In this paper, we explore the need to improve systems awareness to support early-phase decision-making. This research uses the Norwegian energy industry as context. This industry deals with highly complex engineering systems that shall operate remotely for 25+ years. Through an in-depth study in a systems supplier company, we find that engineers are not sufficiently aware of the systems operational context and do not focus on the context in the early phase. We identified the lack of a holistic mindset and the challenge of balancing internal strategy and customers’ needs as the prevalent barriers. To support the concept evaluation, the subsea system suppliers need to raise systems awareness in the early phase. The study identifies four aspects that are important to consider when developing and implementing approaches to improve systems awareness in the early phase.

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This article applies formal system engineering methods in early‐phase concept studies in the subsea oil and gas industry to identify early‐phase needs, and reduce late‐phase design changes. The oil industry is changing, demanding more cost efficient, flexible, and modularized systems. In order to improve their offering, suppliers within this industry are turning towards the systems engineering domain. To better understand the problem, we investigated the engineering processes at the supplier, went into details of technical project reports, and interviewed main stakeholders at the supplier. Based on our research we propose to adjust the early phase of the project execution process for the company, and adapt to a system engineering framework. At an early stage we recommend using unformal models to communicate decisions and to set up a foundation for applying more formal models in the later phases. A case study from field development illustrates the new process and methods. Some of these systems engineering methods have already been adopted in the company to improve the front‐end engineering studies.

This research investigates the application of A3 Architecture Overviews (A3AOs) in subsea front-end engineering studies. A3AO is a valuable method to support multi-disciplinary communication and share architectural knowledge in complex engineering domains. The method captures key information of an overall system and displays the system overview in a standard A3 format. In this paper, we investigate the beneficial impact a global subsea supplier can gain by applying A3AO as part of their concept evaluation process in the early phase study. Through interviews and a survey, we identified company's main challenges in concept evaluation of tie-in and connection system. We then applied the A3AO method to investigate its mitigating effect in early phase concept evaluation. We found that the company resources found the A3AO to provide a holistic system overview and support knowledge sharing. Overall, our research supports the use of A3AO as a method to promote common understanding.

This paper evaluates the use of architectural reasoning to explore the problem space in a system development project in the oil and gas industry. The suppliers in this industry have traditionally been tailor-making their systems for each delivery project. To improve the systems offering across the client and project portfolio, the suppliers must put more effort in the conceptual phase to explore the design space. Architectural reasoning is the process of transferring problem and solution know-how into a new systems architecture. In this paper we review literature on architectural reasoning in the conceptual phase, and on application in the oil and gas industry. To evaluate the use of architectural reasoning in the industry, we perform a case study in a subsea supplier company. From the case study, we are identifying a work-flow for architectural reasoning, utilizing the market needs, design, and domain knowledge to evolve the system. Evaluating the tools and working methods, we find that working in a multi-disciplinary team is key to support the reasoning process. We find that the team is utilizing the design and domain knowledge to improve the system architecture. However, the team lacks methods to make this knowledge explicit and to quantify the issues they are identifying.