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Keywords: prediction
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Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, April 10–11, 2019
Paper Number: SPE-193838-MS
... 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) outperform the others in terms of the quality of the estimated parameters and the prediction accuracy (reliability...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, April 10–11, 2019
Paper Number: SPE-193855-MS
... & 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 of petroleum engineers...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, April 10–11, 2019
Paper Number: SPE-193904-MS
... 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...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, April 10–11, 2019
Paper Number: SPE-193896-MS
... i = 0 , i = 1 , ⋯ , N c . Artificial Intelligence Upstream Oil & Gas saturation pressure machine learning PVT measurement phase splitting calculation reservoir simulation calculation neural network conventional method prediction stability test three-component...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, April 10–11, 2019
Paper Number: SPE-193918-MS
... Artificial Intelligence approximation machine learning Upstream Oil & Gas minimization problem history matching proposal distribution reservoir simulation gmm approximation covariance matrix reservoir simulator prediction reservoir simulation run Reynolds posterior PDF...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, April 10–11, 2019
Paper Number: SPE-193912-MS
... 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...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, February 20–22, 2017
Paper Number: SPE-182660-MS
... well optimization problem Fluid Dynamics prediction data-driven model relative permeability curve Abstract We develop a new data-driven model for the assisted history matching of production data from a reservoir under waterflood. Although the model is developed from production data...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, February 20–22, 2017
Paper Number: SPE-182637-MS
... Yet having an ensemble of predictions tells us little about whether those predictions are biased (too high or too low) or whether the range of predictions represents the true uncertainty range. All too often, the range of uncertainty is underestimated, and the bias is upwards. The UK Department...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, February 20–22, 2017
Paper Number: SPE-182696-MS
... production predictor. Two possible explanations could be that (i) this coefficient could be correlated to another measurement that was selected by Lasso, or (ii) fluid levels can change greatly in this previously produced region while extracting little additional oil. Because the step two predictions...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Symposium, February 23–25, 2015
Paper Number: SPE-173298-MS
... Abstract History matching is commonly performed in reservoir simulations to calibrate model parameters and to improve prediction accuracy. History matching problems often have non-unique solutions, i.e., there exist different combinations of parameter values that all yield the simulation...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Symposium, February 23–25, 2015
Paper Number: SPE-173315-MS
... matching field water cut data, two model parameters, the Koval factor and the producer drainage volume, are estimated. Nevertheless, it is challenging to use the Koval approach as a predictive model directly since the injection contribution into each producer in a future time horizon must be evaluated...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Symposium, February 23–25, 2015
Paper Number: SPE-173213-MS
... units, where each unit has two specific parameters: transmissibility and control pore volume. By solving the mass material balance and front tracking equations for the control units, the interwell fluid rates and saturations are obtained so that phase producing rates can be predicted. INSIM is applied...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Symposium, February 23–25, 2015
Paper Number: SPE-173206-MS
... heterogeneous system. The data-driven component allows for discovering secondary physical flow or production trends that reside hidden in the data that in turn, may aid at complementing and extending the predictability of the whole surrogate model. The data-driven component relies on machine learning techniques...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Symposium, February 18–20, 2013
Paper Number: SPE-163584-MS
... Abstract The present paper proposes a novel non-intrusive model reduction approach based on Proper Orthogonal Decomposition (POD), the Discrete Empirical Interpolation Method (DEIM) and Radial Basis Function (RBF) networks to efficiently predict production of oil and gas reservoirs. Provided...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Symposium, February 18–20, 2013
Paper Number: SPE-163612-MS
..., predictions are shown and conclusions are drawn. reservoir simulation neighborhood ml scheme grid flow in porous media slope limiter Fluid Dynamics application cell center finite-volume method gradient Upstream Oil & Gas prediction flux interface tracer concentration Péclet number...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Symposium, February 18–20, 2013
Paper Number: SPE-163675-MS
... matching multiple data assimilation p-impedance data porosity reservoir simulation seismic data prediction EnKF Emerick ensemble data assimilation Reynolds ensemble Kalman filter Reservoir Characterization Upstream Oil & Gas correspond vector manual history initial ensemble history...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Symposium, February 21–23, 2011
Paper Number: SPE-140882-MS
.... The target application is the design of new multi-lateral complex wells within an established large study. The optimized well is included in the prediction scenario of a history matched model. The target full field models are very large (millions of cells) and have large amounts of wells (hundreds...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Symposium, February 21–23, 2011
Paper Number: SPE-141893-MS
... artificial intelligence upstream oil & gas coefficient expansion ensemble kalman filter state variable ensemble size 86 different run permeability field history matching prediction probabilistic collocation method kalman filter Sequential data assimilation with continuous production...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Symposium, February 21–23, 2011
Paper Number: SPE-141216-MS
... Abstract Because of its ease of implementation, computational efficiency and the fact that it generates multiple history-matched models, which conceptually allows one to characterize the uncertainty in reservoir description and future performance predictions, the ensemble Kalman filter (EnKF...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Symposium, February 21–23, 2011
Paper Number: SPE-141336-MS
... Abstract Although there is intense interest in employing the ensemble Kalman filter (EnKF) for assisted history matching, it is well known that the approximation of covariance matrices from a finite ensemble of states and a finite ensemble of predicted data vectors can lead to a large...

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