bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Efficient Fully-Implicit MFD-MUSCL Method Based on a Novel Multislope Limiting Procedure
Authors: Jiang Jiamin , Younis Rami M. 
Published: February 2017

Abstract
Standard reservoir simulation schemes use single-point upstream weighting for computing the convective fluxes when multi-phase/component fluids are present. These schemes are only first-order and may cause high viscosity effect. Second-order schemes provide a better resolution and reduce the smoothing near the shocks. In reservoir simulation practice, implicit discretisations capable of taking large time steps are preferred over explicit schemes. However, even for a simple first-order method, solving a large nonlinear system is often very expensive; Extra coupling and nonlinearity of the discretised equations can be introduced from a higher-order spatial discretisation. It has been shown that strong nonlinearity as well as lack of continuous differentiability in numerical flux function and flux limiter can cause serious nonlinear convergence problem.
Cell-centered finite-volume (CCFV) discretizations may offer several attractive features, especially for fluid flow in heterogeneous or fractured porous media. The objectives of this work are to develop a fully-implicit CCFV framework that could achieve second-order spatial accuracy on smooth solutions, while at the same time maintain robustness and nonlinear convergence performance. We develop a novel multislope MUSCL method which interpolates the required values at the edge centroids in a simpler way by taking advantage of some geometric features of the triangular mesh. Through the edge centroids, the numerical diffusion caused by mesh skewness is significantly reduced, and optimal second-order accuracy can be achieved. An improved gradually-switching piecewise-linear flux-limiter is introduced according to mesh non-uniformity in order to prevent spurious oscillations. The smooth flux-limiter can achieve high accuracy without degrading nonlinear convergence behavior.
For the discretization of pressure and Darcy velocities, a mimetic finite difference method that provides flux- continuity and an accurate total velocity field is used. The fully-coupled MFD-MUSCL framework is adapted to accommodate a lower-dimensional discrete fracture-matrix (DFM) model. Several numerical tests with discrete fractured system are carried out to demonstrate the efficiency and robustness of the numerical model. The results show that the high-order MUSCL method effectively reduces numerical diffusion, leading to improved resolution of saturation fronts compared with the first-order method. In addition, it is shown that the developed multislope method and adaptive flux limiter exhibit superior nonlinear convergence compared with other alternatives.
Enhanced Constrained Pressure Residual ECPR Preconditioning for Solving Difficult Large Scale Thermal Models
Authors: Li Gary , Wallis John 
Published: February 2017

Abstract
The Constrained Pressure Residual (CPR1,2) preconditioning method with FGMRES3 acceleration, although very efficient for iterative solution of isothermal fully implicit linear systems, may encounter significant convergence difficulties for large scale thermal cases. This is in part due to the fact that for these problems pressures are not the only strong variables. For such cases this paper describes an enhanced version of CPR (ECPR) preconditioning which is simple to implement and can significantly decrease iterations by 50% −80% or more depending on problem difficulty, converge where CPR fails, and substantially reduce work.
The two-stage ECPR preconditioning uses an expanded variable set for the first stage. The key step involves a fast strong variable selection algorithm which can substantially increase the robustness of CPR. For the approximate solution of this enhanced first stage we used FGMRES with equilibrated ILUC as the preconditioning. The improved strength of this step allows the use of a less expensive second stage preconditioning such as block ILU(0) rather than block ILU (1)2.
The block ILU(0) /ECPR method showed significant improvement in convergence rate for these difficult thermal cases over the original block ILU(1)/CPR method. We observed over 50% reduction in the number of iterations for our moderately difficult test cases over entire runs and reduction in run times. For individual linear systems requiring many CPR iterations test cases show iterations can be reduced by 80% or more using ECPR. In one particular test case CPR failed to converge after 100 iterations but ECPR converged in 7 iterations.
A General Modeling Framework for Simulating Complex Recovery Processes in Fractured Reservoirs at Different Resolutions
Authors: Hui Mun-Hong Robin , Karimi-Fard Mohammad , Mallison Bradley , Durlofsky Louis J. 
Published: February 2017

Abstract
A comprehensive methodology for gridding, discretizing, coarsening, and simulating discrete-fracture-matrix models of naturally fractured reservoirs is described and applied. The model representation considered here can be used to define the grid and transmissibilities, at either the original fine scale or at coarser scales, for any connectivity-list-based finite-volume flow simulator. For our fine-scale mesh, we use a polyhedral gridding technique to construct a conforming matrix grid with adaptive refinement near fractures, which are represented as faces of grid cells. The algorithm uses a single input parameter to obtain a suitable compromise between fine-grid cell quality and the fidelity of the fracture representation. Discretization using a two-point flux approximation is accomplished with an existing procedure that treats fractures as lower-dimensional entities (i.e., resolution in the transverse direction is not required). The upscaling method is an aggregation-based technique in which coarse control volumes are aggregates of fine-scale cells, and coarse transmissibilities are computed using a general flow-based procedure. Numerical results are presented for waterflood, sour gas injection, and gas condensate primary production. Coarse-model accuracy is shown to generally decrease with increasing levels of coarsening, as would be expected. We demonstrate, however, that by using our methodology, two orders of magnitude of speedup can be achieved with models that introduce less than about 10% error (with error appropriately defined). This suggests that the overall framework may be very useful for the simulation of realistic discrete-fracture-matrix models.
Non-Equilibrium Reservoir Simulation of Solvent-Steam Processes, Based on Mass and Heat Transfer Inside a Pore
Authors: Bayestehparvin Bita , Abedi Jalal , Ali S. M. 
Published: February 2017

Abstract
Steam processes involving injection of a solvent with steam, such as CSS (Cyclic Steam Stimulation), SAGD (Steam-Assisted Gravity Drainage), and steam injection in isothermal mode are currently receiving a great deal of attention in Alberta. These combination processes are designed to reduce energy consumption and the emission of greenhouse gases over steam alone. Field results have been mixed and the original intent remains elusive. This paper addresses questions regarding the mechanism of solvent dissolution of bitumen and the expected improvement in oil recovery, if any, when a solvent is injected with steam.
The unique aspect of this work is that instantaneous phase equilibrium is not assumed, as is customary in numerical simulators for solvent-steam applications. Nor is partial equilibrium assumed based on an empirical factor or concept. Rather, equilibrium is based on an analytical model of the dissolution and mobilization of a drop of bitumen inside a pore by solvent and heat. The results of this part of the study show that solvent requires at least three times as long to mobilize bitumen as by heat conduction. Such a delay is implicit in the nature of diffusion and dispersion of a solvent. Several boundary conditions are tested for a spherical drop.
A new thermal compositional simulator with the single drop model was developed for this study and several thermal processes for solvent injection were investigated for non-equilibrium phase behaviour. The results were compared to the case of instantaneous equilibrium, confirming the reason for the lack of success with solvent injection.
The results and extensions of this work will be of great interest in heavy oil production because they serve to explain the expected performance and frequent lack of success in solvent-steam injection. Use of the developed simulator would make it possible to determine whether solvent injection is a good choice in a given situation.
Unveiling the Causes of Fingering in Steam-Assisted Heavy Oil Recovery - Linear Instability Analysis and Numerical Simulation
Authors: Zhu Da , Gates Ian D. 
Published: February 2017

Abstract
Given the high viscosity of the oil, bitumen from oil sands reservoirs in western Canada is recovered by using steam which, due to its temperature, lowers its viscosity. One of the key issues faced by the operators is the steam conformance of the depletion chamber around wells. The greater the fingering phenomena of steam at the edge of chamber, the worse is the chamber uniformity and utilization of the well, and the greater are the green house gas emissions and water use per unit oil recovered. Fingering has long been explained as the penetration of steam phase into the oil phase which arises from an unfavourable mobility ratio. In this paper, we introduce linear instability analyses (Orr-Sommerfeld and Rayleigh-Taylor/Saffman-Taylor instability) of the interface between steam and oil layers and conduct a series of numerical simulations to reveal that fingering in the steam-assisted heavy oil recovery at the top of the steam chamber is created due to solution gas exsolution whereas fingering at the chamber edge is due to viscous shear instability. The results show that non-ideal steam conformance is inevitable even in homogeneous reservoirs.
Model-Based Well Location Optimization – A Robust Approach
Authors: Ramirez B. A. , Joosten G. J. , Kaleta M. P. , Gelderblom P. P. 
Published: February 2017

Abstract
Well Location Optimization (WLO) plays a critical role in maximizing recovery from hydrocarbon reservoirs. Identification of well locations typically relies on expert subsurface knowledge and ad-hoc flow simulation scenarios. Such approaches, due to the complexity and uncertainty of subsurface descriptions, can easily miss highly profitable possibilities. This situation has motivated the development of model-based optimization workflows that can assist subsurface specialists in choosing improved well locations. In practice, model-based optimization workflows are implemented in a deterministic fashion and applied to low, mid and high simulation scenarios. The workflow proposed in this paper takes into account model uncertainty in the optimization outcomes.
Deterministic WLO entails optimizing the easting and northing coordinates of wells with respect to an objective function (e.g. net present value or ultimate recovery). The workflow applies to fixed well trajectories. For each new proposed well location, there is a mapping of the new well connections to the reservoir simulation grid. The approach has shown resilience in handling realistic scenarios including non-uniform areal gridding, pinch out features, local grid refinements or tartan gridding, and non-uniform layering dimensions.
Deterministic optimization techniques may anchor the results to particular portions of the uncertainty space, while robust optimization techniques propagate the effect of uncertain parameters on the optimization outcomes. The trade-off between the investigated span of uncertainty space and available computational capability needs to be made. Due to the continuous growth in computational capabilities, it is ever more attainable.
Robust optimization incorporates the effect of uncertain parameters in the optimization process. It is a natural extension of the history matching process in brownfields, but it is also applicable to green fields where dynamic data is yet to be received. For such cases, a robust optimization workflow will yield outcomes that take into account the uncertainty of parameters described by a prior distribution, e.g. only conditioned to static data.
The WLO workflow involves (i) executing deterministic WLO, (ii) examining the deterministic WLO results on a set of reservoir simulation models that capture the uncertainty space, and (iii) executing robust WLO. This workflow was tested with gradient-based optimization methods, derivative-free methods, and hybrid methods, the latter being the most optimal of the three.
The robust implementation of this workflow handles realistic reservoir simulation scenarios by exploiting coarse-grained parallelism and has been applied successfully to field cases with up to fifty wells. In this paper, WLO is showcased on one synthetic case and one field case to illustrate the capabilities of the workflow.
Nonlinear Convergence for Near-Miscible Problem: A Mystery Unveiled for Natural Variable Simulator
Authors: Cao Hui , Zaydullin Rustem , Obi Eguono 
Published: February 2017

Abstract
In reservoir simulation, the nonlinear solver is used to solve the nonlinear system of conservation and phase-equilibrium equations at each time step. The convergence behavior of this solver is vital for the robustness and performance of any reservoir simulator.
Based on the basic solution variables used in a reservoir simulator, we have natural variable based simulators with phase-dependent solution variables (e.g. phase saturations), and mass/overall variable based simulators without phase-dependent solution variables. The nonlinear convergence behavior between these two types of simulators has been compared previously. It has been found that natural variable based simulator works well for immiscible and miscible flow, while sometimes it has apparent convergence issues for near-miscible flow.
In this paper, we analyze the fundamental physical and numerical causes for the nonlinear convergence difficulty encountered for near-miscible flow for a natural variable based simulator and study why the same issues do not appear in both immiscible and miscible flow. Based on our analysis, some carefully designed user tuning parameters can be applied to partially alleviate the convergence issue for near-miscible flow. Unfortunately this is neither general nor good enough. To fundamentally solve this issue, we propose a straightforward nonlinear solver modification which is applicable for all types of displacements (immiscible/miscible/near-miscible/mixed). We demonstrate improvements on the real field cases with different types of flow. For near-miscible and mixed type of displacements, the improvements in Newton iterations can be significant, while for the other types, our nonlinear solver modification does not show the adverse effect. For reference, we also compared the convergence behavior of the proposed solver with that from a standard mass variable based commercial simulator.
Dynamic Mesh Adaptivity for Immiscible Viscous Fingering
Authors: Adam A. , Pavlidis D. , Percival J. R. , Salinas P. , Loubens R. De , Pain C. C. , Muggeridge A. H. , Jackson M. D. 
Published: February 2017

Abstract
The unstable displacement of one fluid by another in a porous medium occurs frequently in various branches of enhanced oil recovery. It is now well known that when the invading fluid is of lower viscosity than the resident fluid, the displacement front is subject to a Saffman-Taylor instability and is unstable to transverse perturbations. These instabilities can grow, leading to fingering of the invading fluid. Numerical simulation of viscous fingering is challenging. The physics is controlled by a complex interplay of viscous and diffusive forces and it is necessary to ensure physical diffusion dominates numerical diffusion to obtain converged solutions. This typically requires the use of high mesh resolution and high order numerical methods. This is computationally expensive, particularly in 3D. We use IC-FERST, a novel control volume finite element (CVFE) code that uses dynamic mesh adaptivity on unstructured meshes to simulate 2D and 3D viscous fingering with higher accuracy and lower computational cost than conventional methods. We provide evidence that these unstructured mesh simulations in fact yield better results that are less influenced by grid orientation error than their structured counterparts. We also include the effect of capillary pressure and show three examples that are very challenging to simulate using more conventional approaches.
Integration of Distributed Gauss-Newton with Randomized Maximum Likelihood Method for Uncertainty Quantification of Reservoir Performance
Authors: Chen Chaohui , Gao Guohua , Li Ruijian , Cao Richard , Chen Tianhong , Vink Jeroen C. , Gelderblom Paul 
Published: February 2017

Abstract
Although it is possible to apply traditional optimization algorithms together with the Randomized Maximum Likelihood (RML) method to generate multiple conditional realizations, the computation cost is high. This paper presents a novel method that integrates the Distributed Gauss-Newton (DGN) method with the RML method to generate multiple realizations conditioned to production data synchronously.
RML generates samples from an approximate posterior by finding a large ensemble of maximum posteriori points, from a distribution function in which the data and prior mean values have been perturbed with Gaussian noise. Rather than performing these optimizations in isolation, using large sets of simulations to evaluate the finite difference approximations of the gradients used to optimize each perturbed realization, we use a concurrent implementation, in which simulation results are shared among optimizations whenever these results are helping to converge a specific optimization. In order to improve sharing of results, we relax the accuracy of the finite difference approximations for the gradients, by using more widely spaced simulation results. To avoid trapping in local optima, a novel global search algorithm integrated with DGN and RML is applied. In this way we can significantly increase the number of conditional realizations that sample the approximate posterior, while reducing the total number of simulations needed to converge the optimization processes needed to obtain these realizations.
The proposed workflow has been applied to field examples on liquid rich shale or tight oil reservoirs developed with hydraulically fractured horizontal wells. The uncertain parameters include stimulated rock volume (SRV) and matrix properties, such as permeability and porosity, and hydraulic-fracture properties, such as conductivity, height, and half length. The case studies involve a sensitivity analysis to identify key parameters, a history matching study to generate history-matched realizations with the proposed method, and an uncertainty quantification of production forecasting based on those conditioned models. The new approach is able to enhance the confidence level of the Estimated Ultimate Recovery (EUR) assessment by accounting for production forecasting results generated from all history-matched realizations. Numerical results indicate that the new method is very efficient compared with traditional methods. Hundreds of history-matched, or rather data-conditioned, realizations can be generated in parallel within 20-40 iterations. The computational cost (CPU usage) is reduced by a factor of 10 to 25 when compared to the traditional RML approach.
Parallel Multiobjective Optimization for the Coupled Compositional /Geomechanical Modeling of Pulse Testing
Authors: Min Baehyun , Wheeler Mary F. , Sun Alexander Y. 
Published: February 2017

Abstract
This paper presents numerical simulation results of pulse testing experiments carried out at a test site of a carbon capture and geological storage project in Mississippi, USA. The primary objective of this study is to validate the effectiveness of pulse testing as a monitoring tool for detecting potential CO2 leakage pathways with application to the test site. Detrending followed by Fourier transform is adopted to decompose sinusoidal pressure anomalies induced by a periodic injection of CO2 into frequencies used as target parameters of history matching. The secondary objective is to calibrate the geologic model of the test site by reducing the discrepancy between observed and simulated Fourier parameters and assess uncertainties associated with the compositional brine-CO2 flow. An assisted history matching tool that mounts global- and multi-objective evolutionary algorithms is developed, integrated with an in-house flow-geomechanics simulator, and employed to manage pulse testing simulations with a low computational cost in high-performance parallel computing environments. Grid cells in the test site are locally refined using enhanced-velocity that allows nonmatching grids on interfaces between subdomains. Experiments performed with one pulser well and two monitoring wells under steady-state conditions are considered baselines for subsequent experiments that convert one monitoring well into a production well as an artificial CO2 leakage pathway. The difference between the pressure anomalies obtained from the baseline and leak experiments are captured as a signal of CO2 leakage detection with reliability in the simulation results. A trade-off relationship between the matching qualities at the two monitoring wells is revealed more clearly by invoking multi-objective history matching than conventional global-objective history matching. This comparative study to investigate the significance of multi-objective optimization in subsurface characterization represents that diversity-preservation in the ensemble composed of qualified geologic models has the advantage of reducing bias for uncertainty quantification.