... lags. This study introduces an innovative alternative using Artificial Neural Networks (ANNs) to accurately predict formation tops. The ANNs model incorporates drilling mechanical parameters and ROP to address limitations in existing techniques, aiming to predict genuine ROP changes irrespective...

... the strength of six machine learning-based regression models, namely the Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine Regression (SVR), Polynomial Regression (PR), and Multiple Linear Regression (MLR), a comprehensive strategy for the Rate...

.... drilling fluid formulation drilling fluid selection and formulation artificial neural network gel strength artificial intelligence correlation coefficient drilling fluids and materials machine learning drilling operation elkatatny dataset aape accuracy drilling fluid property offshore...

... and covers statistics of crude oils of Pakistan, Iran, UAE, and Malaysia. Resultantly, this allows to move step forward towards the creation of a generalized model. Multiple CI algorithms are considered, including Artificial Neural Networks (ANN) and Artificial Neural Fuzzy Inference Systems (ANFIS). Models...

... simulation analysis artificial intelligence floating production system simulation turret-moored fpso ocean turret cg ann model artificial neural network offshore technology conference fpso floating production storage and offloading presented international conference hpc system sway motion...

... of the soil parameters is based on deepwater CPTu data from Brazilian offshore basins. Three analytical approaches are used in this study (linear and non-linear regressions: second and third degrees). Moreover, Artificial Neural Networks (ANN) (dense, convolutional and recurrent networks) are also employed...

... parameters and operational drilling parameters are both considered. Moreover, an artificial neural network is employed to conduct supervised learning. The four metrics: accuracy, precision, f 1 score and recall are used to evaluate the model. The trained artificial neural network model is employed...

... environments out of the sample. In this paper, the improved methodology used for the Coral South FLNG project is presented. A Radial Basis Function (RBF) Artificial Neural Network (ANN) is used to model the variables impacting the SBS offloading operability. The ANN learns from the results of simulations...

... of the remaining allowable life, allowing key field decisions to be made quickly and accurately. riser machine learning wire thickness armor wire stress tensile armor wire riser annulus outer sheath breach upstream oil & gas thickness artificial neural network flexible riser local analysis...

... well log data application artificial neural network neural network transfer function OA A A C T T re o re A D in m a N R T lo s b K I W p in p b ( ( N b t li s s u c s n TC 2296 rtificial pplicatio rvind Kumar opyright 2012, Offsho his paper was prepare his paper was selecte viewed by the Offsho...

... . Artificial neural networks(ANN) have recently attracted a great deal of attention for their ability to "learn," or "estimate" the mapping operator from training data which describe the mapping relationship. Among neural networks for function approximation, the feed forward network trained by error back...

... as the representative for the class. Neural Network In the recent years, artificial neural networks have gained more and more importance in the field of pattern recognition. Their ability to solve classifi- cation problems where com monly used pattern recognition methods (e.g. distance classifiers) fail, makes them...