PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

An empirical method for predicting waterflooding performance in low-permeability porous reservoirs combining static and dynamic data: a case study in Chang 6 formation, Jingan Oilfield, Ordos Basin, China

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
After waterflooding, the distribution of the remaining oil in low-permeability porous reservoirs is quite complicated. Strong heterogeneity of formations makes the waterflooding performance more complex. Therefore, accurate prediction and evaluation of the spatial distribution of the remaining oil and the waterflooding performance of low-permeability reservoirs are essential for understanding the waterflooding process and improving oil recovery. In the study, an empirical method is proposed to predict waterflooding performance combined with static and dynamic data for porous reservoirs. Static data, including logging curves, core porosity and permeability data, are adopted to classify the formation into three hydraulic flow units (HFUs). The proportions of the thicknesses of different HFUs (HFUp) are proposed to characterize the remaining oil distribution. In addition, a waterflooding performance prediction method based on the Koval method was built using dynamic production data. The results show that the HFUp plays the key role in predicting the distribution of the remaining oil in the research well group. The K-factor-based waterflooding prediction method is highly correlated with the history matching in low-permeability waterflooded layers. The study also found Type 3 HFUp shows a great effect in predicting the duration of the low water-cut oil production. Therefore, the empirical method can provide a quick and intuitive evaluation of waterflooding performance in space and time of low-permeability waterflooded reservoirs with the local average K-factor and the HFUp results. The empirical method is of great significance to evaluate the remaining oil, infilling of well pattern, and improving oil recovery.
Czasopismo
Rocznik
Strony
1693--1703
Opis fizyczny
Bibliogr. 34 poz., rys., tab.
Twórcy
autor
  • Xi’an Shiyou University, Xi’an 710065, China
autor
  • Research Institute of Exploration and Development, PetroChina Changqing Oilfield Company, Xi’an 710021, China
  • National Engineering Laboratory for Exploration and Development of Low-Permeability Oil & Gas Fields, Xi’an 710018, China
autor
  • China National Logging Corporation, Beijing 100101, China
autor
  • State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102200, China
autor
  • Xi’an Shiyou University, Xi’an 710065, China
  • School of Earth Sciences and Engineering, Xi’an Shiyou University, Xi’an 710065, China
autor
  • Xi’an Shiyou University, Xi’an 710065, China
autor
  • Wuhan Geomatics Institute, Wuhan 430022, China
Bibliografia
  • 1. Abu-Hashish MF, Al-Shareif AW, Hassan NM (2022) Hydraulic flow units and reservoir characterization of the Messinian Abu Madi formation in West El Manzala development Lease, onshore Nile Delta Egypt. J Afr Earth Sci. https://doi.org/10.1016/j.jafrearsci.2022.104498
  • 2. Al-Ibadi H, Stephen K, MacKay E (2020) Heterogeneity effects on low salinity water flooding. In: Soc Pet Eng SPE Eur Featur 82nd EAGE Conf Exhib https://doi.org/10.2118/200547-ms
  • 3. Aljuboori FA, Lee JH, Elraies KA, Stephen KD (2020) The effectiveness of low salinity waterflooding in naturally fractured reservoirs. J Pet Sci Eng. https://doi.org/10.1016/j.petrol.2020.107167
  • 4. Al-Mudhafar WJ (2019) Integrating lithofacies and well logging data into smooth generalized additive model for improved permeability estimation: Zubair formation, South Rumaila oil field. Mar Geophys Res 40:315–332. https://doi.org/10.1007/s11001-018-9370-7
  • 5. Amaefule JO, Altunbay M, Tiab D, Kersey DG, Keelan DK (1993) Enhanced reservoir description: using core and log data to identify hydraulic (flow) units and predict permeability in uncored intervals/ wells. In: Proceeding of SPE annual Technical Conference and Exhibition Omega, pp 205–220. https://doi.org/10.2523/26436-ms
  • 6. Belayneh MW, Matthai SK, Blunt MJ, Rogers SF (2009) Comparison of deterministic with stochastic fracture models in water-flooding numerical simulations. Am Assoc Pet Geol Bull 93:1633–1648. https://doi.org/10.1306/07220909031
  • 7. Chen X, Yao G, Cai J, Huang Y, Yuan X (2017) Fractal and multifractal analysis of different hydraulic flow units based on micro-CT images. J Nat Gas Sci Eng 48:145–156. https://doi.org/10.1016/j.jngse.2016.11.048
  • 8. Chen H, Li H, Li Z, Li S, Wang Y, Wang J, Li B (2020) Effects of matrix permeability and fracture on production characteristics and residual oil distribution during flue gas flooding in low permeability/tight reservoirs. J Pet Sci Eng. https://doi.org/10.1016/j.petrol.2020.107813
  • 9. Chen S, Gong Z, Li X, Wang H, Wang Y, Zhang Y (2021) Pore structure and heterogeneity of shale gas reservoirs and its effect on gas storage capacity in the Qiongzhusi formation. Geosci Front. https://doi.org/10.1016/j.gsf.2021.101244
  • 10. Friesen OJ, Dashtgard SE, Miller J, Schmitt L, Baldwin C (2017) Permeability heterogeneity in bioturbated sediments and implications for waterflooding of tight-oil reservoirs, Cardium formation, Pembina Field, Alberta. Canada Mar Pet Geol 82:371–387. https://doi.org/10.1016/j.marpetgeo.2017.01.019
  • 11. Gu S, Liu Y, Chen Z, Ma C (2014) A method for evaluation of water flooding performance in fractured reservoirs. J Pet Sci Eng 120:130–140. https://doi.org/10.1016/j.petrol.2014.06.002
  • 12. Hearn CL, Ebanks WJ, Tye RS, Ranganathan V (1983) Geological factors influencing reservoir performance of the hartzog draw field, Wyoming. In: Soc Pet Eng AIME, SPE
  • 13. Jiang Z, Mao Z, Shi Y, Wang D (2018) Multifractal characteristics and classification of tight sandstone reservoirs: a case study from the Triassic Yanchang Formation, Ordos Basin China. Energies. https://doi.org/10.3390/en11092242
  • 14. Jiang Z, Fu J, Li G, Mao Z, Zhao P (2021) Using resistivity data to study the waterflooding process: a case study in tight sandstone reservoirs of the Ordos Basin, China. Geophysics 86:B55–B65. https://doi.org/10.1190/geo2020-0401.1
  • 15. Jiang Z, Liu Z, Zhao P, Chen Z, Mao Z (2022) Evaluation of tight waterflooding reservoirs with complex wettability by NMR data: a case study from Chang 6 and 8 members, Ordos Basin NW China. J Pet Sci Eng 213:110436. https://doi.org/10.1016/j.petrol.2022.110436
  • 16. Kassab MA, Elgibaly A, Abbas A, Mabrouk I (2021) Identification and distribution of hydraulic flow units of heterogeneous reservoir in Obaiyed gas field, Western Desert, Egypt: a case study. Am Assoc Pet Geol Bull 105:2405–2424. https://doi.org/10.1306/06222119083
  • 17. Khurpade PD, Kshirsagar LK, Nandi S (2021) Characterization of heterogeneous petroleum reservoir of Indian Sub-continent: an integrated approach of hydraulic flow unit–Mercury intrusion capillary pressure–Fractal model. J Pet Sci Eng. https://doi.org/10.1016/j.petrol.2021.108788
  • 18. Kou Z, Wang H, Alvarado V, Nye C, Bagdonas DA, McLaughlin JF, Quillinan SA (2022) Effects of carbonic acid-rock interactions on CO2/Brine multiphase flow properties in the upper minnelusa sandstones. SPE J. https://doi.org/10.2118/212272-pa
  • 19. Koval EJ (1963) A method for predicting the performance of unstable miscible displacement in heterogeneous media. Soc Pet Eng J 3:145–154. https://doi.org/10.2118/450-pa
  • 20. Li J, Liu Y, Gao Y, Cheng B, Meng F, Xu H (2018) Effects of microscopic pore structure heterogeneity on the distribution and morphology of remaining oil. Pet Explor Dev 45:1112–1122. https://doi.org/10.1016/S1876-3804(18)30114-9
  • 21. Ma X, Guo S, Shi D, Zhou Z, Liu G (2019) Investigation of pore structure and fractal characteristics of marine-continental transitional shales from Longtan Formation using MICP, gas adsorption, and NMR (Guizhou, China). Mar Pet Geol 107:555–571. https://doi.org/10.1016/j.marpetgeo.2019.05.018
  • 22. Mollaei A, Delshad M (2019) Introducing a novel model and tool for design and performance forecasting of waterflood projects. Fuel 237:298–307. https://doi.org/10.1016/j.fuel.2018.09.125
  • 23. Mou D, Wang ZW, Huang YL, Xu S, Zhou DP (2015) Lithological identification of volcanic rocks from SVM well logging data: case study in the eastern depression of Liaohe Basin. Acta Geophys Sin 58:1785–1793. https://doi.org/10.6038/cjg20150528
  • 24. Nasralla RA, Mahani H, van der Linde HA, Marcelis FHM, Masalmeh SK, Sergienko E, Brussee NJ, Pieterse SGJ, Basu S (2018) Low salinity waterflooding for a carbonate reservoir: experimental evaluation and numerical interpretation. J Pet Sci Eng 164:640–654. https://doi.org/10.1016/j.petrol.2018.01.028
  • 25. Nooruddin HA, Hossain ME (2011) Modified Kozeny-Carmen correlation for enhanced hydraulic flow unit characterization. J Pet Sci Eng 80:107–115. https://doi.org/10.1016/j.petrol.2011.11.003
  • 26. Qiao J, Zeng J, Jiang S, Feng S, Feng X, Guo Z, Teng J (2019) Heterogeneity of reservoir quality and gas accumulation in tight sandstone reservoirs revealed by pore structure characterization and physical simulation. Fuel 253:1300–1316. https://doi.org/10.1016/j.fuel.2019.05.112
  • 27. Rendel PM, Mountain B, Feilberg KL (2022) Fluid-rock interaction during low-salinity water flooding of North Sea chalks. J Pet Sci Eng, 110484
  • 28. Sari A, Chen Y, Myers MB, Seyyedi M, Ghasemi M, Saeedi A, Xie Q (2020) Carbonated waterflooding in carbonate reservoirs: experimental evaluation and geochemical interpretation. J Mol Liq. https://doi.org/10.1016/j.molliq.2020.113055
  • 29. Vledder P, Fonseca JC, Wells T, Gonzalez I, Ligthelm D (2010) Low salinity water flooding: proof of wettability alteration on a field wide scale. Proc SPE Symp Improv Oil Recover 1:200–209. https://doi.org/10.2118/129564-ms
  • 30. Wang M, Yang S, Li M, Wang S, Yu P, Zhang Y, Chen H (2021) Influence of heterogeneity on nitrogen foam flooding in low-permeability light oil reservoirs. Energy Fuels 35:4296–4312. https://doi.org/10.1021/acs.energyfuels.0c04062
  • 31. Wang H, Kou Z, Bagdonas DA, Phillips EHW, Alvarado V, Johnson AC, Jiao Z, McLaughlin JF, Quillinan SA (2022) Multiscale petrophysical characterization and flow unit classification of the Minnelusa eolian sandstones. J Hydrol. https://doi.org/10.1016/j.jhydrol.2022.127466
  • 32. Yan J, Fan J, Wang M, Li Z, Hu Q, Chao J (2018) Rock fabric and pore structure of the Shahejie sandy conglomerates from the Dongying depression in the Bohai Bay Basin, East China. Mar Pet Geol 97:624–638. https://doi.org/10.1016/j.marpetgeo.2018.07.009
  • 33. Zhao P, Wang Z, Sun Z, Cai J, Wang L (2017) Investigation on the pore structure and multifractal characteristics of tight oil reservoirs using NMR measurements: Permian Lucaogou Formation in Jimusaer Sag, Junggar Basin. Mar Pet Geol 86:1067–1081. https://doi.org/10.1016/j.marpetgeo.2017.07.011
  • 34. Zhou X, Wang Y, Zhang L, Zhang K, Jiang Q, Pu H, Wang L, Yuan Q (2020) Evaluation of enhanced oil recovery potential using gas/water flooding in a tight oil reservoir. Fuel. https://doi.org/10.1016/j.fuel.2020.117706
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-93421363-7543-492d-bd32-baea6917ead7
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.