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Keywords: artificial neural networks
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Proceedings Papers
Proc. ASME. OMAE2022, Volume 10: Petroleum Technology, V010T11A016, June 5–10, 2022
Paper No: OMAE2022-81524
..., artificial neural network (ANN) was used to develop a model that can predict the filtrate invasion of nano-based mud under wide range of temperature and pressure up to 350 °F and 500 Psi, respectively. Seven types of nanoparticles with size and concentration ranges from 15 to 50 nm and 0 to 2.5 wt...
Proceedings Papers
Proc. ASME. OMAE2021, Volume 10: Petroleum Technology, V010T11A007, June 21–30, 2021
Paper No: OMAE2021-63094
... most fluid-related issues in drilling. The review discusses various ML methods, their theory, applications, limitations, and achievements. machine learning digital twinning artificial neural networks artificial intelligence drilling fluids automation Proceedings of the ASME 2021 40th...
Proceedings Papers
Proc. ASME. OMAE2021, Volume 10: Petroleum Technology, V010T11A008, June 21–30, 2021
Paper No: OMAE2021-63653
... operations, instead of using mechanistic or empirical methods. The selected models include Artificial Neural Networks, Random Forest, and AdaBoost. The training of the models is determined using the experimental data regarding cuttings transport tests collected in the last 40 years at The University of Tulsa...
Proceedings Papers
Djoni E. Sidarta, Nicolas Tcherniguin, Ho-Joon Lim, Philippe Bouchard, Mengchen Kang, Aurelien Leridon
Proc. ASME. OMAE2021, Volume 1: Offshore Technology, V001T01A033, June 21–30, 2021
Paper No: OMAE2021-63326
... has posed a real challenge to the The use of an Artificial Neural Network (ANN) for detection ANN model as its prediction accuracy has decreased significantly. This paper presents an adaptive method that can of mooring line failure has been a growing subject of discussion be implemented...
Proceedings Papers
Lucas Pereira Cotrim, Henrique Barros Oliveira, Asdrubal N. Queiroz Filho, Ismael H. F. Santos, Rodrigo Augusto Barreira, Eduardo Aoun Tannuri, Anna Helena Reali Costa, Edson Satoshi Gomi
Proc. ASME. OMAE2021, Volume 1: Offshore Technology, V001T01A003, June 21–30, 2021
Paper No: OMAE2021-62674
... This work was nanced in part by the Coordenac¸a o de Aperfeic¸oamento de Pessoal de N´ vel Superior (CAPES Finance V001T01A003-10 Copyright © 2021 by ASME floating offshore platforms artificial intelligence artificial neural networks mooring system design Abstract Abstract The current design...
Proceedings Papers
Amir Muhammed Saad, Florian Schopp, Asdrubal N. Queiroz Filho, Rodrigo Da Silva Cunha, Ismael H. F. Santos, Rodrigo Augusto Barreira, Eduardo Aoun Tannuri, Edson Satoshi Gomi, Anna Helena Reali Costa
Proc. ASME. OMAE2021, Volume 1: Offshore Technology, V001T01A002, June 21–30, 2021
Paper No: OMAE2021-62413
... . In Offshore Technology Conference, Offshore Technology Conference, pp. 1 12. V001T01A002-10 Copyright © 2021 by ASME mooring system failure detection floating offshore platforms artificial neural networks multilayer perceptron Abstract Abstract A failure in the mooring line of a platform...
Proceedings Papers
Proc. ASME. OMAE2021, Volume 2: Structures, Safety, and Reliability, V002T02A037, June 21–30, 2021
Paper No: OMAE2021-62304
... they treat constraints. Constraints using machine learning in the form of artificial neural networks are unavoidable in engineering, aimed at preventing failures and (ANN). A surrogate model is afterwards utilized in optimization meeting all sorts of requirements we put on the system. In based on ANN...
Proceedings Papers
Proc. ASME. OMAE2020, Volume 6B: Ocean Engineering, V06BT06A019, August 3–7, 2020
Paper No: OMAE2020-18967
...ACTIVE ABSORPTION OF RANDOM WAVES IN WAVE FLUME USING ARTIFICIAL NEURAL NETWORKS Áureo I. W. Ramos COPPE/UFRJ Rio de Janeiro, RJ, Brazil Antonio C. Fernandes COPPE/UFRJ Rio de Janeiro, RJ, Brazil Vanessa M. Thomaz CT/UFRJ Rio de Janeiro, RJ, Brazil ABSTRACT A wave flume is primarily intended...
Proceedings Papers
Gabriel Mattos Gonzalez, Marcos Queija de Siqueira, Marina Leivas Simão, Paulo Maurício Videiro, Luis Volnei Sudati Sagrilo
Proc. ASME. OMAE2020, Volume 2A: Structures, Safety, and Reliability, V02AT02A063, August 3–7, 2020
Paper No: OMAE2020-18868
...ON THE USE OF ARTIFICIAL NEURAL NETWORKS FOR ESTIMATING THE LONG-TERM MOORING LINES RESPONSE CONSIDERING WIND SEA AND SWELL Gabriel Mattos Gonzalez1, Marcos Queija de Siqueira, Marina Leivas Simão, Paulo Maurício Videiro, Luis Volnei Sudati Sagrilo Federal University of Rio de Janeiro Rio de...
Proceedings Papers
Proc. ASME. OMAE2007, Volume 4: Materials Technology; Ocean Engineering, 401-409, June 10–15, 2007
Paper No: OMAE2007-29171
... 22 05 2009 A large number of ocean activities call for real time or on-line forecasting of wind wave characteristics including significant wave height ( Hs ). The work reported in this paper uses statistics, and artificial neural networks trained with an optimization technique called...
Proceedings Papers
Proc. ASME. OMAE2003, Volume 1: Offshore Technology; Ocean Space Utilization, 275-284, June 8–13, 2003
Paper No: OMAE2003-37148
... an artificial neural network based model has been developed. The methodology of a response based approach applied to a turret-moored FPSO is presented in the flow chart shown in Fig. 1. The procedures involved entail the following tasks: 1) Building up a mathematical model in order to determine the loads...
Proceedings Papers
Proc. ASME. OMAE2004, 23rd International Conference on Offshore Mechanics and Arctic Engineering, Volume 2, 703-710, June 20–25, 2004
Paper No: OMAE2004-51065
... approaches based on LEFM have been proposed in this regard. Each of them uses different methods for estimating Stress Intensity Modification Factor (Y). In this research two types of Artificial Neural Networks (ANN) are trained for predicting the Y factor: Radial Basis Function (RBF) and Multi Layer...