bluuebunny/crossref_metadata_2025_split huggingface dataset:
Simulation of Radio Frequency Heating of Heavy Oil Reservoir Using Multi-Physics Coupling of Reservoir Simulation with Electromagnetic Solver
Authors: Li Gary , Guan Xiaoyue , Wang Hanming , Du Song , Wu Dagang , Chen Ji 
Published: March 2019

Abstract
Steam-Assisted Gravity Drainage (SAGD) is one of the popular methods for heavy oil production. The process is efficient and economical. However, it requires the use of large quantity of water and disposal of waste water can be costly. In addition, burning of natural gas for steam generation contributes to additional carbon dioxide generation, a known greenhouse gas, which is also undesirable. A method to heat up the in-situ oil without the use of injected water is highly desirable. Radio frequency (RF) heating of heavy oil reservoir is a potential method for oil recovery without steam injection. The evaluation of the potential of such method requires the coupling of a reservoir simulator with an electromagnetic (EM) simulator.
This paper describes the development and implementation of a flexible interface in a reservoir simulator that allows the runtime loading of third party software libraries with additional physics. Data is exchanged between the reservoir simulator and externally loaded software libraries through memory, therefore there is minimal communication overhead. The implementation allows for iterative coupling, explicit coupling and periodic coupling. This paper describes the mathematical coupling of the mass and energy conservation equations in the reservoir simulator with the Maxwell equations in an external library. The electromagnetic properties in the reservoir are highly dependent on temperature and water saturation, this dependence is accounted for in the coupled code using table look-up properties.
Canadian heavy oil and reservoir properties were used in our simulation investigation. We found that RF heating alone can be effective in heating up the in-situ water and reducing heavy oil viscosity by several orders of magnitude. As the in-situ water near wellbore was vaporized by RF heating, electrical conductivities were reduced to zero and thus allowed the EM wave to propagate further into the formation and heat up the water further away from the wellbore. With properly designed RF heating field pilots and tuning of EM and reservoir parameters, the coupled reservoir/EM simulator can be a powerful tool for the evaluation and optimization of RF heating operations.
The interface is sufficiently flexible to allow different types of multi-physics coupling. In addition to RF heating, it has also been used for reaction kinetics and geomechanics coupling with a reservoir simulator. It has been used for large scale coupled full field simulation with over 30 million cells.
Robust Optimization of ASP Flooding Under Oil Price Uncertainty
Authors: Li Heng , Dang Cuong , Mirzabozorg Arash , Yang Chaodong , Nghiem Long 
Published: March 2019

Abstract
This paper presents a novel workflow to optimize Alkaline, Surfactant, and Polymer (ASP) flooding under oil price uncertainty. ASP flooding is a relatively expensive recovery process and the optimization approach must be able to account for the change in oil price to provide realistic economical predictions. A workflow, combining time-event optimization with Robust Optimization has been developed and applied to the optimization of the NPV of the ASP process under uncertain time varying oil prices. Several forecasts of the future oil price from different energy organizations and financial institutions were used to account for the uncertainty of the oil price in the NPV calculations.
The proposed workflow introduces the parameterization of time-dependent events to identify the best time to start the ASP flooding project after the secondary water flooding, and to determine the size and composition of different chemical slugs. The Robust optimization workflow is compared to the Nominal Optimization workflow, which is carried out using the average oil price forecase to calculate NPV. Results show that Robust Optimization increases the project Net Present Value (NPV) by over 18% compared with Nominal Optimization. Robust Optimization generates optimal ASP design with desirable performance under different oil price forecast scenarios. In addition to ASP flooding, the Robust Optimization workflow can be efficiently applied to other recovery processes for both conventional and unconventional reservoirs.
Comparison of Higher Order Schemes on Complicated Meshes and Reservoirs
Authors: Kvashchuk Anna , Klöfkorn Robert , Sandve Tor Harald 
Published: March 2019

Abstract

Scope
Accurate numerical modeling of fluid transport is essential in reservoir management. Higher-order methods help to improve accuracy by reducing the numerical diffusion, which is common for all first order methods. In this paper, we present an implementation of a MUSCL-type second-order finite volume method and demonstrate its capabilities on 2D and 3D unstructured grids. This includes corner point grids that are typically used in reservoir modeling.

Methods, Procedures, Process
A second order finite volume method is compared to standard first order method in terms of accuracy, performance and an ability to handle nonlinearities. There are several ways to build a second order finite volume method. In this paper we choose an optimization-based strategy to compute the steepest possible linear reconstruction. At the same time, a steepness-limiting procedure is included in the optimization as constraint. This ensures that the steepest possible reconstruction that does not lead to oscillations is computed. As a result, sharper fronts compared to standard schemes are obtained.

Results, Observations, Conclusions
The paper demonstrates the described method on several benchmark cases with emphasis on relevant for practical reservoir simulation test cases. In particular, we use Norne field open data set, which enables cross validation with other implementations. We test the method on the transport case, where an analytical solution is known, to verify convergence behavior and to isolate the errors. Furthermore, the performance of first- and second-order methods is compared on multiphase flow problems typical for improved oil recovery: solvent and CO2 injection. The second order method shows superior performance in terms of accuracy.

Novel/Additive Information
This paper verifies the desirable properties of higher order method for reservoir simulation. Moreover, all the described implementations are available in an open source reservoir simulator Open Porous Media (OPM). As a result, these methods are accessible for reservoir engineers and can be used with industry standard modeling setups.
Accelerated Compositional Simulation of Tight Oil and Shale Gas Reservoirs Using Proxy Flash Calculation
Authors: Wang Shihao , Sobecki Nicolas , Ding Didier , Wu Yu-Shu , Zhu Lingchen 
Published: March 2019

Abstract
In this work, we present the development of a compositional simulator accelerated by proxy flash calculation. We aim to speed up the compositional modeling of unconventional formations by stochastic training.
We first developed a standalone vapor-liquid flash calculation module with the consideration of capillary pressure and shift of critical properties induced by confinement. We then developed a fully connected network with 3 hidden layers using Keras. The network is trained with Adam optimizer. 250,000 samples are used as training data, while 50,000 samples are used as testing data. Based on the trained network, we developed a forward modeling (prediction) module in a compositional simulator. Therefore, during the simulation run, the phase behavior of the multicomponent system within each grid block at each iteration is obtained by simple interpolation from the forward module.
Our standalone flash calculation module matches molecular simulation results well. The accuracy of the trained network is up to 97%. With the implementation of the proxy flash calculation module, the CPU time is reduced by more than 30%. In the compositional simulator, less than 2% of CPU time is spent in the proxy flash calculation.
The novelty of this work lies in two aspects. We have incorporated the impacts of both capillary pressure and shift of critical properties in the flash calculation, which matches molecular simulation results well. We developed a proxy flash calculation module and implemented it in a compositional simulator to replace the traditional flash calculation module, speeding the simulation by 30%.
A Framework for Incorporating Nanopores in Compositional Simulation to Model the Unusually High GOR Observed in Shale Reservoirs
Authors: Luo Sheng , Lutkenhaus Jodie , Nasrabadi Hadi 
Published: March 2019

Abstract
The fluid phase behavior in shale reservoirs differs from the conventional fluid phase behavior because of the nanopores in shale rock. In many shale oil reservoirs, the producing gas-oil ratio (GOR) is significantly higher than what would be produced from a bulk-state conventional reservoir. The phase behavior alteration in nanopores arises from the fluid-pore wall interaction within the nano-scale confining geometry. Recently, many approaches have been developed to thermodynamically model the fluid phase behavior in nanopores. An adequate and efficient method to incorporate the nano-confined phase behavior in engineering practice is highly desired.
In this work, we present a practical framework to incorporate the nanopores in compositional simulation for shale reservoirs. This approach enables engineers to incorporate the macro- to nano-scale phase equilibria from a pore-size-dependent equation of state into current compositional simulators, which use cubic equation of state. The procedures include bulk fluid characterization, simulation of multi-scale confined depletion by a pore-size-dependent equation of state, re-training the cubic equation of state, tuning relative permeability and applying in composition simulation. The simulation predicts higher GOR with nanopores, in agreement with the higher-than-normal GOR observed with shale oil wells, and the GOR fluctuations are found in association with various reservoir drainage areas. The simulated production behavior gives mechanistic insight into the effect of nano-confinement fluid behavior on shale reservoir depletions.
Nonlinear Upscaling of Two-Phase Flow Using Non-Local Multi-Continuum Approach
Authors: Leung Wing T. , Chung Eric T. , Efendiev Yalchin , Vasilyeva Maria , Wheeler Mary 
Published: March 2019

Abstract
The objective of this work is to design upscaled model concepts for multi-phase flow and transport. Our approaches are based on recent developments in multiscale simulations and their relations to upscaling.
We propose a novel multi-phase upscaling technique, which employs rigorous multiscale concepts based on the Constraint Energy Minimization (CEM-GMsFEM). CEM-GMsFEM concepts utilize local spectral problems and an energy minimization principle to design multiscale basis functions, which are supported in oversampled regions. A coarse-grid solution defined by these basis functions provides first-order accuracy with respect to the coarse-mesh size and is independent of high contrast of the permeability. The degrees of freedom in multiscale methods represent the coordinates of the solution in the multiscale space. To design an upscaled model, we modify these basis functions such that the degrees of freedom have physical meanings, in particular, the averages of the solution in each continuum. This allows deriving rigorous upscaled models and account for both local and non-local on the effects. The transmissibilities in our upscaled models are non-local and take into account non-neighboring connections.
To extend this approach to nonlinear problems in the context of two-phase flow, we develop non-linear upscaling, where the pressures and saturations are interpolated within an oversampled region based on average values of these quantities. Multicontinua concepts are used to localize the problem to the oversampled regions. Our upscaled model shares some similarities with the pseudo-relative permeability approach with the following differences: (1) the upscaled relative permeabilities depend non-locally on the saturations; and (2) local problems, formulated in oversampled regions, involve constraints and require multi-contiuum concepts.
The numerical results will utilize upscaled methods to predict the solution of single-and two-phase flow dynamics. We will describe upscaled equations, which include the non-local neighborhood connections. Our results demonstrate that the proposed approaches provide a good accuracy and robustness. We consider various types of heterogeneities. The proposed concepts will benefit developing coarse-grid and upscaled models for many applications involving multi-phase flow and transport.
Multilevel Strategies and Geological Parameterizations for History Matching Complex Reservoir Models
Authors: Liu Yimin , Durlofsky Louis J. 
Published: March 2019

Abstract
In this study we explore the use of multilevel derivative-free optimization for history matching, with model properties described using PCA-based parameterization techniques. The parameterizations applied in this work are optimization-based PCA (O-PCA) and convolutional neural network-based PCA (CNN-PCA). The latter, which derives from recent developments in deep learning, is able to represent accurately models characterized by multipoint spatial statistics. Mesh adaptive direct search (MADS), a pattern search method that parallelizes naturally, is applied for the optimizations required to generate posterior (history matched) models. The use of PCA-based parameterization reduces considerably the number of variables that must be determined during history matching (since the dimension of the parameterization is much smaller than the number of grid blocks in the model), but the optimization problem can still be computationally demanding. The multilevel strategy introduced here addresses this issue by reducing the number of simulations that must be performed at each MADS iteration. Specifically, the PCA coefficients (which are the optimization variables after parameterization) are determined in groups, at multiple levels, rather than all at once. Numerical results are presented for 2D cases, involving channelized systems (with binary and bimodal permeability distributions) and a deltaic-fan system, using O-PCA and CNN-PCA parameterizations. O-PCA is effective when sufficient conditioning (hard) data are available, but it can lead to geomodels that are inconsistent with the training image when these data are scarce or nonexistent. CNN-PCA, by contrast, can provide accurate geomodels that contain realistic features even in the absence of hard data. History matching results demonstrate that substantial uncertainty reduction is achieved in all cases considered, and that the multilevel strategy is effective in reducing the number of simulations required. It is important to note that the parameterizations discussed here can be used with a wide range of history matching procedures (including ensemble methods), and that other derivative-free optimization methods can be readily applied within the multilevel framework.
Flexible Discretizations of the Three-Component Three-Phase Flow Problem
Authors: Cappanera Loic , Riviere Beatrice 
Published: March 2019

Abstract
A discontinuous Galerkin method of first order is proposed to solve the three-phase flow problem in three-dimensional heterogeneous reservoirs. The formulation is based on the compositional model and the primary unknowns are the total mass fraction of gas, the aqueous phase saturation and the liquid phase pressure. The algorithm is sequential and controls the nonlinearity with a subiteration scheme. Robustness of the method is shown on reservoirs with different heterogeneities: random permeability field, reservoir with barriers and layered reservoir. The algorithm easily handles phase appearance and disappearance, as well as mass transfer between the vapor and liquid phase.
Markov Chain Monte Carlo Uncertainty Quantification with a Least-Squares Support Vector Regression Proxy
Authors: dos Santos Sousa Emilio Paulo , Reynolds Albert C. 
Published: March 2019

Abstract
Important decisions in the oil industry rely on reservoir simulation predictions. Unfortunately, most of the information available to build the necessary reservoir simulation models are uncertain, and one must quantify how this uncertainty propagates to the reservoir predictions. Recently, ensemble methods based on the Kalman filter have become very popular due to its relatively easy implementation and computational efficiency. However, ensemble methods based on the Kalman filter are developed based on an assumption of a linear relationship between reservoir parameters and reservoir simulation predictions as well as the assumption that the reservoir parameters follows a Gaussian distribution, and these assumptions do not hold for most practical applications. When these assumptions do not hold, ensemble methods only provide a rough approximation of the posterior probability density functions (pdf 's) for model parameters and predictions of future reservoir performance. However, in cases where the posterior pdf for the reservoir model parameters conditioned to dynamic observed data can be constructed from Bayes’ theorem, uncertainty quantification can be accomplished by sampling the posterior pdf. The Markov chain Monte Carlos (MCMC) method provides the means to sample the posterior pdf, although with an extremely high computational cost because, for each new state proposed in the Markov chain, the evaluation of the acceptance probability requires one reservoir simulation run. The primary objective of this work is to obtain a reliable least-squares support vector regression (LS-SVR) proxy to replace the reservoir simulator as the forward model when MCMC is used for sampling the posterior pdf of reservoir model parameters in order to characterize the uncertainty in reservoir parameters and future reservoir performance predictions using a practically feasible number of reservoir simulation runs. Application of LS-SVR to history-matching is also investigated.
New and Improved Physical Property Models for Chemical Flooding Simulators
Authors: Lashgari Hamid , Pope Gary , Balhoff Mathew , Tagavifar Mohsen 
Published: March 2019

Abstract
Significant advances have been made in chemical enhanced oil recovery (EOR) in recent years including the development of hybrid methods that combine surfactants, polymers, alkali, co-solvents, gas and heat in novel ways. New and improved chemical and physical property models have been developed to more accurately simulate these processes at the field scale. We present improved models for relative permeability, capillary pressure, the effect of polymer viscoelasticity on residual oil saturation, the effect of pH on surfactant adsorption, polymer partitioning between aqueous and microemulsion phases, and the effect of co-solvent on microemulsion viscosity. Several simulations are presented to demonstrate how the models can be used to match experimental data.
Integrated Framework for Modelling of Thermal-Compositional Multiphase Flow in Porous Media
Authors: Khait Mark , Voskov Denis 
Published: March 2019

Abstract
Various novel computing architectures, like massively parallel and multi-core, as well as computing accelerators, like GPUs or TPUs, keep regularly expanding. In order to exploit the benefits of these architectures to the full extent and speed up reservoir simulation, the source code has to be inevitably rewritten, sometimes almost completely. We demonstrate how to extract complex physics-related computations from the main simulation loop, leaving only an algebraic multilinear interpolation kernel instead. In combination with linear solvers, which usually have made available soon once the new architecture is introduced, the approach accommodates execution of the entire nonlinear loop on the latest hardware and computational architectures. We describe the integrated simulation framework built on top of this technique and show the applicability of the approach to various challenging physical and chemical problems. All simulation engines along with linear solvers, well controls, interpolation engines, and state operator evaluators are implemented in C++11 and exposed into Python coupling the flexibility of the script language with the performance of C++.
Implicit High-Resolution Compositional Simulation With Optimal Ordering of Unknowns and Adaptive Spatial Refinement
Authors: Klemetsdal Ø.S. , Møyner O. , Lie K.-A. 
Published: March 2019

Abstract
High-resolution discretizations can be advantageous in compositional simulation to reduce excessive numerical diffusion that tends to mask shocks and fingering effects. In this work, we outline a fully implicit, dynamic, multilevel, high-resolution simulator for compositional problems on unstructured polyhedral grids. We rely on four ingredients: (i) sequential splitting of the full problem into a pressure and a transport problem, (ii) ordering of grid cells based on intercell fluxes to localize the nonlinear transport solves, (iii) higher-order discontinuous Galerkin (dG) spatial discretization with order adaptivity for the component transport, and (iv) a dynamic coarsening and refinement procedure. For purely cocurrent flow, and in the absence of capillary forces, the nonlinear transport system can be perturbed to a lower block-triangular form. With counter-current flow caused by gravity or capillary forces, the nonlinear system of discrete transport equations will contain larger blocks of mutually dependent cells on the diagonal. In either case, the transport subproblem can be solved efficiently cell-by-cell or block-by-block because of the natural localization in the dG scheme. In addition, we discuss how adaptive grid and order refinement can effectively improve accuracy. We demonstrate the applicability of the proposed solver through a number of examples, ranging from simple conceptual problems with PEBI grids in two dimensions, to realistic reservoir models in three dimensions. We compare our new solver to the standard upstream-mobility-weighting scheme and to a second-order WENO scheme.