Estimation of water saturation based on optimized models in tight gas sandstone reservoirs: a case study of Triassic Xujiahe Formation in northwestern Sichuan Basin

• Autorzy: Xia Xiaoyong , Han Bing , Xie Bing , Lai Qiang , Wang Yuexiang , Zhu Shaowu

Identyfikatory

DOI

10.1007/s11600-024-01393-5

• Języki publikacji: EN

Abstrakty

EN

Water saturation estimation faced a great challenge in tight gas sandstone reservoirs because of the effect of pore structure and strong heterogeneity. The classic Archie’s equation cannot be always well used. To quantify the effect of pore structure to rock resistivity in tight gas sandstones, taking Triassic Xujiahe Formation of northwestern Sichuan Basin as an example, 35 core samples were recovered and applied for resistivity experiments in laboratory under the simulated formation temperature and pressure environment, and 18 of them were simultaneously applied for nuclear magnetic resonance (NMR) and high- pressure mercury injection experimental measurements. Relationships between pore structure and resistivity parameters were analyzed. The results clearly illustrated that cementation exponent (m) and saturation exponent (n) were heavily affected by pore structure. Rocks with superior pore structure contained relatively higher cementation exponent and lower saturation exponent, and vice versa. Afterward, we raised a parameter of pore size index, which was defined as the ratio of macropore and micro-pore percentage contents, to characterize rock pore structure, and established a model to calculate optimal saturation exponent from NMR data. Meanwhile, cementation exponent prediction model was also raised by combining with porosity and irreducible water saturation (S_wirr). Combining with calculated cementation exponent and saturation exponent, we optimized the Archie’s equation to predict water saturation in our target tight gas sands. Field examples illustrated that the predicted cementation exponent and saturation exponent matched well with core-derived results. The absolute errors between predicted cementation exponent and saturation exponent with core-derived results were lower than 0.05 and 0.07, separately. By using optimized Archie’s equation, water saturations were precisely predicted from well logging data in our target tight gas sandstone reservoirs; whereas, the classic Archie’s equation underestimated formation water saturation.

Słowa kluczowe

EN

water saturation; NMR measurements; pore structure; tight gas sandstones; optimized saturation models

PL

nasycenie wodą; pomiary NMR; struktura porów; piaskowce typu tight-gas

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2025
• Tom: Vol. 73, no. 1
• Strony: 407--419
• Opis fizyczny: Bibliogr. 47 poz.

Twórcy

autor

Xia Xiaoyong

• Research Institute of Exploration and Development, Southwest Oil and Gas Field Company, PetroChina, Chengdu, Sichuan, China

autor

Han Bing

• Research Institute of Exploration and Development, Southwest Oil and Gas Field Company, PetroChina, Chengdu, Sichuan, China
• Present Address: Research Institute of Exploration and Development, Southwest Oil and Gas Field Company, PetroChina, Chengdu, Sichuan, China

autor

Xie Bing

• Research Institute of Exploration and Development, Southwest Oil and Gas Field Company, PetroChina, Chengdu, Sichuan, China

autor

Lai Qiang

• Research Institute of Exploration and Development, Southwest Oil and Gas Field Company, PetroChina, Chengdu, Sichuan, China

autor

Wang Yuexiang

• Research Institute of Exploration and Development, Southwest Oil and Gas Field Company, PetroChina, Chengdu, Sichuan, China

autor

Zhu Shaowu

• Southwest Petroleum Logging Company, China Petroleum Logging Co. Ltd, Chongqing, China

Bibliografia

• 1. Abu-Shanab MM, Hamada GM, El Oraby M (2005) DMR technique improves tight gas porosity estimate. Oil Gas J 103(47):54-57
• 2. Ahmed AS, Revil A, Byrdina S, Coperey A, Gailler L, Grobbe N, Viveiros F, Silva C, Jougnot D, Ghorbani A, Hogg C, Kiyan D, Rath V, Heap MJ, Grandis H, Humaida H (2018) 3D electrical conductivity tomography of volcanoes. J Volcanol Geoth Res 356:243-263. https://doi.Org/10.1016/j.jvolgeores.2018.03.017
• 3. Ammar AI (2021) Development of numerical model for simulating resistivity and hydroelectric properties of fractured rock aquifers. J Appl Geophys 189:104319. https://doi.org/10.1016/j.jappgeo.2021.104319
• 4. Archie GE (1942) The electrical resistivity log as an aid in determining some reservoir characteristics. Trans AIME 146:54-62
• 5. Clavier C, Coates G, Dumanoir J (1984) Theoretical and experimental bases for the dual-water model for interpretation of shaly sands. SPE J 24(2):153-168. https://doi.org/10.2118/6859-PA
• 6. Coates GR, Xiao LZ, Prammer MG (2000) NMR logging principles and applications: Houston. Gulf Publishing Company, Texas, pp 1-256
• 7. Dunn KJ, Bergman DJ, Latorraca GA (2002) Nuclear magnetic resonance: petrophysical and logging applications. Handbook of geophysical exploration: seismic exploration, New York, Pergamon, vol 32, pp 1-293
• 8. Ferraris P, Carnegie AJ (2005) Integrated downhole fluid-identification applications for complex reservoir development. In: Proceeding of SPE Latin American and Caribbean petroleum engineering conference. https://doi.org/10.2118/94860-MS
• 9. Freedman R, Minh CC, Gubelin G, Freeman JJ, McGinness T, Terry B, Rawlence D (1998) Combining NMR and density logs for petrophysical analysis in gas-bearing formations. In: SPWLA 39th annual logging symposium
• 10. 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 Processes 11(4):1030. https://doi.org/10.3390/pr11041030
• 11. Givens WW (1987) A conductive rock matrix model (CRMM) for the analysis of low-contrast resistivity formations. Log Analyst 28(2):138-151
• 12. Givens WW, Schmidt EJ (1988) A generic electrical conduction model for low-contrast resistivity sandstones. In: SPWLA 29th annual logging symposium
• 13. Guo LX, Ma GW, Chen GQ (2023) A micro frost heave model for porous rock considering pore characteristics and water saturation. Comput Geotech 166:106029. https://doi.org/10.1016/j.compgeo.2023.106029
• 14. Gupta AR, Kamal (2023) A theoretical approach for water saturation estimation in shaly sandstones. Geoenergy Sci Eng 228:212001. https://doi.org/10.1016/j.geoen.2023.212001
• 15. Hou YT, Liu D, Zhao TP, Zhou JY, Tian LL, Kou XP, Zhu BD, Nie X (2023) Numerical simulation of electrical properties of carbonate reservoirs using digital rocks. Processes 11(7):2214. https://doi.org/10.3390/pr11072214
• 16. Jain V, Saumya S, Vij J, Singh J, Singh B, Pattnaik S, Oli A, Kumar P, Collett TS (2019) New technique for accurate porosity estimation from logging-while-drilling nuclear magnetic resonance data, NGHP-02 expedition, offshore, India. Mar Pet Geol 108:570-580. https://doi.org/10.1016/j.marpetgeo.2018.11.001
• 17. Li X, Zhao WZ, Zhou CC, Wang TS, Li CL (2012) Dual-porosity saturation model of low-porosity and low-permeability clastic reservoirs. Pet Explor Dev 39(1):88-98. https://doi.org/10.1016/S1876-3804(12)60019-6
• 18. Li X, Li CL, Li B, Liu XF, Yuan C (2020) Response laws of rock electrical property and saturation evaluation method of tight sandstone. Pet Explor Dev 47(1):214-224. https://doi.org/10.1016/S1876-3804(20)60020-9
• 19. Li JQ, Xiao XH, Chi RQ, Wen HC, Zhang WJ (2023) Formation characterization and type prediction based on geophysical well log data in horizontal well: a case study of Triassic Chang 8 formation in shunning region. Central Ordos Basin Processes 11(8):2297. https://doi.org/10.3390/pr11082297
• 20. Liu XF, Sun JM, Wang HT (2009) Numerical simulation of rock electrical properties based on digital cores. Appl Geophys 6(1):1-7. https://doi.org/10.1007/s11770-009-0001-6
• 21. Liu XP, Hu XX (2010) A novel method to identify pore fluid in low porosity/low permeability gas zones. In: Proceeding of SPE Latin
• 22. American and Caribbean petroleum engineering conference. https://doi.org/10.2118/118970-MS
• 23. Liu XP, Hu XX, Xiao L (2012) Effects of pore structure to electrical properties in tight gas reservoirs: an experimental study. In: Proceeding of SPE/EAGE European unconventional resources conference and exhibition. https://doi.org/10.2118/150926-MS
• 24. Mahmood A, Aboelkhair H, Attia A (2023) Investigation of the effect of tortuosity, hydrocarbon saturation and porosity on enhancing reservoir characterization. Geoenergy Sci Eng 227:211855. https://doi.org/10.1016/j.geoen.2023.211855
• 25. Mao ZQ, Tan TD, Lin CZ, Wang Q (1997) The laboratory studies on pore structure and electrical properties of core samples fully-saturated with brine water. Acta Petrolei Sinica 18(3):51-55. https://doi.org/10.7623/syxb199703008
• 26. Mao ZQ, Gong FH, Liu CY, Zhu DC, Zuo ZG (1999) Experimental study on the gensis of low resistivity pay zone in north Region of Tarim Basin (I). Well Logging Technol 23(4):243-245
• 27. Mao ZQ, Xiao L, Wang ZN, Jin Y, Liu XG, Xie B (2013) Estimation of permeability by integrating nuclear magnetic resonance (NMR) logs with mercury injection capillary pressure (MICP) data in tight gas sands. Appl Magn Reson 44(4):449-468. https://doi.org/10.1007/s00723-012-0384-z
• 28. Mo XW, He DH, Li ZB, Wen XC, Li GR (2001) The application of three-water conduction model in the interpretation of low-resistivity reservoir. J Jilin Univ: Earth Sci Edition 31(1):92-95
• 29. Picket GR (1966) A review of current techniques for determination of water saturation from logs. J Petrol Technol 18(11):1425-1433. https://doi.org/10.2118/1446-PA
• 30. Poupon A, Leveaux J (1971) Evaluation of water saturation in shaly formations. In: SPWLA 12th annual logging symposium
• 31. Qian C, Li XZ, Zhang Q, Shen WJ, Guo W, Lin W, Han LL, Cui Y, Huang YZ, Pei XY, Yu ZC (2023) Reservoir characteristics of different shale lithofacies and their effects on the gas content of Wufeng-Longmaxi formation, southern Sichuan Basin. China Geoenergy Sci Eng 225:211701. https://doi.org/10.1016/j.geoen. 2023.211701
• 32. Rasmus JC (1985) A summary of the effects of various pore geometrics and their wettabilities on measured and in-situ values of cementation and saturation exponents. Petrophysics 28(2):152-164
• 33. Sarihi A, Vargas-Murillo B (2015) A method to compute water saturation in tight rocks accounting for conductivity of clay minerals. In: Proceeding of Abu Dhabi international petroleum exhibition and conference. https://doi.org/10.2118/177550-MS
• 34. Simandoux P (1963) Dielectric measurements on porous media and application to the measurements of watersaturation: study of behavior of argillaceous formations. Revue De L’instiut Francase Du Petrol 18:193-215
• 35. Song YJ, Wang Q, Yin CH (1995) A new S-B conductivity model for determining water saturation of shaly sands. Well Logging Technol 19(4):244-249
• 36. Umezawa R, Katsura M, Nakashima S (2021) Effect of water saturation on the electrical conductivity of microporous silica glass.
• 37. Transp Porous Media 138(1):225-243. https://doi.org/10.1007/s11242-021-01601-6
• 38. Walsh SDC, Vogler D (2020) Simulating electropulse fracture of granitic rock. Int J Rock Mech Min Sci 128:104238. https://doi.org/10.1016/j.ijrmms.2020.104238
• 39. Waxman MH, Smits LJM (1968) Electrical conductivities in oil-bearing shaly sands. Soc Petrol Eng J 8(2):107-122. https://doi.org/10.2118/1863-A
• 40. Waxman MH, Thomas EC (1974) Electrical conductivities in shaly sands—I. The relation between hydrocarbon saturation and resistivity index; II. The temperature coefficient of electrical conductivity. J Petroleum Technol 26(2):213-225. https://doi.org/10.2118/4094-PA
• 41. Waxman MH, Thomas EC (2007) Technical Note: An addendum to electrical conductivities in shaly sands—I. The relation between hydrocarbon saturation and resistivity index; II. The temperature coefficient of electrical conductivity. SPE J 12(4):392. https://doi.org/10.2118/109632-PA
• 42. Xiao L, Zou CC, Mao ZQ, Shi YJ, Liu XP, Jin Y, Guo HP, Hu XX (2013) Estimation of water saturation from nuclear magnetic resonance (NMR) and conventional logs in low permeability sandstone reservoirs. J Petrol Sci Eng 108:40-51. https://doi.org/10.1016/j.petrol.2013.05.009
• 43. Xiao L, Mao ZQ, Jin Y (2015) Tight gas sandstone reservoirs evaluation from nuclear magnetic resonance (NMR) logs: case studies. Arab J Sci Eng 40(4):1223-1237. https://doi.org/10.1007/s13369-015-1608-y
• 44. 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 Petrol Sci Eng 147:154-167. https://doi.org/10.1016/j.petrol.2016.05.015
• 45. Xiao L, Li JR, Mao ZQ, Lu J, Yu HY, Guo HP, Li GR (2018) A method to determine nuclear magnetic resonance (NMR) T2cutoff based on normal distribution simulation in tight sandstone reservoirs. Fuel 225:472-482. https://doi.org/10.1016/j.fuel.2018.03.178
• 46. Xie WB, Yin QL, Zeng JB, Wang GW, Feng C, Zhang P (2023) Fractal-based approaches to pore structure investigation and water saturation prediction from NMR measurements: a case study of the gas-bearing tight sandstone reservoir in Nanpu Sag. Fractal Fract 7(3):273. https://doi.org/10.3390/fractalfract7030273
• 47. Zhang LH, Pan BZ, Li ZB, Mo XW, Xia ZL, Xu WL (2010) New three water conduction model and its application in the evaluation of low porosity and low permeability reservoir. Petroleum Geophys Prospect 45(3):431-435

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).