Session: 01-01-01 Offshore Platforms-I

Paper Number: 120988

120988 - Forecasting Mooring Tension of Semi-Submersible Oil Production and Storage Platform Using a Novel Transformer Neural Network Model 

The harsh operating environment of deepwater semi-submersible production platforms poses severe challenges to the safe operation and maintenance of their production processes. Using artificial intelligence technology to improve the operation and maintenance level of large-scale offshore engineering equipment has become an inevitable trend. Accurate mooring tension prediction for semi-submersible production platforms can effectively identify potential damage and ensure the platform's safe and efficient production operations. Compared with the traditional cyclic neural network, the Transformer neural network has faster calculation speed, better generalization ability and scalability, and better captures long-distance dependencies. This study takes the first deepwater semi-submersible production platform, "Deep Sea One", in the South China Sea as the research object. It uses long-term actual monitoring of mooring tension data during the platform production process and proposes a new type of transformer based on the wavelet transform and a novel Transformer neural network architecture. Mooring tension prediction method for semi-submersible production platform. At the same time, the proposed method is compared with traditional mainstream neural network models such as CNN, RNN, and LSTM to prove the effectiveness of the proposed method in predicting mooring dynamic tension on semi-submersible production platforms. In addition, the proposed method is also suitable for other time series data prediction in shipbuilding and ocean engineering and has a certain promotion value.

Presenting Author: Yang Chen Harbin Engineering University

Presenting Author Biography: Name: Chen Yang
Age: 26
Education: Ph.D.
Institution: School of Ship Engineering, Harbin Engineering University
Research methods: Intelligent prediction of ship and marine engineering time series, intelligentization and digitization of ship and marine equipment, digital twin of ship and marine equipment.

Authors:

Yang Chen Harbin Engineering University
Lihao Yuan Harbin Engineering University