Numerical simulation for dynamic positioning in pack ice

This thesis investigates the numerical modelling of Dynamic Position (DP) in pack ice. A two-dimensional numerical model for ship-ice interaction was developed using the Discrete Element Method (DEM). A viscous-elastic ice rheology was adopted to model the dynamic behaviour of the ice floes. Both the ship-ice and the ice-ice contacts were considered in the interaction force. The environment forces and the hydrodynamic forces were calculated by empirical formulas. After the current position and external forces were calculated, a Proportional-Integral-Derivative (PID) control and thrust allocation algorithms were applied on the vessel to control its motion and heading. The numerical model was coded in Fortran 90 and validated by comparing computation results to published data. Validation work was first carried out for the ship-ice interaction calculation, and former researchers’ simulation and model test results were used for the comparison. With confidence in the interaction model, case studies were conducted to predict the DP capability of a sample Arctic DP vessel.

An application of evolutionary algorithms for WAG optimisation in the Norne Field

Water-alternating-gas (WAG) is an enhanced oil recovery method combining the improved macroscopic sweep of water flooding with the improved microscopic displacement of gas injection. The optimal design of the WAG parameters is usually based on numerical reservoir simulation via trial and error, limited by the reservoir engineer’s availability. Employing optimisation techniques can guide the simulation runs and reduce the number of function evaluations. In this study, robust evolutionary algorithms are utilized to optimise hydrocarbon WAG performance in the E-segment of the Norne field. The first objective function is selected to be the net present value (NPV) and two global semi-random search strategies, a genetic algorithm (GA) and particle swarm optimisation (PSO) are tested on different case studies with different numbers of controlling variables which are sampled from the set of water and gas injection rates, bottom-hole pressures of the oil production wells, cycle ratio, cycle time, the composition of the injected hydrocarbon gas (miscible/immiscible WAG) and the total WAG period. In progressive experiments, the number of decision-making variables is increased, increasing the problem complexity while potentially improving the efficacy of the WAG process. The second objective function is selected to be the incremental recovery factor (IRF) within a fixed total WAG simulation time and it is optimised using the same optimisation algorithms. The results from the two optimisation techniques are analyzed and their performance, convergence speed and the quality of the optimal solutions found by the algorithms in multiple trials are compared for each experiment. The distinctions between the optimal WAG parameters resulting from NPV and oil recovery optimisation are also examined. This is the first known work optimising over this complete set of WAG variables. The first use of PSO to optimise a WAG project at the field scale is also illustrated. Compared to the reference cases, the best overall values of the objective functions found by GA and PSO were 13.8% and 14.2% higher, respectively, if NPV is optimised over all the above variables, and 14.2% and 16.2% higher, respectively, if IRF is optimised.