The objective of the paper is to optimise the Steel Lazy Wave Riser configuration based on an analytical approach and implement the riser design automation in cloud-based digital subsea field. The methodology demonstrates the effective implementation of multi-objective optimisation methods based Genetic Algorithm (GA) and hybrid GA with Radial Basis Function(RBF).

Steel Lazy Wave Riser (SLWR) configuration is considered for the riser design optimisation and automation. The configuration consists of multiple design variables such as top angle, sag bend elevation, hog bend elevation, length of the buoyancy section and the module diameter. The riser configuration optimisation is time-consuming and exhaustive method due to nonlinear time domain analysis and a large number of load cases. Therefore, a novel analytical optimisation technique is developed to reduce the computational cost as well as the complexity using automation. The riser design automation procedures consist of the following steps: 1. The multi-objective optimisation algorithm is used to find the optimal configuration of the riser systems in static condition 2. The optimised configurations in the static state are used for further optimisation based on fatigue and extreme load conditions. 3. Use the final optimal configuration for generating the orcaflex simulation file and perform the design automation in the cloud-based subsea digital field.

The proposed optimisation and design automation method minimises both the maximum riser stress and the cumulative fatigue damage of the SLWR configurations. It also demonstrates the significant reduction in computational cost as well automation helps to reduce the standard error occurs due to manual inputs. The all necessary design data are received from the cloud-based field development and perform the calculation in the cloud. The whole procedures are presented in a very efficient and time-saving manner in cloud-based field platform.

The traditional design of the riser configuration is very experience-dependent as well as very time-consuming. But the riser design optimisation using the optimisation algorithm based on GA can save considerable computational time and also less need for design experience.

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