..., 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...

... 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...

... 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...

... 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...

... 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...

... . 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...

... 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...

...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...

...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...

... 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...

... 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...

... 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...