CIESC Journal ›› 2019, Vol. 70 ›› Issue (1): 388-397.doi: 10.11949/j.issn.0438-1157.20180501

• Process safety • Previous Articles     Next Articles

Risk evaluation model of deepwater drilling blowout accident based on risk entropy and complex network

Xiangkun MENG1(),Guoming CHEN1(),Chunliang ZHENG1,2,Xiangfei WU1,Gaogeng ZHU1   

  1. 1. Center for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, Shandong, China
    2. Offshore Oil Engineering Qingdao Co., Ltd., Qingdao 266520, Shandong, China
  • Received:2018-05-14 Revised:2018-06-21 Online:2019-01-05 Published:2018-10-29
  • Contact: Guoming CHEN E-mail:wsdy1002@163.com;offshore@126.com

Abstract:

To control the cascading risk of deepwater blowout, a quantitative evaluation method associated with risk uncertainty and evolution of deepwater drilling system was proposed, based on risk entropy theory and complex network theory. The complicated accident scenario was converted into an intuitive network computing. Firstly, according to the drilling process, a complex network that includes 59 nodes and 102 edges was constructed to represent the accident scene and calculate the clustering coefficient. Secondly, referring to the Shannon entropy theory, the risk entropy was introduced to characterize the uncertainty of risk transmission, in view of its randomness and fuzziness. Finally, the shortest path formulation of the blowout network was described, and the formulation was then converted into a liner programming and a solution of the shortest path of every initial event was provided by utilizing the Dijkstra algorithm. The results show that the clustering coefficient of deepwater drilling blowout network is 0.132. The evolutional structure has the characteristics of small world with low aggregation but high evolutionary of nodes. In addition, shallow gas during drilling has the greatest influence on the blowout accident. The blowout accident has the greatest impact, but the risk of all initial events can be caused by a few steps to cause the blowout accident to verify the feasibility of the method in the quantitative risk assessment of complex process systems.

Key words: deepwater drilling blowout, safety, process systems, uncertainty, entropy, complex network, shortest path

CLC Number: 

  • TE 58

Fig.1

Schematic of MPD flow process"

Table 1

Risk factors of deepwater drilling blowout"

编号风险因素编号风险因素编号风险因素
v1钻进操作因素v21损坏固井质量v41溢流进入隔水管
v2工艺因素v22表层套管下沉v42关闭环形BOP
v3自然因素v23地层压力高于井底压力v43环形BOP密封失效
v4设备因素v24井底压力过大v44环形BOP机械故障
v5固井质量差v25压裂地层v45液压系统故障
v6起钻过快v26溢流v46环形BOP操作失误
v7循环漏失v27溢流流体进入井筒环空v47环形BOP关闭失败
v8抽汲压力过大v28早期溢流监测失效v48关闭闸板BOP
v9钻井液密度过低v29钻井液流量指示器失效v49剪切闸板密封失效
v10气侵钻井液v30气体流量传感器失效v50闸板BOP机械故障
v11钻井液密度过大v31随钻测压工具失效v51闸板BOP操作失误
v12钻遇高渗地层v32气体含量传感器失效v52水合物阻塞BOP组
v13钻遇浅层气v33控制电路失效v53自动关断失效
v14钻遇浅层水流v34钻井监督显示器故障v54RCD密封失效
v15套管破裂v35录井工对泥浆池增量判断失误v55分流系统机械故障
v16钻杆失效v36司钻与录井工协作失误v56RCD操作失误
v17隔水管系统失效v37误报警导致司钻判断失误v57不压井起下作业装置失效
v18泥浆泵失效v38未监测到井涌v58分流失效
v19井口系统失效v39井涌判断失误v59井喷
v20动力故障v40井涌后未停钻

Fig.2

Complex network of risk evolution for blowout accident"

Table 2

Level of fuzzy language and probability"

语言变量梯形模糊数定性描述概率表征
非常低(VL)(0, 0, 0.1, 0.2)基本不可能发生(0, 10-6)
低(L)(0.1, 0.25, 0.25, 0.4)全寿命周期内可能发生(10-6, 10-3)
中等(M)(0.3, 0.5, 0.5, 0.7)全寿命周期内有时发生(10-3, 10-2)
高(H)(0.6, 0.75, 0.75, 0.9)全寿命周期内发生数次(10-2, 10-1)
非常高(VH)(0.8, 0.9, 1, 1)经常会发生(10-1, 1)

Table 3

Computation of expert opinions on path e2"

ParameterValue
Sij(Ei, Ej)(1, 0.825, 0.825)
AA(Ei)(0.913, 0.913, 0.825)
RA(Ei)(0.344,0.344, 0.311)
CC(Ei)(0.402, 0.369, 0.229)
RAG(0.646, 0.784, 0.807, 0.923)

Table 4

Weights of edges"

EdgeDirectionProbabilityEntropyEdgeDirectionProbabilityEntropyEdgeDirectionProbabilityEntropy
e1v1~v51.00×10-36.91e35v21~v58.02×10-10.22e69v42~v441.00×10-36.91
e2v1~v64.84×10-23.03e36v21~v72.07×10-11.58e70v42~v455.25×10-47.55
e3v2~v95.00×10-23.00e37v22~v78.22×10-22.50e71v42~v464.35×10-33.14
e4v2~v103.00×10-510.41e38v23~v260.990.01e72v43~v470.990.01
e5v2~v115.00×10-23.00e39v24~v258.00×10-10.22e73v44~v432.12×10-23.85
e6v3~v121.50×10-11.90e40v25~v237.35×10-10.31e74v44~v459.02×10-34.71
e7v3~v132.69×10-11.31e41v25~v260.990.01e75v44~v470.990.01
e8v3~v144.00×10-510.13e42v26~v270.990.01e76v45~v470.990.01
e9v4~v156.40×10-47.35e43v27~v282.07×10-11.58e77v46~v451.00×10-36.91
e10v4~v165.00×10-47.60e44v27~v291.10×10-49.12e78v46~v470.990.01
e11v4~v175.10×10-47.58e45v27~v301.10×10-49.12e79v47~v480.990.01
e12v4~v181.60×10-36.44e46v27~v313.00×10-35.81e80v48~v493.55×10-23.34
e13v4~v192.00×10-36.21e47v27~v321.10×10-36.81e81v48~v501.00×10-36.91
e14v4~v206.25×10-47.38e48v27~v331.00×10-49.21e82v48~v516.00×10-47.42
e15v5~v72.70×10-23.61e49v27~v341.10×10-49.12e83v48~v524.22×10-35.47
e16v6~v85.40×10-10.62e50v27~v351.00×10-36.91e84v49~v530.990.01
e17v7~v239.00×10-10.11e51v27~v361.00×10-36.91e85v50~v492.85×10-23.56
e18v8~v78.57×10-22.46e52v27~v371.00×10-36.91e86v50~v530.990.01
e19v8~v239.20×10-10.08e53v28~v380.990.01e87v51~v501.25×10-12.08
e20v9~v239.10×10-10.09e54v29~v380.990.01e88v51~v524.25×10-10.86
e21v10~v96.70×10-10.40e55v30~v380.990.01e89v51~v530.990.01
e22v10~v238.00×10-10.22e56v31~v380.990.01e90v52~v530.990.01
e23v11~v248.90×10-10.12e57v32~v380.990.01e91v53~v541.00×10-36.91
e24v12~v258.20×10-20.20e58v33~v380.990.01e92v53~v553.60×10-35.63
e25v13~v260.990.01e59v34~v380.990.01e93v53~v564.30×10-35.45
e26v14~v211.50×10-11.90e60v35~v368.56×10-22.46e94v53~v574.30×10-35.45
e27v14~v222.00×10-11.61e61v35~v390.990.01e95v54~v580.990.01
e28v15~v260.990.01e62v36~v390.990.01e96v55~v543.85×10-10.95
e29v16~v260.990.01e63v37~v390.990.01e97v55~v580.990.01
e30v17~v260.990.01e64v38~v407.00×10-10.36e98v56~v558.55×10-22.46
e31v18~v260.990.01e65v39~v403.00×10-11.20e99v56~v573.40×10-23.38
e32v19~v260.990.01e66v40~v410.990.01e100v56~v580.990.01
e33v20~v182.15×10-11.54e67v41~v420.990.01e101v57~v580.990.01
e34v20~v260.990.01e68v42~v437.00×10-22.66e102v58~v590.990.01

Fig.3

Diagram of shortest path for deepwater blowout caused by v1"

"

Initial eventShortest pathRisk entropyProbability
v1v1v6v8v23v28v43v49v56v5917.113.32×10-8
v2v2v9v23v28v43v49v56v5916.586.30×10-8
v3v3v13v28v38v43v49v56v5914.803.73×10-7
v4v4v19v28v38v43v49v56v5919.702.78×10-9
1 TamimN, LaboureurD M, MentzerR A, et al. A framework for developing leading indicators for offshore drillwell blowout incidents[J]. Process Safety & Environmental Protection, 2017, 106: 256-262.
2 孙宝江, 王志远, 公培斌, 等. 深水井控的七组分多相流动模型[J]. 石油学报, 2011, 32(6): 1042-1049.
SunB J, WangZ Y, GongP B, et al. Application of a seven-component multiphase flow model to deepwater well control[J]. Acta Petrolei Sinica, 2011, 32(6): 1042-1049.
3 褚道余. 深水井控工艺技术探讨[J]. 石油钻探技术, 2012, 40(1): 52-57.
ChuD Y. Well control technology in deepwater well[J]. Petroleum Drilling Techniques, 2012, 40(1): 52-57.
4 BhandariJ, AbbassiR, GaraniyaV, et al. Risk analysis of deepwater drilling operations using Bayesian network[J]. Journal of Loss Prevention in the Process Industries, 2015, 38: 11-23.
5 PraneshV, PalanichamyK, SaidatO, et al. Lack of dynamic leadership skills and human failure contribution analysis to manage risk in deep water horizon oil platform[J]. Safety Science, 2017, 92: 85-93.
6 SkogdalenJ E, UtneI B, VinnemJ E. Developing safety indicators for preventing offshore oil and gas deepwater drilling blowouts[J]. Safety Science, 2011, 49(8): 1187-1199.
7 LiW, ZhangL, LiangW. An accident causation analysis and taxonomy (ACAT) model of complex industrial system from both system safety and control theory perspectives[J]. Safety Science, 2017, 92: 94-103.
8 MengX, ChenG, ShiJ, et al. STAMP-based analysis of deepwater well control safety[J]. Journal of Loss Prevention in the Process Industries, 2018, 55(5): 41-52.
9 XueL, FanJ, RausandM, et al. A safety barrier-based accident model for offshore drilling blowouts[J]. Journal of Loss Prevention in the Process Industries, 2013, 26(1): 164-171.
10 VenkatasubramanianV. Systemic failures: challenges and opportunities in risk management in complex systems[J]. AIChE Journal, 2011, 57(1): 2-9.
11 DengY, LiQ, LuY. A research on subway physical vulnerability based on network theory and FMECA[J]. Safety Science, 2015, 80: 127-134.
12 刘文颖, 王佳明, 谢昶, 等. 基于脆性风险熵的复杂电网连锁故障脆性源辨识模型[J]. 中国电机工程学报, 2012, 32(31): 142-149.
LiuW Y, WangJ M, XieC, et al. Brittleness source identification model for cascading failure of complex power grid based on brittle risk entropy[J]. Proceedings of the CSEE, 2012, 32(31): 142-149.
13 蔡晔, 曹一家, 谭玉东, 等. 基于标准化结构熵的电网结构对连锁故障的影响[J]. 电工技术学报, 2015, 30(3): 36-43.
CaiY, CaoY J, TanY D, et al. Influences of power grid structure on cascading failure based on standard structure entropy[J]. Transactions of China Electrotechnical Society, 2015, 30(3): 36-43.
14 苟竞, 刘俊勇, 刘友波, 等. 基于能量熵测度的电力系统连锁故障风险辨识[J]. 电网技术, 2013, 37(10): 2754-2761.
GouJ, LiuJ Y, LiuY B, et al. Energy entropy measure based risk identification of power system cascading failures[J]. Power System Technology, 2013, 37(10): 2754-2761.
15 孟祥坤, 陈国明, 朱红卫. 海底管道泄漏风险演化复杂网络分析[J]. 中国安全生产科学技术, 2017, 13(4): 26-31.
MengX K, ChenG M, ZhuH W. Complex network analysis on risk evolution of submarine pipeline leakage[J]. Journal of Safety Science and Technology, 2017, 13(4): 26-31.
16 陈长坤, 纪道溪. 基于复杂网络的台风灾害演化系统风险分析与控制研究[J]. 灾害学, 2012, 27(1): 1-4.
ChenC K, JiD X. Risk analysis and control for the evolution disaster system of typhoon based on complex network[J]. Journal of Catastrophology, 2012, 27(1): 1-4.
17 付建民, 李成美, 东静波, 等. 数据不确定条件下安全仪表系统SIL等级验证方法研究[J]. 中国石油大学学报(自然科学版), 2017, 41(3): 129-135.
FuJ M, LiC M, DongJ B, et al. Study on method of SIL verification of safety instrumented systems under data uncertainty[J]. Journal of China University of Petroleum (Edition of Natural Science), 2017, 41(3): 129-135.
18 魏心泉, 王坚. 基于熵的火灾场景介观人群疏散模型[J]. 系统工程理论与实践, 2015, 35(10): 2473-2483.
WeiX Q, WangJ. A mesoscopic evacuation model based on multi-agent and entropy with leading behavior under fire conditions[J]. System Engineering- Theory & Practice, 2015, 35(10): 2473-2483.
19 胡瑾秋, 郭家洁. 基于尺度效应的过程安全事故概率估计[J]. 化工学报, 2017, 68(12): 4848-4856.
HuJ Q, GuoJ J. Accident probability estimation of process safety based on scale effect[J]. CIESC Journal, 2017, 68(12): 4848-4856.
20 胡瑾秋, 张来斌, 王安琪. 炼化装置故障链式效应定量安全预警方法[J]. 化工学报, 2016, 67(7): 3091-3100.
HuJ Q, ZhangL B, WangA Q. Quantitative safety early warning method of fault propagation for petrochemical plants[J]. CIESC Journal, 2016, 67(7): 3091-3100.
21 胡瑞敏, 吕海涛, 陈军. 基于风险熵和Neyman-Pearson准则的安防网络风险评估研究[J]. 自动化学报, 2014, 40(12): 2737-2746.
HuR M, LüH T, ChenJ. Risk evaluation model of security and protection network based on risk entropy and Neyman- Pearson criterion[J]. Acta Automatica Sinica, 2014, 40(12): 2737-2746.
22 LevesonN. A new accident model for engineering safer systems[J]. Safety Science, 2004, 42(4): 237-270.
23 LevesonN G, StephanopoulosG. A system-theoretic, control-inspired view and approach to process safety[J]. AIChE Journal, 2014, 60(1): 2-14.
24 AbimbolaM, KhanF, KhakzadN, et al. Safety and risk analysis of managed pressure drilling operation using Bayesian network[J]. Safety Science, 2015, 76(1): 133-144.
25 AbimbolaM, KhanF, KhakzadN. Dynamic safety risk analysis of offshore drilling[J]. Journal of Loss Prevention in the Process Industries, 2014, 30(3): 74-85.
26 KhakzadN, KhanF, AmyotteP. Quantitative risk analysis of offshore drilling operations: a Bayesian approach[J]. Safety Science, 2013, 57: 108-117.
27 ZareiE, AzadehA, KharzadN, et al. Dynamic safety assessment of natural gas stations using Bayesian network[J]. Journal of Hazardous Materials, 2017, 321: 830-840.
28 董海波, 顾学康. 基于模糊故障树方法的钻井平台井喷概率计算[J]. 中国造船, 2013, 54(1): 155-165.
DongH B, GuX K. Probability calculation of blowout of drilling platform based on fuzzy fault tree method[J]. Shipbuilding of China, 2013, 54(1): 155-165.
29 OnisawaT. An approach to human reliability in man-machine systems using error possibility[J]. Fuzzy Sets and Systems, 1988, 27(2): 87-103.
30 LavasaniS M, ZendeganiA, CelikM. An extension to fuzzy fault tree analysis (FFTA) application in petrochemical process industry[J]. Process Safety & Environmental Protection, 2015, 93(2): 75-88.
[1] Xia XIONG, Zuohua LIU, Deyin GU, Facheng QIU, Liang WANG, Changyuan TAO, Yundong WANG. Chaotic mixing process of fly ash in acid leaching tank intensified by rigid-flexible impeller [J]. CIESC Journal, 2019, 70(5): 1693-1701.
[2] Junmiao TANG, Haizhen YU, Xuhua SHI, Chudong TONG. Dynamic monitoring of chemical processes based on latent variable auto-regressive algorithm [J]. CIESC Journal, 2019, 70(3): 987-994.
[3] Kaixiang PENG, Chuanfang ZHANG, Liang MA, Jie DONG, Ruihua JIAO, Peng TANG. System-levels-based holographic fault diagnosis for complex industrial processes [J]. CIESC Journal, 2019, 70(2): 590-598.
[4] Junren BAI, Jun YI, Qian LI, Ling WU, Xuemei CHEN. Multi-objective optimization of QPSO for thereaction-regeneration process [J]. CIESC Journal, 2019, 70(2): 750-756.
[5] Jian TANG, Junfei QIAO. Dioxin emission concentration soft measuring approach of municipal solid waste incineration based on selective ensemble kernel learning algorithm [J]. CIESC Journal, 2019, 70(2): 696-706.
[6] Haisheng CHEN, Tengfei WANG, Kejin HUANG, Yang YUAN, Xing QIAN, Liang ZHANG. Decentralized control system designs for reactive distillation columns with external recycle [J]. CIESC Journal, 2019, 70(2): 440-449.
[7] Shan DOU, Guangyu ZHANG, Zhihua XIONG, Huangang WANG. Danger situation awareness of chemical industry park based on multiple source data fusion [J]. CIESC Journal, 2019, 70(2): 460-466.
[8] Bo DAI, Zeyu ZHOU, Yan ZHANG, Shuangshuang LIN, Xuejun LIU. Design of safe distance monitoring system for hazardous chemicals storage stack [J]. CIESC Journal, 2019, 70(2): 707-715.
[9] Peng MU, Xiangbai GU, Qunxiong ZHU. Modeling and optimization of ethylene cracking feedstock scheduling based on P-graph [J]. CIESC Journal, 2019, 70(2): 556-563.
[10] Jiong DING, Qi CHEN, Qiyue XU, Suijun YANG, Shuliang YE. ARC thermal inertia correction method based on C80 data merging [J]. CIESC Journal, 2019, 70(1): 417-424.
[11] Yi ZHU, Hao WANG, Liping CHEN, Zichao GUO, Zhongqi HE, Wanghua CHEN. Calculate time to maximum rate under adiabatic condition by numerical calculation method [J]. CIESC Journal, 2019, 70(1): 379-387.
[12] XIONG Xiaojun, HE Ting, LIN Wensheng. Process of BOG treating in LNG receiving station with normal temperature compressor [J]. CIESC Journal, 2018, 69(S2): 425-430.
[13] XU Wenxing, LIANG Jingjing, BIAN Weibin, DAI Bo, TAO Guanliang, LIU Cai. Spontaneous path selection for hazardous chemical transportation based on quasi-decision tree pruning [J]. CIESC Journal, 2018, 69(S2): 324-329.
[14] ZHU Linlin, HUANGFU Lixia, GUO Kaihua. Current status and recent progress of LNG navigation safety standards [J]. CIESC Journal, 2018, 69(S2): 1-8.
[15] ZHOU Xinzhi, SHAO Lun, CUI Ke, YANG Yang, ZHOU Yu, ZHANG Ruobin. Research on multi-stage power control system of lignite microwave drying production line [J]. CIESC Journal, 2018, 69(S2): 274-282.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LING Lixia, ZHANG Riguang, WANG Baojun, XIE Kechang. Pyrolysis Mechanisms of Quinoline and Isoquinoline with Density Functional Theory[J]. , 2009, 17(5): 805 -813 .
[2] LEI Zhigang, LONG Aibin, JIA Meiru, LIU Xueyi. Experimental and Kinetic Study of Selective Catalytic Reduction of NO with NH3 over CuO/Al2O3/Cordierite Catalyst[J]. , 2010, 18(5): 721 -729 .
[3] SU Haifeng, LIU Huaikun, WANG Fan, LÜXiaoyan, WEN Yanxuan. Kinetics of Reductive Leaching of Low-grade Pyrolusite with Molasses Alcohol Wastewater in H2SO4[J]. , 2010, 18(5): 730 -735 .
[4] WANG Jianlin, XUE Yaoyu, YU Tao, ZHAO Liqiang. Run-to-run Optimization for Fed-batch Fermentation Process with Swarm Energy Conservation Particle Swarm Optimization Algorithm[J]. , 2010, 18(5): 787 -794 .
[5] SUN Fubao, MAO Zhonggui, ZHANG Jianhua, ZHANG Hongjian, TANG Lei, ZHANG Chengming, ZHANG Jing, ZHAI Fangfang. Water-recycled Cassava Bioethanol Production Integrated with Two-stage UASB Treatment[J]. , 2010, 18(5): 837 -842 .
[6] Gao Ruichang, Song Baodong and Yuan Xiaojing( Chemical Engineering Research Center, Tianjin University, Tianjin 300072). LIQUID FLOW DISTRIBUTION IN GAS - LIQUID COUNTER - CONTACTING PACKED COLUMN[J]. , 1999, 50(1): 94 -100 .
[7] Su Yaxin, Luo Zhongyang and Cen Kefa( Institute of Thermal Power Engineering , Zhejiang University , Hangzhou 310027). A STUDY ON THE FINS OF HEAT EXCHANGERS FROM OPTIMIZATION OF ENTROPY GENERATION[J]. , 1999, 50(1): 118 -124 .
[8] Luo Xiaoping(Department of Industrial Equipment and Control Engineering , South China University of Technology, Guangzhou 510641)Deng Xianhe and Deng Songjiu( Research Institute of Chemical Engineering, South China University of Technology, Guangzhou 5106. RESEARCH ON FLOW RESISTANCE OF RING SUPPORT HEAT EXCHANGER WITH LONGITUDINAL FLUID FLOW ON SHELL SIDE[J]. , 1999, 50(1): 130 -135 .
[9] Jin Wenzheng , Gao Guangtu , Qu Yixin and Wang Wenchuan ( College of Chemical Engineering, Beijing Univercity of Chemical Technology, Beijing 100029). MONTE CARLO SIMULATION OF HENRY CONSTANT OF METHANE OR BENZENE IN INFINITE DILUTE AQUEOUS SOLUTIONS[J]. , 1999, 50(2): 174 -184 .
[10]

LI Qingzhao;ZHAO Changsui;CHEN Xiaoping;WU Weifang;LI Yingjie

.

Combustion of pulverized coal in O2/CO2 mixtures and its pore structure development

[J]. , 2008, 59(11): 2891 -2897 .