Production Test data is a critical parameter for several production operations and reservoir management workflow. High quality Production test data is vital for better understanding of the flow behavior and rates to ensure optimized production and maximize the asset value.
Current Production Testing practice in oil industry includes separator testing or MPFM at well heads or at degassing station. However, the frequency of testing varies between one to three months for each well which may not sufficient to realize the full potential of Digital Oil Field (DOF) workflows. Virtual Flow Metering (VFM) technique along with well model results shall provide continuous well rates that would significantly improve the quality of decisions made through the production workflow. This brings in a varied Production testing environment for different well categories and types.
In order to continuously provide the real time production parameters to the DOF workflows, it is essential to integrate the different Production testing techniques through an Integrated Production Testing Framework. In this paper, the authors discuss an Integrated Production Testing Framework that comprise of validated real time and historical data, integrated workflows and the enabling technologies that includes calibrated well models, trained neural network models and visualization tools. Production test data obtained using traditional methods (PTS) and MPFM will be at low to medium frequency. VFM using neural network model estimates the flow rates continuously between the actual tests at a high frequency. This framework is suitable for production, injection wells that are installed with MPFM, PTS, water cut meters and covers different production testing scenarios Like PLT, ESP testing, Long-term MRT and step rate injectivity testing.
The framework enables implementation of continuous estimated rates, exception based alarms, automatic well test validation, track well test operations, guidance for reservoir monitoring program, KPI monitoring, precise back allocation leading to better production optimization and reservoir management across oil and gas producing assets.
This paper discusses an integrated approach to manage different Production testing methodologies to streamline the usage of the data for different workflows..