地球科学进展 ›› 2018, Vol. 33 ›› Issue (5): 532 -544. doi: 10.11867/j.issn.1001-8166.2018.05.0532

新学科·新发展·新技术 上一篇    下一篇

智能化学示踪剂技术及其在油藏监测中的应用
高兴军 1( ), 徐薇薇 2, 余义常 1, 李艳然 3, 李蕾 1   
  1. 1.中国石油勘探开发研究院,北京 100083
    2.大庆油田采油一厂,黑龙江 大庆 163000
    3.河南油田研究院,河南 南阳 473132
  • 收稿日期:2017-12-23 修回日期:2018-04-05 出版日期:2018-05-20

Intelligent Chemical Tracer Technology and Its Application to Reservoir Surveillance

Xingjun Gao 1( ), Weiwei Xu 2, Yichang Yu 1, Yanran Li 3, Lei Li 1   

  1. 1.Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
    2.No.1 Oil Production Plant, Daqing Oilfield Limited Company, Daqing Heilongjiang 163000, China
    3.Research Institute of He’nan Oilfield, Nanyang He’nan 473132, China;
  • Received:2017-12-23 Revised:2018-04-05 Online:2018-05-20 Published:2018-06-13
  • About author:

    First author:Gao Xingjun(1972-),male,Mingshui County,Heilongjiang Province,Senior Engineer. Research areas include production geology and production well-logging. E-mail:gaoxingjun@petrochina.com.cn

深海油藏开发环境复杂,开采成本非常高,水平井及流入控制装置(ICD)得到了广泛应用,但长水平井及多分支井的产液剖面监测、水突破时间及见水井段的判断等一直是难题。挪威RESMAN公司和英国Tracerco公司研发的智能化学示踪剂技术因其风险低、寿命长,在水平井监测中日益受到重视。由于目前国内在该领域缺乏相关研究,故综述了智能化学示踪剂技术在深海水平井中的应用,系统介绍了智能化学示踪剂技术的原理、设计、装配、放置、取样及解释方法,并结合国外油田的具体实例,一方面阐述了高频瞬态取样方法及应用示踪剂团冲洗模型、到达模型进行产液剖面解释的技术思路,另一方面介绍了低频稳态取样方法及应用示踪剂通量模型进行见水时间及见水位置分析的技术思路。智能化学示踪剂技术实现了对油藏生产的持续监测,不需要改变水平井完井设计,对油井生产干扰小,油藏适用范围广,在碳酸盐岩储层、砂岩储层、页岩储层、含H2S和CO2气体以及温度高达137 ℃的油藏均有成功应用的实例,该技术应用前景十分广阔。

Deepwater oilfield development is very high cost venture in complex reservoir and production conditions. Reservoir development that relies on long horizontal wells and inflow control devices is common practice. Inflow profile monitoring and identification of the time and location of water breakthrough in long horizontal wells are challenging issues due to well production intervention. Intelligent chemical tracer technology, mainly developed by RESMAN and Tracerco, plays an important role in horizontal well monitoring because of its almost no risk and long duration, which gains increasing attention. Because of the inadequate study on intelligent tracer in China, this paper summarized the application of intelligent chemical tracer technology in deep-sea horizontal wells based on examples from overseas oilfields, and comprehensively introduced its basic principles, tracer system design, assembly, placement, sampling and interpretation of intelligent chemical tracer technology. Interpretation process of liquid production profiles along horizontal wells based on high frequency transient tracer sampling and tracer flushing and tracer reaching model was described. The identification process of the time and location of water breakthrough was explained through the low-frequency steady-state sampling and tracer flux model. The intelligent tracer technology achieves the continuous monitoring of oil and water production without changing the horizontal well completion design and without production intervention during monitoring, which has been proved to be suitable in a large range of reservoir condition, such as carbonate, sandstone, shale reservoirs and reservoirs with H2S/CO2 and temperature up to 137 ℃. More and more successful application cases of intelligent tracer make it to become a very potential and efficient technology.

中图分类号: 

图1 智能示踪剂原理示意图 [ 7 ]
(a)智能示踪剂材料安装于ICD环形空间;(b) 接触地层流体后释放示踪剂分子;(c)带有示踪剂分子的地层流体被开采出来
Fig.1 The schematic diagram of intelligent tracer [ 7 ]
(a)Intelligent tracers installed in ICD screen;(b)Contact with liquid causes tracer molecules to be released from the matrix;(c)Flow of liquid from the reservoir flushes out the oil/water with high tracer concentration
图1 智能示踪剂原理示意图 [ 7 ]
(a)智能示踪剂材料安装于ICD环形空间;(b) 接触地层流体后释放示踪剂分子;(c)带有示踪剂分子的地层流体被开采出来
Fig.1 The schematic diagram of intelligent tracer [ 7 ]
(a)Intelligent tracers installed in ICD screen;(b)Contact with liquid causes tracer molecules to be released from the matrix;(c)Flow of liquid from the reservoir flushes out the oil/water with high tracer concentration
图2 智能示踪剂的装配图 [ 3 , 4 , 11 ]
(a)智能示踪剂系统安装于绕丝筛管内;(b)智能示踪剂系统安装于割缝筛管中;(c)细绳状智能示踪剂装配图;(d)标记井名、载体序列号和示踪剂系统编号的示踪剂载体
Fig.2 The assembly of intelligent tracer system [ 3 , 4 , 11 ]
(a)Tracer systems installed in a wire-wrap screen of ICD;(b)Intelligent tracer systems installed in slotted base pipe;(c)Installing matrix filaments into premium screens;(d)Assembled carriers labeled with well name, carrier sequence number, and tracer system serial numbers
图2 智能示踪剂的装配图 [ 3 , 4 , 11 ]
(a)智能示踪剂系统安装于绕丝筛管内;(b)智能示踪剂系统安装于割缝筛管中;(c)细绳状智能示踪剂装配图;(d)标记井名、载体序列号和示踪剂系统编号的示踪剂载体
Fig.2 The assembly of intelligent tracer system [ 3 , 4 , 11 ]
(a)Tracer systems installed in a wire-wrap screen of ICD;(b)Intelligent tracer systems installed in slotted base pipe;(c)Installing matrix filaments into premium screens;(d)Assembled carriers labeled with well name, carrier sequence number, and tracer system serial numbers
图3 智能示踪剂在水平井段中的放置图 [ 7 ]
Fig.3 The placement of intelligent tracer in horizontal section [ 7 ]
图3 智能示踪剂在水平井段中的放置图 [ 7 ]
Fig.3 The placement of intelligent tracer in horizontal section [ 7 ]
图4 智能示踪剂在单一生产层段中的放置图 [ 3 ]
Fig.4 The placement of intelligent tracer in a single production section [ 3 ]
图4 智能示踪剂在单一生产层段中的放置图 [ 3 ]
Fig.4 The placement of intelligent tracer in a single production section [ 3 ]
图5 估算示踪剂团迁移的时间和体积 [ 1 ]
Fig.5 The estimated tracer migration time and volume [ 1 ]
图5 估算示踪剂团迁移的时间和体积 [ 1 ]
Fig.5 The estimated tracer migration time and volume [ 1 ]
图6 示踪剂测试样品及瞬态取样实例 [ 2 , 13 ]
(a)顶部取样点取得的样品;(b)开井后生产曲线、取样及分析频率
Fig.6 The tracer test samples and a case of transient sampling [ 2 , 13 ]
(a)Example of standard sample collected at the topside sampling point;(b)Surface production rate for the re-start of the well showing oil and water rate, obtained samples and samples sent for analysis
图6 示踪剂测试样品及瞬态取样实例 [ 2 , 13 ]
(a)顶部取样点取得的样品;(b)开井后生产曲线、取样及分析频率
Fig.6 The tracer test samples and a case of transient sampling [ 2 , 13 ]
(a)Example of standard sample collected at the topside sampling point;(b)Surface production rate for the re-start of the well showing oil and water rate, obtained samples and samples sent for analysis
图7 示踪剂团形成及冲洗过程 [ 1 ]
(a)关井时示踪剂仍释放出来并在示踪剂系统周围积累形成示踪剂团;(b)重新开井后示踪剂团通过ICD出口释放出来
Fig.7 The process of formation and flushing of tracer group [ 1 ]
(a)Demonstrates a production shutdown where tracers will still be released and accumulated into a shot of tracer material around the tracer systems and in the completion void;(b)Sketch of how the tracer shot is flushed through the ICD nozzle during the restart of production
图7 示踪剂团形成及冲洗过程 [ 1 ]
(a)关井时示踪剂仍释放出来并在示踪剂系统周围积累形成示踪剂团;(b)重新开井后示踪剂团通过ICD出口释放出来
Fig.7 The process of formation and flushing of tracer group [ 1 ]
(a)Demonstrates a production shutdown where tracers will still be released and accumulated into a shot of tracer material around the tracer systems and in the completion void;(b)Sketch of how the tracer shot is flushed through the ICD nozzle during the restart of production
图8 以2种不同流速冲洗下的示踪剂通量变化 [ 1 ]
Fig.8 The tracer flux change law at two different flow rates [ 1 ]
图8 以2种不同流速冲洗下的示踪剂通量变化 [ 1 ]
Fig.8 The tracer flux change law at two different flow rates [ 1 ]
图9 示踪剂团到达模型解释流程 [ 7 , 13 ]
(a)双分支井的测深MD与垂深TVD关系图(左图为右图目的层深度局部放大);(b)不同累积产液量下各类示踪剂绝对浓度;(c)不同累积产液量下各类示踪剂浓度归一化;(d)各类示踪剂峰值与均匀产液条件下模拟结果的比较;(e)假设上部分支井比下部多产液30%条件下模拟结果与实际对比;(f)通过调整各层段产液比例使模拟结果与实际结果一致;(g)最优拟合后解释各层段产液的比例
Fig.9 The explanation process of tracer group reaching model [ 7 , 13 ]
(a)TVD vs MD for the dual-lateral wells studied, left figure show the enlarged view of target zone in right figure;(b)Tracer responses as a function of cumulative produced liquids;(c) Normalized tracer concentrations as a function of cumulative produced liquids;(d) Comparison between the measured tracer response and simulation prediction assuming uniform production in the well;(e) Comparison between the measured tracer response and simulation prediction assuming 30% higher uniform production in the upper lateral;(f) Best fit between the measured tracer response and simulation prediction by adjusting production profile along each lateral;(g)Percentage of total production from each section giving the best fit between measured and predicted tracer responses
图9 示踪剂团到达模型解释流程 [ 7 , 13 ]
(a)双分支井的测深MD与垂深TVD关系图(左图为右图目的层深度局部放大);(b)不同累积产液量下各类示踪剂绝对浓度;(c)不同累积产液量下各类示踪剂浓度归一化;(d)各类示踪剂峰值与均匀产液条件下模拟结果的比较;(e)假设上部分支井比下部多产液30%条件下模拟结果与实际对比;(f)通过调整各层段产液比例使模拟结果与实际结果一致;(g)最优拟合后解释各层段产液的比例
Fig.9 The explanation process of tracer group reaching model [ 7 , 13 ]
(a)TVD vs MD for the dual-lateral wells studied, left figure show the enlarged view of target zone in right figure;(b)Tracer responses as a function of cumulative produced liquids;(c) Normalized tracer concentrations as a function of cumulative produced liquids;(d) Comparison between the measured tracer response and simulation prediction assuming uniform production in the well;(e) Comparison between the measured tracer response and simulation prediction assuming 30% higher uniform production in the upper lateral;(f) Best fit between the measured tracer response and simulation prediction by adjusting production profile along each lateral;(g)Percentage of total production from each section giving the best fit between measured and predicted tracer responses
图10 瞬态法解释SP16井产液剖面过程及结果 [ 3 ]
(a)SP16井重启后示踪剂浓度与累计产液关系图;(b)SP16井的示踪剂团到达模型;(c)SP16井示踪剂团冲洗模型达到最佳匹配时各层段相应的 k系数;(d)SP16井各层段的长度及示踪剂分布;(e)SP16井各层段总贡献、每英尺贡献比例以及平均贡献比例
Fig.10 The production profile interpretation by intelligent tracer data for well SP16 [ 3 ]
(a)Re-start tracer responses vs cummulative produced liquids for SP16;(b)Tracer shot arrival modeling for SP16;(c)Flush-out modeling for SP16 re-start campaigns with tracer response, fitted model curve, and corresponding volume rate dependent k-factor;(d)Compartments length and tracer distribution for SP16;(e)Compartments contribution based on intelligent tracer modeling for SP16, contribution per foot of different monitored compartments (% per ft), and the average contribution per foot of total length of monitored production(% per ft)
图10 瞬态法解释SP16井产液剖面过程及结果 [ 3 ]
(a)SP16井重启后示踪剂浓度与累计产液关系图;(b)SP16井的示踪剂团到达模型;(c)SP16井示踪剂团冲洗模型达到最佳匹配时各层段相应的 k系数;(d)SP16井各层段的长度及示踪剂分布;(e)SP16井各层段总贡献、每英尺贡献比例以及平均贡献比例
Fig.10 The production profile interpretation by intelligent tracer data for well SP16 [ 3 ]
(a)Re-start tracer responses vs cummulative produced liquids for SP16;(b)Tracer shot arrival modeling for SP16;(c)Flush-out modeling for SP16 re-start campaigns with tracer response, fitted model curve, and corresponding volume rate dependent k-factor;(d)Compartments length and tracer distribution for SP16;(e)Compartments contribution based on intelligent tracer modeling for SP16, contribution per foot of different monitored compartments (% per ft), and the average contribution per foot of total length of monitored production(% per ft)
表1 应用示踪剂团冲洗模型和到达模型解释产液剖面 [ 3 ]
Table 1 Utilize the tracer group flushing model and reaching model to interpret the production profile [ 3 ]
表1 应用示踪剂团冲洗模型和到达模型解释产液剖面 [ 3 ]
Table 1 Utilize the tracer group flushing model and reaching model to interpret the production profile [ 3 ]
图11 G-25-3井水示踪剂通量和含水率随时间的变化 [ 1 ]
Fig.11 The change of water-related tracer flux and water content with time in well G-25-3 [ 1 ]
图11 G-25-3井水示踪剂通量和含水率随时间的变化 [ 1 ]
Fig.11 The change of water-related tracer flux and water content with time in well G-25-3 [ 1 ]
图12 G-25-3井油示踪剂通量随时间的变化 [ 1 ]
Fig.12 The change of oil-related tracer flux in the samples with time in well G-25-3 [ 1 ]
图12 G-25-3井油示踪剂通量随时间的变化 [ 1 ]
Fig.12 The change of oil-related tracer flux in the samples with time in well G-25-3 [ 1 ]
图13 相对示踪剂通量及含水率随时间的变化 [ 1 ]
(a)相对示踪剂通量随时间的变化;(b)层段2,3,4的累计相对示踪剂通量与含水率关系
Fig.13 The change of relative tracer flux and water content with time [ 1 ]
(a)Relative tracer flux between water and oil tracer for all pairs of oil and water tracer systems vs. time;(b)Cumulative relative tracer flux of zones 2, 3 and 4 with the water-cut vs. time
图13 相对示踪剂通量及含水率随时间的变化 [ 1 ]
(a)相对示踪剂通量随时间的变化;(b)层段2,3,4的累计相对示踪剂通量与含水率关系
Fig.13 The change of relative tracer flux and water content with time [ 1 ]
(a)Relative tracer flux between water and oil tracer for all pairs of oil and water tracer systems vs. time;(b)Cumulative relative tracer flux of zones 2, 3 and 4 with the water-cut vs. time
[1] Andrew Montes, Fridtjof Nyhavn, Gaute Oftedal, et al.Application of inflow well tracers for permanent reservoir monitoring in north amethyst subsea tieback ICD Wells in Canada[C]//SPE Middle East Intelligent Energy Conference and Exhibition, Dubai, UAE. Richardson, Texas:Society of Petroleum Engineers,2013:1-15.
[2] Svein Mjaaland, Erlend Gudding, Christian A A.Wireless inflow monitoring in a subsea field development: A case study from the Hyme Field, Offshore Mid-Norway[C]//SPE Annual Technical Conference and Exhibition, Amsterdam, The Netherlands. Richardson, Texas:Society of Petroleum Engineers,2014:1-13.
[3] Kuck M D, Nofziger L, Gentil P, et al.Production monitoring by intelligent chemical inflow tracers in long horizontal heavy oil wells for the Nikaitchuq Field, Northern Alaska[C]//International Petroleum Technology Conference, Doha, Qatar. 2014:1-10.
[4] Ralf Napalowski, Richard loro, Christian Andresen. Successful application of well inflow tracers for water breakthrough surveillance in the pyrenees development, offshore Western Australia[C]//SPE Asia Pacific Oil and Gas Conference and Exhibition, Perth, Australia. Richardson, Texas: Society of Petroleum Engineers,2012:1-15.
[5] Henry Edmundson.A revolution in reservoir surveillance for subsea production environments[J]. Scandinavian Oil-Gas Magazine,2013, (7/8):61-63.
[6] Zhang Dekai.ESMAN intelligent tracer technology substitute PLT[J]. Petroleum Knowledge, 2017,(1): 39-39.
[张德凯. ESMAN智能示踪剂替代生产测井[J]. 石油知识,2017,(1):39-39.]
URL    
[7] Brock Williams, Brent Brough.Wireless reservoir surveillance in deepwater completions[C]//SPE Deepwater Drilling and Completions Conference, Galveston, Texas, USA. Richardson, Texas, USA:Society of Petroleum Engineers,2012:1-11.
[8] Nutricato G, Repetto C.Application of chemical tracers for clean-up and production inflow monitoring with onshore wells in Italy[C]//International Petroleum Technology Conference, Beijing, China,2013:1-11.
[9] Spencer J, Bucior D, Catlett R, et al.Evaluation of horizontal wells in the eagle ford using oil-based chemical tracer technology to optimize stimulation design[C]//SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, USA. Richardson, Texas:Society of Petroleum Engineers,2013:1-9.
[10] Nyhavn F, Dyrli A D.Permanent tracers embedded in downhole polymers prove their monitoring capabilities in a hot offshore well[C]//SPE Annual Technical Conference and Exhibition, Florence, Italy. Richardson, Texas: Society of Petroleum Engineers,2010:1-15.
[11] Hailu K A, Gibbons G, Fridtjof N.Monitoring multilateral flow and completion integrity with permanent intelligent well tracers[C]//SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, USA. Richardson, Texas:Society of Petroleum Engineers,2013:1-15.
[12] Aleks A, Serge H, Tony H.Designing a high resolution chemical surveillance network in a deepwater field off NW Borneo, East Malaysia[C]//Offshore Technology Conference-Asia, Kuala Lumpur, Malaysia, 2014:1-11.
[13] Christian A, Brock W, Mike M.Interventionless surveillance in a multi-lateral horizontal well[C]//ADC/SPE Drilling Conference and Exhibition, San Diego, California, USA. Richardson, Texas:Society of Petroleum Engineers,2012:1-9.
[14] Wang Pinxian.Chinese Earth Science at its turning point[J]. Advances in Earth Science, 2016,31(7):665-667.
[汪品先. 迎接我国地球科学的转型[J]. 地球科学进展,2016,31(7):665-667.]
doi: 10.11867/j.issn.1001-8166.2016.07.0665     URL    
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[15] 冯佳睿, 高志勇, 崔京钢, 周川闽. 深层、超深层碎屑岩储层勘探现状与研究进展[J]. 地球科学进展, 2016, 31(7): 718-736.
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