Second-Stage Challenge for the PRODML Standard: Adaptive Production Optimization

Abstract

Abstract PRODuction xML (PRODML™) was started jointly by BP, Chevron, ExxonMobil, Shell, and Statoil in early 2005 as a data exchange mechanism to support production optimization within a ‘digital oil field’ context. These companies have been joined by Aspentech, ConocoPhillips, Euriware, Halliburton, InfoSys, Invensys, Kongsberg Intellifield, Matrikon, OSISoft, P2ES, Pioneer, Petroleum Experts, Schlumberger, TietoEnator, and Weatherford. Energistics® has stewardship of PRODML and fosters further development. There is significant industry interest in implementing digital oil field strategies. Corporate and government initiatives anticipate significant, sustained improvements in recovery and operating efficiencies while maintaining safe operations. This will require robust, trustworthy, implementation of measurement, optimization and automation technologies. Version 1.0 of the PRODML standard, released in 2006, enables a range of production optimization use cases to handle an information hierarchy which includes time series data. This lays a foundation for adaptive optimization involving interaction between applications and data stores from multiple vendors. Such optimization is important both for situations with low-frequency changes, such as waterfloods, and for those requiring agility, such as compliance with pipeline, liquefied natural gas, and power-generation customer-export schedules that may cycle within a day. PRODML V1.0 provides a means of transferring data between applications incorporated in simple, common use cases. However, it did not address the task of accommodating changes to the physical configuration of the network, such as the addition of a well or a sensor, without having to manually reconfigure applications. Such changes are commonplace. In 2007, the PRODML work group focused on managing changes in production network configuration and in the capabilities of system components. The result enables optimization and reporting architectures and data management processes to adapt to changes faster with less effort and fewer errors. PRODML has therefore become a tool which can be used in implementing robust, trustworthy optimization and automation processes. Several example use-cases are included to illustrate how PRODML can be applied.