Tight Oil Horizontal Well Production Profile Interpretation Method Based on Distributed Temperature Sensing

doi:10.3969/j.issn.1006-6535.2023.04.013

Abstract

Abstract: To address the problem of difficult quantitative diagnostic techniques for tight oil horizontal well production profiles, the tight oil horizontal well temperature profile predicting model is used as the forward model, and a tight oil horizontal well distributed temperature sensing (DTS) data inversion model is established based on the simulated annealing (SA) algorithm, forming a DTS-based tight oil horizontal well production profile interpretation method, to achieve the quantitative inversion interpretation of the tight oil horizontal well production profile and fracture parameters. The result shows that the tight oil horizontal well temperature profile is sensitive to the following factors in descending order: fluid production, fracture half-length, permeability, integrated heat transfer coefficient of wellbore, porosity, fracture conductivity, and thermal conductivity of reservoir rocks, and the main controlling factors affecting the temperature profile of tight oil horizontal wells are fracture half-length and formation permeability distribution. The inversion model was used to invert the measured DTS data from three different production stages of one field well, and the average compliance rate between the production profile interpretation results and the commercial software interpretation results was 87.39%, which verified the reliability of the production profile interpretation method for tight oil horizontal wells. The study results have important guidance for the quantitative interpretation of the tight oil horizontal well production profile.

Key words: DTS, tight oil, production profile, inversion interpretation, temperature profile, well temperature prediction

CLC Number:

TE33.2

Luo Hongwen, Zhang Qin, Li Haitao, Zhu Hanbin, Liu Wenqiang, Xiang Yuxing, Ma Hansong, Li Ying. Tight Oil Horizontal Well Production Profile Interpretation Method Based on Distributed Temperature Sensing[J]. Special Oil & Gas Reservoirs, 2023, 30(4): 104-112.

References

[1] 胡素云,朱如凯,吴松涛,等.中国陆相致密油效益勘探开发[J].石油勘探与开发,2018,45(4):737-748.
HU Suyun,ZHU Rukai,WU Songtao,et al.Profitable exploration and development of continental tight oil in China[J].Petroleum Exploration and Development,2018,45(4):737-748.
[2] 邱振,李建忠,吴晓智,等.国内外致密油勘探现状、主要地质特征及差异[J].岩性油气藏,2015,27(4):119-126.
QIU Zhen,LI Jianzhong,WU Xiaozhi,et al.Exploration status,main geologic characteristics and their differences of tight oil between America and China[J].Lithologic Reservoirs,2015,27(4):119-126.
[3] 苏皓,雷征东,张荻萩,等.致密油藏体积压裂水平井参数优化研究[J].岩性油气藏,2018,30(4):140-148.
SU Hao,LEI Zhengdong,ZHANG Diqiu,et al.Volume fracturing parameters optimization of horizontal well in tight reservoir[J].Lithologic Reservoirs,2018,30(4):140-148.
[4] 曾慧勇,陈立峰,陈亚东,等.压裂-驱油一体化工作液研究进展[J].油气地质与采收率,2022,29(3):162-170.
ZENG Huiyong,CHEN Lifeng,CHEN Yadong,et al.Research progress on fracturing-oil displacement integrated working fluid[J].Petroleum Geology and Recovery Efficiency,2022,29(3):162-170.
[5] 刘子军.基于Pearson 相关系数的低渗透砂岩油藏重复压裂井优选方法[J].油气地质与采收率,2022,29(2):140-144.
LIU Zijun.Method for selecting repeated fracturing wells in low-permeability sandstone reservoirs based on Pearson correlation coefficient[J].Petroleum Geology and Recovery Efficiency,2022,29(2):140-144.
[6] 徐中一,方思冬,张彬,等.页岩气体积压裂水平井试井解释新模型[J].油气地质与采收率,2020,27(3):120-128.
XU Zhongyi,FANG Sidong,ZHANG Bin,et al.A new well test model for horizontal wells by stimulated reservoir volume in shale gas reservoirs[J]. Petroleum Geology and Recovery Efficiency,2020,27(3):120-128.
[7] 马玉娟.大庆长垣油田三类油层压裂驱油提高采收率技术及其应用[J].大庆石油地质与开发,2021,40(2):103-109.
MA Yujuan.Application of fracturing-flooding EOR technique in type III oil reservoirs in Daqing Placanticline Oilfield[J]. Petroleum Geology & Oilfield Development in Daqing,2021,40(2):103-109.
[8] 周海军,黄佳.渤海油气田光纤式井下压力温度监测系统的研发与应用[J].机电信息,2020,42(24):54-55.
ZHOU Haijun,HUANG Jia.Development and application of optical fiber downhole pressure and temperature monitoring system in Bohai Oil and Gas Field[J].Mechanical and Electrical Information,2020,42(24):54-55.
[9] 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.Horizontal well production profile interpretation based on distributed temperature sensing[D].Chengdu:Southwest Petroleum University,2016.
[10] YOSHIDA N.Modeling and interpretation of downhole temperature in a horizontal well with multiple fractures[D].Texas:Texas A & M University,2016.
[11] YOHIOKA 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.
[12] YOHIOKA K,ZHU D,HILL A D.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] 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.Research on prediction and interpretation model of wellbore temperature in horizontal well[D].Chengdu:Southwest Petroleum University,2016.
[14] LI Z,ZHU D.Predicting flow profile of horizontal well by downhole pressure and distributed-temperature data for water drive-reservoir[J].SPE Production & Operations,2010,25(3):296-304.
[15] LUO Hongwen,LI Haitao,TAN Yongsheng,et al.A novel inversion approach for fracture parameters and inflow rates diagnosis in Multistage Fractured Horizontal Wells[J].Journal of Petroleum Science and Engineering,2020,184:106585.
[16]ZHANG Shuang,ZHU Ding.Inversion of downhole temperature measurements in multistage fracture stimulation in horizontal wells[C].SPE187322,2017:302-307.
[17] LUO Hongwen,LI Ying,LI Haitao,et al.Simulated annealing algorithm-based inversion model to Interpret flow Rate profiles and fracture parameters for horizontal wells in unconventional gas reservoirs[J].SPE Journal,2021,26(4):1679-1699.
[18] LUO Hongwen,LI Haitao,LU Yu,et al.Inversion of distributed temperature measurements to interpret the flow profile for a multistage fractured horizontal well in low-permeability gas reservoir[J].Applied Mathematical Modelling,2020,77:360-377.
[19] 丁超,王攀,秦亚东,等.基于非稳态热传导的SAGD开发指标预测模型[J].岩性油气藏,2022,35(1):160-168.
DING Chao,WANG Pan,QIN Yadong,et al.SAGD production performance prediction model based on unsteady heat transfer[J].Lithologic Reservoirs,2022,35(1):160-168.
[20] BIRD R B,STEWART W E,LIGHTFOOT E N,et al.Transport phenomena[J].John Wiley & Sons,1960,28(2):338-359.
[21] 黄连忠,万晓跃,孙永刚,等.基于模拟退火算法的船舶航速优化研究[J].船舶,2018,29(增刊1):8-17.
HUANG Lianzhong,WAN Xiaoyue,SUN Yonggang,et al.Research on ship speed optimization based on simulated annealing algorithm[J].Ship & Boat,2018,29(S1):8-17.
[22] 张志伟,暴景阳,肖付民,等.利用模拟退火算法反演多波束测量声速剖面[J].武汉大学学报(信息科学版),2018,43(8):1234-1241.
ZHANG Zhiwei,BAO Jingyang,XIAO Fumin,et al.Inversion of sound velocity profile in multibeam survey based on simulated annealing algorithm[J].Geomatics and Information Science of Wuhan University(Edition of Information Science),2018,43(8):1234-1241.
[23] 罗红文,李海涛,李颖,等.低渗透气藏压裂水平井产出剖面与裂缝参数反演解释[J].石油学报,2021,42(7):936-947.
LUO Hongwen,LI Haitao,LI Ying,et al.Inversion and interpretation of production profile and fracture parameters of fractured horizontal wells in low-permeability gas reservoirs[J].Acta Petrolei Sinica,2021,42(7):936-947.
[24] 刘全新,方光建.利用模拟退火算法实现地震叠前反演[J].岩性油气藏,2010,22(1):87-92.
LIU Quanxin,FANG Guangjian.Using simulated annealing algorithm to pre-stack seismic inversion[J].Lithologic Reservoirs,2010,22(1):87-92.
[25] 陈华根,吴健生,王家林,等.模拟退火算法机理研究[J].同济大学学报(自然科学版),2004,32(6):802-805.
CHEN Huagen,WU Jiansheng,WANG Jialin,et al.Mechanism study of simulated annealing algorithm[J].Journal of Tongji University(Natural Science),2004,32(6):802-805.