... reservoir accuracy modeling data-driven predictive analytic penetration optimization Introduction In recent years, penetration rate prediction (ROP) has been given priority attention by drilling engineers because it is directly related to cost and it is completely controlling the overall...

... dynamics asia government europe government norway government prediction data correlation coefficient accuracy artificial intelligence machine learning anfis ann logging data saudi arabia government log analysis module well logging training data permeability prediction artificial...

... indicate that developed ML model can predict NPV with around 96% accuracy (testing score). This gives great confidence in predictions from the trained hybrid model that can be used as a proxy model for reservoir simulations. Coupling the trained hybrid model with Particle Swarm Optimization (PSO...

...-validation. The final model's average accuracy was 77.1% across all folds. Multiple evaluation metrics, including Accuracy, Confusion Matrix, Precision, Recall, and Area Under the ROC Curve (AUC), were used to assess the model and each class against the rest. Feature importance showed an even distribution...

... dynamics upstream oil & gas flow in porous media machine learning united states government reservoir geomechanics geologist rock type reservoir characterization permeability geology artificial intelligence algorithm porosity society simulation dataset highest accuracy svm algorithm...

.... This allows simultaneous modeling and feature selection thus fault detection and diagnosis. They trained fault-specific two-class SVM to detect faulty operations and the feature selection algorithm to improve accuracy and perform fault diagnosis. This showed that loss of information is minimized when using...

... dimension reduction upstream oil & gas accuracy steam injection algorithm oil production prediction scenario field data stimulation development targeting lower carbon cyclic steam stimulation development targeting Cyclic Steam Development Thermal production methods have long been used...

... learning neural network risk management intermediate depth workflow two-step prediction-correction procedure risk factor design specification presented algorithm probability upstream oil & gas information accuracy false negative failure mitigation framework pi-ram reduction false...

... to validate the effectiveness of the various imputation methods; allowing comparisons to be made against the original dataset. The study found that with various sizes of missing data percentages withing the sets, the "filled in" data could be used with surprising accuracy for further analytics. This paper...

... Abstract Accurate oil production monitoring is essential for scheduling well work and optimizing the economic performance of primary and enhanced recovery projects. The significance of water cut monitoring accuracy on oil rate determination is discussed and illustrated. This paper provides...

... Abstract Basin-wide heterogeneity of production in unconventional resources creates additional risk in field development planning. In the past few years, several data-driven models have been developed to increase the accuracy in predicting the recovery from shale gas and tight oil wells...

... performance was controlled and validated for quality assurance using multifold cross validation. Results were analyzed and compared by identifying their accuracy and precision. All models performed well and although the error difference between their performances was minor, the one with highest score...

... engineers’ unproductive time such as data manipulation and allowing them to focus on value-added works like analysis and optimization. thermal method SAGD machine learning neural network temperature change accuracy prediction model Artificial Intelligence Upstream Oil & Gas target...

... faults, or fault intersections without sacrificing either their geometry or their hydromechanical properties. Sacrificing the curved geometry of faults results in a loss of accuracy in computing the induced stresses in the basement region, which is known to host most of the recorded induced seismic...

... and is affected by solver algorithms and initial guesses. In addition to the innate limitations, due to the lack of bottomhole pressure data in the Lost Hills and its high oil viscosity (∼ 20 °API), the accuracy of Lost Hills CRM solution is not enough to be used in daily operations. Stochastic CRM (SCRM...

... and support vector machine. Classification techniques were used to generate an accurate suggestion of where the pumping of a hydraulic fracturing stage starts and ends in a high-frequency treating plot. Results show that flag predictions have a training and validation accuracy of approximately 90 percent...

... was assessed primarily through the time-weighted average of root mean square amplitude and then classed into severity levels. Performance of the classification results was assessed using the predictive accuracy and weighted macro-average of precision obtained using cross validation and presented as confusion...

.... machine learning neural network flowline corrosion Artificial Intelligence Upstream Oil & Gas materials and corrosion riser corrosion classification detection region proposal network model architecture average precision accuracy computer vision technique Pipeline Corrosion deep learning...

... maintenance job would be economic. The logistic regression model ultimately yielded the best classification results (71% prediction accuracy for the producer jobs and 77% for the injector jobs) and proved to be the ML algorithm with the best balance of accuracy, interpretability, and ease of implementation...

... of the results. circulation Artificial Intelligence similar case information new case drilling parameter Upstream Oil & Gas accuracy reasoner database remedial method drilling blind recommendation cement plug similarity drilling report remediation Circulation Event operation case...