Abstract
A huge amount of sensors data is daily being generated from a single well site, posing challenges for data transmission, storage and analysis. Marrat reservoir is adeep carbonate reservoirs in Greater Burgan Oilfield and is prone to asphaltene deposition. The present study leverages Edge computing technology which may revolutionize data processing, analysis, and algorithm-based decision making to enhance well performance in a cost-effective manner.
This case study illustrates a pilot project of an on-prem edge computing infrastructure. The project particularly focuses on monitoring well performance and tackling asphaltene problems. The utilization of edge computing in this context introduces two outstanding solutions: the virtual flow meter (VFM) and asphaltene problem detection module. The VFM solution is based on a combination of data-driven and physics workflows. While the Asphaltene module quantifies the asphaltene risk by utilizing fluid data and asphaltene precipitation envelope for well operating conditions. Numerous physical well tests and fluid sampling have been carried out to validate both the solutions.
The data-driven Virtual flow meter (VFM) has demonstrated a very high model accuracy over the test period, which signifies the reliability and potential of edge computing in the field. This solution seeks to estimate flow rates without the need for conventional flow meters, resulting in optimizing the frequency of wells production measurements and operating expenses. Through the estimation of real-time flow rates and asphaltene risk, a proactive wellbore clean-out job could be run to prevent any production losses due to asphaltene blockages. This study shows an opportunity of field scale implementation, which potentially will help in managing and optimizing reservoir performance.
The edge computing technology provides an innovative approach which enables real-time access to all the pertinent well information. The developed modules can be reliably used to identify potential well issues for early resolution and avoid significant production deferral. Asphaltene detection module will not only reduce the downtime but also deliver insights for production assurance, reservoir management and field development planning.
