scholarly journals Dynamic effects of fault modeling on stair-step and corner-point grids

2021 ◽  
Vol 11 (3) ◽  
pp. 1323-1338
Author(s):  
Faruk O. Alpak ◽  
Tianhong Chen
Keyword(s):  
Reservoir Simulation ◽  
Corner Point ◽  
Flow Simulation ◽  
Fault Modeling ◽  
Added Value ◽  
Dynamic Effects ◽  
Flux Approximation ◽  
Grid Convergence ◽  
Simulation Results ◽  
Similar Accuracy

AbstractFault modeling has become an integral element of reservoir simulation for structurally complex reservoirs. Modeling of faults in general has major implications for the simulation grid. In turn, the grid quality control is very important in order to attain accurate simulation results. We investigate the dynamic effects of using stair-step grid (SSG) and corner-point grid (CPG) approaches for fault modeling from the perspective of dynamic reservoir performance forecasting. We have performed a number of grid convergence and grid-type sensitivity studies for a variety of simple, yet intuitive faulted flow simulation problems with gradually increasing complexity. We have also explored the added value of the multipoint flux approximation (MPFA) method over the conventional two-point flux approximation (TPFA) to increase the accuracy of reservoir simulation results obtained on CPGs. Effects of fault seal modeling on grid-resolution convergence and grid-type sensitivity have also been briefly examined. For simple geometries, both SSG and CPG can be used for fault modeling with similar accuracy in conjunction with the pillar-grid approach. This is evidenced by the fact that simulation results from SSG and CPG converge to identical solutions. SSG and CPG yield different results for more complex geometries. Simulation results approach to a converged solution for relatively fine SSGs. However, a SSG only provides an approximation to the fault geometry and reservoir volumes when the grid is coarse. On the other hand, non-orthogonality errors are increasingly evident in relatively more complex faulted models on CPGs and such errors cannot be addressed by grid refinement. It has been observed that MPFA partially addresses the discretization errors on non-orthogonal grids but only from the total flux accuracy perspective. However, transport related errors are still evident. Grid convergence behaviors and grid effects are quite similar with or without fault seal modeling (i.e., dedicated fault-zone modeling by use of scaled-up seal factors) for simple geometries. However, in more complex test cases, we have observed that it is more difficult to achieve converged results in conjunction with fault seal modeling due to increased heterogeneity of the underlying problem.

Hybrid Fluid Flow Simulation Combining Full Physics Simulation and Artificial Intelligence

2021 ◽  
Author(s):  
Mokhles Mezghani ◽  
Mustafa AlIbrahim ◽  
Majdi Baddourah
Keyword(s):  
Artificial Intelligence ◽  
Reservoir Simulation ◽  
History Matching ◽  
Hybrid Approach ◽  
Flow Simulation ◽  
Latin Hypercube Sampling ◽  
Simulation Models ◽  
Fine Scale ◽  
Simulation Results ◽  
Optimization Loop

Abstract Reservoir simulation is a key tool for predicting the dynamic behavior of the reservoir and optimizing its development. Fine scale CPU demanding simulation grids are necessary to improve the accuracy of the simulation results. We propose a hybrid modeling approach to minimize the weight of the full physics model by dynamically building and updating an artificial intelligence (AI) based model. The AI model can be used to quickly mimic the full physics (FP) model. The methodology that we propose consists of starting with running the FP model, an associated AI model is systematically updated using the newly performed FP runs. Once the mismatch between the two models is below a predefined cutoff the FP model is switch off and only the AI model is used. The FP model is switched on at the end of the exercise either to confirm the AI model decision and stop the study or to reject this decision (high mismatch between FP and AI model) and upgrade the AI model. The proposed workflow was applied to a synthetic reservoir model, where the objective is to match the average reservoir pressure. For this study, to better account for reservoir heterogeneity, fine scale simulation grid (approximately 50 million cells) is necessary to improve the accuracy of the reservoir simulation results. Reservoir simulation using FP model and 1024 CPUs requires approximately 14 hours. During this history matching exercise, six parameters have been selected to be part of the optimization loop. Therefore, a Latin Hypercube Sampling (LHS) using seven FP runs is used to initiate the hybrid approach and build the first AI model. During history matching, only the AI model is used. At the convergence of the optimization loop, a final FP model run is performed either to confirm the convergence for the FP model or to re iterate the same approach starting from the LHS around the converged solution. The following AI model will be updated using all the FP simulations done in the study. This approach allows the achievement of the history matching with very acceptable quality match, however with much less computational resources and CPU time. CPU intensive, multimillion-cell simulation models are commonly utilized in reservoir development. Completing a reservoir study in acceptable timeframe is a real challenge for such a situation. The development of new concepts/techniques is a real need to successfully complete a reservoir study. The hybrid approach that we are proposing is showing very promising results to handle such a challenge.

Evaluation of Singular Value Decomposition (SVD) Enhanced Upscaling in Reservoir Simulation

2020 ◽  
Author(s):  
Xu Zhou ◽  
Mayank Tyagi
Keyword(s):  
Reservoir Simulation ◽  
Homogeneous Model ◽  
Flow Simulation ◽  
Reservoir Modeling ◽  
Base Case ◽  
Fine Grid ◽  
Heterogeneous Model ◽  
Grid Model ◽  
Simulation Results ◽  
Simulation Time

Abstract Reservoir upscaling is an important step in reservoir modeling for converting highly detailed geological models to simulation grids. It substitutes a heterogeneous model that consists of high-resolution fine grid cells with a lower resolution reduced-dimension homogeneous model using averaging schemes. Its objective is to use a coarse grid model to represent a fine grid model, thus to reduce simulation time. The benefit of upscaling in reservoir simulation is that it efficiently saves simulation time, and effectively preserves key features of data for flow simulation. Singular Vector Decomposition (SVD) is a matrix decomposition method. It has been used for image processing and compressing. It has been proved to be capable of providing a good compression ratio, and effectively saves digital image storage space. SVD also has been used in noise suppression and signal enhancement. It has been shown to be effective in reducing noise components arising from both the sound sampling and delivery system. This study evaluates the effect of SVD in parameterization and upscaling for reservoir simulation. A two-phase flow reservoir model was created using data from the SPE tenth comparative solution project [1]. Simulation results show that SVD is valid in the parameterization of permeability values. The reconstructed permeability matrices using certain amount of singular values are good approximations of the original permeability values. Simulation results from SVD processed permeability values are similar to that using the original values. SVD is then applied on the upscaled permeability value to evaluate the effectiveness on upscaling. Simulation results were compared between the base case, upscaled case, and SVD upscaled case. The simulation results did not show a significant improvement in the accuracy of predicting oil production by applying SVD on the upscaled permeability values. It could be because the reconstructed permeability matrix has the same dimension before and after the SVD processing, thus the model accuracy and efficiency are not significantly improved. Future work includes adding more cases to further explore the effect of SVD on upscaling. The number of grid blocks may be increased, and more layers can be added to investigate whether SVD enhance upscaling for larger scale reservoir simulation models.

Study on the Cyclic Loading Effects to the Railway Ballast

2013 ◽  
Vol 724-725 ◽  
pp. 1736-1739
Author(s):  
Xue Jun Wang ◽  
Bin Hua ◽  
Yi Lin Chi ◽  
Xue Yu Zhao ◽  
Fu Yu Li
Keyword(s):  
Cyclic Loading ◽  
Research Method ◽  
Flow Simulation ◽  
Railway Ballast ◽  
Compaction Rate ◽  
Simulation Process ◽  
Loading Effects ◽  
Simulation Results ◽  
Frequency Changes ◽  
Particle Flow Simulation

When ballast materials are subjected to cyclic loading, as a result, the change of particles micromechanical properties will lead to ballast degradation, permanent deformations on the railways step by step. In this paper, it presented a coupling discrete particle-flow simulation model of the railway ballast for cyclic tamping loading. Tamping frequency changes from 25HZ to 60HZ in numerical simulation process. Simulation results that the ballast compaction rate increases linearly with frequency up to a characteristic frequency 35HZ and then it declines in inverse proportion to tamping frequency. The aim of this paper is to study on the effects on the railway ballast under cyclic loading. The study shows that the discrete element method is a valid method for investigation of the microscopic properties of railway ballast now, while we have no other better research method.

Accurate Multi-Phase Flow Simulation in Faulted Reservoirs Using Mimetic Finite Difference Methods on Polyhedral Cells

2021 ◽  
Author(s):  
Rencheng Dong ◽  
Faruk O. Alpak ◽  
Mary F. Wheeler
Keyword(s):  
Hybrid Method ◽  
Corner Point ◽  
Flow Simulation ◽  
Phase Flow ◽  
Reference Solution ◽  
Pressure Equation ◽  
Discretization Methods ◽  
Multi Phase Flow ◽  
Polyhedral Cells ◽  
Multi Phase

Abstract Faulted reservoirs are commonly modeled by corner-point grids. Since the two-point flux approximation (TPFA) method is not consistent on non-orthogonal grids, multi-phase flow simulation using TPFA on corner-point grids may have significant discretization errors if grids are not K-orthogonal. To improve the simulation accuracy, we developed a novel method where the faults are modeled by polyhedral cells, and mimetic finite difference (MFD) methods are used to solve flow equations. We use a cut-cell approach to build the mesh for faulted reservoirs. A regular orthogonal grid is first constructed,and then fault planes are added by dividing cells at fault planes. Most cells remain orthogonal while irregular non-orthogonal polyhedral cells can be formed with multiple cell divisions. We investigated three spatial discretization methods for solving the pressure equation on general polyhedral grids, including the TPFA, MFD and TPFA-MFD hybrid methods. In the TPFA-MFD hybrid method, the MFD method is only applied to part of the domain while the TPFA method is applied to rest of the domain. We compared flux accuracy between TPFA and MFD methods by solving a single-phase flow problem. The reference solution is obtained on a rectangular grid while the same problem is solved by TPFA and MFD methods on a grid with distorted cells near a fault. Fluxes computed using TPFA exhibit larger errors in the vicinity of the fault while fluxes computed using MFD are still as accurate as the reference solution. We also compared saturation accuracy of two-phase (oil and water) flow in faulted reservoirs when the pressure equation is solved by different discretization methods. Compared with the reference saturation solution, saturation exhibits non-physical errors near the fault when pressure equation is solved by the TPFA method. Since the MFD method yields accurate fluxes over general polyhedral grids, the resulting saturation solutions match the reference saturation solutions with an enhanced accuracy when the pressure equation is solved by the MFD method. Based on the results of our simulation studies, the accuracy of the TPFA-MFD hybrid method is very close to the accuracy of the MFD method while the TPFA-MFD hybrid method is computationally cheaper than the MFD method.

Flow Analysis of Spray Jet and Direct Jet Nozzle for Fire Water Monitor

2010 ◽  
Vol 139-141 ◽  
pp. 913-916 ◽  
Author(s):  
Guo Liang Hu ◽  
Wei Gang Chen ◽  
Zhi Gang Gao
Keyword(s):  
Flow Analysis ◽  
Flow Characteristics ◽  
Flow Simulation ◽  
Jet Flow ◽  
Extended Length ◽  
Jet Nozzle ◽  
Simulation Results ◽  
Steady Velocity ◽  
Nozzle Structure

In order to investigate the influence rules between the jet nozzle of fire water monitor and the jet performances, two typical jet nozzle, the spray jet and direct jet nozzle was designed to analysis the jet flow characteristics. Flow simulation of the jet nozzle was completed using fluent kits. The outlet velocity of the spray jet nozzle and direct jet nozzle were investigated in detail, and the influence rules of the nozzle structure on the outlet velocity was also discussed. The simulation results show that the steady velocity of the jet nozzle is about 34m/s that coinciding the contour magnitude, and the better extended length of the direct jet nozzle is about 50mm length that can improve the jet performances. The results can verify the reasonableness of the designed nozzle, it also can optimize the nozzle structure and increase the jet performance of the fire water monitor.

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