E&P companies are increasingly instrumenting its fields and introducing new technologies with the objective to proactively monitor and surveillance of its wells, reservoir, and facilities for better operations and management. This brings in wide variety of technologies from multiple vendors in providing smart sensors, downhole equipment, instruments, and solutions along with existing legacy systems to support the digital journey. These technology implementations enable improvement in specific areas of the operations. However, in order to continuously build the digital infrastructure and realize its full potential, it requires seamless integration of real time data, technologies, workflows and business process.There is no single vendor integrated solution that covers the entire spectrum of the digital oil field environment. In this scenario, in general the operating companies have the key role to orchestrate these technology components to achieve the integration and seamless workflows. This paper discusses the business need of an Integrated Digital Oil Field Foundation (IDOFF) Framework (based on industry standards and protocols shall form the core foundation that integrates next generation products, technologies of the multi-vendor digital environment enabling smart workflows including in-house solutions.The IDOFF shall comprise of data standards, business process, data model and architecture, business logic, workflow automation, analytics environment. In IDOFF, the multi-vendor technologies are integrated seamlessly without additional effort in moving data between workflows and applications to achieve higher level of collaboration. The authors discuss the importance of operators to play a key role in orchestrating and building the digital oil field framework that supports multi-vendor environment to enable integrated solutions.

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..