针对柱塞气举井产量与井筒积液影响因素复杂、具有动态变化性等难点,开展了数据驱动的气井井筒积液与产量预测模型研究及应用。建立了瞬态多相流模拟器OLGA柱塞气举井生产动态仿真模型,获取不同气藏—井筒—排采参数组合方案的气井生产动态预测仿真样本;利用Spearman秩相关系数法分析了气举井地层因素、井筒参数、井口动态、排采工作制度对气井产气量、产液量、井筒积液量的关联关系及程度;应用卷积神经网络(Convolutional Neural Network,CNN)进行模型训练,建立了柱塞气举井的产气量、产液量、积液量的CNN预测模型;并在长庆神11站的气井群进行了部署验证。研究及现场应用表明:OLGA柱塞气举井生产动态仿真模型可用于柱塞气举井的产气量、产液量、积液量的仿真;建立的CNN预测模型预测精度较高,可解释性强,可以作为柱塞气举井排采工作制度优化的技术基础。

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.