摘要
油气井生产过程中,持续、精确地透明化监测有关生产质量的各项参数,对于确保作业成功率及后续井筒的完整性维护具有重要的作用。本文提出了一种基于数字孪生和深度学习的油气井生产质量透明化管控方法,构建了高度精细化的数字孪生三维模型,设计了孪生模型生产质量数据交互机制以及油气井生产过程实时响应与动作映射机制。基于映射的生产质量相关数据,运用PSO-CNN-GRU-Attention算法构建油气井生产质量预测模型,通过CNN网络提取油气井生产质量的关键特征要素,基于GRU-Attention挖掘关键特征要素之间的关联关系,运用PSO对网络参数进行寻优。实验结果表明,油气井数字孪生透明化监测与管控平台可以实现生产参数和质量的有效监测与预测,所提出的油气井生产质量透明化管控方法具有显著的优越性。
During the production process of oil and gas wells,continuous,accurate and transparent monitoring of various parameters related to production quality plays an important role in ensuring the success rate of operations and the subsequent maintenance of wellbore integrity.Hence,a method for transparent control of oil and gas well production quality based on digital twins and deep learning is proposed.A high-fidelity three-dimensional digital twin model is established to achieve millimeter-level geometric reconstruction of wellbore structures,while a multi-source data dynamic interaction mechanism and a real-time response-action mapping collaborative mechanism are developed to bridge physical-virtual space interactions.Leveraging the mapped production quality data,a novel hybrid prediction model based on PSO-CNN-GRU-Attention is constructed through multi-algorithm fusion.Convolutional Neural Networks(CNN)extract spatial-temporal features of production quality parameters,Gated Recurrent Units(GRU)with attention mechanisms capture long-term dependency relationships among key features,and Particle Swarm Optimization(PSO)dynamically adjusts hyperparameters for optimal model configuration.The experimental results show that the oil and gas well digital twin transparent monitoring and control platform can realize the effective monitoring and prediction of production parameters and quality.The proposed oil and gas well production quality transparent control method has significant advantages.
作者
冉瑞平
孙长浩
刘长春
王立平
黄凯
穆泽宇
RAN Ruiping;SUN Changhao;LIU Changchun;WANG Liping;HUANG Kai;MU Zeyu(Shenzhen Operations Company of Oilfield Chemistry Division of China Oilfield Services Co.,Ltd.,Shenzhen,Guangdong 518000,China;College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing,Jiangsu 210016,China;Huabei Well Testing Branch,China National Logging Corporation,Langfang,Hebei 065007,China)
出处
《油气井测试》
2025年第1期55-61,共7页
Well Testing
基金
中国航天科工集团基础科研项目“数字孪生仿真与开发研究”(SCA24003)。
作者简介
第一作者:冉瑞平,男,1978年出生,工程师,专科,2001年毕业于重庆石油高等专科学校石油地质专业,主要从事油气田油气井及油气井工具、附件方面的研究。电话:18217216932,Email:18217216932@163.com。通信地址:深圳市南山区中海油田服务股份有限公司油田化学事业部深圳作业公司,邮政编码:518000;通讯作者:刘长春,Email:liuchangchun@nuaa.edu.cn。
