## Search

Now showing items 1-10 of 10

JavaScript is disabled for your browser. Some features of this site may not work without it.

AuthorSun, Shuyu (10)Zhang, Tao (4)Kou, Jisheng (2)Li, Aifen (2)Li, Jingfa (2)View MoreDepartmentEarth Science and Engineering Program (10)Physical Sciences and Engineering (PSE) Division (10)Earth Science and Engineering (3)Computational Transport Phenomena Lab (2)Computational Bioscience Research Center (1)View MoreJournalJournal of Computational Physics (4)Computational Geosciences (1)Fuel (1)Industrial & Engineering Chemistry Research (1)Journal of Scientific Computing (1)KAUST Acknowledged Support UnitComputational Transport Phenomena Laboratory (1)KAUST Grant Number

BAS/1/1351-01-01 (10)

BAS/1/1624-01-01 (1)REP/1/2879-01 (1)URF/1/2993-01 (1)PublisherElsevier BV (5)American Chemical Society (ACS) (1)arXiv (1)Springer International Publishing (1)Springer Nature (1)View MoreSubjectActive-set reduced-space (1)Adsorption (1)Applications of Gaussian random field (1)Artificial neural networks (1)Black oil model (1)View MoreTypeArticle (8)Book Chapter (1)Preprint (1)Year (Issue Date)
2019 (10)

Item AvailabilityEmbargoed (7)Metadata Only (2)Open Access (1)

Now showing items 1-10 of 10

- List view
- Grid view
- Sort Options:
- Relevance
- Title Asc
- Title Desc
- Issue Date Asc
- Issue Date Desc
- Submit Date Asc
- Submit Date Desc
- Results Per Page:
- 5
- 10
- 20
- 40
- 60
- 80
- 100

Advances in Gaussian random field generation: a review

Liu, Yang; Li, Jingfa; Sun, Shuyu; Yu, Bo (Computational Geosciences, Springer Science and Business Media LLC, 2019-08-05) [Article]

Gaussian (normal) distribution is a basic continuous probability distribution in statistics, it plays a substantial role in scientific and engineering problems that related to stochastic phenomena. This paper aims to review state-of-the-art of Gaussian random field generation methods, their applications in scientific and engineering issues of interest, and open-source software/packages for Gaussian random field generation. To this end, first, we briefly introduce basic mathematical concepts and theories in the Gaussian random field, then seven commonly used Gaussian random field generation methods are systematically presented. The basic idea, mathematical framework of each generation method are introduced in detail and comparisons of these methods are summarized. Then, representative applications of the Gaussian random field in various areas, especially of engineering interest in recent two decades, are reviewed. For readers’ convenience, four representative example codes are provided, and several relevant up-to-date open-source software and packages that freely available from the Internet are introduced.

A coupled Lattice Boltzmann approach to simulate gas flow and transport in shale reservoirs with dynamic sorption

Zhang, Tao; Sun, Shuyu (Fuel, Elsevier BV, 2019-03-01) [Article]

Gas flow in a shale reservoir is hard to model and simulate as certain complex mechanisms should be included, for example, diffusion and sorption. As a mesoscopic approach, Lattice Boltzmann Methods can capture the flow behavior in both scales: free flow through the conventional pore channels and gas transport with sorption in the tight matrix with very small pores. In this paper, two Lattice Boltzmann (LB) schemes are presented to recover the Navier-Stokes equations and advection diffusion equation respectively, to model the flow and transport in the two scales. The Navier-Stokes type LB scheme is constructed to model the free flow in fractures and conventional pore channels in matrix, and the convection diffusion type LB scheme is constructed to model the transport in tight matrix with very small pores. Chapman-Enskog expansions are derived to show the equivalence of the two LB schemes with the two macroscopic equations. Dynamic sorption is included in the advection diffusion with the dependance of gas concentration and free flow velocities, and the absorbed amount can affect the free flow velocity as well. In our simulation of gas flow and transport in shale reservoirs, the media is generated through two methods, reading a realistic media image and generating using a pore-network model. The rock characteristics are preserved in our generated porous media, with the method we proposed to link the LB scheme with the pore network modeling method. The simulation results are reasonable to prove that our schemes are robust and efficient, and the effect of porosity and sorption parameters are presented. Furthermore, the interaction of the two-scale gas flow and transport is analyzed, and we show that the increasing adsorbed gas amount in matrix may not slow down the free flow velocity as this increase may be resulted from changes in the rock characteristics. The scheme is simple to understand and implement, because only a few modifications are needed to construct the LB schemes on the two scales.

Acceleration of the NVT Flash Calculation for Multicomponent Mixtures Using Deep Neural Network Models

Li, Yiteng; Zhang, Tao; Sun, Shuyu (Industrial & Engineering Chemistry Research, American Chemical Society (ACS), 2019-06-15) [Article]

Phase equilibrium calculation, also known as flash calculation, has been extensively applied in petroleum engineering, not only as a standalone application for a separation process but also as an integral component of compositional reservoir simulation. Previous research devoted numerous efforts to improve the accuracy of phase equilibrium calculations, which place more importance on safety than speed. However, the equation-of-state-based flash calculation consumes an enormous amount of computational time in compositional simulation and thus becomes a bottleneck to the broad application of compositional simulators. Therefore, it is of vital importance to accelerate flash calculation without much compromise in accuracy and reliability, turning it into an active research topic in the past two decades. With the rapid development of computational techniques, machine learning brings another wave of technology innovation. As a subfield of machine learning, the deep neural network becomes a promising computational technique due to its great capacity to deal with complicated nonlinear functions, and it thus attracts increasing attention from academia and industry. In this study, we establish a deep neural network model to approximate the iterative flash calculation at given moles, volume, and temperature, known as the NVT flash. A dynamic model designed for NVT flash problems is iteratively solved to generate data for training the neural network. In order to test the model’s capacity to handle complex fluid mixtures, three real reservoir fluids are investigated, including one Bakken oil and two Eagle Ford oils. Compared to previous studies that follow the conventional flash framework in which stability testing precedes phase splitting calculation, we incorporate stability test and phase split calculation together and accomplish two steps by a single deep learning model. The trained model is able to identify the single vapor, single liquid, and vapor–liquid states under the subcritical region of the hydrocarbon mixtures. A number of examples are presented to show the accuracy and efficiency of the proposed deep neural network. It is found that the trained model makes predictions at most 244 times faster than the iterative NVT flash calculation for the given cases and meanwhile preserves high accuracy.

A fully implicit constraint-preserving simulator for the black oil model of petroleum reservoirs

Yang, Haijian; Sun, Shuyu; Li, Yiteng; Yang, Chao (Journal of Computational Physics, Elsevier BV, 2019-07-05) [Article]

Due to the rapid advancement of supercomputing resource, there is a growing interest in developing parallel algorithms for the large-scale reservoir simulation. In this paper, we present a parallel and fully implicit simulator for the black oil model based on the variational inequality (VI) framework, which can be used to enforce important mathematical and physical properties to obtain accurate constraint-preserving solutions. In other words, this framework ensures the predicted solution to stay within the physical range. In the proposed approach, the black oil model is reformulated as a variational inequality system that naturally satisfies the basic boundedness requirement of the solution, and then a fully implicit finite volume method is applied to discretize the model equations. In addition to that, a number of nonlinear and linear fast solver technologies, including a variant of inexact Newton methods and the domain decomposition based preconditioners, are employed to guarantee the robustness and parallel scalability of the simulator. A particular emphasis of the proposed framework is placed on the parallel and algorithmic performance of the variational inequality approach across large-scale and heterogeneous problems. Several numerical results pertaining to the problems in one, two and three dimensions are presented to illustrate the efficiency, robustness, and the overall performance of the fully implicit constraint-preserving simulator.

Accelerating flash calculation through deep learning methods

Li, Yu; Zhang, Tao; Sun, Shuyu; Gao, Xin (Journal of Computational Physics, Elsevier BV, 2019-05-29) [Article]

In the past two decades, researchers have made remarkable progress in accelerating flash calculation, which is very useful in a variety of engineering processes. In this paper, general phase splitting problem statements and flash calculation procedures using the Successive Substitution Method are reviewed, while the main shortages are pointed out. Two acceleration methods, Newton's method and the Sparse Grids Method are presented afterwards as a comparison with the deep learning model proposed in this paper. A detailed introduction from artificial neural networks to deep learning methods is provided here with the authors' own remarks. Factors in the deep learning model are investigated to show their effect on the final result. A selected model based on that has been used in a flash calculation predictor with comparison with other methods mentioned above. It is shown that results from the optimized deep learning model meet the experimental data well with the shortest CPU time. More comparison with experimental data has been conducted to show the robustness of our model.

Numerical Approximation of a Phase-Field Surfactant Model with Fluid Flow

Zhu, Guangpu; Kou, Jisheng; Sun, Shuyu; Yao, Jun; Li, Aifen (Journal of Scientific Computing, Springer Nature, 2019-03-07) [Article]

Modeling interfacial dynamics with soluble surfactants in a multiphase system is a challenging task. Here, we consider the numerical approximation of a phase-field surfactant model with fluid flow. The nonlinearly coupled model consists of two Cahn–Hilliard-type equations and incompressible Navier–Stokes equation. With the introduction of two auxiliary variables, the governing system is transformed into an equivalent form, which allows the nonlinear potentials to be treated efficiently and semi-explicitly. By certain subtle explicit-implicit treatments to stress and convective terms, we construct first and second-order time marching schemes, which are extremely efficient and easy-to-implement, for the transformed governing system. At each time step, the schemes involve solving only a sequence of linear elliptic equations, and computations of phase-field variables, velocity and pressure are fully decoupled. We further establish a rigorous proof of unconditional energy stability for the first-order scheme. Numerical results in both two and three dimensions are obtained, which demonstrate that the proposed schemes are accurate, efficient and unconditionally energy stable. Using our schemes, we investigate the effect of surfactants on droplet deformation and collision under a shear flow, where the increase of surfactant concentration can enhance droplet deformation and inhibit droplet coalescence.

Darcy-scale phase equilibrium modeling with gravity and capillarity

Sun, Shuyu (Journal of Computational Physics, Elsevier BV, 2019-09-05) [Article]

The modeling of multiphase fluid mixture and its flow in porous media is of great interest in the field of reservoir simulation. In this paper, we formulate a novel energy-based framework to model multi-component two-phase fluid systems at equilibrium. Peng-Robinson equation of state (EOS) is used to model the bulk properties of each phase, though our framework works well also with other equations of state. Our model reduces to the conventional compositional grading if restricted to one spatial vertical dimension together with the assumption of monodisperse pore-size distribution (all pores being one size). However, our model can be combined with a general distribution of pore size, which can generate interesting behaviors of capillarity in porous media. In particular, the model can be used to predict the capillary pressure of two-phase fluid as a function of saturation, with a given pore-size distribution. This model is the quantitative study of the first time in the literature for the capillarity of a two-phase fluid with partial miscibility. We proposed an unconditional-stable energy-decay numerical algorithm based on convex-concave splitting, which has been demonstrated to be both robust and efficient using numerical examples. To verify our model, we simulate the compositional grading of a binary fluid mixture consisting of carbon dioxide and normal decane. To demonstrate powerful features of our model, we provide an interesting example of fluid mixture in a porous medium with wide pore size distribution, where the competition of capillarity and gravity is observed. This work represents the first effort in the literature that rigorously incorporates capillarity and gravity effects into EOS-based phase equilibrium modeling.

Recent Progress on Phase Equilibrium Calculation in Subsurface Reservoirs Using Diffuse Interface Models

Zhang, Tao; Li, Yiteng; Cai, Jianchao; Sun, Shuyu (Springer International Publishing, 2019-11-16) [Book Chapter]

Compositional multiphase flow in subsurface porous media is becoming increasingly attractive due to issues related with enhanced oil recovery, greenhouse effect and global warming, and the urgent need for development in unconventional oil/gas reservoirs. One key effort prior to construct the mathematical model governing the compositional multiphase flow is to determine the phase compositions of the fluid mixture, and then calculate other related physical properties. In this paper, recent progress on phase equilibrium calculations in subsurface reservoirs have been reviewed and concluded with authors’ own analysis. Phase equilibrium calculation is the main approach to perform such calculation, which could be conducted using two different types of flash calculation algorithms: The NPT flash and NVT flash. NPT flash calculations are proposed early, well developed within the last few decades and now become the most commonly used method. However, it fails to remain the physical meanings in the solution as a cubic equation, derived from equation of state, is often needed to solve. Alternatively, NVT flash can handle the phase equilibrium calculations as well, without the pressure known a priori. Recently, Diffuse Interface Models, which were proved to keep a high consistency with thermodynamic laws, have been introduced in the phase calculation, incorporating the realistic equation of state (EOS), e.g. Peng-Robinson EOS. In NVT flash, Helmholtz free energy is minimized instead of Gibbs free energy used in NPT flash, and this energy density is treated with convex-concave splitting technique. A semi-implicit numerical scheme is designed to process the dynamic model, which ensures the thermodynamic stability and then preserve the fast convergence property. A positive definite coefficient matrix is designed to meet the Onsager Reciprocal Principle so as to keep the entropy increasing property in the presence of capillary pressure, which is required by the thermodynamic laws. The robustness of the proposed algorithm is verified via two numerical examples, one of which has up to seven components. In the complex fluid mixture, special phenomena could be capture from the global minimum of TPD functions as well as the phase envelope resulted from the phase equilibrium calculations. It can be found that the boundary between the single-phase and vapor–liquid phase regions will move in the presence of capillary pressure, and then the area of each region will change accordingly. Some remarks have been concluded at the end, as well as suggestions on potential topics for future studies.

A phase-field moving contact line model with soluble surfactants

Zhu, Guangpu; Kou, Jisheng; Yao, Jun; Li, Aifen; Sun, Shuyu (Journal of Computational Physics, Elsevier BV, 2019-12-04) [Article]

A phase-field moving contact line model is presented for a two-phase system with soluble surfactants. With the introduction of some scalar auxiliary variables, the original free energy functional is transformed into an equivalent form, and then a new governing system is obtained. The resulting model consists of two Cahn-Hilliard-type equations and incompressible Navier-Stokes equation with variable densities, together with the generalized Navier boundary condition for the moving contact line. We prove that the proposed model satisfies the total energy dissipation with time. To numerically solve such a complex system, we develop a nonlinearly coupled scheme with unconditional energy stability. A splitting method based on pressure stabilization is used to solve the Navier-Stokes equation. Some subtle implicit-explicit treatments are adopted to discretize convection and stress terms. A stabilization term is artificially added to balance the explicit nonlinear term associated with the surface energy at the fluid-solid interface. We rigorously prove that the proposed scheme can preserve the discrete energy dissipation. An efficient finite difference method on staggered grids is used for the spatial discretization. Numerical results in both two and three dimensions demonstrate the accuracy and energy stability of the proposed scheme. Using our model and numerical scheme, we investigate the wetting behavior of droplets on a solid wall. Numerical results indicate that surfactants can affect the wetting properties of droplet by altering the value of contact angles.

A full multigrid multilevel Monte Carlo method for the single phase subsurface flow with random coefficients

Liu, Yang; Li, Jingfa; Sun, Shuyu; Yu, Bo (arXiv, 2019-10-10) [Preprint]

The subsurface flow is usually subject to uncertain porous media structures. In most cases, however, we only have partial knowledge about the porous media properties. A common approach is to model the uncertain parameters of porous media as random fields, then the statistical moments (e.g. expectation) of the Quantity of Interest(QoI) can be evaluated by the Monte Carlo method. In this study, we develop a full multigrid-multilevel Monte Carlo (FMG-MLMC) method to speed up the evaluation of random parameters effects on single-phase porous flows. In general, MLMC method applies a series of discretization with increasing resolution and computes the QoI on each of them. The effective variance reduction is the success of the method. We exploit the similar hierarchies of MLMC and multigrid methods and obtain the solution on coarse mesh $Q^c_l$ as a byproduct of the full multigrid solution on fine mesh $Q^f_l$ on each level $l$. In the cases considered in this work, the computational saving due to the coarse mesh samples saving is $20\%$ asymptotically. Besides, a comparison of Monte Carlo and Quasi-Monte Carlo (QMC) methods reveals a smaller estimator variance and a faster convergence rate of the latter approach in this study.

The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.