... machines have become the leading drivers for maintaining economic prosperity and providing a giant job creation platform. Consequently, this will render some jobs no longer in use ( Hofmann & Rüsch, 2017 ). The formation of Artificial Intelligence, Big Data, Machine Learning, Robots, and other...

... measurement machine learning validation upstream oil & gas correlation accuracy reservoir simulation gravity multiple non-linear regression mnlr artificial intelligence crude oil application neural network support vector machine svm study petroleum technology factor correlation error...

... reservoir surveillance production monitoring energy economics supply and demand pricing scenario mmbbl hubbert reserves evaluation social responsibility modeling & simulation market analysis machine learning forecast transition projection oil end-use optimization framework alalade oil...

... Regression model, well test data and well performance model to predict the expected water cut while changing the operating conditions of a well. We had used four wells to demonstrate the application of machine learning to produced water prediction under different operating conditions. Well performance model...

... and classification machine learning technique that generates a prediction model in the form of an ensemble of weak prediction models. This method creates a model step by step and then generalizes it by permitting the optimization of any differentiable loss function. Gradient boosting is an iterative process...

... curve is its inability to adapt predictions to different past operational scenarios and uncertainties. With the emergence of big data and increasing computational power, machine learning techniques are increasingly being used to solve problems like this in the oil and gas industry. The objective...

... correlations, with varying range of applicability and predictive capability, are typically relied upon by researchers to predict this parameter. This work therefore presents the development of a machine learning approach for predicting drift velocity in horizontal and non-horizontal pipelines. Python...

... saturation basin-ward (South-west), in the direction of the frontier prospect to site more wells. lithology well logging environment artificial intelligence machine learning structural geology reservoir characterization elso-12a sequence elso-15a reconstructing deposition environment...

... recovery factors of 3.83%, 3.81%, 2.9%, 1.85% and 6.1% respectively. sagd machine learning reservoir artificial intelligence upstream oil & gas modeling & simulation composition stb injection condensate recovery society steam-assisted gravity drainage enhanced recovery thermal...

... liquid viscosity determination journal neural network artificial neural network algorithm validity upstream oil & gas machine learning artificial neural network approach drift velocity prediction Introduction The world fossil fuels are produced from both conventional oil resources...

... flow assurance flowrate experiment algorithm temperature prediction neural network machine learning plot molecule gas-dominated system regression pressure ridge regression pipeline evaluation artificial intelligence hydrate dataset hydrate risk management cross-correlation plot...

... algorithms have been applied, however, these algorithms are limited to certain applications due to the available data utilized and high computation time. It is hence pertinent to consider a robust model that can meet up with these requirements. In this study, a proposed hybrid machine learning probabilistic...

... of Barium Sulphate (BaSO 4 ) and Calcium Carbonate (CaCO 3 ) oilfield scales built using machine learning. Thermodynamic and compositional properties including temperature, pressure, P H , CO 2 mole fraction, Total Dissolved Solids (TDS), and ion compositions of water samples from wells where BaSO 4...

.... In this study, a voting classifier machine learning model was developed to predict the lithology of different lithologies using an assembly of different classification algorithms: Support Vector Machine (SVM), Logistic Regression, Random Forest Classifier, K-Nearest Neighbor, and Multilayer Perceptron (MLP...

... is to achieve a high optimal rate of penetration in safe and stable drilling conditions. Several machine learning models have been developed to predict ROP, however, there have been few studies that consider the different optimization algorithms needed to optimize the conventional developed models other than...

.... algorithm artificial neural network reservoir neural network fracture aperture prediction upstream oil & gas artificial intelligence learning algorithm dataset deep learning machine learning architecture normalization ann geothermal reservoir determination coefficient neuron...

... of seasonal changes, and production anomalies in the life of the reservoir. dataset deep learning neural network artificial intelligence machine learning reservoir upstream oil & gas production forecasting algorithm determination epoch prediction machine learning algorithm information...

... due to the optimization algorithm used. neural network prediction permeability flow in porous media upstream oil & gas machine learning sequential gaussian simulation dataset predictive model algorithm artificial neural network variogram fluid dynamics artificial intelligence...

... to have key influence on the dependent variable Bubblepoint pressure The random forest model developed uses ensemble learning approach, combines predictions from multiple machine learning algorithms by averaging all predictions to make a more accurate prediction. The ‘forest’ generated by the random...

... enhanced recovery upstream oil & gas artificial intelligence gas injection method cobol chemical flooding methods programming language correlation mmp evolutionary algorithm miscibility pressure empirical correlation screenshot machine learning glaso correlation petroleum engineer...