A Semi-Implicit Approach for Integrated Reservoir and Surface-Network Simulation

2014 ◽  
Vol 17 (04) ◽  
pp. 559-571 ◽  
Author(s):  
Jialing Liang ◽  
Barry Rubin
Keyword(s):  
Explicit Knowledge ◽  
Coupled Model ◽  
Network Models ◽  
Computational Time ◽  
New Approach ◽  
Fully Implicit ◽  
Implicit Approach ◽  
Surface Network ◽  
Well Tests ◽  
Stability And Accuracy

Summary Conventionally, methods of coupling reservoirs and surface networks are categorized into implicit and explicit approaches. The term "implicit coupling" indicates that the two simulators solve unknowns together, simultaneously, or iteratively, whereas "explicit coupling" indicates that the two simulators solve unknowns sequentially and exchange their boundary conditions at the last coupled time tn. The explicit approach is straightforward to implement in existing reservoir and surface-network models and is widely used. Explicit coupling does have drawbacks, however, because well rate and pressure oscillations are often observed. In this paper, a new semi-implicit method for coupled simulation is presented. This technique stabilizes and improves the accuracy of the coupled model. The "semi-implicit coupling" overcomes the problems found in explicit-coupling methods without requiring the complexity of a fully implicit coupled model. The new approach predicts inflow-performance-relationship (IPR) curves at the next coupled time tn+1 by simultaneously conducting well tests for all wells in the reservoir before actually taking the required timestep. All wells first flow simultaneously to the next coupled time tn+1 with the well rates unchanged from the last coupled timestep. The timestep is rewound, and all well rates are reduced by a uniform fraction and then simultaneously flow again to tn+1. By extrapolating the resulting well pressures, the well's shut-in pressures at time tn+1 are determined, and thus, straight-line IPRs are produced. The new IPR curves approximate better each well's drainage region at tn+1 and each well's shut-in pressure at tn+1 which helps to stabilize the explicitly coupled model. The new coupling technique normally does not require iteration between the reservoir and surface network and normally has the stability and accuracy characteristics of an implicitly coupled approach. Because the well tests already account for individual well-drainage regions, an explicit knowledge of the well-drainage region is not required. Because of the stabilized IPR, the approach also was found to reduce the overall computational time compared with explicit coupling. Applications of the new approach are presented that show significant improvements surpassing explicit coupling in both stability and accuracy.

scholarly journals Multi-model inference of network properties from incomplete data

2006 ◽  
Vol 3 (2) ◽  
pp. 123-136 ◽  
Author(s):  
Michael P. H. Stumpf ◽  
Thomas Thorne
Keyword(s):  
Network Models ◽  
Network Data ◽  
Global Networks ◽  
New Approach ◽  
Model Inference ◽  
Novel Approach ◽  
Eukaryotic Species ◽  
Network Properties ◽  
Experimental Approaches ◽  
Saccaromyces Cerevisiae

Summary It has previously been shown that subnets differ from global networks from which they are sampled for all but a very limited number of theoretical network models. These differences are of qualitative as well as quantitative nature, and the properties of subnets may be very different from the corresponding properties in the true, unobserved network. Here we propose a novel approach which allows us to infer aspects of the true network from incomplete network data in a multi-model inference framework. We develop the basic theoretical framework, including procedures for assessing confidence intervals of our estimates and evaluate the performance of this approach in simulation studies and against subnets drawn from the presently available PIN network data in Saccaromyces cerevisiae. We then illustrate the potential power of this new approach by estimating the number of interactions that will be detectable with present experimental approaches in sfour eukaryotic species, inlcuding humans. Encouragingly, where independent datasets are available we obtain consistent estimates from different partial protein interaction networks. We conclude with a discussion of the scope of this approaches and areas for further research

scholarly journals Coupling Methodology for Studying the Far Field Effects of Wave Energy Converter Arrays over a Varying Bathymetry

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2899 ◽  
Author(s):  
Gael Verao Fernandez ◽  
Philip Balitsky ◽  
Vasiliki Stratigaki ◽  
Peter Troch
Keyword(s):  
Numerical Simulations ◽  
Wave Energy ◽  
Near Field ◽  
Coupled Model ◽  
Propagation Model ◽  
Irregular Waves ◽  
Computational Time ◽  
Far Field ◽  
Field Effects ◽  
Numerical Tool

For renewable wave energy to operate at grid scale, large arrays of Wave Energy Converters (WECs) need to be deployed in the ocean. Due to the hydrodynamic interactions between the individual WECs of an array, the overall power absorption and surrounding wave field will be affected, both close to the WECs (near field effects) and at large distances from their location (far field effects). Therefore, it is essential to model both the near field and far field effects of WEC arrays. It is difficult, however, to model both effects using a single numerical model that offers the desired accuracy at a reasonable computational time. The objective of this paper is to present a generic coupling methodology that will allow to model both effects accurately. The presented coupling methodology is exemplified using the mild slope wave propagation model MILDwave and the Boundary Elements Methods (BEM) solver NEMOH. NEMOH is used to model the near field effects while MILDwave is used to model the WEC array far field effects. The information between the two models is transferred using a one-way coupling. The results of the NEMOH-MILDwave coupled model are compared to the results from using only NEMOH for various test cases in uniform water depth. Additionally, the NEMOH-MILDwave coupled model is validated against available experimental wave data for a 9-WEC array. The coupling methodology proves to be a reliable numerical tool as the results demonstrate a difference between the numerical simulations results smaller than 5% and between the numerical simulations results and the experimental data ranging from 3% to 11%. The simulations are subsequently extended for a varying bathymetry, which will affect the far field effects. As a result, our coupled model proves to be a suitable numerical tool for simulating far field effects of WEC arrays for regular and irregular waves over a varying bathymetry.

Development of a fully implicit approach with intensive variables for compositional reservoir simulation

2018 ◽  
Vol 169 ◽  
pp. 317-336 ◽  
Author(s):  
Bruno Ramon Batista Fernandes ◽  
Francisco Marcondes ◽  
Kamy Sepehrnoori
Keyword(s):  
Reservoir Simulation ◽  
Fully Implicit ◽  
Implicit Approach

A Physics-Based Proxy for Surface and Subsurface Coupled Simulation Models

2021 ◽  
Author(s):  
Changdong Yang ◽  
Jincong He ◽  
Song Du ◽  
Zhenzhen Wang ◽  
Tsubasa Onishi ◽  
...  
Keyword(s):  
Network Model ◽  
Reservoir Simulation ◽  
Coupled Model ◽  
Simulation Models ◽  
Production Performance ◽  
Proxy Model ◽  
Surface Network ◽  
Run Time ◽  
Multi Phase ◽  
Subsurface Reservoir

Abstract Full-physics subsurface simulation models coupled with surface network can be computationally expensive. In this paper, we propose a physics-based subsurface model proxy that significantly reduces the run-time of the coupled model to enable rapid decision-making for reservoir management. In the coupled model the subsurface reservoir simulator generates well inflow performance relationship (IPR) curves which are used by the surface network model to determine well rates that satisfy surface constraints. In the proposed proxy model, the CPU intensive reservoir simulation is replaced with an IPR database constructed from a data pool of one or multiple simulation runs. The IPR database captures well performance that represents subsurface reservoir dynamics. The proxy model can then be used to predict the production performance of new scenarios – for example new drilling sequence – by intelligently looking up the appropriate IPR curves for oil, gas and water phases for each well and solving it with the surface network. All necessary operational events in the surface network and field management logic (such as facility constraints, well conditional shut-in, and group guide rate balancing) for the full-coupled model can be implemented and honored. In the proposed proxy model, while the reservoir simulation component is eliminated for efficiency. The entirety of the surface network model is retained, which offers certain advantages. It is particularly suitable for investigating the impact of different surface operations, such as maintenance schedule and production routing changes, with the aim of minimizing production capacity off-line due to maintenance. Replacing the computationally intensive subsurface simulation with the appropriate IPR significantly improves the run time of the coupled model while preserving the essential physics of the reservoir. The accuracy depends on the difference between the scenarios that the proxy is trained on and the scenarios being evaluated. Initial testing with a complex reservoir with more than 300 wells showed the accuracy of the proxy model to be more than 95%. The computation speedup could be an order of magnitude, depending largely on complexity of the surface network model. Prior work exists in the literature that uses decline curves to replicate subsurface model performance. The use of the multi-phase IPR database and the intelligent lookup mechanism in the proposed method allows it to be more accurate and flexible in handling complexities such as multi-phase flow and interference in the surface network.

scholarly journals Thermal modeling of hydrogen storage by absorption in a magnesium hydrides tank

2014 ◽  
Vol 5 ◽  
pp. A21 ◽  
Author(s):  
Karim Lahmer ◽  
Rachid Bessaïh ◽  
Angel Scipioni ◽  
Mohammed El Ganaoui
Keyword(s):  
Hydrogen Absorption ◽  
Numerical Scheme ◽  
Computational Time ◽  
Governing Equations ◽  
Time Step ◽  
Kinetic Reaction ◽  
Fully Implicit ◽  
Temporal Profiles ◽  
Tank Geometry ◽  
Reaction Equations

This paper summarizes numerical results of hydrogen absorption simulated in an axisymmetric tank geometry containing magnesium hydride heated to 300 °C and at moderate storage pressure 1 MPa. The governing equations are solved with a fully implicit finite volume numerical scheme used by a commercial software FLUENT. The effect of the different kinetic reaction equations modeling hydrogen absorption was studied by the introduction of a specific subroutine at each time step in order to consider which one will provide results close to available experimental results. Spatial and temporal profiles of temperature and concentration in hydride bed are plotted. Results show that suitable method for our two-dimensional study is a CV-2D technique because it generates the smallest error especially during the beginning of the reaction. Also, its computational time is the shortest one compared to the other methods.

Mathematical Models for Model-Based Control in Offshore Pipelay Operations

2009 ◽  
Author(s):  
Gullik A. Jensen ◽  
Thor I. Fossen
Keyword(s):  
Mathematical Models ◽  
Dynamic Models ◽  
Controller Design ◽  
Computational Time ◽  
Beam Model ◽  
Time Dynamic ◽  
Model Based ◽  
On Line ◽  
Stability And Accuracy ◽  
Control Application

This paper considers mathematical models for model-based controller design in offshore pipelay operations. Three classes of models for control design are discussed, real-world models suitable for controller design verification, controller and observer models which are used on-line in the control system implementation. The control application place requirements on the model with respect to the computational time, dynamic behavior, stability and accuracy. Models such as the beam model, two catenary models, as well as general finite element (FE) models obtained from computer programs were not able to meet all of the requirements, and two recent dynamic models designed for control are presented, which bridge the gap between the simple analytical and more complex FE models. For completeness, modeling of the pipelay vessel, stinger and roller interaction, soil and seabed interaction and environmental loads are discussed.

scholarly journals Effective PCB Decoupling Optimization by Combining an Iterative Genetic Algorithm and Machine Learning

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1243 ◽  
Author(s):  
Riccardo Cecchetti ◽  
Francesco de Paulis ◽  
Carlo Olivieri ◽  
Antonio Orlandi ◽  
Markus Buecker
Keyword(s):  
Genetic Algorithm ◽  
Power Delivery ◽  
Computational Time ◽  
Test Cases ◽  
Experimental Measurements ◽  
New Approach ◽  
Power Delivery Network ◽  
Capacitor Placement ◽  
Minimum Number ◽  
Artificial Neural Network Ann

An iterative optimization for decoupling capacitor placement on a power delivery network (PDN) is presented based on Genetic Algorithm (GA) and Artificial Neural Network (ANN). The ANN is first trained by an appropriate set of results obtained by a commercial simulator. Once the ANN is ready, it is used within an iterative GA process to place a minimum number of decoupling capacitors for minimizing the differences between the input impedance at one or more location, and the required target impedance. The combined GA–ANN process is shown to effectively provide results consistent with those obtained by a longer optimization based on commercial simulators. With the new approach the accuracy of the results remains at the same level, but the computational time is reduced by at least 30 times. Two test cases have been considered for validating the proposed approach, with the second one also being compared by experimental measurements.

Advanced Robust Optimization Approach for Design Optimization With Interval Uncertainty Using Sequential Quadratic Programming

2013 ◽  
Author(s):  
Jianhua Zhou ◽  
Mian Li
Keyword(s):  
Robust Optimization ◽  
Linear Equations ◽  
Variable Parameter ◽  
Optimal Solution ◽  
Interval Uncertainty ◽  
Computational Time ◽  
Optimization Approach ◽  
Engineering Optimization ◽  
Loop Optimization ◽  
New Approach

Uncertainty is inevitable in real world. It has to be taken into consideration, especially in engineering optimization; otherwise the obtained optimal solution may become infeasible. Robust optimization (RO) approaches have been proposed to deal with this issue. Most existing RO algorithms use double-looped structures in which a large amount of computational efforts have been spent in the inner loop optimization to determine the robustness of candidate solutions. In this paper, an advanced approach is presented where no optimization run is required to be performed for robustness evaluations in the inner loop. Instead, a concept of Utopian point is proposed and the corresponding maximum variable/parameter variation will be obtained by just solving a set of linear equations. The obtained robust optimal solution from the new approach may be conservative, but the deviation from the true robust optimal solution is very small given the significant improvement in the computational efficiency. Six numerical and engineering examples are tested to show the applicability and efficiency of the proposed approach, whose solutions and computational time are compared with those from a similar but double-looped approach, SQP-RO, proposed previously.

Statistical Estimation and Elimination of Thermal Network Model Errors

1969 ◽  
Vol 91 (4) ◽  
pp. 554-560 ◽  
Author(s):  
B. H. Browne
Keyword(s):  
General Theory ◽  
Complex Systems ◽  
Prediction Accuracy ◽  
Statistical Estimation ◽  
Network Models ◽  
Canonical Forms ◽  
Model Errors ◽  
New Approach ◽  
Thermal Network ◽  
Model Predictions

A new approach is developed for estimating and correcting digital thermal network errors based upon achieving statistical reconciliation between model predictions and observed results. The development is preceded by a review of the general theory of thermal networks and the development of the canonical forms of networks and their error models. Application of the technique promises improved thermal prediction accuracy of complex systems using simplified network models.

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