Data Driven Versus Transient Multiphase Flow Simulator for Virtual Flow Meter Application

被引:0
作者
Ishak, Mohd Azmin B. [1 ]
Ismail, Idris B. [1 ]
Al-Qutami, Tareq Aziz Hasan [2 ]
机构
[1] Univ Teknol PETRONAS, Elect & Elect Engn Dept, Seri Iskandar, Perak, Malaysia
[2] PETRONAS Res Sdn Bhd, Facil Future, Seri Iskandar, Perak, Malaysia
来源
2020 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT AND ADVANCED SYSTEMS (ICIAS) | 2021年
关键词
Multiphase Flow; Virtual Flow metering; data-driven transient flow simulator; ensemble method;
D O I
10.1109/ICIAS49414.2021.9642589
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This study aims to evaluate two independent approaches of Virtual Flow Meter (VFM) i.e., using Transient Multiphase Flow Simulator (TMFS) and data-driven using Diverse Ensemble Learning Neural Network (DELNN). The main objective of using the Virtual Flow Meter (VFM) developed from this study is to implement in real time as a mean of troubleshooting and validating the measurement provided by a physical Multiphase Flow Meter (MPFM) for well testing operation. The result of the study showed both VFM flow rate estimates were less than 10% of full-scale error for both oil and gas flow rates compared to the measured flow rate respectively. Additionally, both VFM also independently managed to track a similar trend of deviation in gas flow rate which help to identify failure in the Multiphase Flow Meter (MPFM) internal measurement devices. The result of the study proved that by employing two independent VFM approaches in parallel, we could position VFM with higher confidence as a reliable solution either as a backup or as a mean of troubleshooting solution to physical MPFM as well as an analytic tool to plan well testing procedure.
引用
收藏
页数:4
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