... petroleum engineer artificial neural network correlation coefficient performance validation algorithm predict downhole condition pressure Introduction and (4) b’ = [ 0.247 Q + 1.38 ( Q , + Q = T Q = ] Where QS = volumetric flow rate of solid...

... machine learning artificial intelligence accuracy extraction machine learning approach remote dataset pollution information society ecosystem oil spill detection solberg journal monitoring classification health artificial neural network Introduction Oil spill pollution plays...

... capture the underlying trend in the time-series dataset with the Levenberg-Marguardt optimized-neural network having a faster convergence time of 10 seconds, higher regression value of 0.999 and lower MSE value of 0.0489. The structure of an artificial neural network is shown in Fig. 2 while...

... from gas flaring is known to have deleterious effects on the environment and they constitute a major source of global warming. Flare gas volume, temperature, gas composition and other meteorological factors are major parameters in gas flaring processes. Using Artificial Neural Network, a model...

... thermal conductivity of rocks which is cost effective, and practically achievable. Model development During the model development stage of this study, 3 machine learning algorithms alongside Artificial Neural Networks were employed in the prediction of thermal conductivity of geothermal rocks...

... for the purpose of accurately predicting monthly natural gas spot prices. Henry Hub natural gas spot price data from January 2001 to November 2021 were utilized alongside four machine learning algorithms namely; Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest Regressor...

... ties to mathematical optimization, which provides the field with methods, theory, and application domains. algorithm artificial neural network deep learning machine learning architecture upstream oil & gas artificial intelligence learning algorithm dataset reservoir neural network...

... of determination of 0.989% after GA optimization. artificial intelligence rop algorithm upstream oil & gas evolutionary algorithm dataset neural network machine learning prediction correlation driven model genetic algorithm drilling artificial neural network application accuracy...

... were utilized to populate the petrophysical parameter in this study. Sequential Gaussian Simualtion (SGS) Abstract This study attempts to estimate permeability from well logs data and also predict values from existing rock sections to points that are missing using Artificial Neural Network...

..., the application of Artificial Neural Network is therefore investigated for drift velocity determination, in a bid to develop an improved generalised model. Basics of Artificial Neural Network (ANN) In the past three decades, Neural Networks have become of interest to fluid engineers, and supervised...

...), Ridge and Lasso regression; Support Vector Regression, Artificial Neural Networks (ANN) as well as Classification and Regression Tree (CART) based algorithms including Decision Trees, Random Forest, eXtreme Gradient Boosting (XGBoost), Gradient Boosting and Extremely Randomized Trees (ERT...

... Several researchers have in these recent times have resorted to the use of Artificial Neural Network (ANN) to predict oil and gas production rates in other to address the weaknesses and limitations of the decline curve empirical and theoretical correlation methods. Artificial Neural Networks...

... known as an activation function once the firing threshold of the neuron has been exceeded. Artificial Neural Network (ANN) is a Supervised Machine Learning method which employs a collection of interconnected artificial neurons to extract patterns from a given data set. In designing an ANN model...

... Abstract An artificial neural network (ANN) was developed to predict skin, a formation damage parameter in oil and gas drilling, well completion and production operations. Four performance metrics: goodness of fit ( R 2 ), mean square error (MSE), root mean square error (RMSE), average...

... layers, which could lead to over estimation or under estimation of the acquifer susceptibility. Artificial Neural Network (ANN) An Artificial Neural Network (ANN) is a processing mathematical model that's bases its principle of functioning by the way biological nervous systems, like the brain...

... Abstract With the evolution of technology, data is a major sector of engagement in the oil and gas industry. Various studies are done on the subject of artificial intelligence and Artificial Neural Networks are commonly employed. The study seeks to apply artificial intelligence...

... in a lot of redundancy because the down-hole equipment is exposed to mechanical failure. ANN Theoretical Framework Model Development Li et al (2014) presented a combined approach involving a calculation procedure using multiphase correlation and Artificial Neural Network models. Back...

... for the use of tools of big data, data analytics, artificial intelligent models alongside a real time transient model to improving leak detection. Belsito et al. (1998) used an Artificial Neural Network (ANN) to build leak detection systems. This system can detect and locate leaks down to 1% of flow rates...

... the effectiveness of Artificial Neural Network (ANN) for predicting hydrate formation temperature to the effectiveness of other hydrate temperature prediction correlations such as: Towler and Mokhtab correlation, Hammerschmidt correlation and Bahadori and Vuthalaru correlation. The ANN was trained using 459 hydrate...

...Methods The artificial neural networks (ANN) can be described as mathematical test systems in wherein data is processed in a similar way to that of a biological neural system works in terms of its intricacy and functionality ( Mohaghegh, 2000 ). A basic meaning of an artificial neural system...