reservoirs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data availability

There is no permission to share the data of this article.

References

  • Al-Javad SN, Abd Ahmed M, Saleh AH (2020) Integrated reservoir characterization and quality analysis of the carbonate rock types, case study, southern Iraq. J Pet Explor Prod Technol 10(8):3157–3177. https://doi.org/10.1007/s13202-020-00982-6

    Article  Google Scholar 

  • Al-Jawad SN, Saleh AH (2020) Flow units and rock type for reservoir characterization in carbonate reservoir : case study, south of Iraq. J Pet Explor Prod Technol 10(1):1–20. https://doi.org/10.1007/s13202-019-0736-4

    Article  Google Scholar 

  • Al-mahrooqi S, Mookerjee A, Walton W, Scholten S, Archer R, Al-busaidi J, Al-busaidi H, Oman S (2011). Well Logging and Formation Evaluation Challenges in the Deepest Well in the Sultanate of Oman ( HPHT Tight Sand Reservoirs ). Soc Pet Eng J, 1–9.

  • Aminian K, Ameri S (2005) Application of artificial neural networks for reservoir characterization with limited data. J Petrol Sci Eng 49:212–222. https://doi.org/10.1016/j.petrol.2005.05.007

    Article  Google Scholar 

  • 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. J Petrol Sci Eng 145:230–237. https://doi.org/10.1016/j.petrol.2016.05.019

    Article  Google Scholar 

  • Beigi M, Jafarian A, Javanbakht M, Wanas HA, Matten F, Tabatabaei A (2017) Facies analysis, diagenesis and sequence stratigraphy of the carbonate-evaporite succession of the Upper Jurassic Surmeh Formation: Impacts on reservoir quality (Salman Oil Field, Persian Gulf, Iran). J Afr Earth Sc 129:179–194

    Article  Google Scholar 

  • Chongwain GC, Osinowo OO, Ntamak-Nida MJ, Nkwanyang TL (2019) Lithological typing, depositional environment, and reservoir quality characterization of the “ M - Field”, offshore Douala Basin, Cameroon. J Pet Explor Prod Technol 9(3):1705–1721. https://doi.org/10.1007/s13202-019-0648-3

    Article  Google Scholar 

  • Dasgupta SN, Aminzadeh F (2013) Geophysics for Petroleum Engineers. Elsevier

    Google Scholar 

  • Edwards DA, Brenner H, Karsh T (1994). Interfacial Transport Processes and Rheology,. In J Pet Explor Prod Technol (Vol. 10)

  • Farfour M, Yoon WJ, Kim J (2015) Seismic attributes and acoustic impedance inversion in interpretation of complex hydrocarbon reservoirs. J Appl Geophys 114:68–80. https://doi.org/10.1016/j.jappgeo.2015.01.008

    Article  Google Scholar 

  • Haijun Y, Sun SZ, Lulu C, Youjun X (2011). A new method of formation evaluation for fractured and caved carbonate reservoirs : A case study from the Lundong area , Tarim Basin , China. Pet.Sci., 446–454. https://doi.org/10.1007/s12182-011-0162-9

  • Jonge-anderson ID, Ma J, Wu X, Stow D (2022) Determining reservoir intervals in the Bowland Shale using petrophysics and rock physics models. Geophys J Int 228:39–65

    Article  Google Scholar 

  • Jurcic H, Plc INA, Cogelja Z (2012). Petrophysical Parameters Evaluation in Unconventional Reservoirs by Well Logging and Mud Logging Data Interactive Correlation Method. Soc Pet Eng

  • Kadkhodaie A, Kadkhodaie R,(2022a). Chapter 3 - Tight gas sandstones rock typing. In Kadkhodaie A, Kadkhodaie R (Ed.). Reservoir characterization of tight gas sandstones: exploration and development. (pp. 1–22). Elsevier. https://doi.org/10.1016/B978-0-323-90180-2.00010-4

  • Kadkhodaie A, Kadkhodaie R (2022b). Chapter 4 - Pore type and pore size distribution. In Kadkhodaie A, Kadkhodaie R (Ed.). Reservoir characterization of tight gas sandstones: exploration and development. (pp. 1–22). Elsevier. https://doi.org/10.1016/B978-0-323-90180-2.00010-4

  • Kadkhodaie A, Kadkhodaie R (2022c). Chapter 5 - Porosity and permeability evolution. In Kadkhodaie A, Kadkhodaie R (Ed.). Reservoir characterization of tight gas sandstones: exploration and development. (pp. 1–22). Elsevier. https://doi.org/10.1016/B978-0-323-90180-2.00010-4

  • Kazatchenko E, Mousatov A (2002). Primary and Secondary Porosity Estimation of Carbonate Formations Using Total Porosity and the Formation Factor. Soc Pet Eng

  • Kendalp CGSC, Bowen B, Alsharhan A, Cheong D, Stoudt D, Geology C, Box PO, Ain AI, Emirates UA (1991) Eustatic controls on carbonate facies in reservoirs, and seals associated with Mesozoic hydrocaron fields of the Arabian Gulf and the Gulf of Mexico. Marire Geolog 102:215–238

    Article  Google Scholar 

  • Lasemi Y, Jalilian AH (2010) The Middle Jurassic basinal deposits of the Surmeh Formation in the Central Zagros Mountains, southwest Iran : facies, sequence stratigraphy, and controls. Carbonates Evaporites 25:283–295. https://doi.org/10.1007/s13146-010-0032-3

    Article  Google Scholar 

  • Leisi A, Falahat R (2021) Investigation of some porosity estimation methods using seismic data in one of the south Iranian oil fields. J Pet Sci Res 31(119):88–97. https://doi.org/10.22078/pr.2021.4438.3007

    Article  Google Scholar 

  • Leisi A, Saberi MR (2022) Petrophysical parameters estimation of a reservoir using integration of wells and seismic data: a sandstone case study. Earth Sci Inf. https://doi.org/10.1007/s12145-022-00902-8

    Article  Google Scholar 

  • Li Y, Zhang T, Dai Z, Huang L, Xu Y, Wang X, Fang R, Zhang X, Li T (2020). Quantitative evaluation methods of tight reservoirs based on multi-feature fusion : A case study of the fourth member of Shahejie Formation in Liaohe Depression Yang Li a, Tingshan Zhang a, Zongyang Dai a, Lei Huang b, Yuxuan School of Geoscience and. J Pet Sci Eng https://doi.org/10.1016/j.petrol.2020.108090

  • Lim J (2005) Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea. J Petrol Sci Eng 49:182–192. https://doi.org/10.1016/j.petrol.2005.05.005

    Article  Google Scholar 

  • Lucia, F. J. (2007). Carbonate Reservoir Characterization. springer.

  • Masoud M, Meddaugh WS, Masud ME (2020) Reservoir Characterization and Geostatistical Model of the Cretaceous and Cambrian-Ordovician Reservoir Intervals, Meghil Field, Sirte Basin. Society of Petroleum Engineers, Libya

    Book  Google Scholar 

  • Mohammed OK, Glover PWJ, Ll RE (2021). Reservoir quality estimation using a new ternary diagram approach applied to carbonate formations in north-western Iraq. J Pet Sci Eng 196(March 2020). https://doi.org/10.1016/j.petrol.2020.108024

  • Nizamuddin S, Hossain ME, Abdulazeez A, Hassan MR (2016). Permeability Prediction Based on Leaky-Capillary-Tubes Using Genetic. Soc Pet Eng

  • Ozkan A, Cumella SP, Milliken KL, Laubach SE (2011) Prediction of lithofacies and reservoir quality using well logs, Late Cretaceous Williams Fork Formation, Mamm Creek field, Piceance Basin, Colorado. Am Assoc Petr Geol Mem 95(10):1699–1723. https://doi.org/10.1306/01191109143

    Article  Google Scholar 

  • Qadri SMT, Islam A, Shalaby MR, El-Aal AKA (2021) Reservoir quality evaluation of the Farewell sandstone by integrating sedimentological and well log analysis in the Kupe South Field, Taranaki Basin - New Zealand. J Pet Explor Prod Technol 11(1):11–31. https://doi.org/10.1007/s13202-020-01035-8

    Article  Google Scholar 

  • Revil A, Jardani A, Sava P, Haas A (2015). The Seismoelectric Method, Theory and applications. Wiley Blackwell

  • Selley RC, Sonnenberg SA (2015) Elements of Petroleum Geology. Elsevier. https://doi.org/10.1016/B978-0-12-386031-6.09001-9

    Article  Google Scholar 

  • Sfidari E, Amini A, Kadkhodaie-ilkhchi A, Chehrazi A, Zamanzadeh SM (2018) Depositional facies, diagenetic overprints and sequence stratigraphy of the upper Surmeh reservoir (Arab Formation) of offshore Iran. J Afr Earth Sc. https://doi.org/10.1016/j.jafrearsci.2018.07.025

    Article  Google Scholar 

  • Sfidari E, Sharifi M, Amini A, Zamanzadeh SM, Kadkhodaie A (2020). Reservoir quality of the Surmeh ( Arab-D ) reservoir in the context of sequence stratigraphy in Salman Field , Persian Gulf. J Pet Sci Eng https://doi.org/10.1016/j.petrol.2020.108180

  • Shalaby MR, Binti Sapri SH, Islam A (2020) Integrated reservoir characterization and fluid flow distribution of the Kaimiro Formation, Taranaki Basin, New Zealand. J Pet Explor Prod Technol 10(8):3263–3279. https://doi.org/10.1007/s13202-020-01005-0

    Article  Google Scholar 

  • El Sharawy M, Nabawy B (2016). Determination of electrofacies using wireline logs based on multivariate statistical analysis for the Kareem Formation , Gulf of Suez , Egypt. Environ Earth Sci https://doi.org/10.1007/s12665-016-6214-0

  • Silva GM, Souza VDe, Davolio A, Schiozer DJ, Petr P (2020). Journal of Petroleum Science and Engineering Improving fluid modeling representation for seismic data assimilation in compositional reservoir simulation. J Pet Sci Eng 194(June), 107446 https://doi.org/10.1016/j.petrol.2020.107446

  • Tyler N, Major RP, Bebout DG, Kerans C, Lucia FJ, Ruppel SC, Holtz MH (1992) Styles of heterogeneity in dolomitized platform carbonate reservoirs : Examples from the Central Basin Platform of the Permian Basin, southwestern USA. J Petrol Sci Eng 6:301–339

    Article  Google Scholar 

  • Yang P, Sun SZ, Liu Y, Li H, Dan G, Jia H (2012). Origin and architecture of fractured-cavernous carbonate reservoirs and their influences on seismic amplitudes. The Leading Edge.

Download references

Acknowledgements

We express our gratitude to the unknown reviewers that helped us improve the quality of this research paper.

Funding

No funding was obtained for this study.

Author information

Authors and Affiliations

  1. Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran

    Saeed Aftab & Ahsan Leisi

  2. Earth Sciences Department, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran

    Ali Kadkhodaie

Authors
  1. Saeed Aftab
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahsan Leisi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Kadkhodaie
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All the authors had good and active cooperation in writing the article and the article has been completed with the participation of all the authors.

Saeed Aftab main writer of the manuscript and programmer. Ahsan Leisi was a co-author in the field of data analysis and visualization. Ali Kadkhodaie is supervisor and results interpreter.

Corresponding author

Correspondence to Ali Kadkhodaie.

Ethics declarations

Competing interests

The authors declare no competing interests.

Consent for publication

We transfer to Springer Nature the non-exclusive publication rights and we warrant that our contribution is original and that we have full power to make this grant. We accept responsibility for releasing this material. This transfer of publication rights covers the non-exclusive right to reproduce and distribute the article, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.

Conflict of interest

There are no financial competitors related to this article.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aftab, S., Leisi, A. & Kadkhodaie, A. Reservoir petrophysical Index (RPI) as a robust tool for reservoir quality assessment. Earth Sci Inform 16, 2457–2473 (2023). https://doi.org/10.1007/s12145-023-01049-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-023-01049-w

Keywords

Search

Navigation