.... chemical spill detection algorithm 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...

..., with various internal and external factors influencing the production process, including geological location, time, production equipment, and internal geological factors. Traditional methods of prediction, such as curve analysis and mathematical modeling, have limitations in terms of accuracy due...

... and ensuring fair comparison between different features or variables, thereby improving the accuracy and efficacy of machine learning models. The following equation was employed for this: Data from an offshore field were gathered from the start of production for this study's model development. 13,500 data...

... network wellbore design drilling fluid property annular pressure drilling drilling fluids and materials artificial intelligence well control drilling fluid management & disposal random forest regressor conference drilling fluid chemistry accuracy dataset wellbore integrity machine...

... techniques for assessing asphaltene stability are time-consuming and expensive. Extant-developed models provide quick stability outcomes, but high accuracy remains a major drawback of these models. Considering these limitations, this study developed a hybrid supervised machine learning (ML) model...

... individual models showed high R 2 score above 0.9 and lower RMSE below 0.1, further optimization using ensemble voting regression indicated improved accuracy in model performance as overall R 2 score and RMSE values rose above 0.9 and further below 0.01 respectively. In general, the SVM showed better...

...-effectiveness and accuracy in precision. Several intelligent systems have been utilized to develop models that effectively predict PVT properties. An example of such a system is the ensemble intelligent system. This method functions by using multiple learning algorithms to ensure better performance over...

... learning technique petroleum engineer reservoir characterization correlation compressive strength uc prediction selection prediction geological subdiscipline accuracy unconfined compressive strength real-time prediction random forest Introduction Geomechanical properties of rock...

... techniques. The result showed that the CatBoost model predictions demonstrate 98% accuracy compared to measurements. Furthermore, its forecast suggests that in-situ superficial gas velocity is the most influential variable affecting flow pattern, followed by superficial liquid velocity, inclination angle...

... leakages across the pipeline. Machine learning algorithms which include Recurrent Neural Networks, and K-nearest neighbourhood are built and trained with operational data to derive the optimal learning model. Also, each model's performance metrics were evaluated to measure the model's accuracy...

... engineering predict permeability model africa government norway government reservoir characterization accuracy regression natural ga science variance node Introduction Traditionally, permeability has been determined using laboratory and field tests which can be time-consuming, prone to human...

... level. Results from this approach can also be used in the absence of a reliable well test. nigeria government artificial intelligence africa government prediction formula upstream oil & gas algorithm engineering equation wellhead machine learning approach accuracy ml algorithm...

... Abstract Time and cost estimation and the accuracy of it, are central to engineering design and forms the basis for economic analysis of projects. There are several factors that could result in cost or schedule overruns ranging from unplanned non-productive time, inefficiencies, changes...

... model was trained with nine out of thirty-three features, while the Support Vector Regression (SVR) model used twenty-eight features for its training. The SVR model had a higher variance (0.48), accuracy (92.41%), and lower Mean Absolute Percentage Error (MAPE) of 7.59% compared to the LR model's...

... an appreciable degree of accuracy as shown in Figure 1 . The resulting information from the analysis estimation for pre-flush acidization volume for different flow reservoir geometries was conclusive as different parameters were varied to estimate the volume of acid. The modified flow geometry was further...

... accurate. The root means square error (RMSE) for the Nigerian, and Global correlations are 0.0033, and 0.0256 respectively. The two correlations are reliably better in terms of accuracy than the existing correlations. The new correlations would facilitate a more accurate reservoir characterization...

... and their parameters (distribution coefficient, C0, and the drift velocity, uD) in a modified form. The drift flux parameters were incorporated to cater for the impacts of mixing of the liquid and gas droplets. The newly developed Model predicted the critical velocity with a fitting accuracy of coefficient...

... by the model. Figure 9 Fitted vs Residual Plot showing the error margin made by the model predictions. The further the dots are from the dotted line, the larger the error made in the predictions by the model. High accuracy by the model can be inferred from the dots clustered around the dotted line...

... with 11 variables with 80% of the dataset utilized for training and 20% for testing purposes. A 10-fold cross validation technique was implemented for model validation purposes. The accuracy of each model was evaluated using the Root Mean Square error metric. After model evaluation, all four models...

... Table 4.1 Average comparative result of the optimizers OPTIMIZER MAP RMSE VALIDATION ACCURACY MAPE ADAM 27594.564008213747 166.116116039997 96.4173134736893% 112.44575692779681 SDG 83072.612280458359 288.223198720121 77.459785901114% 245.4297640023119...