基于LSTM-WGAN模型的柱塞-泡排复合排采系统预测控制方法  

LSTM-WGAN-based model predictive control method of plunger-foam compound drainage system

作　　者：赖欣[1] 钱中程 杨肖 刘永辉 叶长青[3] 王强 Lai Xin;Qian Zhongcheng;Yang Xiao;Liu Yonghui;Ye Changqing;Wang Qiang(School of Mechanical Engineering,Southwest Petroleum University,Chengdu 610500,China;Petroleum Engineering School,Southwest Petroleum University,Chengdu 610500,China;Engineering and Technology Research Institute,PetroChina Southwest Oil&Gas Field Company,Chengdu 610017,China)

机构地区：[1]西南石油大学机电工程学院,成都610500 [2]西南石油大学石油与天然气工程学院,成都610500 [3]中国石油西南油气田分公司工程技术研究院,成都610017

出　　处：《电子测量与仪器学报》2025年第2期11-20,共10页Journal of Electronic Measurement and Instrumentation

基　　金：中国石油-西南石油大学创新联合体科技合作项目(2020CX020203);四川省自然科学基金面上项目(2025ZNSFSC0428)资助。

摘　　要：高效的生产过程和智能化管理是天然气井可持续发展的关键,目前实际生产中页岩气开采仍然面临着井底积液造成气井产能下降的问题。为提高天然气井的产能和排水效率,充分利用泡沫排水采气和柱塞气举的优点,设计了一套“双元合一”的柱塞-泡排复合排采装置,提出了一种新颖的基于长短期记忆网络(LSTM)和Wasserstein生成对抗网络(WGAN)的复合排采LSTM-WGAN预测控制方法。利用基于密度的抗噪聚类算法(DBSCAN)对数据进行预处理,避免异常数据对模型预测的影响。通过生成器和判别器相互对抗并更新各自梯度方向的权重,不断优化使油套压差、水气比预测值逼近真值,从而准确预测下一时刻的油套压差和水气比。通过柱塞-泡排复合排采智能管理系统,实施预测的柱塞泡排投放策略。实验结果表明,LSTMWGAN模型的误差最小,与LSTM模型相比,LSTM-WGAN模型的油套压差和水气比预测结果的均方根误差、均方误差、平均绝对误差分别降低了2.64%、5.13%、11.75%和8.81%、8.07%、6.60%。LSTM-WGAN预测模型可以准确地预测油套压差和水气比,指导柱塞-泡排复合排采系统发出正确的投放泡排球和柱塞指令,实现了泡排-柱塞的全智能化投放。Efficient production process and smart management are key to the sustainable development of natural gas wells.At present,shale gas mining in actual production still faces the problem of liquid loading in wellbores causing the gas well production capacity to decrease.In this paper,a“dual-element integration”plunger-foam compound drainage device is designed to improve the productivity and drainage efficiency of gas wells,taking full advantages of both shale gas foam drainage and plunger drainage gas recovery systems.A novel LSTM-WGAN predictive control method based on Long short-term memory networks(LSTM)and Wasserstein generative adversarial networks(WGAN)is proposed.Density-based spatial clustering of applications with noise(DBSCAN)is used to preprocess the data to avoid the impact of abnormal data on model prediction.The generator and the discriminator compete with each other and update the weights of their respective gradient directions,and the predicted values of oil-casing pressure difference and water-gas ratio are continuously optimized to approach the true value.This enables the model to accurately predict the oil-casing pressure difference and water-gas ratio at the next moment.The predicted plunger-foam drainage strategy is implemented through the plunger-foam drainage composite drainage intelligent management system.Compared with LSTM models,the LSTM-WGAN model reduces the root mean square error(RMSE),mean square error(MSE),and mean absolute error(MAE)of the predicted oil-casing pressure difference and watergas ratio by 2.64%,5.13%,11.75%and 8.81%,8.07%,6.60%,respectively.The experimental results demonstrate that the prediction model can accurately predict the oil-casing pressure difference and water-gas ratio data,guide the plunger-foam compound drainage system to issue correct instructions to deploy foam and plungers,and the intelligent delivery of plunger-foam is realized.

关 键 词：复合排采 预测控制 LSTM-WGAN 油套压差 水气比 井底积液 

分 类 号：TH86[机械工程—仪器科学与技术] TN06[机械工程—精密仪器及机械]