...-preview, resulting in higher F1 scores and improved accuracy. These LLMs demonstrated the ability to synthesize diverse information and provided clear market insights. For long-term forecasting, we integrate news data and LCFS trading data with California’s gasoline and diesel prices, annual CO 2...

... for comparison. geologist dataset production enhancement geology unconventional play steam-assisted gravity drainage petroleum play type enhanced recovery random forest xgboost accuracy neural network downhole intervention data mining reservoir oil reservoir stimulation treatment...

... detection from SEM images. A 75% accuracy score was achieved from the model training, indicating a promising start. This model was found to differentiate successfully between detrital quartz grains and their diagenetic quartz overgrowths; it was also demonstrated that it could identify porosity and other...

... of detected technologies to cover 12 unique real-time telemetry modules such as pressure, temperature, casing collar locator, and gamma ray sondes. For this method, over 50 acquisitions are analyzed with an accuracy of 94%. Electronic firing head signatures for three unique types of firing heads...

..., where GOR increases over time. Results demonstrate the ensemble machine learning model's high accuracy, surpassing traditional Gilbert-type choke correlations, which are constrained by the applicable range of operations variables like GOR and choke diameter. These findings suggest that this approach...

... exceptional accuracy, achieving R2 scores exceeding 98% with RMSE values below 3%. Particularly, the Random Forest model surpassed traditional approaches, achieving an accuracy of approximately 99.7%. These results suggest that these models remain capable of generating reliable and useful projections...

... with amplified confidence. Second, a machine learning model, crafted leveraging the engineered features, comes to the fore, showcasing an impressive accuracy rate that surpasses 85%. What makes this accomplishment particularly remarkable is that such precision was attained by relying solely on conventional logs...

... the effectiveness of the machine learning models in predicting the CO 2 trapping mechanism with a negligible prediction error while ensuring a low computational time. Each model demonstrated acceptable accuracy (R 2 >0.93), with the XGBoost model showing the best accuracy with an R 2 value of 0.999, 0.995...

... 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...

... network artificial intelligence data mining hyperparameter journal reservoir accuracy 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...

...-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...

... norway government prediction fluid dynamics asia government europe government geology well logging module training data artificial intelligence neural network data correlation coefficient accuracy flow in porous media artificial intelligence machine learning anfis permeability prediction...

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

... Network (ANN) approach was relatively fast with a negligible difference in accuracy when compared for air influx and carbon dioxide influx for kick prognosis. The ANN resulted in an accuracy of about 90% and 93% for air-based kick prognosis. While the accuracy was 92% and 94% for carbon dioxide-based...

... steam stimulation development targeting oil production prediction scenario accuracy steam injection algorithm principal component reservoir pressure simulation augmented machine learning approach dimension reduction Cyclic Steam Development Thermal production methods have long been...

... accuracy false negative pi-ram failure mitigation framework reduction downhole practice false positive machine learning neural network design specification presented algorithm probability artificial intelligence casing and cementing intermediate depth workflow two-step prediction...

... 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...

... 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...

... 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...