... Abstract Numerous machine learning algorithms are applied in the oil and gas industry. However, the data used in these studies are difficult to obtain due to various limitations. Due to the lack of benchmark datasets, it is challenging to make performance comparisons across different algorithms...

... Abstract Lost circulation is one of the most common issues affecting drilling safety, known for its sudden occurrence. Traditional expert diagnosis methods heavily rely on expert experience, exhibiting a high degree of subjectivity and lag. Conventional machine learning approaches struggle...

... optimization results. Various approaches have been widely researched to construct the ship performance model, such as empirical white-box models based on experimental tests and physical knowledge, data-driven black-box models using machine learning methods, and gray-box models combining the above two...

... Abstract This paper explores the applicability of machine learning techniques for the generation of tailored wave sequences. For this purpose, a fully convolutional neural network was implemented for relating the target wave sequence at the target location in time domain to the respective...

... Abstract This study proposes an alternative approach to predict wave elevation near multi-column semi-submersible structures by applying machine-learning methods from experimental data. The most common approach to this problem is to apply linear potential theory numerical programs to calculate...

... in the scene of polar regions can be applied to support obstacle avoidance algorithm for Polar region navigation. By comparing PA, MIoU and other parameters with the other methods, IBNet performs obvious advantages in semantic segmentation task of icebergs in polar regions. ships in ice machine learning...

... by this study, lies in the advancements of machine learning and deep neural networks. In this research, the spatio-temporal relationship between wind and wave conditions is established using the XGBoost machine learning method and Informer deep neural networks. This approach enables effective predictions...

... the potential of this approach in enhancing ship safety. multivariate time series forecasting whipping response machine learning Proceedings of the ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering OMAE2024 June 9-14, 2024, Singapore, Singapore OMAE2024-126186...

.... This work employs a machine learning approach utilising a surrogate wave model trained on the relationship between the wave conditions at discrete measurement locations to wave conditions across the entire model domain. The surrogate model can then be run with real-time data inputs from the discrete...

... platforms for mooring line rupture detection, considering platform oscillation. This work develops, implements, and tests an alert system for potential cable breaks on offshore platforms. Beyond operational value, it contributes to technological innovation through machine learning, overcoming conventional...

... Abstract This paper presents a Neural Operator (NOp) for estimating wave height from wind data. The NOp is a machine learning model to approximate functions from one function space to another. It is trained to map wind data to wave height. Each function is represented as a graph, with vertices...

... to monitor fatigue damage accumulation. This lacuna precipitates pronounced ambiguities in maintenance prediction, highlighting the urgent need for a rigorously systematic approach to address this knowledge void. To address these issues, this paper introduces a machine learning-based indirect measurement...

... and environmental impact in the offshore industry. offshore maintenance machine learning corrosion inspection Proceedings of the ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering OMAE2024 June 9-14, 2024, Singapore, Singapore OMAE2024-127615 DEEP LEARNING S ROLE...

... buckling pressure prediction accuracy through machine learning. The model is trained using data from NASA design codes and finite element analysis in ANSYS. Twenty-four-layer composite cylindrical shells with a thickness of 2.52mm, constructed from carbon-epoxy prepreg tape (USN-125), were subjected...

... sensors to observe the environment. The proposed methodology is based on a machine learning approach that has recently set benchmark results in various domains: model-based reinforcement learning. By randomizing the port environments during training, the trained model can navigate in scenarios...

... currents in environments with more complicated geometries. It may also be possible to use encoding models that are aware of temporal evolution to predict a full time series instead of just a snapshot. neural networks machine learning computational fluid dynamics wave modelling Proceedings...

... Abstract Lost circulation is one of the common risks in the drilling process, accurate and efficient lost circulation risk diagnosis is very important to ensure drilling safety. The risk diagnosis method based on machine learning has high efficiency and strong robustness, and is receiving more...

... fluid dynamics finite element analysis machine learning Bayesian network Proceedings of the ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering OMAE2023 June 11-16, 2023, Melbourne, Australia OMAE2023-102743 BAYESIAN NETWORK MODELLING OF AERO-MECHANICAL PERFORMANCE...

... mechanics designs. Therefore, accurately predicting the wellbore temperature is important to control the well pressure. This paper presents mathematical and machine learning modeling. The transient temperature model is developed based on energy balance that describes the heat transfer phenomenon...

... decided to be kept due to those outliers were not occurred by error measurement. Therefore, a robust regression technique (Huber Regression) and two types of ensemble learning (Random Forest and Gradient Boosting) were chosen because those machine learning algorithms were not sensitive to the presence...