... simulation is essential for various reservoir engineering processes such as history matching and field development plan optimization but is typically an intensive and time-consuming process. The aim of this study is to compare various deep-learning algorithms for constructing a machine-learning (ML) proxy...

... government modeling & simulation dataset united states government artificial intelligence prediction permeability prediction figure algorithm reservoir characterization pore porosity gaussian process regression model segmentation correlation toolkit flow in porous media machine learning...

... Machine learning can also be helpful to improve NMR models and predict petrophysical properties for carbonate rocks such as porosity, permeability, and irreducible water saturation. To apply ML, the selected algorithm is trained on the available dataset to establish a relationship between...

... meaningful dimensionless parameters to be incorporated into the machine learning Sonin (2001) claimed prior to obtaining a quantitative answer that dimensional analysis reduces a complex physical problem to the simplest and most economic form. Astarita (1997) noted on the estimation of orders...

... specific gravity, viscosity, composition etc. The challenge is to develop a new approach which overcomes the current shortcomings. In this paper a new machine learning based model has been developed using Interactive Multivariate Linear Regression (I-MLR) method by integrating a large number of datasets...

... keeping the parametrization of uncertainties as practical as possible. reservoir optimization problem history matching neural network upstream oil & gas reservoir simulation artificial intelligence machine learning boundary evolutionary algorithm north kuwait criteria asia government...

..., and geologicalwise. Ultimately, an equation to correlate between the results was developed graphically and using logistic regression techniques. permeability flow in porous media equation probe permeameter upstream oil & gas machine learning darcy asia government permeameter spe formation...

... engineer neural network machine learning concentration fno kaolinite co 2 permeability tariq exhibition simulator subsurface storage climate change society mole fraction abdulraheem mineral prediction anorthite Introduction Mineral trapping is recognized as the most secure carbon...

... management reservoir simulation reservoir characterization netherlands government injection chemical flooding methods tubing correlation diameter permeability realization subsurface storage artificial intelligence machine learning workflow co2 injection experiment united states government...

... is parametrized by 3 guide points (shown as red dots in Figure 2 ), with each point being defined by 3 coordinates (x, y, z). asset and portfolio management machine learning upstream oil & gas artificial intelligence reservoir optimization problem uae government asia government optimization...

... zoomed-in view of a local region in the reservoir, which is required to take actionable decisions. This work proposes using super-resolution techniques, recently developed using machine learning methods, to obtain fine-scale flow behavior given flow behavior from a low-resolution simulation...

... bias by automating the Pc-based pore network characterization and applying machine learning (ML) to capillary pressure modeling. We have also built advanced analytics dashboards to allow for QC and interactive adjustments by the user. The solution defines the pore system modality based on the automated...

... with a modern machine learning approach to "Evergreen" forecasts in reservoir assets. Consequently, the simulation resulted in a sub-optimal shortcut between model updates and inconsistencies in production forecasting. Moreover, applying deep learning methods to focus on the critical reservoir properties...

... government composition scale inhibition equation of state wax inhibition scale remediation machine learning remediation of hydrates asia government hydrate inhibition envelope enhanced recovery wax remediation eos asphaltene remediation production chemistry hydrate remediation flow assurance...

... Abstract In giant reservoirs, production sustainability strongly depends on the identification of opportunities for infill drilling. This paper presents the use of Machine Learning to speed-up and improve the efficiency of the evaluation of future infill wells, in an effort to optimize field...

... and high model complexity. realization parameterization history matching machine learning workflow reservoir simulation ensemble localization pda mismatch information upstream oil & gas artificial intelligence streamline production data analysis fpcm computation similarity fluid...

... asia government calculation neural network machine learning upstream oil & gas china government pvt measurement artificial intelligence prediction molar composition reduction diagram accuracy simulation application fraction mole fraction procedure sour ga composition...

... simulation tracer asia government machine learning transformation nanoprobe temporal resolution nanoprobe tracer algorithm producer resolution tracer breakthrough spe annual technical conference united states government artificial intelligence breakthrough exhibition presented chem-probe...

... design often results in unfavorable oil recovery. This study investigates WAG optimization in a sandstone field using a hybrid numerical-machine learning (ML) model. In this work, we present a hybrid neural approach for optimizing the WAG injection process that can be easily integrated as a workflow...

... machine learning seismic inversion geostatistical inversion realization interparticle pore correction facies abu dhabi estimation rpm rock physics modelling uae government geostatistical seismic inversion prediction correlation deterministic inversion Introduction The two adjacent...