石油钻探取心工程风险的量化分析

Quantitative Analysis of Oil Coring Project Risk

取心工程存在很大的风险性和不确定性,做好风险分析和预测可以有效规避风险。结合取心工程特性对影响取心工程的风险因素和风险事件进行有效地识别、分析,建立了风险评估指标体系和风险评估的数学模型。利用蒙特卡洛模拟与层次分析法(AHP)相结合的方法对取心风险进行分析计算,综合考虑了多种不确定性风险因素进行组合抽样,列出了蒙特卡洛模拟的计算流程,得到具有可靠度的量化的风险概率分布。同时,根据模拟结果可以得到各指标的不同风险指数,量化了每个风险因素对工程风险的贡献,对计算结果进行了概率统计分析,找出敏感性较大的风险因素或事件。该风险分析方法对各个不确定性参数进行了全面抽样,涵盖了风险发生的各种可能性组合,输入参数的不确定性得以传递并在计算结果中得到反映,避免了信息的丢失,提高了计算结果的准确度。同时,针对不同组合的风险值计算出了其出现概率的大小,更有利于之后的风险管理,提高取心作业的成功率和岩心收获率。通过实例分析,该方法具有较好的评价效果,预测结果合理可靠,预测取心工程风险是可行的、合理的。

Risk prediction and analysis is essential for risk control because of the uncertainty of coring project.Combined with the characteristics of coring project,the risk factors and events were recognized and analyzed effectively;the risk assessment system and mathematic model were established.Using Monte Carlo simulation method as well as AHP,the risk of coring project could be analyzed and calculated.With the consideration of various uncertainly risk factors,combination sampling method was applied to get a quantized risk probability distribution.Meanwhile,according to the simulation results,the risk index of each factor was available which quantized its corresponding contribution to the risk of the project.Then,the risk factors or events with high index could be found based on the analysis of probability,which is helpful in risk management of coring project and promoted the success ratio and recovered the rate of coring.This risk analysis method covered a variety of possibilities and delivered the uncertainties of parameters;its counting process reduced the loss of information,then the result is more accurate.Meanwhile,the sensitivity analysis could be carried on the base of data samples for deeper insight.On one hand,this study established a new evaluation methodology and supplied decision basis for the risk analysis and management of hydrate coring project;on the other hand,it made the foundational research work for subsequent more perfect hydrate coring project evaluation.Research showed that this method can achieve a good evaluation effectiveness and its prediction results are reasonable and practicable.