论文详情
复杂地层钻井风险程度判别方法研究
石油钻采工艺
2015年 37卷 第3期
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Title
Research on method of discriminating drilling risk degree in complex formations
Authors
GUAN Zhichuan
ZHAO Tingfeng
SHENG Yanan
WEI Kai
单位
中国石油大学华东石油工程学院,山东青岛 266580
中国石油渤海钻探工程有限公司,天津 300280
Organization
Petroleum Engineering College, China University of Petroleum, Qingdao 266580, China
Bohai Drilling Engineering Co. Ltd., CNPC, Tianjin 300280, China
摘要
由于地质构造复杂和地质特征参数难以预测,复杂地层钻井具有高风险与高投入的特点。因此,井下工程风险程度的合理判别,对于钻井工程方案的风险决策具有重要的意义。针对这一问题,建立了基于风险概率和模糊理论的风险程度判别模型。首先,对常见井下工程风险进行风险概率计算;然后,在此基础上结合井史资料中的事故记录,利用模糊数学理论构造不同程度区间的隶属度函数;最后,根据隶属度进行区间划分,从而实现工程风险的程度判别。应用该方法对西部某地区的钻井资料进行实例分析,结果表明,该方法的判别结果与工程实际情况基本吻合,满足工程实际需要,能够为钻井工程方案设计提供风险判断依据。
Abstract
Due to the fact that the geological structures are complex and the geological feature parameters are hard to predict, the drilling operation in complex formations is characterized by high risk and high investment. Therefore, reasonable discrimination of downhole engineering risk degrees is of great significance to risk decision for drilling engineering program. For this problem, a model is built for discriminating the risk degree based on risk probability and fuzzy theory. Firstly, calculate the risk probability of common downhole engineering risks. Then, based on which and in conjunction with the incident records in the well history data use fuzzy mathematical theory to construct the subordinating degree function between various degree intervals. Finally, delineate the intervals according to subordinating degree, hence realizing the discrimination of engineering risk degrees. After outlining its discriminating principle, then the drilling data from somewhere in the west is taken as an example. Example analysis shows that the discriminating result of this method almost agrees with the actual engineering and satisfies the real need of drilling engineering and can provide basis for risk discrimination for the design of drilling engineering program.
关键词:
钻井风险;
概率;
隶属度函数;
模糊判别;
Keywords:
drilling risk;
probability;
subordinating degree function;
fuzzy discrimination;
DOI
10.13639/j.odpt.2015.03.003