A Full Field Model Study of the East Velma West Block Sims Sand Unit Reservoir

1985 ◽  
Vol 37 (08) ◽  
pp. 1429-1440 ◽  
Author(s):  
John D. Bolling
Keyword(s):  
Field Model ◽  
Full Field ◽  
Model Study

A Phase-Field Model Study of Microstructural Effects on the Corrosion of Mg Alloys

2020 ◽  
Vol MA2020-01 (14) ◽  
pp. 975-975
Author(s):  
David Montiel ◽  
Stephen DeWitt ◽  
Alexander F. Chadwick ◽  
Katsuyo Thornton
Keyword(s):  
Phase Field ◽  
Field Model ◽  
Phase Field Model ◽  
Mg Alloys ◽  
Model Study ◽  
Microstructural Effects

scholarly journals Full field model for interleave-chirped arrayed waveguide gratings

2013 ◽  
Vol 21 (6) ◽  
pp. 6928 ◽  
Author(s):  
Bernardo Gargallo ◽  
Pascual Muñoz
Keyword(s):  
Field Model ◽  
Full Field ◽  
Arrayed Waveguide Gratings ◽  
Waveguide Gratings ◽  
Arrayed Waveguide

scholarly journals 2D and 3D simulation of grain growth in olivine aggregates using a full field model based on the level set method

2018 ◽  
Vol 283 ◽  
pp. 98-109 ◽  
Author(s):  
Jean Furstoss ◽  
Marc Bernacki ◽  
Clément Ganino ◽  
Carole Petit ◽  
Daniel Pino-Muñoz
Keyword(s):  
Grain Growth ◽  
Level Set Method ◽  
Level Set ◽  
Field Model ◽  
3D Simulation ◽  
Full Field ◽  
Model Based ◽  
2D And 3D

History Matching of the Norne Full-Field Model With an Iterative Ensemble Smoother

2014 ◽  
Vol 17 (02) ◽  
pp. 244-256 ◽  
Author(s):  
Yan Chen ◽  
Dean S. Oliver
Keyword(s):  
History Matching ◽  
Oil And Gas ◽  
Field Model ◽  
Model Parameters ◽  
Data Types ◽  
Full Field ◽  
Ensemble Smoother ◽  
Main Fault ◽  
Iterative Ensemble Smoother ◽  
Partial Communication

Summary Although ensemble-based data-assimilation methods such as the ensemble Kalman filter (EnKF) and the ensemble smoother have been extensively used for the history matching of synthetic models, the number of applications of ensemble-based methods for history matching of field cases is extremely limited. In most of the published field cases in which the ensemble-based methods were used, the number of wells and the types of data to be matched were relatively small. As a result, it may not be clear to practitioners how a real history-matching study would be accomplished with ensemble-based methods. In this paper, we describe the application of the iterative ensemble smoother to the history matching of the Norne field, a North Sea field, with a moderately large number of wells, a variety of data types, and a relatively long production history. Particular attention is focused on the problems of the identification of important variables, the generation of an initial ensemble, the plausibility of results, and the efficiency of minimization. We also discuss the challenges encountered in the use of the ensemble-based method for complex-field case studies that are not typically encountered in synthetic cases. The Norne field produces from an oil-and-gas reservoir discovered in 1991 offshore Norway. The full-field model consists of four main fault blocks that are in partial communication and many internal faults with uncertain connectivity in each fault block. There have been 22 producers and 9 injectors in the field. Water-alternating-gas injection is used as the depletion strategy. Production rates of oil, gas, and water of 22 producers from 1997 to 2006 and repeat-formation-tester (RFT) pressure from 14 different wells are available for model calibration. The full-field simulation model has 22 layers, each with a dimension of 46 × 112 cells. The total number of active cells is approximately 45,000. The Levenberg-Marquardt form of the iterative ensemble smoother (LM-EnRML) is used for history matching. The model parameters that are updated include permeability, porosity, and net-to-gross (ntg) ratio at each gridblock; vertical transmissibility at each gridblock for six layers; transmissibility multipliers of 53 faults; endpoint water and gas relative permeability of four different reservoir zones; depth of water/oil contacts; and transmissibility multipliers between a few main fault blocks. The total number of model parameters is approximately 150,000. Distance-based localization is used to regularize the updates from LM-EnRML. LM-EnRML is able to achieve improved data match compared with the manually history-matched model after three iterations. Updates from LM-EnRML do not introduce artifacts in the property fields as in the manually history-matched model. The automated workflow is also much less labor-intensive than that for manual history matching.

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