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238 Intelligent Digital Oil and Gas Fields

based on Unified Modeling Language (UML), which provides a common,
standardized vocabulary for domain experts to define and understand the
asset model. In the framework of UML, they propose a template tool to pro-
vide the scenario management with a mechanism to automatically configure
and invoke the forecasting tool for a particular scenario with underlying
uncertainty modeling.
Bravo et al. (2011) further acknowledge that the challenges in resource
negotiation, ineffective communication language, and delayed decision-
making protocols can have a deteriorating effect on the execution of
today’s IAM workflows. They propose addressing these challenges by
implementing distributed artificial intelligence (AI)-based architecture,
designed for automated production management, which they call an inte-
grated production management architecture (IPMA). The IPMA frame-
work has three layers (see Fig. 6.15):
• Connectivity layer: defines data acquisition, treatment, and interpretation
mechanisms.
• Semantic layer: consists of an ontological framework that facilitates effec-
tive information interchange between production applications. An
ontological framework provides a robust and evolving vocabulary that

Fig. 6.15 Integrated production management architecture (IPMA). (Modified from
Bravo, C., Saputelli, L., Castro. J.A., Rios, A., Rivas, F., Aguilar-Martin, J., 2011. Automation
of the Oilfield Asset via an Artificial Intelligence (AI)-Based Integrated Production Manage-
ment Architecture (IPMA). SPE-144334-MS. https://doi.org/10.2118/144334-MS.)