A Data Centric Digital Platform for Rigid Pipeline Engineering

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Authors:

Sylvain Truche, Kevin Le Prin, Mohamed Ben Abdallah, Matthieu Minguez

Journal / Event:

Offshore Technology Conference, Houston, Texas, USA, May 2022

Abstract:

Rigid pipeline engineering, alike most of the engineering activities, has to manage a deep transformation in the way it is performed. This trend is seen throughout the industry. This transformation is driven by the massive introduction and development of digital products and services, which as of today drive what is called a Digital Transformation. The paper briefly provides some insights on how such a transformation is reshaping the current established order in term of organization, schedule, product, processes, human resources, and business strategy. While this transformation is still moving on, a specific focus on Data is getting a massive momentum, as Data is becoming, within a general consensus, a major sought-after assest, a business field on its own. Based on that new paradigm, the notion of single digital thread is introduced to highlight the need to manage the flow of data which is produced through the different digital products, platforms, or services. In the Company, this notion of a single digital thread is symbolized by Subsea Studio™ (here after called SS) to which the different digital products, platforms and services are connected. Once such a context is properly defined, the focus is done on the digital platform called Smart Pipeline Design ARENA (hereafter named ARENA) which is to be seen as a product configurator dedicated to rigid pipeline engineering. The authors firstly present what ARENA is about, what ARENA intends to offer and how ARENA is embedded in the digital ecosystem and strategy of the company. To conclude, the authors introduce how the platform and the massive use of Artificial Intelligence algorithms can impact rigid pipeline engineering and more generally improve the business performance. This is achieved providing (i) some insights on the way automation and optimization are implemented in standard engineering process flow and (ii) most importantly the savings which have to be expected in terms of direct engineering cost (i.e. man hours) and technology & procurement cost.

Keywords: Artificial Intelligence, Engineering Process, Piping, Digital transformation, Industrial Equipment