Rock physics-based analysis to discriminate lithology and pore fluid saturation of carbonate reservoirs: a case study

• Autorzy: Makarian Esmael , Elyasi Ayub , Moghadam Rasoul Hamidzadeh , Khoramian Reza , Namazifard Pedram

Identyfikatory

DOI

10.1007/s11600-023-01029-0

• Języki publikacji: EN

Abstrakty

EN

Rock physics templates (RPT) and modeling contribute significantly to accurately and fast characterization of hydrocarbon reservoirs. This study strives to characterize lithology and pore fluid saturation for a carbonate reservoir in southwest Iran using RPT by well-logging data in a zone with a thickness of almost 100 m. To discriminate lithology, Greenberg–Castagna and Gardner models were applied. Also, velocity ratio - Gamma-ray (GR) and density - GR templates were implemented to investigate more. Results show that the researched zone’s lithology consists of a considerable amount of limestone, followed by dolomite, and a small amount of shale, without sandstone, which matches excellently with the lithology column and geophysical logs. For fluid discrimination, two different rock physical directions were impalement. Firstly, the rock physics model of the study area was built through Xu and Payne’s model. Then, velocity ratio - acoustic impedance template (Vsub>p/Vsub>s–AI) was applied to modeled data scaled with resistivity and porosity and then successfully validated with oil and water saturation. Findings show that PRT organizes data concerning similar features (here, aspect ratio), causing easy, fast, and more accurate analysis, and fluid content in the study includes oil and water, which the figure for oil is much more. In different oil and water saturation, non-modeled data were investigated through Vsub>p/Vsub>s − Vsub>s, Vsub>s − Vsub>p, and shear impudence (SI) - AI template supported by pore-pressure (PP) information to further research fluid distribution and its effect in the second direction. Regarding the rock physical analysis, the main reason for the decrease in seismic velocities and impedances is high pore pressure due to high oil saturation.

Słowa kluczowe

EN

rock physics; Greenberg and Castagna model; Gardner model; lithology identification; fluid discrimination; carbonate reservoir

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2023
• Tom: Vol. 71, no. 5
• Strony: 2163--2180
• Opis fizyczny: Bibliogr. 66 poz., rys.

Twórcy

autor

Makarian Esmael

• Department of Mining Engineering, Sahand University of Technology, Tabriz, Iran

autor

Elyasi Ayub

• CAPE Consultant Group, Tehran, Iran

autor

Moghadam Rasoul Hamidzadeh

• Department of Mining Engineering, Sahand University of Technology, Tabriz, Iran

autor

Khoramian Reza

• Department of Petroleum Engineering, Nazarbayev University, Nur-Sultan, Kazakhstan

autor

Namazifard Pedram

• Department of Mining Engineering, Sahand University of Technology, Tabriz, Iran

Bibliografia

• 1. Abe SJ, Akinyemi OD, Olatunbosun MO (2022) Rock physics diagnostic for enhancing characterization of reservoir sands within offshore field, Niger Delta. J Emerg Trends Eng Appl Sci 13(2):60–68
• 2. AbdollahieFard I, Sherkati S, McClay K, Haq BU (2019) Tectono-sedimentary evolution of the Iranian Zagros in a global context and its impact on petroleum habitats. Dev Struct Geol Tecton 3:17–28. https://doi.org/10.1016/B978-0-12-815048-1.00002-0
• 3. Afghah M (2016) Biostratigraphy, facies analysis of upper cretaceous-lower paleocene strata in South Zagros Basin (Southwestern Iran). J Afr Earth Sc 119:171–184. https://doi.org/10.1016/j.jafrearsci.2016.04.002
• 4. Ahmad QA, Ehsan MI, Khan N, Majeed A, Zeeshan A, Ahmad R, Noori FM (2022) Numerical simulation and modeling of a poroelastic media for detection and discrimination of geo-fluids using finite difference method. Alex Eng J 61(5):3447–3462. https://doi.org/10.1016/j.aej.2021.08.064
• 5. Alavi M (2004) Regional stratigraphy of the Zagros fold-thrust belt of Iran and its proforeland evolution. Am J Sci 304(1):1–20. https://doi.org/10.2475/ajs.304.1.1
• 6. Albakr MA, Abd N, Hasan S, Al‐Sharaa GH (2022) Reservoir characterization and density velocity analysis by using rock physics and integrated multi‐types post‐stack inversion to identify hydrocarbon possibility and litho‐prediction of Mishrif formation in kumaite and Dhafriyah oil fields, Southern Iraq. Geophys Prospect. https://doi.org/10.1111/1365-2478.13266
• 7. Asquith GB, Krygowski D, Henderson S, Hurley N (2004) Basic well log analysis. AAPG methods in exploration series, vol 16. https://doi.org/10.1306/Mth16823
• 8. Assefa S, McCann C, Sothcott J (2003) Velocities of compressional and shear waves in limestones. Geophys Prospect 51:1–13. https://doi.org/10.1046/j.1365-2478.2003.00349.x
• 9. Avseth P, Mukerji T (2002) Seismic lithofacies classification from well logs using statistical rock physics. Petrophysics 43(21):70–81
• 10. Avseth P, Mukerji T, Mavko G (2010) Quantitative seismic interpretation: applying rock physics tools to reduce interpretation risk. Cambridge University Press
• 11. Borgomano JV, Pimienta LX, Fortin J, Guéguen Y (2019) Seismic dispersion and attenuation in fluid-saturated carbonate rocks: effect of microstructure and pressure. J Geophys Res: Solid Earth 124:12498–12522. https://doi.org/10.1029/2019JB018434
• 12. Boxberg MS, Prévost JH, Tromp J (2015) Wave propagation in porous media saturated with two fluids. Transp Porous Media 107:49–63. https://doi.org/10.1007/s11242-014-0424-2
• 13. Cao Q, Ye X, Liu Y, Wang P, Jiang K (2022) Effect of different lithological assemblages on shale reservoir properties in the Permian Longtan Formation, southeastern Sichuan Basin: Case study of Well X1. PLoS ONE 17(8):e0271024. https://doi.org/10.1371/journal.pone.0271024
• 14. Crain ER (2013) Welcome to Crain’s Petrophysical Handbook. Online Shareware Petrophysics Training and Reference Manual. http://www.spec2000.net
• 15. Chenin J, Bedle H (2022) Unsupervised machine learning, multi-attribute analysis for identifying low saturation gas reservoirs within the deepwater Gulf of Mexico, and Offshore Australia. Geosciences 12(3):132. https://doi.org/10.3390/geosciences12030132
• 16. Cross NE, Singh SK, Al-Enezi A, Behbehani S (2022) Mixed carbonate-clastic reservoir characterization of the mid-Cretaceous Mauddud Formation (Albian), north Kuwait—implications for field development. AAPG Bull 106(2):289–319. https://doi.org/10.1306/08182119209
• 17. Das B, Chatterjee R (2018) Well log data analysis for lithology and fluid identification in Krishna-Godavari Basin, India. Arab J Geosci 11(10):1–12. https://doi.org/10.1007/s12517-018-3587-2
• 18. Deng C, Pan H, Fang S, Konaté AA, Qin R (2017) Support vector machine as an alternative method for lithology classification of crystalline rocks. J Geophys Eng 14(2):341–349. https://doi.org/10.1088/1742-2140/aa5b5b
• 19. Di L, Ping Y, Zhonghong W, Weiguang L, Ping C, Wei X (2021) Relative rock physics-driven seismic facies discrimination. In: SEG/AAPG/SEPM 1st international meeting for applied geoscience & energy. https://doi.org/10.1190/segam2021-3593763.1
• 20. Dong S, Wang Z, Zeng L (2016) Lithology identification using kernel fisher discriminant analysis with well logs. J Pet Sci Eng 143:95–102. https://doi.org/10.1016/j.petrol.2016.02.017
• 21. Elyasi A, Goshtasbi K, Hashemolhosseini H (2016) A coupled thermo-hydro-mechanical simulation of reservoir CO2 enhanced oil recovery. Energy Environ 27(5):524–541. https://doi.org/10.1177/0958305X16665545
• 22. Fawad M, Rahman MJ, Mondol NH (2021) Seismic reservoir characterization of potential CO2 storage reservoir sandstones in Smeaheia area, Northern North Sea. J Pet Sci Eng 205:108812. https://doi.org/10.1016/j.petrol.2021.108812
• 23. Fawad M, Hansen JA, Mondol NH (2020) Seismic-Fluid Detection-A Review. Earth Sci Rev 2020:103347. https://doi.org/10.1016/j.earscirev.2020.103347
• 24. Foster D, Zhao Z, Kumar D, Dralus D, Sen M (2021) Frequency-dependent AVO attributes for fluid saturation and thin-bed mapping. In: SEG/AAPG/SEPM 1st international meeting for applied geoscience & energy. https://doi.org/10.1190/segam2021-3582997.1
• 25. Gardner GHF, Gardner LW, Gregory AR (1974) Formation velocity and density - the diagnostic basics for stratigraphic traps. Geophysics 39:770–780. https://doi.org/10.1190/1.1440465
• 26. Gassmann F (1951) Elastic waves through a packing of spheres. Geophysics 16(4):673–685. https://doi.org/10.1190/1.1437718
• 27. Gelinsky S (2020) Reservoir characterization supported by rock physics diagnostics. In: Offshore technology conference Asia, Kuala Lumpur, Malaysia, November 2–August 19, 2020, Offshore Technology Conference, 2020, OTC-30359-MS. https://doi.org/10.4043/30359-MS
• 28. Grana D, Parsekian AD, Flinchum BA, Callahan RP, Smeltz NY, Li A, et al. (2022). Geostatistical rock physics inversion for predicting the spatial distribution of porosity and saturation in the critical zone. Math Geosc. https://doi.org/10.1007/s11004-022-10006-0
• 29. Greenberg ML, Castagna JP (1992) Shear-wave velocity estimation in porous rocks: theoretical formulation, preliminary verification and applications. Geophys Prospect 40:195–209. https://doi.org/10.1111/j.1365-2478.1992.tb00371.x
• 30. Hossain S, Junayed TR, Haque AKM (2022) Rock physics diagnostics and modelling of the Mangahewa Formation of the Maui B gas field, Taranaki Basin, offshore New Zealand. Arab J Geosci 15(13):1–21. https://doi.org/10.1007/s12517-022-10436-4
• 31. Huang C, Zhang X, Liu S, Li N, Kang J, Xiong G (2021) Construction of pore structure and lithology of digital rock physics based on laboratory experiments. J Pet Explor Prod Technol 11(5):2113–2125. https://doi.org/10.1007/s13202-021-01149-7
• 32. Ibrahim AF, Elkatatny S, Abdelraouf Y, Al Ramadan M (2022) Application of various machine learning techniques in predicting water saturation in tight gas sandstone formation. J Energy Resour Technol 144(8):083009. https://doi.org/10.1115/1.4053248
• 33. Islam MA, Yunsi M, Qadri SM, Shalaby MR, Haque AKM (2021) Three-dimensional structural and petrophysical modeling for reservoir characterization of the Mangahewa formation, Pohokura Gas-Condensate Field, Taranaki Basin, New Zealand. Nat Resour Res 30(1):371–394. https://doi.org/10.1007/s11053-020-09744-x
• 34. Karimian Torghabeh, A., Qajar, J., & Dehghan Abnavi, A. (2022). Characterization of a heterogeneous carbonate reservoir by integrating electrofacies and hydraulic flow units: a case study of Kangan gas field, Zagros basin. J Pet Explor Prod Technol. https://doi.org/10.1007/s13202-022-01572-4
• 35. Kordi M (2019) Sedimentary basin analysis of the neo-tethys and its hydrocarbon systems in the Southern Zagros Fold-Thrust Belt and Foreland Basin. Earth Sci Rev 191:1–11. https://doi.org/10.1016/j.earscirev.2019.02.005
• 36. Kuster GT, Toksöz MN (1974) Velocity and attenuation of seismic waves in two-phase media: part I. Theoretical Formulations Geophysics 39:587–606. https://doi.org/10.1190/1.1440450
• 37. Lan T, Zong Z, Yanwen F (2022) An improved seismic fluid identification method incorporating squirt flow and frequency-dependent fluid-solid inversion. Interpretation 11(1):1–66. https://doi.org/10.1190/int-2022-0053.1
• 38. Li H, Zhang J, Gao Q, Li X, Yang Z (2022) Quantitative prediction of porosity and gas saturation based on a new dual-porosity rock-physics model and Shuey’s Poisson ratio for tight sandstone reservoirs. J Pet Sci Eng 216:110826. https://doi.org/10.1016/j.petrol.2022.110826
• 39. Li H, Zhang J (2018) Well log and seismic data analysis for complex pore-structure carbonate reservoir using 3D rock physics templates. J Appl Geophys 151:175–183. https://doi.org/10.1016/j.jappgeo.2018.02.017
• 40. Li S, Peng Z (2017) Seismic acoustic impedance inversion with multi-parameter regularization. J Geophys Eng 14:520–532. https://doi.org/10.1088/1742-2140/aa5e67
• 41. Mavko G, Mukerji T, Dvorkin J (2020) The rock physics handbook, 3. Cambridge University Press, UK
• 42. Mehrabi H, Bagherpour B, Honarmand J (2020) Reservoir quality and micrite textures of microporous intervals in the upper cretaceous successions in the Zagros Area, SW Iran. J Pet Sci Eng 192:107292. https://doi.org/10.1016/j.petrol.2020.107292
• 43. Mirhashemi M, Khojasteh ER, Manaman NS, Makarian E (2022) Efficient sonic log estimations by geostatistics, empirical petrophysical relations, and their combination: two case studies from Iranian hydrocarbon reservoirs. J Pet Sci Eng 213:110384. https://doi.org/10.1016/j.petrol.2022.110384
• 44. Mondal I, Singh KH (2022) Core-log integration and application of machine learning technique for better reservoir characterisation of Eocene carbonates. Indian Offshore Energy Geosci 3(1):49–62. https://doi.org/10.1016/j.engeos.2021.10.006
• 45. Nazeer A, Abbasi SA, Solangi SH (2016) Sedimentary facies interpretation of Gamma Ray (GR) log as basic well logs in Central and Lower Indus Basin of Pakistan. Geodesy Geodyn 7:432–443. https://doi.org/10.1016/j.geog.2016.06.006
• 46. Pang M, Ba J, Carcione JM, Picotti S, Zhou J, Jiang R (2019) Estimation of porosity and fluid saturation in carbonates from rock-physics templates based on seismic Q. Geophysics 84(6):1–12. https://doi.org/10.1190/geo2019-0031.1
• 47. Ødegaard E, Avseth PA (2004) Well log and seismic data analysis using rock physics templates. First Break 22(10):37–43. https://doi.org/10.3997/1365-2397.2004017
• 48. Qing F, Yan J, Wang J, Hu Q, Wang M, Geng B, Chao J (2020) Pore Structure and fluid saturation of near-oil source low-permeability turbidite sandstone of the Dongying Sag in the Bohai Bay Basin, East China. J Pet Sci Eng 196:108106. https://doi.org/10.1016/j.petrol.2020.108106
• 49. Radwan AA, Nabawy BS, Shihata M, Leila M (2022) Seismic interpretation, reservoir characterization, gas origin and entrapment of the Miocene-Pliocene Mangaa C sandstone, Karewa Gas Field, North Taranaki Basin, New Zealand. Mar Pet Geol 135:105420. https://doi.org/10.1016/j.marpetgeo.2021.105420
• 50. Singh A, Ojha M (2022) Machine learning in the classification of lithology using downhole NMR data of the NGHP-02 expedition in the Krishna-Godavari offshore Basin, India. Mar Pet Geol 135:105443. https://doi.org/10.1016/j.marpetgeo.2021.105443
• 51. Taheri A, Makarian E, Manaman NS, Ju H, Kim TH, Geem ZW, RahimiZadeh K (2022) A fully-self-adaptive harmony search GMDH-type neural network algorithm to estimate shear-wave velocity in porous media. Appl Sci 12(13):6339. https://doi.org/10.3390/app12136339
• 52. Teillet T, Fournier F, Zhao L, Borgomano J, Hong F (2021) Geophysical pore type inversion in carbonate reservoir: Integration of cores, well logs, and seismic data (Yadana field, offshore Myanmar). Geophysics 86(3):B149–B164. https://doi.org/10.1190/geo2020-0486.1
• 53. Temizel C, Odi U, Balaji K, Aydin H, Santos JE (2022) Classifying facies in 3D digital rock images using supervised and unsupervised approaches. Energies 15(20):7660. https://doi.org/10.3390/en15207660
• 54. Wang G, Lai J, Liu B, Fan Z, Liu S, Shi Y, Zhang H, Chen J (2020) Fluid property discrimination in dolostone reservoirs using well logs. Acta Geol Sin-Engl Ed 94:831–846. https://doi.org/10.1111/1755-6724.14526
• 55. Wang X, Zuo R, Wang Z (2022a) Lithological mapping using a convolutional neural network based on stream sediment geochemical survey data. Nat Resour Res 31(5):2397–2412. https://doi.org/10.1007/s11053-022-10096-x
• 56. Wang YR, Zong ZY, Yin XY (2022b) Fluid discrimination incorporating amplitude variation with angle inversion and squirt flow of the fluid. Pet Sci. https://doi.org/10.1016/j.petsci.2022.03.007
• 57. Wei D, Gao Z, Fan T, Zhang C, Tsau JS (2020) The rock-fabric/petrophysical characteristics and classification of the micropores hosted between the calcite and dolomite crystals. J Pet Sci Eng 193:107383. https://doi.org/10.1016/j.petrol.2020.107383
• 58. Wood AB (1955) A textbook of sound: being an account of the physics of vibrations with special reference to recent theoretical and technical developments. 3rd rev edn. Macmillan, New York
• 59. Xie JY, Zhang JJ, Xiang W, Fang YP, Xue YJ, Cao JX, Tian RF (2022) Effect of microscopic pore structures on ultrasonic velocity in tight sandstone with different fluid saturation. Pet Sci. https://doi.org/10.1016/j.petsci.2022.06.009
• 60. Xu C, Ding P, Di B, Wei J (2021) Investigation of fluid effects on seismic responses through a physical modeling experiment. Interpretation 9(1):T213–T222. https://doi.org/10.1190/INT-2020-0116.1
• 61. Xu S, Payne MA (2009) Modeling elastic properties in carbonate rocks. Lead Edge 28:66–74. https://doi.org/10.1190/1.3064148
• 62. Xu Z, Ma W, Lin P, Hua Y (2022) Deep learning of rock microscopic images for intelligent lithology identification: neural network comparison and selection. J Rock Mech Geotech Eng 14(4):1140–1152. https://doi.org/10.1016/j.jrmge.2022.05.009
• 63. Yang Q, Malcolm A (2021) Frequency domain full-waveform inversion in a fluid-saturated poroelastic medium. Geophys J Int 225(1):68–84. https://doi.org/10.1093/gji/ggaa579
• 64. Yang S, Wei J (2017) Fundamentals of Petrophysics, Springer Geophysics, vol 2. Springer, Berlin, Heidelberg
• 65. Zhou T, Rose D, Millot P, Grover R, Beekman S, Amin MFM, Zamzuri MDB, Ralphie B, Zakwan ZA (2018) Comprehensive neutron porosity from a pulsed neutron logging tool. In: SPWLA 59th annual logging symposium, London, UK, June 2018, Society of Petrophysicists and Well-Log Analysts, SPWLA-2018-XXX
• 66. Zhou, X., Ba, J., Santos, J. E., Carcione, J. M., Fu, L. Y., & Pang, M. (2021). Fluid discrimination in ultra-deep reservoirs based on a double double-porosity theory. Frontiers in Earth Science, 9, 649984. https://doi.org/10.3389/feart.2021.649984

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).