... the computational expense. Step 4: If the errors of the predicted response between the high-fidelity model and Bayesian MCMC are high, we need to revisit the accuracy of the BiLSTM and the prior information on the uncertainty parameters. We demonstrate the proposed method using a 3D fractured geothermal reservoir...

... Abstract Integrated reservoir studies for performance prediction and decision-making processes are computationally expensive. In this paper, we develop a novel linearization approach to reduce the computational burden of intensive reservoir simulation execution. We achieve this by introducing...

... prediction for no-flow boundary conditions. However, it provides acceptable predictions for constant pressure boundary conditions. We also assessed the capability of the PIML method in handling fractures. The results indicate that the PIML can provide accurate predictions for parallel fractures subjected...

..., the model is tested over large variations of well control settings. Accurate pressure and saturation solutions are predicted along with the injection and production well quantities using the proposed approach. Errors in the predicted quantities of interest are reduced with the increase in the number...

... in this work. machine learning reservoir simulation fluid dynamics artificial intelligence deep learning geomodel flow in porous media history matching es-mda procedure realization prediction application production well neural network upstream oil & gas time step evaluation...

... and works seamlessly on any computational platform (from laptop computers to workstations, to clusters and supercomputers with thousands of processors). reservoir characterization flow in porous media upstream oil & gas flow assurance enthalpy ada cluster water inflow prediction...

... are presented that illustrate homogeneous and drift-flux flow model differences for various well scenarios. thermal method reservoir simulation well model sagd circulation steam holdup light blue circle steam-assisted gravity drainage prediction well trajectory fraction inclination gas holdup...

... features in the CNN structure which are crucial for achieving high accuracy of predictions for the DPDP model closures, and put forward the corresponding CNN architectures. Obtaining a suitable training dataset is challenging because i ) it requires a dedicated effort to map the fracture geometries; ii...

... Abstract We present a novel reservoir simulator time-step selection approach which uses machine-learning (ML) techniques to analyze the mathematical and physical state of the system and predict time-step sizes which are large while still being efficient to solve, thus making the simulation...

... and convergence behavior. flow in porous media reservoir simulation hydraulic fracturing upstream oil & gas modeling & simulation complex reservoir average ratio timestep case 2 locality equation of state fluid dynamics prediction convergence behavior fracture network iteration...

... successfully captured the physics in the high-fidelity model. The Bayesian-LSTM MCMC produces an accurate prediction with narrow uncertainties. The posterior prediction through the high-fidelity model ensures the robustness and precision of the workflow. This approach provides an efficient and high-quality...

... matrix of the total error during history matching. Based on the results, we conclude that a good parameterization of the error model is needed in order to obtain good estimate of physical model parameters and to provide better predictions. In this study, the last three approaches (i.e. 4, 5, 6...

...-driven model Upstream Oil & Gas network model commercial simulator geo-cellular model history matching Artificial Intelligence prediction producer gpsnet model reservoir simulation observation data well case reservoir management simulator well point historical data society...

... reduction methodologies to predict multi-phase flow dynamics. The use of reduced-order model concepts is important for constructing robust deep learning architectures. The reduced-order models provide fewer degrees of freedom and allow handling the cases relevant to reservoir engineering that is limited...

... Muerta unconventional shale formation in Argentina. Given relatively moderate data requirements, we show that it is possible to attain a high level of predictability from hidden field state variables and well production data. As the main conclusion of this work, EDMD stands as a promising data- driven...

... simulations. For the phase stability tests, an ANN model is built to predict the saturation pressures at given temperature and compositions, and consequently the stability can be obtained by comparing the saturation pressure with the system pressure. The prediction accuracy is more than 99% according to our...

...   . The sensitivity matrix G( m ) is a N d h × N m matrix in which the entry of the ℓ th row and j th column is represented by the partial derivative of the ℓ th predicted datum ( ℓ th entry of the vector of reservoir predictions for the history-matching period, d h ) with respect...

... and production forecasts. The bandwidth and computational throughput provided by GPUs allow such simulations to be performed extremely fast using only a modest amount of hardware. Through this case study, we show that by coupling an advanced history matching, prediction and optimization tool with a fast GPU...

... Abstract When many daily measurements of a thermal EOR field are taken throughout production, it is not cost effective to manually interpret trends to update reservoir models, so we developed an automated data-driven approach for production prediction using machine learning techniques...

... identify large-scale geological features such as faults. INSIM-FT remedies this INSIM deficiency. The reliability of INSIM-FT for history-matching, future reservoir performance prediction and reservoir characterization is validated with two synthetic models, and its performance is compared...