Abstract
This study presents a summary of intuitive and comprehensive pore-to-process (P2P) automated workflows that can be utilized for production enhancement and operational efficiency of complex fields. High-frequency data obtained from digital production field instrumentation systems are aggregated and stored into a SQL-based production data management system (PDMS) through which automated workflows are orchestrated using well and network models built in an industry standard steady-state multiphase flow simulation software. Advanced data visualization and analytics dashboards help users monitor well and network performance, identify flow assurance hurdles and optimize overall production.
An automated workflow streamlines the process of validating well tests through various steps, such as calculating standard volumes, establishing stable flow ranges, analyzing production trends, and verifying representative conditions. The well model is calibrated using the validated parameters in the well test determining the productivity index and choke discharge coefficient as parameters. With the latest operating conditions, the calibrated well models are plugged into a network simulation to reduce any uncertainties in results validity as well as to identify network performance issues such as liquid loading, corrosion constraints, and hydrate risk. This valid surface network serves as the foundation for planning and executing what-if workflows, which allow for comparisons between operational base cases and planning scenarios. This tool is crucial for visualizing physical and quantitative changes in the surface network, mitigating future risks and losses that would otherwise be encountered. Also, it can be used to perform production optimization analyses. Depending on the well lift type such as naturally flowing, electrical ESP, gas lift, or PCP, network-based optimization can be automated using optimized parameters like choke size, ESP operating frequency or PCP operating torque. By utilizing these tools, production operations can be optimized to increase efficiency and reduce costs. Additionally, two important analyses are implemented:
H2S & CO2 emission calculation utility to provide an idea of overall emissions in the estimated production rates.
Integrated asset surveillance to capture the overall asset performance for the field.
In conclusion, the combination of well-automated workflows and network optimization workflows allows for the complete utilization of all individual components within a production network, resulting in overall gains. This approach of automating P2P presents a systematic and holistic method for estimating well potential, production gains from workovers, tracking emissions, identifying bottlenecks in the overall production system, and evaluating network performance. By utilizing these methods, production efficiency can be increased while reducing costs and minimizing environmental impact.
While focusing on sustainability as an important KPI, the P2P approach is aimed to be made more detailed for capturing various kinds of additional emission components in the estimated production rates via enhanced workflows. This will provide a quantitative analysis of production-related emissions at the surface network or field level.
