Page 27 - Intelligent Digital Oil And Gas Fields

Introduction to Digital Oil and Gas Field Systems 9

charted and displayed in the IOC. The IOC was also equipped to control
wells and facilities. The IOCs enabled proactive management and addressed
issues such as production bottlenecks.
In 2005, Chevron rolled out its I-Field program (Oran et al., 2008).
For the San Joaquin Valley Business Unit in California, I-Field projects
included collaborative environments (decision support centers, DSC), remote
collaboration and visualization, and standardization. The DOF system yielded
increasesincrewefficiency,betterintegrationofofficeandfieldactivities,opti-
mization of steam systems, surveillance of well events, and a pattern-exception
tool. For its Agbami development in Nigeria (Sankaran et al., 2010; Ibeh et al.,
2015), Chevron implemented intelligent well completions, management by
exception, improved collaboration, standardization and centralized analytics,
and increased focus on safety and environmental risks. The full DOF imple-
mentation in Agbami was a critical success factor in the reliability of intelli-
gent wells and minimizing interventions (Ibeh et al., 2015). The Agbami
DOF system uses downhole sensors, DSC, and sophisticated data capture
and satellite communications. DOF has also been critical for surveillance and
flow assurance. In conjunction, a production optimization and reservoir
management solution was deployed (Paulo et al., 2011) with well test valida-
tion, well rate estimation, and data analytics components.
In 2010, the Kuwait Oil Company (KOC) launched the Kuwait Intel-
ligent Digital Field (KwIDF) program with three pilot projects (Dashti et al.,
2012; Ershaghi and Al-Abbassi, 2012), one of the most ambitious in the
industry, using state-of-the-art communications, sensor devices, collabora-
tion centers, and automated engineering workflows. The KwIDF vision was
to achieve IO for measurement, model, and control of oil field assets, where
informed decisions are made effectively and consistently in a collaborative
work environment for production and reservoir management. Al-Abbasi
et al. (2013) described that DOF is designed to help asset teams meet these
challenges, a new generation of petroleum workflow automation integrates
real-time data with asset models, helping team members to collaborate so
that they can analyze data better and more fully understand the asset prob-
lems. They called this program intelligent workflows or smart flows.
This approach is cutting edge but also more complex. The complexity is
addressed with the use of artificial intelligence technology, such as proxy
models and neural networks, coupled with a visualization engine to provide
an effective visual data-mining tool. The objective of their workflow automa-
tion is to provide integrated solutions to asset opportunities and guide the ope-
rations with instructions based on smart analysis and integrated visualization.