基于动态更新的钻井参数实时优化方法  

Real-time Optimization Method of Drilling Parameters Based on Dynamic Update

作　　者：宋先知[1,2] 张瑞 祝兆鹏 李根生[1,2,4] 效世杰 潘涛 颜志 SONG Xianzhi;ZHANG Rui;ZHU Zhaopeng;LI Gensheng;XIAO Shijie;PAN Tao;YAN Zhi(China University of Petroleum(Beijing),Beijing 102249,China;State Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing 102249,China;Engineering Technology Research Institute,CNPC Great Wall Drilling Company,Panjin,Liaoning 124010,China)

机构地区：[1]中国石油大学(北京) [2]油气资源与工程全国重点实验室·中国石油大学(北京) [3]中国石油长城钻探工程有限公司工程技术研究院 [4]中国工程院

出　　处：《钻采工艺》2025年第1期21-28,共8页Drilling & Production Technology

基　　金：国家杰出青年科学基金项目“油气井流体力学与工程”(编号:52125401);国家重点研发计划项目“变革性技术关键科学问题”(编号:2019YFA0708300)联合资助。

摘　　要：当前,钻井参数的优化主要依赖于钻井专家经验或者传统预测与优化模型,这些方法在面对复杂波动环境下的钻井施工精细指导具有较大的局限性,尤其是随着人工智能在钻井系统中的发展,对智能模型的稳定性、实时性和参数优化随机性方面具有限制。针对上述问题,文章提出了一种动态更新下的钻井参数实时优化方法,并研发了钻井参数智能优化系统。该方法考虑了钻井参数与多个响应参数的动态关联,建立了扭矩智能预测模型、机械钻速智能预测模型和机械比能计算模型;其次,基于现场实时数据流和模型实时更新机制,实现了扭矩、机械钻速和机械比能的动态智能表征;最后,基于平稳提速策略设定提速目标,利用改进的鲸鱼优化算法,综合考虑参数波动和能量损耗进行最优方案决策,实现钻井参数实时优化和平稳有效提速。现场应用表明:机械钻速实时预测具有较高精度,有效满足钻井现场实时需求;且平稳提速策略下的优化效果可观,关键层段平均提速可达30%,机械比能降低15%;此外,通过量评价优化后的钻井参数,该提速策略下的参数均值方差波动较小,可减小井下复杂工况发生概率,更符合现场钻井工艺,具有良好应用前景。In the past,the optimization of drilling parameters mainly relied on the experience of drilling experts or traditional prediction and optimization models,which had great limitations on the fine guidance of wellbore construction in complex downhole environments,especially with the development of artificial intelligence in drilling systems,which limited the stability,real-time and randomness of parameter optimization of intelligent models.To address these challenges,this paper proposes a real-time optimization method for drilling parameters under dynamic update and develops an intelligent optimization system for drilling parameters.The method considers the dynamic correlations between drilling parameters and multiple response parameters,establishing intelligent prediction models for torque,rate of penetration(ROP),and mechanical specific energy(MSE)calculation.Next,based on the real-time data flow in the field and the real-time update mechanism of the model,the dynamic intelligent characterization of torque,ROP and MSE is realized.Finally,the ROP optimization is set based on the steady ROP optimization strategy,and the improved whale optimization algorithm is used to make the optimal plan decision by considering the parameter fluctuation and energy loss,so as to achieve the real-time optimization of drilling parameters and steady and effective ROP optimization.Field applications show that the real-time prediction of ROP has high accuracy,which effectively meets the requirements of real-time drilling scenarios;and the optimization effect under the steady ROP optimization strategy is considerable,with an average ROP of up to 30%in key sections and a reduction of 15%in MES.In addition,through the quantitative evaluation of the optimized drilling parameters,it is found that the fluctuation of mean value variance of the parameters under the ROP optimization strategy is smaller,which can reduce the probability of occurrence of complicated downhole conditions,and is more in line with the on-site drilling process,and has a

关 键 词：机械钻速 动态更新 智能表征 参数优化 最优决策 

分 类 号：TE24[石油与天然气工程—油气井工程]