bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reservoir Modelling with Feature Selection: A Kernel Learning Approach
Authors: Demyanov V. , Foresti L. , Christie M. , Kanevski M. 
Published: February 2011

Abstract
Geological realism is an increasingly important aspect of automated history matching and uncertainty quantification. While there are many algorithms that can automate the process of generating multiple history matched reservoir models, there is no guarantee that the changes made to the reservoir description to achieve a history match preserve geological characteristics.
This paper proposes a novel data-driven approach to modelling the spatial distribution of petrophysical properties in reservoirs. The proposed approach applies an advanced kernel-based approach – Multiple Kernel Learning (MKL) – to integrate multi-scale geological features. Measured reservoir properties and prior knowledge are fused in an intelligent way in order to improve the realism of reservoir models.
We show how we can use MKL to combine prior geological knowledge with seismic data. Geologically tailored features (e.g. large scale continuous fluvial sand bodies, small scale heterogeneity, spatial variability around facies boundaries, etc.) are identified from data using convolution filters to capture changes in gradients, orientations, sizes of geological bodies. Such spatial features are then used as separable input patterns for the MKL model to compute petrophysical reservoir properties through regression in high-dimensional kernel Hilbert space. MKL derives the internal model relations from data without enforcing predefined dependencies by optimising a weighted combination of kernels to fit to the conditioning data.
We used Multiple Kernel Learning to achieve realistic reproduction of geological properties of a complex fluvial reservoir system. It provides flexible control over the model properties by kernel weighting of spatial patterns, which reflect different geological features and data interpretations. MKL is also able to maintain the right balance between model complexity and goodness of fit, yielding robust history matched models.
Finally, Multiple Kernel Learning was integrated into a Bayesian uncertainty quantification framework for a synthetic reservoir to obtain uncertain production forecasts based on the ensemble of multiple history matched models.
A Dirichlet Neumann Representation Method for Simulating Flow in Reservoirs
Authors: Yang Yahan , Li Dachang , Parashkevov Rossen , Wu Xiao-Hui , Usadi Adam 
Published: February 2011

Abstract
Reservoir simulation models multiphase flow in reservoirs. Since geological models usually are heterogeneous and may contain millions of cells, upscaling is often a necessary step to reduce the model size to make them suitable for simulation. When grid coarsening is done to obtain fast simulation results, simulation grid cells can be quite large and may also contain localized geological features. Obviously, the upscaled model, though easier to simulate, is only an approximation of the original model. Errors introduced in the upscaling process generally may be small when flow field is relatively smooth but could be large otherwise. A critical technical challenge in upscaling is to be able to coarsen the reservoir model for fast computational performance while maintaining a high degree of accuracy for the results.
In this paper we propose a new Dirichlet Neumann representation method, DNR for short, for upscaling and simulating flow in reservoirs. With DNR, expressions are derived for flow rates as linear functions of multiple discrete pressure values along the boundary of each coarse block and pressure value at the center of the block. The number of pressure values at the boundary is flexible and may be chosen so that those pressure points provide an adequate representation of the possible pressure profiles and flow distributions during the simulation. Because of this, the error induced by artificial boundary conditions in traditional upscaling methods is greatly reduced. Test results show that DNR produces substantially more accurate results when compared to existing approaches using similar number of unknown variables, especially for reservoirs which contain high permeability streaks or are highly channelized. This method makes it possible to coarsen reservoir models while preserving a high degree of reliability for simulation results. DNR also allows straightforward reconstruction of fine-scale flow solutions and so can be used as part of a multiscale computational procedure.
Accurate Cell-Centered Discretizations for Modeling Multiphase Flow in Porous Media on General Hexahedral and Simplicial Grids
Authors: Wheeler Mary F. , Xue Guangri , Yotov Ivan 
Published: February 2011

Abstract
We introduce an accurate cell-centered method for modeling Darcy flow on general quadrilateral, hexahedral, and simplicial grids. We refer to these discretizations as the multipoint flux mixed finite element (MFMFE) method. The MFMFE method is locally conservative with continuous fluxes and can be viewed within a variational framework as a mixed finite element method with special approximating spaces and quadrature rules. We study two versions of the method: with a symmetric quadrature rule on smooth grids and a non-symmetric quadrature rule on rough grids. The framework allows for handling hexahedral grids with non-planar faces defined via trilinear mappings from the reference cube. Moreover, the MFMFE method allows for local elimination of the velocity, which leads to a cell-centered pressure system. Theoretical and numerical results demonstrate first order convergence on rough grids. Second order superconvergence is observed on smooth grids. We also discuss a new splitting scheme for modeling multiphase flows that can treat higher order transport discretizations for saturations. We apply the MFMFE method to obtain physically consistent approximations to the velocity and a reference pressure on quadrilateral or hexahedral grids, and a discontinuous Galerkin method for saturations. For higher order saturations, we propose an efficient postprocessing technique that gives accurate velocities in the interior of the gridblocks. Computational results are provided for flow in highly heterogenous reservoirs, including different capillary pressures arising from different rock types.
Simulating Two-phase Flow in Porous Media with Anisotropic Relative Permeabilities
Authors: Keilegavlen E. , Nordbotten J. M. , Stephansen Annette 
Published: February 2011

Abstract
In multi-phase flow simulation, the prevailing approach to discretizing flux terms treats the elliptic and the hyperbolic terms in the equations separately. With this concept, the flux is calculated analogously as for single phase flow, and later multiplied by the upstream mobility. This approach is valid when the mobility is a scalar quantity, which is the case for most traditional models. However, tensorial relative permeability (and thus tensorial mobilities) in general arise on all scales, as is seen in both laboratory and field experiments. Furthermore, upscaling methods almost invariably lead to anisotropic relative permeabilities. The saturation dependency of the fluid permeability tensor means that the upstream direction is no longer uniquely defined, which challenges common numerical schemes. In this work, we give examples of how the relative permeability has preferential flow directions on different scales. We then study how to incorporate tensorial relative permeability fields into control volume methods. In particular, we address two immediate challenges: Firstly, the non-determinacy of the upstream direction invalidates the use of upstream weighting for the saturation equation. We discuss the applicability of Godunov methods to handle flow cases where interfaces have either no or two upstream directions. Secondly, there is a marked increase in computational complexity associated with the pressure equation. We discuss the possibility of mitigating this computational complexity through mass lumping methods. The validity and computational efficiency of our approaches is discussed on theoretical terms and with the support of numerical implementations.
Automatic Differentiation Framework for Compositional Simulation on Unstructured Grids with Multi-Point Discretization Schemes
Authors: Zhou Y. , Tchelepi H. A. , Mallison B. T. 
Published: February 2011

Abstract
We present a flexible general-purpose reservoir simulation framework based on Automatic Differentiation (AD). The new AD-based simulator supports unstructured grids, employs a generalized Multi-Point Flux Approximation (MPFA) for spatial discretization, and uses a multi-level Adaptive Implicit Method (AIM) for time discretization. Given the discrete form of the governing nonlinear residual equations and a declaration of the independent variables, the AD library employs advanced expression templates with block data-structures to automatically generate compact computer code for the Jacobian matrix. Test results indicate that the construction of the Jacobian matrix with MPFA is efficient, and the overhead associated with treating a two-point flux approximation (TPFA) as a special case of MPFA is negligible. Our AIM implementation is designed to facilitate a systematic application of the method to new fluid models and variable formulations. The AD simulator allows for any combination of TPFA (Two-Point Flux Approximation), MPFA, FIM (Fully Implicit Method), and AIM. The generic and modular design is amenable to extension, both in terms of modeling additional flow processes and implementing new numerical methods. The AD-based modeling capability is demonstrated for highly nonlinear compositional problems using challenging large-scale reservoir models that include full-tensor permeability fields and non-orthogonal grids. The behaviors of TPFA and several MPFA schemes are analyzed for both FIM and AIM simulations. The implications of using MPFA and AIM on both the nonlinear and linear solvers are discussed and analyzed.
Domain Decomposition Methods Applied to Coupled Flow-Geomechanics Reservoir Simulation
Authors: Florez Horacio , Wheeler Mary , Rodriguez Adolfo A. , Monteagudo Jorge E. 
Published: February 2011

Abstract
In this work, well established Domain Decomposition techniques have been studied in order to carry out efficient simulations of coupled flow/geomechanics problems by taking full advantage of current parallel computer architectures. Different solution schemes can be defined depending upon transmission conditions among sub-domain interfaces. Three different schemes, i.e. Dirichlet-Neumann, Neumann-Neumann and Mortar-FEM, are tested and the advantages and disadvantages of each of them identified. This work will focus in the coupling of different meshes and/or physics on different domains by means of the Mortar-FEM plus the above DD-Schemes. Several examples of coupling of elasticity and poroelasticity in the context of reservoir compaction and subsidence are presented. In order to facilitate the implementation of complex workflows, we have implemented an advanced Python wrapper interface that allows programming capabilities. We have applied this platform to a variety of problems ranging from near-wellbore applications to field level subsidence calculations.
A Practical Approach for Assisted History Matching Using Grid Coarsening and Streamline-Based Inversion: Experiences in a Giant Carbonate Reservoir
Authors: Taware Satyajit , Friedel Torsten , Datta-Gupta Akhil 
Published: February 2011

Abstract
Flow simulation of multimillion grid cell models with hundreds of wells and decades long production history can be extremely time-consuming. This often limits the applicability of assisted history matching techniques. A pragmatic solution to this problem is grid coarsening which is now embedded in many commercial reservoir simulators.
Instead of up-scaling geological models in external packages, grid cells are automatically amalgamated within the simulator while preserving flux distribution and reducing the total number of active cells. The resulting speedup can be significant, often only at small loss of accuracy. Both characteristics are essential elements of any inversion technique in a multimillion grid cell environment. For water-flood history matching, we have utilized a commercial finite-volume simulator and the streamline-based generalized travel time inversion whereby water-cut behavior is matched by adjusting inter-well permeabilities.
To apply the assisted history matching technique on high resolution models, a flux reconstruction method is devised which makes full use of the benefits of automatic grid coarsening. It approximates fluxes in the original geological grid by recalculating transmissibility at the fine scale and redistributing coarse scale fluxes accordingly. The inversion is thus conducted on the fine scale grid, while the forward simulation model uses the coarse grid. While solving for an inexpensive coarse model, the calibration takes into account fine grid resolution. Upscaling is not required and the degree of coarsening can be adjusted during the matching process.
The proposed method was successfully tested on a supergiant carbonate oilfield with about hundred wells and large-scale water injection. The history match improved dramatically at relatively low numerical cost, which also allowed for investigating multiple sensitivities. The results were verified against non-coarsened model. The significant increase in efficiency makes this a potential method of choice for cases, where previously assisted history matching techniques could not be deployed due to excessive run times.
An Expanded Well Model for Accurate Simulation of Well-Reservoir Interactions
Authors: Karimi-Fard M. , Durlofsky L. J. 
Published: February 2011

Abstract
We present a new framework for modeling wells in reservoir simulators. The approach is based on an expanded well model in which the well region is expanded geometrically to include portions of the reservoir. The method proceeds by first defining an underlying fine-scale model, in which the well and any key near-well features such as hydraulic fractures are fully resolved using an unstructured grid. The geometry, grid structure and transmissibilities in the coarse-model well region, and the linkage between the well and reservoir regions, are determined from the solution of a single-phase steady-state pressure equation on the underlying fine grid. The method is compatible with any reservoir simulator that allows general (unstructured) cell-to-cell connections. The expanded well modeling procedure is applied to challenging three-dimensional problems involving production in a low-permeability reservoir, tight-gas production via a hydraulically-fractured well, and production in a gas-condensate reservoir. In the first two cases, where it is possible to simulate the fine-grid unstructured model, results using the expanded well model closely match the reference solutions, while standard approaches lead to significant error. In the gas-condensate example, which involves a nine-component compositional model, the reference solution is not computed, but the solution using the expanded well model is shown to be physically reasonable while standard coarse-grid solutions show large variation under grid refinement.
Joint History Matching of Well Data and Surface Subsidence Observations Using the Ensemble Kalman Filter: A Field Study
Authors: Wilschut F. , Peters E. , Visser K. , Fokker P. A. , van Hooff P. M. 
Published: February 2011

Abstract
The number of reported applications of the Ensemble Kalman Filter (EnKF) for history matching reservoir models is increasing steadily for various reasons. Here, we report on exploiting the capability of EnKF to handle observations from different sources simultaneously. While traditionally only well data are matched, we use surface subsidence observations together with well data. Surface subsidence results from compaction of the reservoir rock through the mechanical response of the subsurface. Compaction is caused by decreasing pore pressures during reservoir depletion. Therefore, the subsidence data contains information about dynamic pressure distributions in the reservoir.
The joint history matching of well data and surface subsidence observations was applied to the Roswinkel gas field. This field has been operated for 25 years, during which nine leveling campaigns generated a valuable data set of subsidence data. An important feature of the reservoir was the uncertainty about its compartmentalization, due to a large number of possibly sealing faults in the anticlinal structure. Therefore, instead of uncertain rock properties, fault transmissibilities were estimated in this study.
In a previous study on Roswinkel, a compaction field was estimated by inverting subsidence measurements. The results indicated several sealing faults in the reservoir, dividing the field into different compartments with independent pressure histories. The post-inversion history match of production data, however, was unsatisfactory. We have now been able to show that estimating the driving parameters, in casu the fault transmissibilities in the reservoir, can be achieved in a consistent way with both production and surface subsidence data for a synthetic case. Furthermore, for the actual Roswinkel case, the joint history match clearly reveals the discreapancy between the data from both sources and the current reservoir simulation model.
The joint history match of land surface movement data together with well production data is a success for EnKF as a flexible method for history matching. But more importantly, it demonstrates the potential of using complementary sources of information for improved reservoir characterization, and the possibility of estimating the driving parameters.
Application of Dynamic Gridding Techniques to IOR/EOR-Processes
Authors: van Batenburg D. W. , Bosch M. , Boerrigter P. M. , de Zwart A. H. , Vink J. C. 
Published: February 2011

Abstract
IOR/EOR processes have moving fronts and require an accurate description at a scale much smaller than typical grid block sizes used in black-oil simulations. Furthermore, they may require extra fluid components and phases, or thermal properties, or sometimes chemical reactions between components. However, simulation of these processes on a high resolution grid throughout the reservoir is still not practical with today's computing power. One solution is to start on a coarse grid that is dynamically adjusted to provide sufficient resolution where needed.
The challenge in such an approach is in identifying where and when to adjust the grid. This paper presents a semi-implicit scheme, that allows for the evaluation of grid adaption criteria within a time step, and guarantees that grid refinements occur where the fronts have propagated at the end of each time step. No assumptions on front movement need to be made in advance. The scheme is robust and accurate, while still providing strict control on the number of blocks by minimizing the extent of refined zone(s). This is of particular importance for nested refinements as the number of blocks rapidly grows with each extra refinement level. The combined use of changes in properties in space and in time allows for the definition of criteria that always reliably position the refinements along the fronts.
Application of this dynamic gridding approach in simulation models with water-flooding, chemical-flooding, gas injection, and in-situ combustion demonstrates that efficient nested dynamic gridding can be implemented in a general purpose simulator to provide sufficient physical and spatial detail in meaningful field or pattern models for IOR/EOR. The key to fast and accurate dynamic gridding is the implicit evaluation of proper criteria for refining and coarsening.
Multiple-Objective Optimization Applied to Well Path Design under Geological Uncertainty
Authors: Schulze-Riegert R. , Bagheri M. , Krosche M. , Kück N. , Ma D. 
Published: February 2011

Abstract
Well placement and design under geological uncertainties defines a major risk for field development processes. Including alternative geological realizations in a manually optimized development plan has been cumbersome and time consuming. In practical cases, a single reference model was often used as a basis for the flow simulation model which included a major risk on underestimating geological uncertainties. Optimization approaches and related optimization methods applied in this field previously included an objective function definition which aggregates multiple objectives, e.g. cumulative well rates or economical indicators. These optimization methods do not allow distinguishing easily between correlated and uncorrelated objectives which defines a major limitation for efficiency.
This paper presents a novel approach individually linking objectives and design parameters to multiple optimization processes which operate in parallel on a single simulation case. This approach increases the efficiency of stochastic optimization processes for complex application scenarios with multiple objectives. The extension to an uncertainty workflow including multiple geological models in the optimization processes is discussed.
Well placement optimization of horizontal wells in independent target formations of the gas condensate field is used to verify the optimization methodology. Alternative geological models are included in the optimization workflow representing geological uncertainties. The specific objective of the case study is to find optimum well trajectories by maximizing cumulative gas production over the life cycle of the field. Performance indicators for all well candidates (design parameters) are related to individual objectives (regional production). Robustness criteria for well candidates are implicitly fulfilled on the basis of risk weighted geological realizations.
Automatic Decomposition for Parallel Reservoir Simulation
Authors: Tarman Mehmet , Wang Kefei , Killough John E. , Sepehrnoori Kamy 
Published: February 2011

Abstract
A large amount of research in domain decomposition methods for parallel computing has been carried out in the last twenty years. This paper presents an automatic domain decomposition framework for parallel reservoir simulation. It emphasizes practical issues and implementation rather than theoretical complexity.
At least three main factors affect the parallel simulator performance when decomposing the domain for the given total number of sub-grids: 1) the active cell distribution in the reservoir, 2) reservoir geometry, and 3) fault locations. This discussion primarily focuses on the active cell distribution to improve the performance of the parallel reservoir simulator by load balancing.
Four domain decomposition algorithms are implemented before determining the best decomposition for the given total number of sub-domains. These four algorithms applied to all factors of the total number of sub-domains. The ideal decomposition for active cells and the offset size between the ideal number and real number from each scenario are then calculated. The smallest offset size determines the best decomposition.
Several examples, such as a synthetic model, a GOM (Gulf of Mexico) model with over 550,000+ grid blocks and a synthetic African reservoir compositional model, illustrate the improved parallel simulator performance resulting from the automatic decomposition framework. For both cases, the reservoir models were decomposed by different algorithms, and the CPU time for each algorithm was summarized. The automatic decomposition framework provides an effective way to improve the parallel simulator performance.