Optimization of insulation on subsea oil and gas equipment

Optimization of insulation on subsea oil and gas equipment can lead to significant cost and weight reduction. A judicious combination of commercially available Finite Element Analysis (FEA) software with evolutionary optimization algorithms can efficiently serve this purpose. In the present study, the optimization of insulation on a gate valve has been presented with due emphasis on the methodology adapted. A parameterized model of the gate valve with several dimensions of interest has been chosen for the numerical experiment. Of the various Design of Experiment (DOE) techniques, the Optimal Space Filling (OSF) method has been considered for generating the samples owing to its robustness and the associated computational cost. The insulation in subsea systems is usually supposed to satisfy two contradicting constraints, it must be ensured that the temperature of the production fluid remains above the Hydrate Formation Temperature (HFT) and at the same time the production fluid temperature cannot be too high, as to create structural damage of the seals in High Pressure High Temperature (HPHT) applications. The sample cases are run simultaneously in a cluster and a response surface is created using genetic aggregation using the various samples those are generated by OSF. The resulting response surface is finally subjected to optimization using the calculus based/gradient based methods. In the present investigation, the optimization is performed using the NonLinear Programming with Quadratic Lagrangian (NLPQL) and Adaptive Single Objective (ASO) Methods. The results obtained show that almost two thirds of the insulation can be effectively removed without compromising on the thermal efficiency of the system. Similar savings have been witnessed in several other cases/subsea geometries that the authors are aware of. The presented case is truly representative of a wide class of subsea applications and the proposed methodology can be suitably adjusted to customer specific requirements to provide suitable results. © 2021, The Author(s).