天然气与石油2025，Vol.43Issue(1)：9-19,11.DOI:10.3969/j.issn.1006-5539.2025.01.002
数据驱动的气井井筒积液与产量预测模型研究及应用
Study and application of a data-driven prediction model for gas well liquid loading and production
摘要
Abstract
To address the challenges posed by the complex and dynamic factors affecting the production and wellbore liquid loading in plunger gas-lift wells,a data-driven prediction model for liquid drainage and gas production in these water-breakthrough wells was developed.A dynamic simulation model for plunger gas-lift wells was established using the transient multiphase flow simulator,generating simulation models for various combinations of reservoir,wellbore,and production parameters.The Spearman rank correlation coefficient method was used to analyze the relationship and degree of association between reservoir formation factors,wellbore parameters,wellhead dynamics,startup/shutdown procedures and gas production rate,liquid production rate,and wellbore liquid loading.A convolutional neural network(CNN)was used for model training to create predictive models for gas production,liquid production,and liquid loading in plunger gas-lift wells.The model was deployed and validated in the gas well clusters at the Shen 11 station of Changqing Oilfield.The research and field application indicate that the OLGA-based simulation model for plunger gas-lift wells can effectively simulate transient gas production,liquid production,and liquid loading.The CNN-based proxy model demonstrated high prediction accuracy and is highly interpretable,serving as a technical foundation for optimizing the production and liquid drainage procedure of plunger gas-lift wells.关键词
柱塞气举/OLGA仿真/井筒积液/产量预测/卷积神经网络Key words
Plunger gas-lift/OLGA simulation/Wellbore liquid loading/Production prediction/Convolutional Neural Network引用本文复制引用
张昀,陈彦润,陈晓刚,赵峥延,王哲,白红升,矫欣雨,檀朝东..数据驱动的气井井筒积液与产量预测模型研究及应用[J].天然气与石油,2025,43(1):9-19,11.基金项目
国家重大科技项目子课题"阜康西部四工河煤层气高效开发先导实验有序排采模式及无杆举升设备智能化实验项目"(2016ZX05043-004) （2016ZX05043-004）
中国石油长庆油田分公司"揭榜挂帅"科研项目"致密气田智能生产与节能控制技术研究"(2023DJ0806) （2023DJ0806）
