bluuebunny/crossref_metadata_2025_split huggingface dataset:
Compositional Reservoir Flow Simulation for Organic-Rich Gas Shale
Authors: Olorode O. M. , Akkutlu I. Y. , Efendiev Y. 
Published: February 2017

Abstract
A new-generation compositional reservoir flow simulation model is presented for organic-rich shale gas formations including convective-diffusive mass balance equations for hydrocarbon components in organic (kerogen), inorganic and fracture continua. The model presented accounts for the presence of dispersed kerogen with sorbed-gas corrected dynamic porosity. Maxwell-Stefan theory is used to predict pressureand composition-dependence of molecular diffusion in the formation. The equations are discretized and solved numerically using control volume finite element method.
The simulation is based on a new multi-scale conceptual flow model. We consider that kerogen is dispersed at a fine scale in the inorganic matrix and that it will be the discontinuous component of total porosity at the reservoir simulation scale, which could be up to six orders of magnitude larger. A simple mass balance equation is introduced to enable kerogen to transfer gas to the inorganic matrix that is collocated in the same grid-block. The convective-diffusive transport takes place between neighboring grid blocks only in the inorganic matrix.
The simulation results show that the multi-scale nature of the rock is important and should not be ignored because this could result in an overestimation of the contribution of the discontinuous kerogen. We also observe that although adsorbed fluid could contribute significantly to storage in the shale formation, its contribution to production could be severely limited by the lack of kerogen continuity at the reservoir scale and by a low degree of coupling between the organic and inorganic pores. The contribution of Maxwell-Stefan diffusion to the overall transport in the shale formation increases as the inorganic matrix permeability is reduced due to pressure decline during production.
Multiphase Thermal Compositional Wellbore Modeling in Hybrid Steam/Solvent Injection
Authors: Almeida Rafael Valladares , Rahnema Hamid 
Published: February 2017

Abstract
Solvent aided steam injection is one of the most promising enhanced oil recovery methods for recovering heavy oil and oil sands. Experiments, simulations, and some field pilots have demonstrated that a hybrid steam and solvent injection can result in better production rates and higher ultimate oil recovery. However, the simulation studies assume that the steam and solvent mixture will be at the sandface in pre-determined pressure, temperature, and steam/solvent gas molar ratios, neglecting the pressure gradients, heat losses, and condensations that occur along the wellbore, which will affect the thermodynamic condition of the mixture at the perforation zones. This paper provides important background information regarding the wellbore numerical modeling in hybrid steam/solvent injection. Detailed formulations of pressure gradient and enthalpy calculations are described, and we investigate the effect of partial pressure on the condensation behavior of steam and solvent. The benefits of the model on the steam and solvent co-injection process optimization are examined with three case studies, where steam is co-injected with a) gas condensate, b) medium weight solvent, and c) heavy weight solvent. Gas condensate is unlikely to condense in the wellbore. However, medium and heavy weight solvents might condense depending on the steam and solvent ratios. To avoid solvent condensation, it is suggested to reduce the solvent ratio compared to steam, or to increase the steam quality at the wellhead. As a result, the solvent partial pressure will be reduced.
Uncertainty Quantification for Foam Flooding in Fractured Carbonate Reservoirs
Authors: Almaqbali A. , Spooner V. E. , Geiger S. , Arnold D. , Mackay E. 
Published: February 2017

Abstract
When simulating foam floods, uncertainties exist in both the foam and reservoir parameters however the combination of these uncertainties are rarely incorporated in forecasting. Foam flooding is an effective enhanced oil recovery method that controls mobility, reduces gas relative permeability, delays gas breakthrough and helps improve sweep efficiency. Thus it is often used in highly heterogeneous reservoirs where significant subsurface uncertainties exist. Foam uncertainties exist as (a) foam stability is controlled by a number of factors such as critical water and surfactant concentrations, brine salinity, and oil saturation which are unknown in the subsurface spatially and (b) foam flood simulation requires the accurate description of multiple parameters used in the foam flood models which are unknown.
This study quantifies and compares the impact of uncertainties associated with foam model parameters with the heterogeneity of a fractured carbonate reservoir, an analogue to the highly prolific Arab D formation. Foam model parameters are not known a-priori but can be tuned to experimental data, which ideally represent a range of foam regimes and reservoir conditions. Geological heterogeneities in fractured carbonate reservoirs are complex and include, matrix wettability, fracture density/orientation and initial saturation distribution.
To quantify uncertainties geological uncertainties in fractured carbonate reservoirs, an automated framework was used to history match the production response of a fractured carbonate field by varying geological parameters. This accounts for the geological uncertainties during a waterflood, which are then combined with foam uncertainties from experimental analysis in the optimisation step, by optimising the mean response of the model to foam flooding across a range of geological and foam scenarios. Our workflow used a combination of Particle Swarm Optimisation for history matching and manual optimisation, the final results of which show a wide range of possible impacts of foam flooding from different but equally well matched reservoirs.
The novelty of our work is that it demonstrates how parameters that control foam stability and hence effectiveness in mobility control are related to both foam properties and geological uncertainty. Carrying these uncertainties into foam model properties from core to field scale will translate into considerably more robust estimates of uncertainty when predicting field-scale recovery compared to simulations that only consider uncertainty in the reservoir model.
A Non-Orthodox MPFA Method Coupled to a High Order Modified Flow Oriented Scheme for the Simulation of Two-Phase Flows in Heterogeneous and Anisotropic Reservoirs
Authors: Contreras F. R. , Souza M. R. , Lyra P. R. , Carvalho D. K. 
Published: February 2017

Abstract
In this paper, we propose a full finite volume approach to simulate two-phase flows of oil and water in heterogeneous and anisotropic petroleum reservoirs in 2-D. The IMPES procedure is used to solve the coupling between the pressure and the saturation equations. The eliptic pressure equation is discretized by a non-orthodox linear Multi-Point Flux Approximation (MPFA-HD) method capable to handle heterogeneous and highly anisotropic media. Following the MPFA-D (MPFA-Diamond) scheme and some existent non-linear cell centered strategies, the key point in the construction of our scheme is the discretization of the flux across each cell face. First, we construct the one-sided fluxes on each cell independently and then, we integrate both one-sided fluxes over the control surface and finally we express the cell edge fluxes as a convex combination of the one side fluxes, to obtain a unique flux expression. On the other hand, differently from the MPFA-D, in our scheme, fluxes on each cell face are explicitly expressed by one cell centered unknown defined on the cells sharing that face and two auxiliary unknowns defined at two face endpoints that do not necessarily belong to the same face shared by the adjacent cells. These auxiliary vertex unknowns are eliminated by a proper interpolation. To solve the saturation equation, we propose a Modified Flow Oriented Scheme (M-FOS). This flow oriented variant explicitly computes the multidimensional numerical fluxes by using higher order accuracy in space. Besides, for problems with distorted meshes, the proposed formulation takes into account the angular distortion of the computational mesh by means of an adaptive weight, that tunes the multidimensional character of the formulation according to the grid distortion. This strategy diminishes the occurrence of Grid Orientation Effects (GOE). In order to avoid spurious oscillations in higher-order approximations, a recently devised Multidimensional Limiting Process (MLP) is adopted. This strategy guarantees monotone solutions and can be used with any polygonal mesh. Finally, an entropy fix strategy is also employed in order to produce convergent solutions. The performance of our numerical formulation is evaluated by solving some benchmark problems.
A Multiscale Restriction-Smoothed Basis Method for Compositional Models
Authors: Møyner O. , Tchelepi H. 
Published: February 2017

Abstract
Compositional simulation is attractive for a wide variety of applications in reservoir simulation and it is especially valuable when modeling gas injection for enhanced oil recovery. Since the nonlinear behavior of gas injection is sensitive to the resolution of the simulation grid used, it is important to use a fine grid to accurately resolve the gas front and the pressure propagation. Unfortunately, discretizing a compositional flow system with many components on a high-resolution geological model leads to very large and poorly conditioned linear systems, and the high computational cost of solving these systems tends to render field- scale simulations infeasible. An additional challenge is the need for phase-equilibrium calculations, which often represent a large fraction of the computational time when both gas and liquid are present.
We present a multiscale solver for compositional three-phase flow problems in which the behavior of the liquid and vapor phases are described by generalized cubic equations of state. The solver relies on a total-mass and overall composition based sequential solution strategy for the flow and transport equations and uses restricted smoothing to compute multiscale basis functions on unstructured grids with general polyhedral cell geometries. The resulting method computes approximate pressure solutions (to within a prescribed residual tolerance) that have conservative fluxes on the reduced computational grid, the original fine-scale grid, or any intermediate partition.
The new method is verified against existing compositional simulators on conceptual models and validated on more complex cases represented on both unstructured and corner-point grids with strong heterogeneity, faults, pinched and eroded cells, etc.
The resulting implementation is the first demonstrated multiscale method applicable to general compositional problems relevant for the petroleum industry, which includes a cubic equation of state and stratigraphic grids.
A General Non-Uniform Coarsening and Upscaling Framework for Reduced-Order Modeling
Authors: Lie K.-A. , Kedia K. , Skaflestad B. , Wang X. , Yang Y. , Wu X.-H. , Hoda N. 
Published: February 2017

Abstract
This paper presents a general framework for constructing effective reduced-order models from an existing high-fidelity reservoir model, irrespective of grid topology. We employ a flexible hierarchical grid coarsening strategy that is designed to preserve geologic features and structures in the underlying model such as environments of deposition and faults. The strategy supports selecting and combining coarsening methods that are targeted to the flow patterns in different parts of the reservoir. This includes, but is not limited to, explicit user-imposed boundaries, using efficient field-wide flow indicators, topological and geometric partitioning and methods for amalgamating and splitting clusters of cells.
Collectively, these schemes enable an automatic strategy that separates a model into flow-dependent compartments that are respectively close to, far away from, or in between regions of sharp flow transients such as wells. These compartments may then be coarsened using different tailored techniques and target grid resolutions providing much more flexibility compared to traditional coarsening methods. We demonstrate that various techniques for flow-based transmissibility upscaling can be deployed on the resulting coarsened model to compute effective model properties. The hierarchical construction strategy allows efficient exploration of the geologic features of a reservoir that most impact flow patterns and well communication. The coarsened models are shown to be rank and trend accurate, enabling a more exhaustive sensitivity analysis if needed. We study the accuracy of the reduced-order model with a particular emphasis on the upscaled model's ability to capture effects of multiple phases in simulation runs compared to the full high-fidelity model.
Online Adaptive POD-DEIM Model Reduction for Fast Simulation of Flows in Heterogeneous Media
Authors: Yang Yanfang , Gildin Eduardo , Efendiev Yalchin , Calo Victor 
Published: February 2017

Abstract
In the paper, we propose an online adaptive POD-DEIM model reduction method for fast multiscale reservoir simulations in highly heterogeneous porous media. The approach uses Proper Orthogonal Decomposition (POD) Galerkin projection to construct a global reduced system. The nonlinear terms are approximated by the Discrete Empirical Interpolation Method (DEIM). To adapt at the online stage the states (velocity, pressure and saturation) of the system, we incorporate new data, as it becomes available. Once the criterion for updates is satisfied, we adapt the reduced system online by updating the POD subspace and the DEIM approximation of the nonlinear functions. These global online basis function updates improve the accuracy of snapshot approximation. Since the adaption is performed infrequently, the new methodology does not add a significant computational overhead due to the adaptation of the reduced bases. Our approach is particularly useful for situations where one needs to solve the reduced system for inputs or controls that result in a solution outside the span of the snapshots generated in the offline stage. Our method is able to construct a robust reduced system even if an initial poor choice of snapshots is used. We demonstrate with a numerical experiment to demonstrate the efficiency of our method.
Data-Driven Metric Learning for History Matching
Authors: Miller Jacob , Thiagarajan Jayaraman J. , Bremer Peer-Timo , Hoda Nazish , Stern Dave , Mifflin Rick 
Published: February 2017

Abstract
History matching, a highly non-unique inverse problem, is critical to calibrate model parameters in many scientific applications. A typical approach to history matching is to start with a uniform sampling of the high-dimensional parameter space and employ a surrogate modeling based black-box optimization to perform sequential sampling. Though this general workflow has been well studied, the problem of choosing an appropriate merit function to compare time-varying simulation outputs has been overlooked. Instead, convenient metrics such as the L2 or the LI-norm are employed. In this paper, we show that choosing an appropriate metric can significantly improve the solutions of sequential sampling. To this end, we propose a metric learning technique, develop a sequential sampling pipleline with the metric, and demonstrate its superiority to the conventional L2-norm metric.
Generation of a Proposal Distribution for Efficient MCMC Characterization of Uncertainty in Reservoir Description and Forecasting
Authors: Li Xin , Reynolds Albert C. 
Published: February 2017

Abstract
Generating an estimate of uncertainty in production forecasts has become almost standard in the oil industry but is often done with procedures that yield at best a highly approximate uncertainty quantification. Formally, the uncertainty quantification of a production forecast can be achieved by generating a correct characterization of the posterior probability density function (pdf) of reservoir model parameters conditional to dynamic data and sampling this pdf correctly. While Markov chain Monte Carlo (MCMC) provides a theoretically rigorous method for sampling any target pdf that is known up to a normalizing constant, in reservoir engineering applications, researchers have found that it may require extraordinarily long chains containing millions to hundreds of million of states to obtain a a correct characterization of the target pdf. When the target pdf has a single mode or has multiple modes concentrated in a small region, it is possible to implement a proposal distribution based essentially on random walk so that the resulting Markov chain Monte Carlo algorithm based on the Metropolis-Hastings acceptance probability can yield a good characterization of the posterior pdf. However, such a method may still require the generation of millions of states in the chain in order to obtain a proper sampling of the posterior pdf. For a high-dimensional multimodal pdf with modes separated by large regions of low or zero probability, characterizing the pdf with MCMC based on random walk is not feasible. While methods such as population MCMC exist for characterizing a multimodal pdf, their computational cost generally makes the application of these algorithm far too costly for field application. In this paper, we design a new proposal distribution based on a Gaussian mixture pdf for use in MCMC where the posterior pdf can be a multimodal pdf, possibly with the modes spread far apart. Simply put, the method generates modes using a gradient-based optimization method and constructs a Gaussian mixture model to use as the basic proposal distribution. Tests on three simple problems are presented to establish the validity of the method. The performance of the new MCMC algorithm is compared with random walk MCMC and is also compared with population MCMC for a target pdf which is multimodal.
Adaptive Coarsening in Physical Representation for the Robust Thermal-Compositional Simulation
Authors: Voskov Denis , Khait Mark 
Published: February 2017

Abstract
The nonlinear nature of flow and transport in porous media requires a linearization of the governing numerical model equations. We propose a new linearization approach and apply it to complex thermal-compositional problems of practical interest. The approach approximates exact physics of a simulation problem, which is similar to an approximate representation of space and time discretization performed in conventional simulation. Maintaining control of the error in approximate physics, we perform an adaptive parametrization to improve the performance of the method and adaptive coarsening to identify the major nonlinearities in the physical description. The global under-relaxation based on inflection points detection in parameterized space of the state variables provides a highly robust strategy to obtain nonlinear solution. This strategy was applied to several challenging compositional problems. In all cases, the proposed strategy improves the robustness and efficiency of the nonlinear solution.
Dual Grid Method for Aquifer Acceleration in Parallel Implicit Field-Scale Reservoir Simulation
Authors: Du Shouhong , Fung Larry S. , Dogru Ali H. 
Published: February 2017

Abstract
For many field-scale reservoir simulation models, aquifer regions (both peripheral and bottom) can represent a large percentage of total active grid blocks. The objective of this work is to build an accurate method respecting the fine-scale heterogeneities, but significantly reduce the computational cost of keeping the aquifer grids in the model, where they are deemed necessary to better describe the variable strength of water influx.
The present method can be considered a specialized two-level grid coarsening or upscaling method. Within the aquifer regions, each column of fine cells are coarsened vertically based on fine-scale z-transmissibility. A coarsened column may consist of a single amalgamated aquifer cell or multiple vertically disconnected aquifer cells separated by flow barriers. The pore volume (PV), compressibility, and lateral flow terms of the coarse cell are restricted from the fine-grid cell. The lateral connectivities within the aquifer regions and between the aquifer and the reservoir are honored, inclusive of the fine-scale description of faults, pinchouts, and null cells. Reservoir regions are not coarsened. Two alternatives exist for the fine-scale pressure reconstruction from the coarse-grid solution. The first method uses the vertical equilibrium concept. The second method performs a 1D solution for the fine-scale pressure. A spatially variable multi-porosity permeability model can also be accommodated. In parallel implementation, besides the load balancing method, the memory balancing method is also necessary. All these aspects will be explained in the paper.
The method has been applied successfully to several complex full-field simulation models where the transient aquifer water influx has been identified as a key factor. These models include dual porosity, dual permeability models with a complex geologic description. We compare simulation results using the accelerated aquifer calculation method against the original fine-grid simulation model without acceleration and demonstrate the efficacy of the new method. For the models tested, the actual speedup factors achieved are also compared against the theoretically computed improvement factors.
The dual grid method strives to significantly reduce computational cost but retain the fine-scale heterogeneity data to accurately represent the water movement within the water zones of the simulation model. The method differs from the standard upscaling and grid coarsening method where the coarse-grid properties are computed a priori. Instead, the fine-scale information is restricted to the coarse grid during Newton's iteration to represent the fine-scale flow behavior. This is analogous to the multiscale method, but we apply this only vertically within the aquifer regions.