摘要
裂缝性漏失给钻井安全及成本控制带来严重挑战。由于发生该漏失类型的地层不是连续性地层,传统分析方法难以满足工程准确预测的要求。基于地震几何属性与工程参数联合建立了裂缝性漏失风险评价方法。该方法以地震几何属性作为漏失通道与储集空间因素,以工程参数及地层原始孔隙压力作为动力因素,建立符合井下漏失发生过程的平衡方程。基于沿井轨迹的地震几何属性、连通指数、井眼液柱与地层压差以及钻井液流变参数等信息,建立区域内已钻井数据集,利用建立的数据集进行人工神经网络训练,建立起动力、通道、储集空间与漏失规模之间的复杂函数关系,从而实现基于待钻井相应基础参数对裂缝性漏失风险的量化预测,并对钻井液使用密度提供指导。利用所建立的方法在渤中凹陷进行了实际应用,对研究区内5口开发评价井7处潜在漏失风险点进行了分析,预测的漏失点位及规模与实际工程表现完全相符,预测漏失速率精度大于85%,满足工程需要,且预测方法可为工程优化设计与实践提供可靠依据。
Lost circulation induced by fracture generally brings serious challenges to drilling safety and cost control.Traditional analysis methods are no longer applicable due to the discontinuity of the formation.A method of risk assessment of lost circulation had established by the combination of seismic geometric attributes and engineering parameters.The seismic geometric attributes are used as the leakage channel and reservoir space factors,and the engineering parameters and the pore pressure of the formation are used as the dynamic factors to establish the balance equation.A drilled data set in the research block can be established based on the seismic geometric attribute,connectivity index,wellbore pressure difference along the well trajectory and rheological parameters of the drilling mud.The data set is used for artificial neural network training to establish the complex functional relationship between power,channel,reservoir space and lost rate.With the trained net and basic parameters of the well to be drilled,the risk of lost circulation induced by fracture can be estimated accurately,and the density of drilling mud could be optimized too.The method had been applied in practice.5 development evaluation wells of Bozhong depression had been analyzed with the evaluation method,7 potential risk points of lost circulation had been detected.The location and leakage scale predicted by the method got a high accuracy compared to the actual situation,and the accuracy of predicted rate got a accuracy more than 85%,which can meet the requirements of engineering.The prediction method can provide a reliable basis for engineering optimization and practice.
作者
周长所
杨进
谢仁军
袁俊亮
ZHOU Changsuo;YANG Jin;XIE Renjun;YUAN Junliang(China University of Petroleum,Beijing 102249,China;CNOOC Research Institute Ltd.,Beijing 100028,China)
出处
《钻采工艺》
CAS
北大核心
2023年第6期14-20,共7页
Drilling & Production Technology
基金
国家科技重大专项“渤海油田高效开发示范工程”(编号:2016ZX05058-002)
中海油十四五重大科技项目“海上低渗及潜山油气田有效开发技术”(编号:KJGG2022-0700)。
关键词
裂缝
漏失
地震几何属性
风险评价
连通指数
fracture
lost circulation
seismic geometric attributes
risk assessment
connectivity index
