Risk assessment of drilling site operation based on the structural equation and Monte Carlo Method

被引:0
作者
Zhao C. [1 ]
Yin H. [1 ]
Wang B. [2 ]
Fan X. [3 ]
Wu H. [4 ]
机构
[1] School of Science, Southwest Petroleum University, Chengdu, 610500, Sichuan
[2] School of Computer Science, Southwest Petroleum University, Chengdu, 610500, Sichuan
[3] State Key Laboratory of Oil & Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, 610500, Sichuan
[4] Exploration Division of PertroChina Southwest Oil & Gasfield Company, Chengdu, 610041, Sichuan
关键词
Drilling site operation; Monte Carlo Method; Potential variable; Risk assessment; Risk degree; Risk factor; Risk level; Structural equation model;
D O I
10.3787/j.issn.1000-0976.2019.02.012
中图分类号
学科分类号
摘要
In the risk assessment of drilling site operation, the relevance between monitored data and actual on-site operation is not strong. In view of this, the structural equation model (SEM) integrated with the Monte Carlo Method (MC) was applied as a new methodology to evaluate the on-drilling-site operation risks. First, risk factors were constituted and their relevance and weights were obtained by the SEM, then the key risk factors were determined. Then, the analog values of potential data were got based upon data distribution. Finally, risk degrees were obtained in combination with SEM risk consequence weights and accident consequence degree risk degrees identified from the on-site operation, and were applied in the judgment of risk levels. This methodology was applied in a case study of a well in a gas field, Sichuan Basin. The following findings were achieved. (1) Compared with the other methods, this new methodology determined the relevance of various factors and disadvantages of each factor weight, providing a new approach for the on-site operation risk assessment. (2) The factors of operation behaviors and environmental considerat ion had little impact on the drilling risks but the man - agement factor was the most influential factor. (3) The two key factors of non-conformity and general equipment defects were determined and their correlation coefficient was 0.57. (4) A series of required sample data was obtained according to the distribution characteristic of each risk variable and probability distribution of drilling risks was thus achieved, which made the assessment results more consistent with the actual situation. In conclusion, this new methodology provides not only a better way to apply the monitored on-site data to evaluate the drilling risk levels, but a reference for risk management i n drilling sites. © 2019, Natural Gas Industry Journal Agency. All right reserved.
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页码:84 / 93
页数:9
相关论文
共 24 条
  • [1] Wang B., Yang X., Zhao C., Xiao B., Drilling site risk assessment based on Bayesian Network, Journal of Southwest Petroleum University (Science & Technology Edition), 37, 2, pp. 131-137, (2015)
  • [2] Yang C., Development and application of risk-assessment system for drilling operations, Petroleum Drilling Techniques, 45, 5, pp. 60-67, (2017)
  • [3] Li Q., Yu L., Liu Z., Gao X., Integrated drilling risk evaluation method and model establishment, Natural Gas Industry, 28, 5, pp. 120-122, (2008)
  • [4] Li H., Han S., Risk assessment of drilling operation base on HAZOP, Journal of Chongqing University of Science and Technology (Natural Sciences Edition), 15, pp. 17-20, (2013)
  • [5] Li H., The building of evaluation method and mathematical mode for drilling risk, Petroleum Drilling Techniques, 31, 6, pp. 97-101, (2003)
  • [6] Jin Y., Wu Q., Hu M., Quantitative risk assessment model for drilling well control based on triangular fuzzy number, Science Technology and Engineering, 14, 35, pp. 186-189, (2014)
  • [7] Qian Z., Pan W., Risk evaluation of international petroleum engineering project based on SEM, Oil-Gasfield Surface Engineering, 32, 10, pp. 13-14, (2013)
  • [8] Li J., Li K., Wang B., An evaluation model of drilling safety based on combined rough set and neural network, Journal of Southwest Petroleum University (Science & Technology Edition), 39, 5, pp. 120-128, (2017)
  • [9] Lin S., Theory of Structural Equation Model and Application of AMOS, (2008)
  • [10] Golob T.F., Structural equation modeling for travel behavior research, Transportation Research Part B, 37, 1, pp. 1-25, (2001)