摘要：In this work, Back Propagation (BP) neural network, Long Short-Term Memory (LSTM) network and Random Forest algorithm are applied to establish an intelligent data-driven model for virtual flow meters in oil and gas development. The actual data of two oil wells in an offshore oil field in the South China Sea are used to construct a dataset. Feature engineering and parameter optimization are carried out in sequence, and three data -driven models are established. The model utility is evaluated in terms of model stability and data volume requirement. Among the three models, the LSTM model shows the highest accuracy with a MAE (Mean Absolute Error) of 3.9 %, the highest stability, and moderate data volume requirement. The BP network exhibits the lowest accuracy with a MAE of 12.1 %, the lowest stability, and the smallest data volume requirement. Random forest exhibits moderate accuracy, high model stability and highest data volume re-quirement. Finally, the transfer learning model based on LSTM model and BP network is proposed and tested. The results show that the data volume requirement of the transfer learning model are reduced by half when the accuracy of the prediction results is not much different from that of the LSTM model. The proposed transfer learning model can use available data more efficiently and improve the generalization ability of the model. This work provides further insights of data-driven model application in virtual flow me-ters, which is of great significance for the intelligent green oil and gas engineering. (c) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.

关键字：Virtual flow meter; Date -driven model; Machine learning; Data volume requirement; Transfer learning

ISSN号：0263-8762

卷、期、页：卷:186: 398-406

发表日期：2022-10-01

影响因子：3.739000

期刊分区（SCI为中科院分区）：三区

收录情况：SCIE（科学引文索引网络版）,EI（工程索引）

发表期刊名称：CHEMICAL ENGINEERING RESEARCH & DESIGN

参与作者：李清平,姚海元,陈海宏

通讯作者：吴冕,亓佳宁,康琦,沈思亨

第一作者：宋尚飞,吴海浩,史博会,宫敬

论文类型：期刊论文

论文概要：宋尚飞,吴冕,亓佳宁,吴海浩,康琦,史博会,沈思亨,李清平,姚海元,陈海宏,宫敬，An intelligent data-driven model for virtual flow meters in oil and gas development，CHEMICAL ENGINEERING RESEARCH & DESIGN，2022，卷:186: 398-406

论文题目：An intelligent data-driven model for virtual flow meters in oil and gas development