Digital Oilfield Down Under: Implementation of an Integrated Production Monitoring and Management System for the Maari Field, Taranaki, New Zealand

Abstract

The Maari oil field, the first OMV operated offshore oil field development, has showcased OMV's impressive technical skills. Following the completion of a field re-development drilling campaign in August 2015, the well configuration currently consists of 10 producers and 1 water injector (with the option to convert a producer into a water injector in the future). Electric Submersible Pumps (ESPs) are installed on all 10 producing wells to provide lift of reservoir fluids to surface. A SCADA system and associated Production Historian Database (PHD) was included in order to capture the high frequency data for well & reservoir surveillance and daily production optimisation of the field. However, there were many challenges in utilising this live data stream from the offshore facility. In particular, it was vital to continuously and effectively monitor and optimise ESP performance in order to improve run life, reduce downtime and ultimately increase production. An integrated decision support system was therefore required for real-time data collection, production monitoring, ESP health check and KPI analysis for proactive decision making and limiting the number of manual processes involved. This paper describes how these challenges were overcome by creating an integrated workflow and aligning the existing system architecture in order to meet the business needs. The system is based on full workflow automation, and has been deployed for data acquisition, validation and analysis by optimising the components of integrated asset management. The system includes an integrated framework connected to various live data sources with different time increments, allowing data aggregation to a reliable intra-day hub. Automated job scheduling has also been built in with a decision support dashboard setup for production analysis and ESP performance monitoring. Based on historical trends, an optimum operating envelope was defined and automatic rules were configured for anomaly detection. The system has provided standardized data access throughout the asset team, streamlining their entire process and resulting in improved efficiency, which has optimised the engineers time for core operational activities. With a secure and automated workflow, and the ability for multiple users to work simultaneously, the system has minimised their downtime, thus improving overall productivity. Utilizing the live data feed for updating of simulation models has allowed quicker comparisons of numerical predictions with analytical forecasts, hence helping to streamline the overall reservoir management of the field. The system has not only assisted the team in meeting their production reporting deadlines, but has also alleviated bottlenecks in their decision-making processes helping to boost overall asset productivity.

AI-driven microbial sequencing analysis for hydrogen storage in the Maari reservoir

“…These two units have separate free-water levels and the M1A reservoir is of varying quality across the wells in Maari. It has a lower a lower net to gross interval than the M2A formation [24]. The best sand quality is found in the south-western area of the eld.…”