Reservoir Simulator Employing A Fine-Grid Model Nested In A Coarse-Grid Model

1980 ◽  
Author(s):  
Michael F. Graham ◽  
G. Thomas Smart
Keyword(s):  
Coarse Grid ◽  
Fine Grid ◽  
Grid Model ◽  
Reservoir Simulator

A Nested-Grid Hydrodynamic/Pollutant Transport Model for Nearshore Areas in Hamilton Harbour

1995 ◽  
Vol 30 (2) ◽  
pp. 205-230 ◽  
Author(s):  
Ioannis K. Tsanis ◽  
Jian Wu
Keyword(s):  
Transport Model ◽  
Pollutant Transport ◽  
Coarse Grid ◽  
Fine Grid ◽  
Flushing Time ◽  
Grid Model ◽  
Hamilton Harbour ◽  
Nested Grid ◽  
Water Elevations ◽  
The Impact

Abstract A nested-grid depth-averaged circulation model was developed and applied to three nearshore areas in Hamilton Harbour: the western basin, LaSalle Park waterfront and the northeastern shoreline. The grid sizes used were 100 m for the whole harbour, and 25 m for the three nearshore areas. General features of current circulation and horizontal mixing times under various wind directions and speeds were obtained for the whole harbour using the coarse-grid model. The fine-grid model (water elevations and current information on the open boundaries were obtained from the whole harbour model) then provided current patterns which were used to drive the pollutant transport model. Simulation results reveal that the current in the fine-grid model is close to the current from the coarse-grid model, while more detailed current structures are explored. The water elevations from the fine-grid model agree well with the elevations from the coarse-grid one. The impact of artificial islands was examined by studying changes in current patterns, pollutant peaks, exposure and flushing time in different locations of concern. The design proposed provides: (i) minimum change in the existing current patterns; (ii) avoidance of pollutant hot spots; and (iii) minimum changes in the flushing time of pollutants.

A New Simulation Layer Optimization and Permeability Upscaling Method for Preserving Critical Reservoir Heterogeneity

2021 ◽  
Author(s):  
Dachang Li ◽  
Corneliu-Liviu Ionescu ◽  
Baurzhan Muftakhidinov ◽  
Byron Haynes ◽  
Bakyt Yergaliyeva
Keyword(s):  
History Matching ◽  
Cost Effective ◽  
Coarse Grid ◽  
Oil Field ◽  
Reservoir Properties ◽  
The Caspian Sea ◽  
Fine Grid ◽  
Grid Model ◽  
Compositional Model ◽  
Permeability Upscaling

Abstract Running a fine grid model with 107 - 109 of cells is possible using a supercomputer with 103 - 106 of CPUs but may not be always cost-effective. The most cost-effective way is to use a coarse grid model that is much smaller but with static/dynamic profiles very close to the fine grid model. This paper proposes a new layer optimization and upscaling method with the aim for creating a consistent coarse grid model. Unlike the industry's existing layer optimization and upscaling methods, the proposed method performs layer optimization and upscaling fully integrated with the Lorenz coefficient and curves (LCC). Coarse grid layers and their permeabilities are created by minimizing the difference between fine and coarse grid LCCs. The process consists of static and dynamic optimizations. The former is measured by LCC while the latter by pressure, GOR, and water-cut. A new LCC-based permeability upscaling method is developed to preserve the fine grid multiphase flow behaviors. A satisfactory coarse grid model is achieved when both static and dynamic criteria are met. The proposed method has been successfully applied to a giant carbonate oil field in the Caspian Sea that consists of a matrix dominated platform and a fracture/karst dominated rim. Due to the field's complex geology and high H2S content (15%), a dual porosity, dual permeability compositional model has been created to model compositional sour crude flow within and between the matrix and fracture/karst features. The reservoir drive mechanisms are fluid expansion, miscible gas injection and aquifer drive. The reservoir is undersaturated and has an abnormally high initial reservoir pressure. The fine-grid static model contains 104 million cells (370×225×625×2) and the optimized upscaled coarse-grid dynamic model has 8.3 million cells (370×225×50×2). The upscaled model can be run efficiently on the company's existing HPC infrastructure with a maximum of 64 CPUs. Excellent matches of the Lorenz coefficient maps for reservoir total/zones and Lorenz curves at all wells between the fine and coarse grid models have been achieved. Matches on the dynamic variables, e.g., pressure, gas breakthrough time, and GOR growth, in all producers are within the defined acceptable tolerances. The high quality of the static and dynamic matches between the coarse- and fine-grid models confirms that the reservoir properties of the coarse-grid model is very close to the fine-grid model and can be used a base model for history matching and uncertainty analysis.

Sensitivity-Coefficient Based Inverse Heat Conduction Method for Identifying Hot Spots in Electronics Packages: A Comparison of Grid-Refinement Methods

2021 ◽  
Author(s):  
Patrick Krane ◽  
David Gonzalez Cuadrado ◽  
Francisco Lozano ◽  
Guillermo Paniagua ◽  
Amy Marconnet
Keyword(s):  
Heat Conduction ◽  
Hot Spots ◽  
Computation Time ◽  
Grid Method ◽  
Sensitivity Coefficient ◽  
Coarse Grid ◽  
Inverse Heat Conduction ◽  
Fine Grid ◽  
Temperature Maps ◽  
Electronics Packages

Abstract Estimating the distribution and magnitude of heat generation within electronics packages is pivotal for thermal packaging design and active thermal management systems. Inverse heat conduction methods can provide estimates using measured temperature profiles acquired using infrared imaging or discrete temperature sensors. However, if the heater locations are unknown, applying a fine grid of potential heater locations across the surface where heat generation is expected can result in prohibitively-large computation times. In contrast, using a more computationally-efficient coarse grid can reduce the accuracy of heat flux estimations. This paper evaluates two methods for reducing computation time using a sensitivity-coefficient method for solving the inverse heat conduction problem. One strategy uses a coarse grid that is refined near the hot spots, while the other uses a fine grid of potential heaters only near the hot spots. These grid-refinement methods are compared using both input temperature maps acquired from a "numerical experiment" (where the outputs of a 3D steady-state thermal model in FloTHERM are used for input temperatures) and temperature maps procured using infrared microscopy on a real electronics package. Compared to the coarse-grid method, the fine-grid method reduces computation time without significantly reducing accuracy, making it more convenient for designing and testing electronics packages. It also avoids the problem of "false hot spots" that occurs with the coarse-grid method. Overall, this approach provides a mechanism to predict hot spot locations during design and testing and a tool for active thermal management.

The Fine Grid Model: GF3

2013 ◽  
pp. 115-128
Author(s):  
P.B. Crean ◽  
T.S. Murty ◽  
J.A. Stronach
Keyword(s):  
Fine Grid ◽  
Grid Model

Calculation of Aerodynamic Noise for Centrifugal Fan of Air-Conditioner

2013 ◽  
Author(s):  
Taku Iwase ◽  
Hideshi Obara ◽  
Hiroyasu Yoneyama ◽  
Yoshinobu Yamade ◽  
Chisachi Kato
Keyword(s):  
Sound Pressure ◽  
Static Pressure ◽  
Pressure Fluctuations ◽  
Coarse Grid ◽  
Aerodynamic Noise ◽  
Absolute Velocity ◽  
Centrifugal Fan ◽  
Air Conditioner ◽  
Simulation Code ◽  
Fine Grid

Flow fields in a centrifugal fan for an indoor unit of an air-conditioner were calculated with finite element method-based large eddy simulation (LES) with the aim of predicting fan performance and aerodynamic noise in this study. The numerical simulation code employed throughout the LES was called FrontFlow/blue (FFB). We compared 10M grid [coarse grid] and 60M grid [fine grid] calculation results for investigation of influence of grid resolution. In the fine grid, the number of grid elements in blade-to-blade direction, and of region between the shroud and the bell mouth increased in particular. By calculating with the fine grid, calculated distributions of absolute velocities at blade exit reasonably agreed with experimental results. Because of this, maximum absolute velocity by fine grid near hub decreased as compared to those by coarse grid. Calculated sound pressure level by fine grid was therefore smaller than that by coarse grid, and the overestimation of sound pressure was suppressed by calculating with fine grid. This decrease of the absolute velocity was a first factor for the improvement of calculation accuracy. Moreover, number of captured streaks on the blade, hub, and shroud surfaces by fine grid increased as compared to those by coarse grid. As a result, size of streak by fine grid became smaller than that by coarse grid. Static pressure fluctuations by fine grid on the blade, hub, and shroud surfaces therefore reduced as compared to those by coarse grid. Aerodynamic noise was related to static pressure fluctuations according to Curle’s equation. This reduction of static pressure fluctuations was therefore a second factor for improvement of calculation accuracy.

scholarly journals Innovative Approaches for Geometric Uncertainty Quantification in an Operational Oil Spill Modeling System

2019 ◽  
Vol 7 (8) ◽  
pp. 259 ◽  
Author(s):  
Dongyu Feng ◽  
Paola Passalacqua ◽  
Ben R. Hodges
Keyword(s):  
Emergency Response ◽  
Oil Spill ◽  
Learning Algorithm ◽  
Coarse Grid ◽  
Data Driven ◽  
Model Integration ◽  
Fine Grid ◽  
Oil Transport ◽  
Uncertainty Model ◽  
Geometric Uncertainty

Reliable and rapid real-time prediction of likely oil transport paths is critical for decision-making from emergency response managers and timely clean-up after a spill. As high-resolution hydrodynamic models are slow, operational oil spill systems generally rely on relatively coarse-grid models to provide quick estimates of the near-future surface-water velocities and oil transport paths. However, the coarse grid resolution introduces model structural errors, which have been called “geometric uncertainty”. Presently, emergency response managers do not have readily-available methods for estimating how geometric uncertainty might affect predictions. This research develops new methods to quantify geometric uncertainty using fine- and coarse-grid models within a lagoonal estuary along the coast of the northern Gulf of Mexico. Using measures of geometric uncertainty, we propose and test a new data-driven uncertainty model along with a multi-model integration approach to quantify this uncertainty in an operational context. The data-driven uncertainty model is developed from a machine learning algorithm that provides a priori assessment of the prediction’s confidence degree. The multi-model integration generates ensemble predictions through comparison with limited fine-grid predictions. The two approaches provide explicit information on the expected scale of modeling errors induced by geometric uncertainty in a manner suitable for operational modeling.

Multigrid Methods with Newton-Gauss-Seidel Smoothing and Constraint Preserving Interpolation for Obstacle Problems

2015 ◽  
Vol 8 (2) ◽  
pp. 199-219 ◽  
Author(s):  
Chunxiao Wu ◽  
Justin W.L. Wan
Keyword(s):  
Obstacle Problem ◽  
Multigrid Method ◽  
Multigrid Methods ◽  
Coarse Grid ◽  
Obstacle Problems ◽  
Hjb Equations ◽  
Fine Grid ◽  
Multigrid Algorithm ◽  
Prolongation Operator ◽  
Approximate Scheme

AbstractIn this paper, we propose a multigrid algorithm based on the full approximate scheme for solving the membrane constrained obstacle problems and the minimal surface obstacle problems in the formulations of HJB equations. A Newton-Gauss-Seidel (NGS) method is used as smoother. A Galerkin coarse grid operator is proposed for the membrane constrained obstacle problem. Comparing with standard FAS with the direct discretization coarse grid operator, the FAS with the proposed operator converges faster. A special prolongation operator is used to interpolate functions accurately from the coarse grid to the fine grid at the boundary between the active and inactive sets. We will demonstrate the fast convergence of the proposed multigrid method for solving two model obstacle problems and compare the results with other multigrid methods.

Multiscale modeling of acoustic wave propagation in 2D media

Geophysics ◽  
2014 ◽  
Vol 79 (2) ◽  
pp. T61-T75 ◽  
Author(s):  
Richard L. Gibson ◽  
Kai Gao ◽  
Eric Chung ◽  
Yalchin Efendiev
Keyword(s):  
Wave Propagation ◽  
Wave Equation ◽  
Acoustic Wave ◽  
Heterogeneous Media ◽  
Coarse Grid ◽  
Basis Functions ◽  
Seismic Survey ◽  
Fine Scale ◽  
Acoustic Wave Propagation ◽  
Fine Grid

Conventional finite-difference methods produce accurate solutions to the acoustic and elastic wave equation for many applications, but they face significant challenges when material properties vary significantly over distances less than the grid size. This challenge is likely to occur in reservoir characterization studies, because important reservoir heterogeneity can be present on scales of several meters to ten meters. Here, we describe a new multiscale finite-element method for simulating acoustic wave propagation in heterogeneous media that addresses this problem by coupling fine- and coarse-scale grids. The wave equation is solved on a coarse grid, but it uses basis functions that are generated from the fine grid and allow the representation of the fine-scale variation of the wavefield on the coarser grid. Time stepping also takes place on the coarse grid, providing further speed gains. Another important property of the method is that the basis functions are only computed once, and time savings are even greater when simulations are repeated for many source locations. We first present validation results for simple test models to demonstrate and quantify potential sources of error. These tests show that the fine-scale solution can be accurately approximated when the coarse grid applies a discretization up to four times larger than the original fine model. We then apply the multiscale algorithm to simulate a complete 2D seismic survey for a model with strong, fine-scale scatterers and apply standard migration algorithms to the resulting synthetic seismograms. The results again show small errors. Comparisons to a model that is upscaled by averaging densities on the fine grid show that the multiscale results are more accurate.

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