Volume 64

Study on Downhole CO₂ Injection Flow Measurement Model Based on Mechanism and Data Fusion Jiao Xinyu, Tan Chaodong, Shi Yipeng, Chen Yanrun, He Jinsong, Yang Aonan

Abstract

The accurate measurement of downhole CO₂ injection rate is of great practical significance for optimizing enhanced oil recovery (EOR) effects, evaluating reservoir performance, and designing and adjusting development plans. To improve the accuracy of CO₂ injection well flow rate measurement, a downhole CO₂ injection flow rate measurement method integrating mechanism and data was proposed. Data were acquired and preprocessed using an indoor experimental platform for CO₂ flow rate measurement, and a mechanistic model, a data-driven model, and a hybrid-driven model were constructed. The accuracies of the three models were compared and analyzed. The research results show that: the mechanistic model can be used for downhole CO₂ flow rate measurement, with most errors ranging from 9% to 20%. Relying on its flexible application of multi-source data, relatively simple construction process, and strong adaptability, the data-driven model exhibits higher accuracy in CO₂ flow rate measurement than the mechanistic model, with an accuracy of 87.3%. The hybrid-driven model integrates the in-depth interpretability of the mechanistic model and the efficient learning ability of the data-driven model for massive data. Its comprehensive performance in accuracy, adaptability, and interpretability surpasses that of single models, with an accuracy of 94.7%. The application of this hybrid-driven model in an oil well in southern China shows that its accuracy is 92.3%, which can meet the actual on-site measurement requirements.

Keywords downhole CO₂ Injection, CO₂ indoor experiment, mechanism-data fusion, hybrid-drive model, flow measurement