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Pore structure characterization and deliverability prediction of fractured tight glutenite reservoir based on geophysical well logging

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Tight glutenite reservoirs characterization and effective hydrocarbon-bearing formation identification faced great challenge due to ultra-low porosity, ultra-low permeability and complicated pore structure. Fracturing fracture-building technique always needed to obtain deliverability because of poor natural productive capacity. Pore structure characterization and friability prediction were essential in improving such type of reservoir evaluation. In this study, fractured tight glutenite reservoirs in Permian Jiamuhe Formation that located in northwest margin of Junggar Basin, northwest China, were chosen as an example, and 25 typical core samples were drilled and simultaneously applied for mercury injection capillary pressure (MICP), nuclear magnetic resonance (NMR) and whole-rock mineral X-ray diffraction experiments. A novel method of synthetizing pseudo-pore-throat radius (Rc) distribution from porosity frequency spectra was established to characterize fractured formation pore structure. Quartz and calcite were considered as the fragile mineral, and rock mineral component ratio method was used to predict brittleness index. Meanwhile, the statistical model raised by Jin et al. (SPE J 20:518-526, 2015) was used to predict two types of fracture toughness. And then, brittleness index and fracture toughness were combined to characterize tight glutenite reservoirs friability. Combining with maximal pore-throat radius (Rmax, reflected rock pore structure) and friability, our target formations were classified into four clusters. In addition, relationships among pore structure, friability and daily hydrocarbon production per meter (DI) were analyzed, and a model to predict DI from welllogging data was established. Comparison of predicted DI with the extracted results from drill stem test (DST) data illustrated the reliability of our raised models. This would be valuable in determining optimal hydrocarbon production intervals and formulating reasonable developed plans.
Czasopismo
Rocznik
Strony
273--286
Opis fizyczny
Bibliogr. 37 poz.
Twórcy
autor
  • Research Institute of Geophysical, Research Institute of Exploration and Development, PetroChina Xingjiang Oilfield Company, Urumchi 830013, China
autor
  • Research Institute of Geophysical, Research Institute of Exploration and Development, PetroChina Xingjiang Oilfield Company, Urumchi 830013, China
autor
  • Research Institute of Geophysical, Research Institute of Exploration and Development, PetroChina Xingjiang Oilfield Company, Urumchi 830013, China
autor
  • Research Institute of Geophysical, Research Institute of Exploration and Development, PetroChina Xingjiang Oilfield Company, Urumchi 830013, China
autor
  • Research Institute of Geophysical, Research Institute of Exploration and Development, PetroChina Xingjiang Oilfield Company, Urumchi 830013, China
Bibliografia
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  • 4. Coates GR, Xiao LZ, Primmer MG (2000) NMR logging principles and applications. Gulf Publishing Company, Houston, pp 1-256
  • 5. Dong XM, Zhang T, Yao WJ, Hu TT, Li J, Jia CM, Guan J (2019) A method to quantitatively characterize tight glutenite reservoir pore structure. In: Proceeding of SPE Reservoir Characterisation and Simulation Conference and Exhibitions. https://doi.org/10. 2118/196649-MS
  • 6. Dubey A, Mohamed MI, Salah M, Algarhy A (2019) Evaluation of the rock brittleness and total organic carbon of organic shale using triple combo. In: SPWLA 60th Annual Logging Symposium. https://doi.org/10.30632/T60ALS-2019_BBB
  • 7. Dunn KJ, Bergman DJ, Latorraca GA (2002) Nuclear magnetic resonance: petrophysical and logging applications. Handbook of geophysical exploration. Pergamon, New York, pp 1-176
  • 8. Gao FM, Xiao L, Zhang W, Cui WP, Zhang ZQ, Yang EH (2023) Low permeability gas-bearing sandstone reservoirs characterization from geophysical well logging data: a case study of Pinghu formation in KQT region, East China. Sea Process 11(4):1030. https://doi.org/10.3390/pr11041030
  • 9. Green DP, Gardner J, Balcom BJ, McAloon MJ, Cano-Barrita PFJ (2008) Comparison study of capillary pressure curves obtained using traditional centrifuge and magnetic resonance imaging techniques. In: SPE Symposium on Improved Oil Recovery. https:// doi.org/10.2118/110518-MS
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  • 11. Hu ML (2021) Research on logging evaluation of effectiveness of sandstone conglomerate reservoir: taking Permian Jiamuhe Formation in Shawan Sag of Junggar Basin as an example. A Dissertation Submitted to China University of Geosciences for Master of Professional Degree, pp 1-68
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  • 16. Jiang ZH, Liu ZD, Zhao PQ, Chen Z, Mao ZQ (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
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  • 20. Kim SM, Mustafa MA, Reza GB (2019) A review of brittleness index correlations for unconventional tight and ultra-tight reservoirs. Geosciences 9:1-21. https://doi.org/10.3390/geosciences9070319
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  • 22. Li HT, Deng SG, Xu F, Niu YF, Hu XF (2021) Multi-parameter logging evaluation of tight sandstone reservoir based on petrophysical experiment. Acta Geophys 69:429-440. https://doi.org/10.1007/ s11600-021-00542-4
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  • 28. Rickman R, Mullen M, Petre E, Grieser B, Kundert D (2008) A practical use of shale petrophysics for stimulation design optimization: all shale plays are not clones of the barnett Shale. In: Proceeding of SPE Annual Technical Conference and Exhibition. https://doi. org/10.1306/12190606068
  • 29. Shao W, Ding Y, Liu Y, Liu S, Li Y, Zhao J (2009) The application of NMR log data in evaluation of reservoir pore structure. Well Logging Technol 33:52-56
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  • 31. Wood DA (2022) Predicting brittleness indices of prospective shale formations from sparse well-log suites assisted by derivative and volatility attributes. Adv Geo-Energy Res 6:334-346. https://doi. org/10.46690/ager.2022.04.08
  • 32. Xiao LZ, Li K (2011) Characteristics of the nuclear magnetic resonance logging response in fracture oil and gas reservoirs. New J Phys 13:045003. https://doi.org/10.1088/1367-2630/13/4/045003
  • 33. Xiao L, Mao ZQ, Zou CC, Jin Y, Zhu JC (2016) A new methodology of constructing pseudo capillary pressure (Pc) curves from nuclear magnetic resonance (NMR) logs. J Pet Sci Eng 147:154-167. https://doi.org/10.1016/j.petrol.2016.05.015
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  • 35. Xiao L, Li JR, Mao ZQ, Yu HY (2020) A method to evaluate pore structures of fractured tight sandstone reservoirs using borehole electrical image logging. AAPG Bull 104:205-226. https://doi. org/10.1306/04301917390
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  • 37. Yuan JL, Zhou JL, Liu SJ, Feng YC, Deng JG, Xie QM, Lu ZH (2017) An improved fracability evaluation method for shale reservoirs based on new fracture toughness prediction models. SPE J 22:1704-1713. https://doi.org/10.2118/185963-PA
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ec185905-2fb3-4b67-9c0d-ec645ca457d3
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