One of the conceptual and systems engineering objectives related to field development is to find the best solutions for surface facilities designed to gather, process and transport oil and gas. However, due to high uncertainty, complexity and labor-intensity associated with the objective and also due to the fact that there are a lot of factors that are hard to summarize, people tend to adopt “locally suitable” solutions incorporating the interests of certain stakeholders (such as geologists, drillers, engineers, constructors, economists, etc.) rather than “generally best” solutions. Issues related to such an approach, which takes into consideration multiple factors related to selection and approval of process equipment, are very urgent for the oil and gas industry. Capital expenditures take a significant part of expenses and their optimization contributes to the general cost-efficiency of field development. This article describes an ontology-based approach, which enables to select process equipment automatically, considering various stakeholders’ and regulatory requirements. The objective is to create architecture and an ontology-based knowledge model for an intelligent support system for oil and gas engineering based on the concept of augmented intelligence for oil and gas systems engineering Oil&Gas AugSE. The article suggests generalized system architecture, with the core being ontology of the facilities and processes related to field development; the authors also developed a multilayer structure for ontological models and databases. The architecture is the basis for the algorithm that enables to configure well pad infrastructure at an oil field automatically.
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