... enhanced recovery viscosity rheological dataset polymer artificial intelligence machine learning prediction accuracy shear rate architecture concentration atb content oil recovery input feature reservoir condition neural network chemical flooding methods neuron polymer flow...

... the prediction accuracy for one feature results in a deterioration in the prediction accuracy of the other. Several attempts were made to create an automated drilling system; however, these attempts focused on the larger picture of the model and ignored the vital components that the calculated and predicted...

... reservoir geomechanics application abdulraheem algorithm presented efficiency rop prediction accuracy drilling data fuzzy logic data mining geological subdiscipline prediction onepetro mahmoud sensor elkatatny machine learning gamal drilling operation conference alsaihati...

.... Model evaluation is a crucial step that occurs iteratively during the model development process to obtain optimized parameters and ensure high prediction accuracy. Finally, the study reports the optimized model parameters and the obtained results, which can be implemented for real-time vibration...

... simulation reservoir surveillance production control waterflooding conformance improvement wct data-driven prediction method petroleum engineer society algorithm accuracy modeling prediction accuracy prediction petroleum exploration random forest regression model water cut oilfield wellhead...

... in actual production. And it may be difficult to guarantee the prediction accuracy with a single method. In order to accurately predict formation pore pressure, this paper systematically reviewed domestic and foreign literatures on formation pore pressure well logging prediction, explained the methods...

... in predicting geohazard impact to pipeline cannot be quantified. Tackling geohazard impact to pipeline problem in a timely manner is a challenge faced by engineers in managing SPL. Machine Learning had provided a tool to quantify the prediction accuracy of geohazard impact to pipeline and provide the knowledge...

... the highest prediction accuracy. Tariq et al. (2016 ) also developed ANN model to predict sonic logs based on GR, RHOB and NPHI. The ANN model yielded high accuracy results. Tariq (2018 ) later experimented with functional networks to predict sonic travel time in carbonate rocks from the three common well...

... show that the proposed model, with fewer inputs and simpler network architecture than the referenced model, achieves an excellent prediction accuracy of 99.9% even for the testing dataset. Proper data preprocessing significantly improves training efficiency and accuracy. Moreover, the inputs...

... carbonate reservoirs can be predicted. It has been proved that this method is reasonable and feasible. With this method, the prediction accuracy on thin reservoirs can be improved greatly. Compared with the conventional geostatistical inversion results, the 3D multi-level lithofacies-controlled inversion...

... forms of input data are considered to improve the prediction accuracy. By using the degree of deviation from normal as the input data for the prediction model have the highest accuracy. However basic machine learning model contains many input parameters which can't obtain easily. The number of input...

... the model. Hyperparameter tuning of the RF model has been done to improve upon the prediction accuracy. After the parameters are tuned, the mean squared error and R 2 value of the training dataset are 1.77 and 0.98; while that for the testing dataset, they are 13.26 and 0.89 respectively. The closeness...

... for accurately predicting ECD by analyzing the implicit relationship of ECD series data through data mining. machine learning BP neural network Artificial Intelligence autocorrelation graph Upstream Oil & Gas prediction result prediction accuracy hydraulic model traditional hydraulic model...