Experimental Study on Temperature Response and Quantitative Relationship with Injection Profile in Water Injection Wells Based on Distributed Temperature Sensing

Authors

  • Tingting Zhou Southwest Petroleum University, Chengdu 610500, China
  • Sihang Xie Southwest Petroleum University, Chengdu 610500, China
  • Hongwen Luo Southwest Petroleum University, Chengdu 610500, China https://orcid.org/0000-0001-8084-4518
  • Haitao Li Southwest Petroleum University, Chengdu 610500, China
  • Ying Li Southwest Petroleum University, Chengdu 610500, China
  • Yixin Zhang Southwest Petroleum University, Chengdu 610500, China
  • Feifei Ran Southwest Petroleum University, Chengdu 610500, China

DOI:

https://doi.org/10.15377/2409-787X.2026.13.4

Keywords:

Water injection well, Enhanced oil recovery (EOR), Injection profile interpretation, Wellbore temperature distribution, Distributed temperature sensing (DTS)

Abstract

Currently, the understanding of temperature profile responses in water injection wells remains insufficient, making it challenging to interpret water injection profiles using distributed temperature sensing (DTS) data and to perform quantitative diagnosis of downhole injection performance. In this study, a physical simulation experiment was conducted to investigate the effects of injection rate, injection time, injection temperature, and injection position on the temperature profile of horizontal wells. The results indicate that as the injection rate increases, the wellbore temperature profile decreases, and the temperature rise from the heel to the toe becomes less pronounced. The temperature drop corresponding to the water-injection intervals exhibits a linear positive correlation with the injection rate, and a quantitative relationship between temperature change (ΔT) and injection rate was established through regression analysis. Injection temperature significantly enhances the temperature field; higher injection temperatures lead to more substantial overall temperature increases along the profile. The injection position determines the fundamental shape of the temperature profile. Regardless of whether injection is applied at the heel, toe, middle section, or both ends, distinct temperature anomalies are observed around the water-injection locations. Based on this characteristic, water-injection positions can be identified from field DTS data, thereby providing a basis for quantitative evaluation of water injection profiles.

References

[1] Song YJ, Shi Y. Numerical simulation study on downhole temperature distribution in polymer injection wells. Prog Geophys. 2007; (5): 1533-8.

[2] Li D, Lin RY, Wang XW. Steam flow behavior in steam injection wells considering reservoir heterogeneity. CIESC J. 2020; 71(12): 5479-88.

[3] Yoshioka K, Zhu D, Hill AD, Dawkrajai P, Lake LW. A comprehensive model of temperature behavior in a horizontal well. In: SPE Annual Technical Conference and Exhibition; 2005 Oct 9-12; Dallas, TX, USA. SPE-95656-MS. https://doi.org/10.2118/95656-MS

[4] Liu W, Li H, Wang Y, Luo H, Shao C. Experimental study on temperature distribution characteristics of horizontal wells in gas reservoir based on DTS test. Fault-Block Oil Gas Field. 2020; 27(2): 228-32.

[5] Feng XW, Zhao Y, Yang P, Zhou J. Application of distributed optical fiber temperature monitoring technology in production and profile interpretation of fractured horizontal wells. Reserv Eval Dev. 2021; 11(4): 542-9. https://doi.org/10.13809/j.cnki.cn32-1825/te.2021.04.010

[6] Nowak TJ. The estimation of water injection profiles from temperature surveys. J Pet Technol. 1953; 5(8): 203-12. https://doi.org/10.2118/953203-G

[7] Smith RC, Steffensen RJ. Interpretation of temperature profiles in water-injection wells. J Pet Technol. 1975; 27(6): 777-84. https://doi.org/10.2118/4649-PA

[8] Hagoort J. Ramey's wellbore heat transmission revisited. SPE J. 2004; 9(4): 465-74. https://doi.org/10.2118/87305-PA

[9] Wang Q, Wu XD. Calculation and influencing factors of wellbore temperature and pressure profiles in CO₂ injection wells. Sci Technol Eng. 2009; (18): 5330-4.

[10] Ran F, Luo H, Li Y, et al. Fiber optic monitoring inversion method for water injection profile in offshore extended-reach wells. Well Logging Technol. 2025; 49(4): 519-30. https://doi.org/10.16489/j.issn.1004-1338.2025.04.004

[11] Zhu HT, Lin BT, Shi LX. Temperature logging inversion method for flow profile in horizontal wells based on Adam optimization algorithm. J China Univ Pet Nat Sci Ed. 2023; 47(2): 99-107.

[12] Zhang XL, Zuo K, Li A. Study on temperature profile prediction model for offshore water injection wells. Mod Chem Res. 2024; (17): 59-61.

[13] Li ZY, Zhu D. Predicting flow profile of horizontal well by downhole pressure and distributed-temperature data for waterdrive reservoir. SPE Prod Oper. 2010; 25(3): 296-304. https://doi.org/10.2118/124873-PA

[14] Li ZY. Interpreting horizontal well flow profiles and optimizing well performance by downhole temperature and pressure data [dissertation]. Texas: Texas A&M University; 2010.

[15] Zhang S, Zhu D. Inversion of downhole temperature measurements in multistage fracture stimulation in horizontal wells. In: SPE Annual Technical Conference and Exhibition; 2017 Oct 9-11; San Antonio, TX, USA. SPE-187322-MS. https://doi.org/10.2118/187322-MS

[16] Cui J, Zhu D. Diagnosis of multiple fracture stimulation in horizontal wells by downhole temperature measurements. In: International Petroleum Technology Conference; 2014 Jan 19-22; Doha, Qatar. IPTC-17700-MS. https://doi.org/10.2523/IPTC-17700-MS

[17] Cui J, Yang C, Zhu D, Datta-Gupta A. Fracture diagnosis in multiple-stage-stimulated horizontal well by temperature measurements with fast marching method. SPE J. 2016; 21(6): 2289-300. https://doi.org/10.2118/174880-PA

[18] Zhang S, Zhu D, Hill AD. Flow profile determination from inversion of distributed temperature measurements. In: SPE Annual Technical Conference and Exhibition; 2019 Sep 30-Oct 2; Calgary, Alberta, Canada. SPE-196002-MS. https://doi.org/10.2118/196002-MS

[19] Huang L, Song HW, Wang MX, Ma WH, Wei BJ. Research on injection and production profile interpretation method based on distributed fiber optic temperature logging. Prog Geophys. 2024; 39(1): 266-79.

[20] Ukil A, Braendle H, Krippner P. Distributed temperature sensing: review of technology and applications. IEEE Sens J. 2012; 12(5): 885-92. https://doi.org/10.1109/JSEN.2011.2162060

[21] Ashry I, Mao Y, Wang B, Hveding F, Bukhamsin AY, Ng TK, et al. A review of distributed fiber-optic sensing in the oil and gas industry. J Lightwave Technol. 2022; 40(5): 1407-31. https://doi.org/10.1109/JLT.2021.3135653

[22] Li XR, Liu XF, Zhang Y, Guo F, Wang XD, Feng YC. Application and progress of oil and gas well monitoring techniques based on distributed optical fiber sensing. Oil Drill Prod Technol. 2022; 44(3): 309-20. https://doi.org/10.13639/j.odpt.2022.03.007

[23] Wang YL, Wu ZP, Wang FY. Research progress of applying distributed fiber optic measurement technology in hydraulic fracturing and production monitoring. Energies. 2022; 15(20): 7519. https://doi.org/10.3390/en15207519

[24] Brown G, Storer D, McAllister K, Al-Asimi M, Raghavan K. Monitoring horizontal producers and injectors during cleanup and production using fiber-optic-distributed temperature measurements. In: SPE Annual Technical Conference and Exhibition; 2003 Oct 5-8; Denver, CO, USA. SPE-84379-MS. https://doi.org/10.2118/84379-MS

[25] Pimenov V, Brown G, Tertychnyi V, Shandrygin A, Popov Y. Injectivity profiling in horizontal wells via distributed temperature monitoring. In: SPE Annual Technical Conference and Exhibition; 2005 Oct 9-12; Dallas, TX, USA. SPE-97023-MS. https://doi.org/10.2118/97023-MS

[26] Ouyang LB, Belanger D. Flow profiling via distributed temperature sensor (DTS) system: expectation and reality. In: SPE Intelligent Energy Conference and Exhibition; 2004 Mar 23-25; Utrecht, The Netherlands. SPE-90541-MS. https://doi.org/10.2118/90541-MS

[27] Yoshioka K, Zhu D, Hill AD, Lake LW. A new inversion method to interpret flow profiles from distributed temperature and pressure measurements in horizontal wells. SPE Prod Oper. 2009; 24(4): 510-21. https://doi.org/10.2118/109749-PA

[28] Reges JEO, Salazar AO, Maitelli CWSP, Carvalho LG, Britto UJB. Flow rates measurement and uncertainty analysis in multiple-zone water-injection wells from fluid temperature profiles. Sensors. 2016; 16(7): 1077. https://doi.org/10.3390/s16071077

[29] Silva WLA, Lima VS, Fonseca DAM, Salazar AO, Maitelli CWSP, Echaiz Espinoza GA. Study of flow rate measurements derived from temperature profiles of an emulated well by a laboratory prototype. Sensors. 2019; 19(7): 1498. https://doi.org/10.3390/s19071498

[30] Echaiz Espinoza GA, Oliveira GP, Lima VS, Fonseca DAM, Silva WLA, Maitelli CWSP, et al. Thermal profiles in water injection wells: reduction in the systematic error of flow measurements during the transient regime. Sensors. 2023; 23(23): 9465. https://doi.org/10.3390/s23239465

[31] Zhang XL, Li A, Wu J, Wang LJ, Zuo K, Zhang ZH. Method to interpret injection profile of water injection well based on DTS. Well Logging Technol. 2024; 48(4): 537-47. https://doi.org/10.16489/j.issn.1004-1338.2024.04.014

[32] Shi S, Liu J, Li M, Sun C, Lei T. Research on numerical simulation and interpretation method of water injection well temperature field based on DTS. Processes. 2025; 13(1): 274. https://doi.org/10.3390/pr13010274

[33] Huang H, Song H, Li M, Shi X. Research on injection profile interpretation method based on DTS logging. Processes. 2025; 13(3): 733. https://doi.org/10.3390/pr13030733

[34] Ramey HJ Jr. Wellbore heat transmission. J Pet Technol. 1962; 14(4): 427-35. https://doi.org/10.2118/96-PA

[35] Sagar RK, Doty DR, Schmidt Z. Predicting temperature profiles in a flowing well. SPE Prod Eng. 1991; 6(4): 441-8. https://doi.org/10.2118/19702-PA

[36] Hasan AR, Kabir CS. Wellbore heat-transfer modeling and applications. J Pet Sci Eng. 2012; 86-87: 127-36. https://doi.org/10.1016/j.petrol.2012.03.021

[37] Duru OO, Horne RN. Modeling reservoir temperature transients and reservoir-parameter estimation constrained to the model. SPE Reserv Eval Eng. 2010; 13(6): 873-83. https://doi.org/10.2118/115791-PA

[38] Hashish RG, Zeidouni M. Analytical approach for injection profiling through warm-back analysis in multilayer reservoirs. J Pet Sci Eng. 2019; 182: 106274. https://doi.org/10.1016/j.petrol.2019.106274

[39] Fahim M, Keshka A, Al Marzooqi A, Alvi A, Salem D, Brown G, et al. Distributed temperature sensing (DTS) enables injectivity visualization to enhance stimulation efficiency. In: SPE Middle East Oil and Gas Show and Conference; 2011 Sep 25-28; Manama, Bahrain. SPE-141239-MS. https://doi.org/10.2118/141239-MS

[40] Wu MW, Zheng YJ, Long TY, Duan Y, Wei M. Interpretation of gas well production profile based on distributed temperature sensing monitoring: a case study of multilayered exploration wells in Yongle, South China Sea. Sci Technol Eng. 2022; 22(6): 2245-51.

[41] Adeyemi T, Wei C, Sharma J, Chen Y. Comparison of gas signature and void fraction in water- and oil-based muds using fiber-optic distributed acoustic sensor, distributed temperature sensor, and distributed strain sensor. SPE J. 2024; 29(7): 3531-52. https://doi.org/10.2118/219753-PA

[42] Wei C, Tabjula JL, Sharma J, Chen Y. The modeling of two-way coupled transient multiphase flow and heat transfer during gas influx management using fiber optic distributed temperature sensing measurements. Int J Heat Mass Transf. 2023; 214: 124447. https://doi.org/10.1016/j.ijheatmasstransfer.2023.124447

[43] Zhu H, Liu W, Luo H, Li H, Ma H, Li Y, et al. Study on the influence law of temperature profile of water injection well. Int J Pet Technol. 2023; 10: 1-13. https://doi.org/10.15377/2409-787X.2023.10.1

[44] Ma H, Luo H, Li H, Xiang Y, Zhang Q, Li Y. Study on the influence law of temperature profile of vertical wells in gas reservoirs. Int J Pet Technol. 2022; 9: 54-66. https://doi.org/10.15377/2409-787X.2022.09.7

[45] Li H, Luo H, Xiang Y, Li Y, Jiang B, Cui X, et al. DTS based hydraulic fracture identification and production profile interpretation method for horizontal shale gas wells. Nat Gas Ind B. 2021; 8(5): 494-504. https://doi.org/10.1016/j.ngib.2021.05.001

[46] Luo H, Li Y, Li H, Cui X, Chen Z. Simulated annealing algorithm-based inversion model to interpret flow rate profiles and fracture parameters for horizontal wells in unconventional gas reservoirs. SPE J. 2021; 26(4): 1679-99. https://doi.org/10.2118/205010-PA

Published

2026-06-16

Issue

Section

Articles

How to Cite

1.
Experimental Study on Temperature Response and Quantitative Relationship with Injection Profile in Water Injection Wells Based on Distributed Temperature Sensing. Int. J. Pet. Technol. [Internet]. 2026 Jun. 16 [cited 2026 Sep. 12];13(1):43-56. Available from: https://www.avantipublishers.com/index.php/ijpt/article/view/1845

Similar Articles

1-10 of 67

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)