深部地层智能压井多解性分析与优化策略  

Multi-Solution Analysis and Optimization Strategy for Intelligent Well Killing in Deep Formation

作　　者：王志远[1] 梁沛智 陈科杉 仉志 张剑波 孙宝江[1] WANG Zhiyuan;LIANG Peizhi;CHEN Keshan;ZHANG Zhi;ZHANG Jianbo;SUN Baojiang(School of Petroleum Engineering,China University of Petroleum(East China),Qingdao,Shandong,266580,China)

机构地区：[1]中国石油大学(华东)石油工程学院,山东青岛266580

出　　处：《石油钻探技术》2024年第2期136-145,共10页Petroleum Drilling Techniques

基　　金：国家自然科学基金基础科学中心项目“超深特深层油气钻采流动调控”(编号:52288101);山东省重点研发计划项目“深水复杂钻井多相流动模拟关键技术与监测装备”(编号:2022CXGC020407);国家自然科学基金联合基金重点项目“深水控压钻完井地层–井筒多场耦合机理与压力调控”(编号:U21B2069)联合资助。

摘　　要：开发深部地层油气资源时普遍存在地质条件复杂、钻井周期长和井筒压力控制困难等问题,采用智能压井方法结合多源实时信息反馈,可实现井筒内气液分布状态和压力变化规律的实时预测与更新,但不同修正系数组合可能得到相同的压力计算结果,导致模型存在多解性难题。为此,分析了不同历史时间节点解空间形态的演变规律,揭示了模型多解性的本质源于少量数据约束下模型训练的不完善性;并对应建立了基于实时信息序列的模型全局训练优化方法及动态随机种群训练优化方法,测试了其对于模型全局最优解的搜索能力及适用条件。测试结果表明,全局训练优化方法在压井初期能够实现精准调控,但计算耗时较长;而动态随机种群训练优化方法在压井初期与预期值略有差异,但计算耗时较少。根据可用计算资源情况选择合适的训练优化方法,可实现多源实时数据约束下模型关于井筒气液流动规律的深度学习。Complex geological conditions,long drilling cycles,and difficult wellbore pressure control are common problems during oil and gas resource development in deep formations.Intelligent well killing methods,combined with multi-source real-time information feedback,can predict and update gas-liquid distribution and pressure change law in the wellbore in real time.However,the combination of different correction coefficients may derive the same pressure calculation result,which leads to the problem of multiple solutions of the model.By analyzing the evolution law of the spatial morphology of the solution at different historical time nodes,it was revealed that the essence of the multi-solution of the model came from the imperfection of the model training under the constraint of sparse data.The global model training optimization method based on real-time information sequence and the dynamic random population training optimization method were established correspondingly,and their search ability and applicable conditions for the global optimal solutions of the model were tested.The results show that the global training optimization method can achieve accurate control in the early stages of well killing,but the calculation time is long.The dynamic random population training optimization method is slightly different from the expected value in the early stage of well killing,but the calculation is rapid.According to available computing resources,a suitable training optimization method can be selected to achieve deep learning of the gas-liquid flow law in the wellbore under the constraints of multi-source real-time data.

关 键 词：深部地层 智能压井方法 模型多解性 多源实时数据约束 训练优化方法 

分 类 号：TE21[石油与天然气工程—油气井工程] TE283