bluuebunny/crossref_metadata_2025_split huggingface dataset:
Scale Up Methodology for Wettability Modification in Fractured Carbonates
Authors: Delshad Mojdeh , Najafabadi Nariman Fathi , Sepehrnoori Kamy 
Published: February 2009

Abstract
Recent research findings suggest that wettability modification holds a great potential for increased oil production from mixed-wet and fractured carbonates. Our current knowledge of the field-scale performance of these processes is very limited despite very favorable results obtained in the laboratory-scale experiments. Reservoir simulation is required to properly scale up the process from laboratory to field conditions and to understand and interpret reservoir data. Without a mechanistic simulation tool and adequate scale up, it is unlikely that a cost-effective process can be developed and applied economically. The predictive simulation and methodology to scale up such a complex process will reduce the risk of failure of field projects.
A chemical compositional reservoir simulator with the capability to model oil recovery from mixed-wet carbonate rocks in both static imbibition and dynamic fractured block experiments using chemicals to alter wettability is used for this scale up study. The simulator captured the key recovery processes of capillary and natural imbibition, wettability alteration, buoyancy, oil mobilization, and viscous pressure gradient in imbibition experiments.
Proper scaling from laboratory to field indicates that the synergy of wettability alteration, ultra low interfacial tension, and emulsification under small viscous pressure gradient provides an attractive and profitable opportunity in fractured carbonate reservoirs. Dimensionless scaling groups and numerical simulations are presented for each experimental condition to aid in understanding the time dependence and up-scaling of the laboratory results to field scale applications.
The oil recovery results of the static imbibition experiment were successfully scaled using a reference time based on gravity emphasizing that the buoyancy was a dominant mechanism in this case. The scale up simulations for the dynamic fractured block experiment indicated favorable conditions for field scale applications with more dominance of viscous forces.
Ensemble-Based Closed-Loop Optimization Applied to Brugge Field
Authors: Chen Yan , Oliver Dean S. 
Published: February 2009

Abstract
In closed-loop reservoir management, geologic properties are updated sequentially using available data, and model-based optimization is used to search for the optimal operating strategy. Ensemble-based methods can be used to solve both data assimilation and production optimization with the gradient approximated from an ensemble. This ensemble-based closed-loop optimization method is flexible and has been illustrated using a small scale problem in Chen et al. (2008). The dimensionality of the model parameter and control parameter space are high in large scale problems. Approximating the gradient using small ensembles might result in unsatisfactory solution of the ensemble-based closed-loop optimization. In this paper the ensemble-based closed-loop optimization is applied to a large scale SPE Benchmark study. Brugge field, a synthetic reservoir, is designed as a common platform to test different methods for closed-loop reservoir management. The problem was designed to mimic real field scenarios, and as a result is by far the largest and most complex test case on closed-loop optimization. The Brugge field model consists of nine layers and the number of active cells is 44550. It has one internal fault and seven rock regions with different relative permeability and capillary pressure functions. There are 20 producers and 10 injectors in the field. Noise corrupted production data are provided monthly. Additionally, time-lapse seismic data are available at year ten. Each well has three different completions that can be controlled independently. The producing life of the reservoir is 30 years and the objective of optimization is to maximize the net present value at the end of 30 years.
Due to the complexity of this test case, several advanced techniques are used in order to improve the solution of the ensemble-based closed-loop optimization. Saturation normalization is used to account for the presence of difference rock regions. Localization is used in the assimilation of production and seismic data to increase the effective size of the ensemble and to alleviate the effect of spurious correlations resulted from the small ensemble. Relative permeability curves and initial oil water contact are estimated together with the gridblock properties. Covariance inflation is used to maintain the desired variability in the ensemble. We expect that these techniques will also be critical in real field application of the ensemble-based closed-loop optimization.
Field Applications of Waterflood Optimization via Optimal Rate Control With Smart Wells
Authors: Alhuthali Ahmed H. , Datta-Gupta Akhil , Yuen Bevan , Fontanilla Jerry P. 
Published: February 2009

Abstract
Waterflood optimization via rate control is receiving considerable attention because of increasing deployments of smart well completions and I-Field technology. The use of inflow control valves (ICV) allows us to optimize the production/injection rates of various segments along the wellbore, thereby maximizing sweep efficiency and delaying water breakthrough. Field scale rate optimization problems, however, involve highly complex reservoir models, production and facility constraints, and a large number of unknowns. In this paper we propose an approach that is computationally efficient and suitable for large field cases. It is based on our previous work that relies on equalizing arrival time of the waterfront at all producers for maximizing the sweep efficiency. We use streamlines to efficiently and analytically compute the sensitivity of the arrival times with respect to well rates. We also account for geologic uncertainty via a stochastic optimization framework using multiple realizations. Analytical forms for gradients and Hessian of the objective functions are derived, making our optimization computationally efficient for large-scale applications. Finally, optimization is performed under operational and facility constraints using a sequential quadratic programming approach.
We demonstrate our approach using two field-scale examples. The first one is a synthetic example called "Brugge" field, a benchmark case based on a North Sea Brent-type field. The production optimization of this field is carried out as part of a closed loop process where the production history is matched prior to the production optimization. The production optimization is performed over multiple realizations for 20 years and involves 30 wells equipped with three ICVs per well. The second example is a super-giant Middle Eastern field which has over 50 years of historical oil production. The optimization is performed for 20 years on a portion of this field which contains nearly 300 wells consisting of conventional vertical and horizontal wells and smart horizontal wells. In both examples, multiple field-related constraints are imposed, such as the maximum well injection and production rates, the maximum allowable drawdown, restriction on high water cut wells and voidage replacement for pressure maintenance. The results clearly demonstrate the viability of our approach, and the benefits of optimal rate control with a considerable increase in cumulative oil production and a substantial decrease in the associated water production.
A Dual Scale Approach to Production Data Integration into High Resolution Geologic Models
Authors: Kim Jong Uk , Datta-Gupta Akhil 
Published: February 2009

Abstract
Inverse problems associated with reservoir characterization are typically under-determined and often have difficulties associated with stability and convergence of the solution. A common approach to address this issue is through the introduction of prior constraints, regularization or reparameterization to reduce the number of estimated parameters.
We propose a dual scale approach to production data integration that relies on a combination of coarse-scale and fine-scale inversions while preserving the essential features of the geologic model. To begin with, we sequentially coarsen the fine-scale geological model by grouping layers in such a way that the heterogeneity measure of an appropriately defined ‘static’ property is minimized within the layers and maximized between the layers. Our coarsening algorithm results in a non-uniform coarsening of the geologic model with minimal loss of heterogeneity and the ‘optimal’ number of layers is determined based on a bias-variance trade-off criterion. The coarse-scale model is then updated using production data via a generalized travel time inversion. The coarse-scale inversion proceeds much faster compared to a direct fine-scale inversion because of the significantly reduced parameter space. Furthermore, the iterative minimization is much more effective because at the larger scales there are fewer local minima and those tend to be farther apart. At the end of the coarse-scale inversion, a fine-scale inversion may be carried out, if needed. This constitutes the outer iteration in the overall algorithm. The fine-scale inversion is carried out only if the data misfit is deemed to be unsatisfactory.
We demonstrate our approach using both synthetic and field examples. The field example involves waterflood history matching of a structurally complex and faulted offshore turbiditic oil reservoir. Permeability and fault transmissibilities are the main uncertainties. The geologic model consists of more than 800,000 cells and 10 years of production data from 8 producing wells. Using our dual scale approach, we are able to obtain a satisfactory history match with a finite-difference model in less than a day in a PC. Compared to a manual history matching, the dual scale approach is shown to better preserve the geological features and the pay/non-pay juxtapositions in the original geologic model.
History Matching With Parametrization Based on the SVD of a Dimensionless Sensitivity Matrix
Authors: Tavakoli Reza , Reynolds Albert C. 
Published: February 2009

Abstract
In gradient based automatic history matching, calculation of the derivatives of all production data with respect to gridblock rock properties (sensitivities) and other model parameters is not feasible for large-scale problems. Thus, the Gauss-Newton method and Levenberg-Marquardt algorithm, which require calculation of all sensitivities to form the Hessian, are seldom viable. For such problems, the quasi-Newton and nonlinear conjugate gradient algorithms present reasonable alternatives as these two methods do not require explicit calculation of the complete sensitivity matrix or the Hessian. Another possibility, the one explored here, is to define a new parameterization to radically reduced the number of model parameters.
We provide a theoretical argument which indicates that reparametrization based on the principal right singular vectors of the dimensionless sensitivity matrix provides an optimal basis for re-parametrization of the vector of model parameters. We present and apply to example problems two algorithms for using this parametrization. Like LBFGS, these algorithms avoid explicit computation of individual sensitivity coefficients. Explicit computation of the sensitivities is avoided by using a partial singular value decomposition based on a form of the Lanczos algorithm. At least for all synthetic problems that we have considered, the reliability, computational efficiency and robustness of the methods presented here are as good as those obtained with quasi-Newton methods.
Numerical Simulation of the In-situ Upgrading of Oil Shale
Authors: Fan Y. , Durlofsky L. J. , Tchelepi H. 
Published: February 2009

Abstract
Oil shale is a highly abundant energy resource, though commercial production has yet to be realized. Thermal in-situ upgrading processes for producing hydrocarbons from oil shale have recently gained attention, however, in part because of promising results reported by Shell using their In-situ Conversion Process. This and similar processes entail heating the oil shale to about 325°C, when the kerogens in the shale decompose through a series of chemical reactions into liquid and gas products. In this paper we present a detailed numerical formulation of the in-situ upgrading process. Our model, which can be characterized as a thermal-compositional, chemical reaction and flow formulation, is implemented into Stanford's General Purpose Research Simulator (GPRS). The formulation includes strongly temperature-dependent kinetic reactions, fully-compositional flow and transport, and a model for the introduction of heat into the formation through downhole heaters. We present detailed simulation results for representative systems. The model and heating patterns are based somewhat on information in Shell patents; chemical reaction and thermodynamic data are from previously reported pyrolysis experiments. After a relatively modest degree of parameter adjustment (with parameters restricted to physically realistic ranges), our results for oil and gas production are in semi-quantitative agreement with available field data. We also investigate various sensitivities and show how the produced hydrocarbon components are impacted by heater temperature and location. The ability to model these effects will be essential for the eventual design and optimization of in-situ upgrading operations.
Data Assimilation of Coupled Fluid Flow and Geomechanics via Ensemble Kalman Filter
Authors: Chang Haibin , Chen Yan , Zhang Dongxiao 
Published: February 2009

AbstractIn reservoir history matching or data assimilation, dynamic data such as production rates and pressures are used to constrain reservoir models and to update model parameters. As such, even if under certain conceptualization the model parameters do not vary with time, the estimate of such parameters may change with the available observations and thus with time. In reality, the production process may lead to changes in both the flow and geomechanics fields, which are dynamically coupled. For example, the variations in the stress/strain field lead to changes in porosity and permeability of the reservoir and hence in the flow field. In weak formations such as the Lost Hills oilfield, fluid extraction may cause a large compaction to the reservoir rock and a significant subsidence at the land surface, resulting in huge economic losses and detrimental environmental consequences. The strong nonlinear coupling between reservoir flow and geomechanics possesses a challenge to construct a reliable model for predicting oil recovery in such reservoirs. On the other hand, the subsidence and other geomechanics observations can provide additional insight into the nature of the reservoir rock and help constrain the reservoir model if used wisely. In this study, the Ensemble Kalman filter (EnKF) approach is used to estimate reservoir flow and material properties by jointly assimilating dynamic flow and geomechanics observations. The resulting model can be used for managing and optimizing production operations and for mitigating the land subsidence. The use of surface displacement observations improves the match to both production and displacement data. Localization is used to facilitate the assimilation of a large amount of data and to mitigate the effect of spurious correlations resulting from small ensembles. Since the stress, strain, and displacement fields are updated together with the material properties in The EnKF, the issue of consistency at the analysis step of the EnKF is investigated. A 3D problem with reservoir fluid-flow and mechanical parameters close to those of the Lost Hills oilfield is used to test the applicability.
An Object Oriented Approach to Goal Based Well Management
Authors: Davidson J. E. , Yang Y. , Netemeyer S. , Banki A. , Do L. 
Published: February 2009

Abstract
A reservoir simulator may be considered a collection of objects: reservoir nodes, producers, injectors, tubing, surface facilities, and drilling rigs. Each of these objects has attributes (variables that describe their state) and can perform actions (manipulation of attributes) on themselves or upon other objects. Engineers assemble these objects together to form a model of reservoir and surface facilities. Equations that describe the physical processes occurring in the reservoir model define some of the interactions between the objects. Engineers can further refine the interactions between the objects by imposing constraints and objectives on the facilities by well management. The interactions between the objects, goals, constraints, policies, physics and numerics can become quite complex. This paper will describe two approaches to controlling the facilities in a reservoir simulation model.
The first approach allows the engineer to define a set of instructions that tests for conditions and modifies the facilities based on those results. These instructions are translated into computer code, compiled and linked at run-time thereby giving engineers almost complete control over the facility network model. But with this flexibility comes the burden of writing and maintaining the instructions. Three different user interfaces for the well management instructions are described and compared.
The second approach allows the engineer to make use of the facility network objects to specify goals, constraints and strategies for production, injection, and well scheduling without having to develop solution algorithms. This paper will discuss the flexibility of these two object-oriented approaches to well management in reservoir simulation.
Local Parameterization of Geostatistical Realizations for History Matching
Authors: Ding D-Y. , Roggero F. 
Published: February 2009

Abstract
Reservoir characterization needs the integration of various data, and especially dynamic information such as production or 4D seismic data through history matching. This paper presents a local domain parameterization technique to update geostatistical realizations for assisted history matching. This technique allows us to change locally geostatistical realizations through the variation of geometrical domains whose geometry and size can be easily controlled and parameterized.
Randomized Maximum Likelihood with Permeability Samples Generated by a Predictor Corrector Technique
Authors: Feng T. , Mannseth T. , Aanonsen S. I. 
Published: February 2009

Abstract
To assess the uncertainty in reservoir descriptions and performance predictions, the a posteriori probability density function (pdf) for the unknown model parameters is needed. The only practical way to evaluate the posterior pdf is by sampling. Permeability samples for performance forecasting should be consistent with all available information. Markov chain Monte Carlo (McMC) samples correctly from the posterior distribution, but is computationally extremely intensive. Randomized maximum likelihood (RML) is a good approximation to McMC that involves history matching of each permeability sample. Although RML is computationally less intensive than McMC, the computational effort to generate a sufficiently large number of samples is therefore huge for all but very small reservoir models.
In this work, we have investigated if the sampling procedure can be made more efficient by using a predictor-corrector approach in the history matching step of RML. The predictor applies sequential history matching to obtain an estimate with few degrees of freedom, utilizing only part of the available information. The corrector downscales the predictor estimate in a two-step procedure involving all available information, including the estimate obtained with the predictor. The first corrector step is a variant of Kriging. The second corrector step is a history match, again involving few degrees of freedom, with basis functions derived from the results of the predictor. The method is demonstrated and compared to regular RML through numerical examples, indicating that predictor-corrector RML is computationally more efficient than regular RML.
Multipoint Flux Approximations via Upscaling
Authors: Potsepaev R. , Farmer C. L. , Fitzpatrick A. J. 
Published: February 2009

Abstract
This paper investigates the control volume method with multipoint flux approximation (MPFA) applied to the discetization of –div(K(x)grad u) = f(x), the equation describing fluid flow through anisotropic porous media. The permeability tensor K(x) is allowed to have discontinuities. Transmissibility coefficients are obtained from local numerical flow experiments (transmissibility upscaling) for each cell face. Monotonicity of the solution matrix is discussed and a version of the method that provides an M-matrix is described. Convergence and numerical flux consistency of the scheme for uniform permeability tensor are studied. This discretization scheme is applied to reservoir simulation on 3D structured grids with distorted geometry, highly anisotropic media and discontinuities in the permeability tensor. Simulation results are presented and compared with results from other two-point and multipoint flux approximations.
Multiphase, 3-D Flow Simulation With Integrated Upscaling, MPFA Discretization, and Adaptivity
Authors: Lambers J. V. , Gerritsen M. G. , Fragola D. 
Published: February 2009

Abstract
We present a multi-point flux approximation (MPFA) scheme for simulation of 3-D multi-phase flow on adapted grids. It is an extension of the VCMP (Variable Compact Multi-Point) scheme, which constructs stencils that are locally adapted to flow through transmissibility upscaling. The proposed integration of upscaling, MPFA stencil selection and gridding leads to an optimized flow discretization that can accurately resolve full-tensor effects, is compact, and leads to low upscaling errors.
The grid adaptivity is driven by both upscaling, in a pre-processing stage, and by solution gradients during run-time. During transmissibility upscaling, the grid is refined to resolve important flow features such as high permeability flow paths. During run-time, we refine the grid locally, as needed, to accurately resolve saturation fronts, and coarsen once the front passes. We do not employ downscaling techniques, but compute transmissibilities for the newly refined regions using the upscaling methodology that can be effectively applied to localized regions.
The numerical tests presented in this paper show that the method leads to higher upscaling accuracy as compared to commonly used upscaling methods. On average, error in total flow is reduced by a factor of two, and the fine-scale velocity field is resolved with greater accuracy as well. They also illustrate that saturation fronts can be resolved accurately using single-phase upscaling only by employing VCMP on adapted grids.
The combined strategy promises to significantly improve the workflow of coarse-scale modeling for flow simulations such as those routinely performed in the industry.
Modeling Polymer Flood in an Unstructured Grid Simulator
Authors: Verma Santosh , Adibhatla Bhargaw , Kaminsky Robert , Wattenbarger Chick , Davidson Jeffrey 
Published: February 2009

Abstract
This paper describes the development of a comprehensive polymer flood modeling capability within a reservoir simulator that uses an unstructured grid framework. The polymer modeling capability includes a number of specialized capabilities. Polymer solution viscosity is modeled as a function of polymer concentration, shear rate, multiple salt components, and reservoir temperature. The polymer solution viscosity model handles both shear thinning and shear thickening. High near-wellbore shear rates are accounted for when determining well flow rates through a mobility correction. Thermal degradation of the polymer is modeled as a function of polymer concentration, ion concentrations, and reservoir temperature. Polymer retention and its impact on flow are also modeled. In addition to describing these capabilities, the numerical schemes used to solve the flow problems are discussed and example applications are presented.