Application for Predicting Vessel Motion Enhances Operability in Offshore Drilling

Abstract

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 35341, “Enhancing Energy Efficiency and Operability in Offshore Drilling Operations: Validation and Benefits of a Machine-Learning-Based Vessel-Motion-Prediction Application,” by Daniel A.C. Canache, Massimiliano Russo, and Rune Haakonsen, Kongsberg Maritime, et al. The paper has not been peer reviewed. Copyright 2024 Offshore Technology Conference. _ This paper delves into the framework of a new application that leverages advanced machine-learning (ML) techniques in conjunction with metocean forecasts to predict vessel motions and thruster loads. The discussion encompasses the key role of diverse input factors in shaping prediction accuracy and outlines strategies to address inherent uncertainties associated with methods, weather forecasts, and the stochastic nature of the underlying problem. Methods, Procedures, and Process Traditional Physics-Based Vessel Model. A physics-based model of the vessel is used to train the ML model. As such, the accuracy of the physics-based model is critical to the success of the ML model. The model is built using vessel-specific hydrodynamic data, mass data, and thruster-control software. The thruster-control software is extracted from the actual vessel to ensure that the same settings are being used in the model. The model is used to execute time-domain simulations, which include second-order effects, from which prediction targets [six degrees-of-freedom (DOF) motions and thruster usage] are extracted. As shown in Fig. 1, four environmental components are typically considered: wind waves, swell waves, wind, and current. To train an ML model capable of predicting in any given weather condition, environmental parameters are varied over thousands of simulations. Physics-Informed Neural Network (PINN) Architecture. The adopted architecture for the ML model is a PINN, a model architecture that offers several advantages. By integrating vessel-specific hydrodynamic data, the model becomes grounded firmly in the underlying physical phenomena. Furthermore, the architecture facilitates the model’s comprehension and assessment of the environment by enabling the compilation of individual loads into an overall load acting on the vessel. Ultimately, the analysis of resultant loads on the vessel provides a means to compare various environmental load cases directly in relation to the vessel’s response. Optimizing the Training Matrix. The optimal training matrix should span the environmental parameter space up to the vessel’s station-keeping capacity while minimizing the number of necessary load cases. Given the computational expense and prolonged execution times associated with time-domain simulations, it is important to maintain a manageable number of cases. While resultant loads facilitate the comparison across different environment load cases, a systematic algorithm is needed for case selection. To address this requirement, a similarity metric incorporating cosine similarity and Euclidean distance was implemented. The downside to the Euclidean distance is that, at low magnitudes, the distance will be low regardless of direction. This is mitigated to some extent by combining the measure with the cosine similarity. The cosine similarity will be high if two vectors are close in direction regardless of their magnitudes. For the specific problem described in the complete paper (vessel responses), it is desirable that the similarities at low loads stay high because the response also will likely be low.