Advances in Automated Design Approval

There are significant trends in the reduction of traditional 2D design, this is being replaced by the sole development of 3D models. This paper will detail how to develop algorithms to automate large aspects of a design review. These techniques significantly increase efficiency, ensure constancy and optimise the accuracy of the design, leading to reduced project costs. Utilising the 3D models enriched metadata and by developing independent algorithms, it is possible to create a cyberphysical model that enables automation of the design review. For example; using the geometrical data in the 3D model to check a hazard with respect to a detector, confirming that the detector is located close to the hazard. There are multiple checks similar this example, cataloguing and scripting these checks can be managed within PLM software. Using algorithmic automation techniques reduces the overall design hours of a project, it checks the consistency of the design. Getting it right first time reduces the number of changes later in the project lifecycle, avoiding expensive rework costs. During the first phase of this initiative, we have found, that automation leads to a reduction of design hours by 10% and increases the accuracy and consistency of the design review. This first phase of automation uses the metadata in the 3D model, where the output from the check leads to a comment on the design. To scale the pilotm which will encompass the inclusion of other data sources, will further enrich the cyberphysical model. Ultimately, by creating a decisions database and using Artificial Intelligence we will be able to close the loop, which will lead to a design that is fully evaluated before it leaves the designer. It is also possible to automate in other phases of the project lifecycle, where image recognition will compare the real asset to the model. This level of automation is unique, there are other low-level forms of automation, but the advancements of this technology has, to our knowledge, not been attempted in the Oil and Gas sector. The development and scaling of this technology is novel and will have a significant impact on the way future projects are executed.