Offshore production facilities play a central role in the oil industry given the growing demand for energy resources. The mooring system of these floating structures is a critical component for safety maintenance. The timely identification of mooring lines failures can prevent environmental pollution, property losses and further system failures. In this paper we propose a system to detect and classify failures of the mooring lines based on the natural period in the longitudinal axis and in the lateral axis of the long drift oscillatory motion of the platform. The proposal starts from the hypothesis that when a line break occurs, the natural period of oscillation of the platform is increased, and this difference may indicate the malfunction of the mooring system. The proof of concept developed for the proposed system demonstrates the potential of using the natural period to detect failures in mooring lines for floating vessels, validating the initial hypothesis that the difference in a natural period appears when a line breaks and that this difference may detect line break.

This paper presents a computing pipeline architecture for semantic search in the domain of Offshore Engineering. The proposed system combines modules such as document retriever, passage retriever, and answer extractor to produce textual responses to queries in natural language such as: “What FPSO motion is mostly affected by viscous damping?” Such responses are often needed in Offshore Engineering activities, and linguistic techniques such as those based on inverted indexes with a syntactic focus tend to perform poorly. Instead, this research explores semantic techniques that take into account the meaning of words in the domain of Offshore Engineering. This paper describes a Linguistic QA pipeline architecture built that provides a way to retrieve answers instantly from a collection of 13,000 unstructured technical documents about Offshore Engineering, reports the achieved results and future work. This paper also presents additional modules under construction that exploit Neural Networks and ontologies approaches for semantic search in the domain of Offshore Engineering.

The current design process of mooring systems for FPSOs is highly dependent on the availability of the platform’s mathematical model and accuracy of dynamic simulations, through which resulting time series motion is evaluated according to design constraints. This process can be time-consuming and present inaccurate results due to the mathematical model’s limitations and overall complexity of the vessel’s dynamics. We propose a Neural Simulator, a set of data-based surrogate models with environmental data as input, each specialized in the prediction of different motion statistics relevant to mooring system design: Maximum Roll, Platform Offset and Fairlead Displacements. The meta-models are trained by real current, wind and wave data measured in 3h periods at the Campos Basin (Brazil) from 2003 to 2010 and the associated dynamic response of a spread-moored FPSO obtained through time-domain simulations using the Dynasim software. A comparative analysis of different model architectures is conducted and the proposed models are shown to correctly capture platform dynamics, providing good results when compared to the statistical analysis of time series motion obtained from Dynasim.

A failure in the mooring line of a platform, if not detected quickly, can cause a riser system failure, extended production downtime, or even environmental damages. Therefore, integrity management and timely detection of mooring failure for floating platforms are critical. In this paper, we propose a new model for an ANN-based mooring failure detection system. The proposal’s idea is to train a Multilayer Perceptron (MLP) to estimate the platform’s future motion based on its motion’s temporal data without failure. A classifier then indicates whether or not there is a failure in the mooring system based on the difference between the predicted and the measured motion. The results with several tests of the implemented system show that our proposal can correctly predict the motion of the platform in most environmental conditions. The system shows a precision, accuracy and F1-score of 99.88%, 99.99% and 99.94%, respectively, for detecting changes in platform motion in near real-time, quickly signaling a possible breakage of mooring lines.