Volume 64

Study on Downhole COâ‚‚ 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 COâ‚‚ 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 COâ‚‚ injection well flow rate measurement, a downhole COâ‚‚ injection flow rate measurement method integrating mechanism and data was proposed. Data were acquired and preprocessed using an indoor experimental platform for COâ‚‚ 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 COâ‚‚ 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 COâ‚‚ 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 COâ‚‚ Injection, COâ‚‚ indoor experiment, mechanism-data fusion, hybrid-drive model, flow measurement