Page 284 - Intelligent Digital Oil And Gas Fields

232 Intelligent Digital Oil and Gas Fields

This type of effort is required to assign stakeholders who are responsible for
the execution of assignments, to assure accountability, and to secure sustain-
ability of collaborative DOF projects.
Many operators as well as service providers consider data IAM architec-
ture as another pillar in support of DOF assets. For end users, the IAM
framework should provide a transparent, easy-to-use user interface for
defining and executing a variety of workflows from reservoir simulation
to economic evaluation and optimization (Soma et al., 2006). At the same
time, from the software implementation perspective, the IAM framework
should facilitate the seamless interaction of diverse and independent appli-
cations that are responsible for various tasks in the workflow. To emphasize
the relevance of data architecture, Soma et al. (2006) and Kozman (2004)
highlight data composition, abstraction and federation, and visual aggrega-
tion as vital building blocks in a service-oriented IAM architecture.
Khedr et al. (2009) present the IAM workflow for optimizing large-scale
artificial lift (AL) and enhanced oil recovery (EOR) strategies to one of the
2
largest offshore fields in the world, covering an areal extent of 1200km .
The field combines three major reservoirs that produce from approximately
450 single- and dual-string wells. The field was initially developed with
peripheral waterflooding strategy, and then converted into a five-spot pat-
tern water-injection scheme. The development plan combines intensive
infill drilling and applications of AL and EOR to different reservoir areas,
including water injection (WI), water alternate gas (WAG), and gas injection
(GI).
The deployed IAM platform couples industry-standard, domain-specific
reservoir and surface network simulation applications using an explicit net-
work balancing algorithm (Ghorayeb et al., 2003) that is well suited for solv-
ing optimization problems with a large number of wells (>500). The IAM
coupling platform uses a general-purpose workflow process controller
(WPC) that supports various coupling schemes, where the purpose of a pro-
cess controller is to keep an external network balanced with reservoir
simulation(s) as the reservoir conditions evolve:
• Tight, iteratively lagged coupling scheme, when the network couples to a sin-
gle reservoir model: The coupling points may be individual well tubing
heads or well groups. The simulator determines the pressure drop from
the well bottom hole to the tubing head using the precalculated VFP
tables. The list of coupling points may be extended to include the well
bottom hole. Fig. 6.12 shows an example of generic tight, iterative