水平井两相流产出剖面光纤监测反演方法

doi:10.13809/j.cnki.cn32-1825/te.2025051

Abstract

Abstract: Distributed temperature sensing (DTS) technology is widely applied in intelligent monitoring of production dynamics in oil and gas wells. To address the challenges in quantitatively analyzing oil-water two-phase flow profile in horizontal wells, a temperature profile prediction model applicable to oil-water two-phase flow in horizontal wells was constructed, comprehensively considering multiple micro-thermal effects, including the Joule-Thomson effect. Simulation and sensitivity analyses of the temperature profile of a reservoir horizontal well were conducted. Meanwhile, the particle swarm optimization (PSO) algorithm was used to establish a DTS data inversion model, innovatively enabling the inversion of multi-dimensional unknown downhole parameters based on a single DTS data source, thereby achieving a quantitative interpretation of the oil-water two-phase flow production profile in horizontal wells. The results showed that: (1) The main influencing factors of the temperature profile in oil-water two-phase horizontal wells, ranked by impact degree from high to low, were single-well production (Q)> permeability (k)> water cut (F_W)> wellbore radius (R_w)> crude oil density (ρ_o)> damage zone radius (R_d)> reservoir thermal conductivity (K_t). (2) Single-well production, permeability, and water cut were the key dominant factors affecting the temperature profile. When inverting measured DTS data, formation permeability could be prioritized as the core target parameter for inversion, and secondary factors could be set as fixed values or assigned reasonable ranges to simplify the problem. (3) By using the PSO inversion model to invert the DTS temperature data of the field well, the production positions of the two-phase fluids could be accurately identified. The interpreted liquid production profile obtained from inversion showed high consistency with field production logging tool (PLT) test results, with a mean absolute error of the average liquid production per section of less than 10%, fully verifying the reliability of the PSO inversion model. Future research can focus on enhancing the model's ability to characterize complex flows and expanding its application to multiphase flow scenarios.

Key words: production profile, inversion method, distributed temperature sensing, particle swarm optimization algorithm, two-phase flow in horizontal well, sensitivity analysis

CLC Number:

TE33.2

XIONG HANLAN,LUO HONGWEN,LI HAITAO, et al. Inversion method for fiber optic monitoring of two-phase flow production profiles in horizontal wells[J]. Petroleum Reservoir Evaluation and Development, 0, (): 2025051-2025051.

References

[1] 朱世琰. 基于分布式光纤温度测试的水平井产出剖面解释理论研究[D]. 成都: 西南石油大学, 2016.
ZHU Shiyan.Theoretical study on horizontal well production profile interpretation based on distributed optical fiber temperature measurement[D]. Chengdu: Southwest Petroleum University, 2016.
[2] DENG R, ZHANG L, WANG L, et al.Accelerated inversion workflow coupling deep learning and ES-MDA for DTS data: A case study of horizontal well in middle east carbonate reservoir[C]// Paper SPE-221236-MS presented at the APOGCE 2024, Perth, Australia, October, 2024.
[3] 罗红文, 李海涛, 安树杰, 等. 致密气藏压裂水平井温度剖面影响因素分析[J]. 特种油气藏, 2021, 28(4): 150-157.
LUO Hongwen, LI Haitao, AN Shujie, et al.Analysis of influencing factors of temperature profile of fractured horizontal wells in tight gas reservoirs[J]. Special Oil & Gas Reservoirs, 2021, 28(4): 150-157.
[4] 蔡珺君. 水平井井筒温度预测及解释模型研究[D]. 成都: 西南石油大学, 2016.
CAI Junjun.Study on prediction and interpretation model of wellbore temperature in horizontal wells[D]. Chengdu: Southwest Petroleum University, 2016.
[5] SOOKPRASONG P A, GILL C C, HURT R S.Lessons learned from DAS and DTS in MultiCluster, MultiStage horizontal well fracturing: Interpretation of hydraulic fracture initiation and propagation through diagnostics[C]// Paper IADC/SPE-170512-MS presented at the IADC/SPE Asia Pacific Drilling Technology Conference, Bangkok, Thailand, August 2014.
[6] UGUETO C G A, HUCKABEE P T, MOLENAAR M M, et al. Perforation cluster efficiency of cemented plug and perf limited entry completions; insights from fiber optics diagnostics[C]// Paper IADC/SPE-179124-MS presented at the SPE Hydraulic Fracturing Technology Conference, Texas, USA, February 2016.
[7] RAMEY H J.Wellbore heat transmission[J]. Journal of Petroleum Technology, 1962, 14(4): 427-435.
[8] HASAN A R, KABIR C S.Aspects of wellbore heat transfer during two-phase flow[J]. SPE Production & Facilities, 1994, 9(3): 211-216.
[9] KABIR C S, HASAN A R, JORDAN D L, et al.A wellbore/reservoir simulator for testing gas wells in high-temperature reservoirs[J]. SPE Formation Evaluation, 1996, 11(2): 128-134.
[10] YOSHIOKA K, ZHU D, HILL A D, et al.Prediction of temperature changes caused by water or gas entry into a horizontal well[J]. SPE Production & Operations, 2007, 22(4): 425-433.
[11] YOSHIOKA K.Detection of water or gas entry into horizontal wells by using permanent downhole monitoring systems[D]. College Station: Texas A&M University, 2007.
[12] YOSHIOKA K, ZHU D, HILL A D, et al.A new inversion method to interpret flow profiles from distributed temperature and pressure measurements in horizontal wells[J]. SPE Production & Operations, 2009, 24(4): 510-521.
[13] 冯晓炜, 赵毅, 杨鹏, 等. 分布式光纤温度监测技术在压裂水平井产剖解释中的应用[J]. 油气藏评价与开发, 2021, 11(4): 542-549.
FENG Xiaowei, ZHAO Yi, YANG Peng, et al.Application of distributed optical fiber temperature monitoring technology in production and profile interpretation of fractured horizontal wells[J]. Petroleum Reservoir Evaluation and Development, 2021, 11(4): 542-549.
[14] ZHAO Z H, YANG H Z, WU J F, et al.Fracture diagnosis with distributed temperature sensing using thermal Embedded Discrete Fracture Model (EDFM)[C]// Paper ARMA 21-2061 presented at the 55th US Rock Mechanics/Geomechanics Symposium held in Houston, Texas, USA, June 2021.
[15] ZHANG S, ZHU D.Inversion of downhole temperature measurements in multistage fracture stimulation in horizontal wells[C]// Paper SPE-187322-MS presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, October 2017.
[16] WU Q, ZHU Z, YAN X, et al.An improved particle swarm optimization algorithm for AVO elastic parameter inversion problem[J]. Concurrency and Computation: Practice and Experience, 2019, 31(9): e4987.
[17] 马海, 王延江, 胡睿, 等. 测井岩性识别新方法研究[J]. 地球物理学进展, 2009, 24(1): 263-269.
MA Hai, WANG Yanjiang, HU Rui, et al.A novel method for well logging lithologic identification[J]. Progress in Geophysics, 2009, 24(1): 263-269.
[18] 罗红文, 艾文斌, 李海涛, 等. 采用PSO算法的页岩气水平井DTS数据反演方法[J]. 石油地球物理勘探, 2023, 58(5): 1202-1210.
LUO Hongwen, AI Wenbin, LI Haitao, et al.Inversion method of distributed temperature sensing data for horizontal wells in shale gas reservoirs based on PSO algorithm[J]. Oil Geophysical Prospecting, 2023, 58(5): 1202-1210.
[19] 罗红文, 李海涛, 李颖, 等. 分段压裂水平井的温度分布预测模型[J]. 科学技术与工程, 2020, 20(29): 11915-11922.
LUO Hongwen, LI Haitao, LI Ying, et al.Temperature prediction model for multistage fractured horizontal well[J]. Science Technology and Engineering, 2020, 20(29): 11915-11922.
[20] 张玺亮, 左凯, 李昂, 等. 海上注水井温度剖面预测模型研究[J]. 当代化工研究, 2024, (17): 59-61.
ZHANG Xiliang, ZUO Kai, LI Ang, et al.Study on the temperature profile prediction model of offshore water injection well[J]. Modern Chemical Research, 2024, (17): 59-61.
[21] SHAW R, SRIVASTAVA S.Particle swarm optimization: A new tool to invert geophysical data[J]. Geophysics, 2007, 72(2): 75-83.

Inversion method for fiber optic monitoring of two-phase flow production profiles in horizontal wells

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 7

Metrics

Comments

Recommended 0

[1]	XIAO Honglin, BU Chunliang, HOU Fu, TANG Huiying, WANG Yiyun, LUO Shangui. Automatic inversion of fracture parameters in tight gas reservoirs based on gas production profile [J]. Petroleum Reservoir Evaluation and Development, 2025, 15(5): 815-823.
[2]	HU Qiujia, LIU Chunchun, ZHANG Jianguo, CUI Xinrui, WANG Qian, WANG Qi, LI Jun, HE Shan. Machine learning-based coalbed methane well production prediction and fracturing parameter optimization [J]. Petroleum Reservoir Evaluation and Development, 2025, 15(2): 266-273.
[3]	LU Cong, LI Qiuyue, GUO Jianchun. Research progress of distributed optical fiber sensing technology in hydraulic fracturing [J]. Petroleum Reservoir Evaluation and Development, 2024, 14(4): 618-628.
[4]	ZHAO Di, MA Sen, CAO Yanhui. Seismic rock physics analysis and prediction model establishment of Shaximiao Formation in Zhongjiang Gas Field [J]. Petroleum Reservoir Evaluation and Development, 2023, 13(5): 608-613.
[5]	XING Cuiqiao, YIN Hongjun, ZHANG Ye, FU Jing. Well test analysis model for ribbon-shaped composite reservoirs with partially permeable faults [J]. Reservoir Evaluation and Development, 2023, 13(2): 215-222.
[6]	ZHANG Chenglin,YANG Xuefeng,ZHAO Shengxian,ZHANG Jian,DENG Feiyong,HE Yuanhan,ZHANG Deliang,WANG Gaoxiang,ZHONG Guanghai. Target position optimization for shale reservoirs in Zigong Block of southern Sichuan Basin [J]. Petroleum Reservoir Evaluation and Development, 2022, 12(3): 496-505.
[7]	FENG Xiaowei,ZHAO Yi,YANG Peng,ZHOU Jincheng. Application of distributed optical fiber temperature monitoring technology in production and profile interpretation of fractured horizontal wells [J]. Petroleum Reservoir Evaluation and Development, 2021, 11(4): 542-549.