The mooring systems give stability to the floating platforms against environmental conditions, stabilizing the platform with mooring lines attached to the seabed. The mooring systems are among the main components that guarantee the safety of the staff and the various operations carried out on the platforms. The current approaches used to monitor mooring lines are inefficient as line tension sensors are expensive to install, maintain, and have durability problems. This article presents the development of two neural network-based machine learning systems: a Multilayer Perceptron (MLP) and a Long Short-Term Memory (LSTM). They are able to detect mooring line failure in near real-time based on the comparison between measured and predicted motion. The implemented systems were trained and evaluated with simulated motion data generated using real environmental conditions measured in the Campos Basin, in Rio de Janeiro, Brazil. The results showed the MLP and LSTM models were able to detect a failure in the mooring lines, with increasing difference between the predicted and the measured motions when there is a line breakage. A comparison between the two machine learning models revealed the LSTM model performed better at predicting the motions of the platform.

Remotely operated vehicle (ROV)-based inspection, maintenance, and repairs (IMR) services are costly because operations are traditionally executed by a hired subsea contractor, who then hires a specialized vessel with an entire crew from the vessel owner or the shipping company. Even though this is an established method considered relatively reliable in comparison to human divers, there is a growing need for more versatile, efficient, and economical IMR methods. Innovations that require no or less use of support vessels are mitigating this challenge. The current ROV classifications do not adapt to these innovations. Hence, the lack of a widely accepted ROV classification. Thus, this paper reviews ROV classifications and proposes a classification that poses no hindrance to innovation and conforms to modern developments. The paper then illustrates and reviews the emerging methods of conducting IMR operations by putting together in a concise, yet resourceful manner the ROV technologies and their various configurations to provide a basic meaningful understanding to the audience. This paper also provides a summary of the comparison of the methods and some of their challenges.

The focus of this work is the extension of nonlinear state estimation methods to gas-lifted systems. The extended Kalman filter (EKF), unscented Kalman filter (UKF) and particle filter (PF) were used to estimate the nonlinear states. Brief descriptions of the filters were first presented starting from the linear Kalman filter. Hypothesis tests on the expectation of the residuals were performed to show how close to optimal the estimation methods are and it showed the UKF estimates to be slightly better than EKF while PF performs the worst. The PF has poor accuracy using residual visualisation, hypothesis test and the root mean squared error (RMSE) values of the residuals. The gas-lifted system exhibits casing heading instability where the states show oscillatory behaviour depending on the value of the input but the results here do not change in a known way for each filter as the input is changed from the non-oscillatory region to the oscillatory region. Therefore, for this noise distribution and model assumption, either the EKF or UKF can be used for nonlinear state estimation with UKF better preferred if computational cost is not considered when control solutions are used in gas-lifted system.