... that both developed methods are well-calibrated probabilistic methods. Also, computer software was developed during this research to make the process of calculations more convenient. production control production monitoring machine learning Bayesian Inference Upstream Oil & Gas mcmc...

... was Logarithmic sigmoid (logsig). The high accuracy of the developed model confirmed the importance of compiling the drilling fluid properties with the drilling parameters. drilling fluid selection and formulation machine learning drilling fluid chemistry neural network drilling fluids and materials...

..., integration of the developed model into the drilling system will allow for real-time prediction of the concentration of the cuttings (i.e. the amount of cuttings present in the wellbore) and, hence, the cleaning efficiency during the drilling operation. machine learning Engineering correlation...

... fuzzy logic solution gas drive reservoir correlation Oil Well society of petroleum engineers oil flow rate artificial neural network reservoir machine learning neural network IPR Inflow Performance Relationship Fetkovich well performance Upstream Oil & Gas flow rate solution gas...

... serve as a practical resource for drilling through the Hartha formation. well control machine learning Artificial Intelligence Upstream Oil & Gas Leverage plot Polymer Mud fcl mud drilling operation operational drilling parameter ROP model ECD model coefficient drilling fluids...

... chemistry drilling fluid property Upstream Oil & Gas plastic viscosity prediction machine learning drilling fluids and materials abdulraheem viscosity artificial neural network real time prediction correlation coefficient apparent viscosity neuron Artificial Intelligence drilling fluid...

... compositional reservoir fluid variations as well as relative permeability in universal workflows. Artificial Intelligence flow in porous media machine learning neural network enhanced recovery Upstream Oil & Gas blind test injection design saturation Fluid Dynamics prediction miscibility...

... production history, the PCM concept should be used as it will produce a higher quality initial geomodel and significantly reduce the time required to obtain a history matched model without the need for significant modifications. machine learning flow in porous media drillstem/well testing History...

... fluid selection and formulation neural network accuracy machine learning drilling fluids and materials Engineering high accuracy Artificial Intelligence drilling fluid formulation mfv viscosity Elkatatny normalized value society of petroleum engineers rheological property ann technique...

... with high level of certainty. neural network transfer function Upstream Oil & Gas two-phase flow machine learning abdulraheem correlation Artificial Intelligence mechanistic model optimization artificial neural network society of petroleum engineers FBHP Tariq multiphase flow...

... drilling service companies are encouraged and challenged to improve the efficiency and accuracy of RSS mechanisms, improving the hole quality and reducing micro-doglegs. Directional Drilling RSS horizontal section machine learning Upstream Oil & Gas point-the-bit mechanism drilling...

... strategic planning and management drilling operation management multi-rig turnkey development drilling project contractor rig Artificial Intelligence Drilling Project Implementation machine learning drilling contractor society of petroleum engineers startup visual harc Directional Drilling...

... Distribution gas reservoir machine learning neural network flow rate fuzzy logic Thickness prediction gas field society of petroleum engineers artificial neural network reservoir IPR curve correlation coefficient Inflow Performance Relationship relative error Standard Deviation Introduction...

... as a handy tool for reservoir engineer to select the best ASP flood parameters to achieve maximum NPV. machine learning Artificial Intelligence viscosity evolutionary algorithm optimization problem flooding parameter enhanced recovery polymer chemical flooding methods Upstream Oil & Gas...

...% training dataset, which reduces the training dataset size up to 25% compared to the traditional methods. neural network knowledge management training dataset coefficient Artificial Intelligence correlation new approach machine learning accuracy artificial neural network Upstream Oil...

... of two wells with unseen data. machine learning drilling fluid selection and formulation drilling fluid chemistry neural network transfer function Elkatatny average absolute percentage error penetration drilling fluid property drilling fluid formulation ROP model prediction neuron...

... others in predicting Z-factor. This work will help in selecting the best artificial intelligence technique for predicting Z-factor. machine learning neural network Upstream Oil & Gas fuzzy logic Artificial Intelligence regression radial basis function network functional network...

... that the delivery of information can be done effectively and efficiently. When the process is well underway, the next thing is how the knowledge gained can be used in their professional lives so that it can be useful in developing their ability to be a more professional and dynamic workforce. machine learning...

...Problem Definition and Approach As seen, several approaches were made to find PVBT or the optimum injection rate with numerical, empirical, or semi-empirical methods. Nevertheless, no one has tried to use machine learning (ML) to approach the problem. Therefore, the aim of this paper is to use...

... of artificial intelligence techniques as a new method for indirect estimation of rock failure parameters are beneficial especially when the amount of core samples are relatively few. Upstream Oil & Gas cohesion artificial neural network machine learning input parameter prediction elemental 0...