含可信度地层破裂压力的钻前预测方法

2017年 24卷 第01期
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Pre-drilling prediction of fracture pressure with credibility
 罗黎敏1 胜亚楠2 刘晓坡1 管志川2 刘书杰3 冯桓榰3
中国海洋石油国际有限公司,北京 100027 中国石油大学(华东)石油工程学院,山东 青岛 266580 中海油研究总院,北京 100028)
CNOOC International Ltd., Beijing 100027, China College of Petroleum Engineering, China University of Petroleum, Qingdao 266580, China CNOOC Research Institute, Beijing 100028, China)
地层破裂压力是钻井工程设计的基础数据,由于海上深井苛刻地层环境的复杂性、基础资料的不完备性以及预测模型的适用性等问题,地层破裂压力的解释结果存在一定的不确定性。文中首先分析了地层破裂压力预测模型中地质力学参数的测井解释方法,然后以相似构造井同一层组内的地质力学参数的测井解释结果为样本,构建样本库,并基于正态信息扩散原理,得到了地质力学参数的概率分布函数,再将地质力学参数的概率分布函数代入地层破裂压力计算模型中,基于Monte Carlo模拟得到待钻井任意井深处的地层破裂压力概率分布,最终建立了具有置信度的地层破裂压力区间。实例分析表明,计算得到的含可信度地层破裂压力更切合钻井工程实际,文中建立的方法对于分析海上深井苛刻地层破裂压力的不确定性具有一定参考价值。
The formation fracture pressure is the basic data of drilling engineering design. Due to the complexity of offshore petroleum geology, incomplete data, the adaptability of the mathematical model and other issues, the true value of formation fracture pressure cannot be obtained. There are some uncertainties in the interpretation of results. In this paper, we firstly analyzed the logging interpretation methods of the calculation parameters in the prediction model of formation fracture pressure. And the sample library was constructed by using the logging interpretation results of the calculated parameters in the same layer of the adjacent wells in the area. Then probability distribution of parameters was built based on the method of normal information diffusion. Finally parameters with probability distribution were inducted into the model and the probability distribution and cumulative probability distribution of formation fracture pressure were built up using the Monte Carlo simulation. An example analysis shows that the results of the formation fracture pressure with credibility are more practical. This method has certain references to the analysis of the uncertainty of formation fracture pressure in complex deep offshore well formation.
地层破裂压力; 不确定性; 概率分布; 正态信息扩散原理; Monte Carlo模拟;
formation fracture pressure; uncertainty; probability distribution; normal information diffusion; Monte Carlo simulation;
10.6056/dkyqt201701025