bluuebunny/crossref_metadata_2025_split huggingface dataset:
Toxicity of orimulsion-400® to early life stages of atlantic herring (Clupea harengus) and mummichog (Fundulus heteroclitus)
Authors: Boudreau Monica , Sweezey Michael J. , Lee Kenneth , Hodson Peter V. , Courtenay Simon C. 
Published: February 2009

AbstractThe toxicity of Orimulsion-400® (PDVSA-BITOR), an emulsion of 70% bitumen in 30% water, was tested during the embryonic development of Atlantic herring (Clupea harengus) and mummichog (Fundulus heteroclitus) in duplicate experiments. Air injection and different salinities were included in the herring assays to examine their effects on Orimulsion-400 toxicity. Water-accommodated fractions (WAFs) of no. 6 fuel oil were tested in the mummichog assays to compare Orimulsion-400 toxicity with that of a heavy fuel oil. Concentrations of Orimulsion-400 as low as 0.001% (v/v) were harmful to both species. In herring, the more sensitive of the two species, this concentration produced 100% abnormal larvae. Similar abnormalities, including pericardial edema and spinal deformities, the same signs of toxicity caused by heavy fuel oils and polycyclic aromatic hydrocarbons (PAHs), were produced in both herring and mummichog. Fish exposed to Orimulsion-400 also suffered from increased mortality, reduced heart rates, premature hatch, and reduced lengths compared to control fish. Orimulsion-400 was approximately 300-fold more toxic than the WAFs of no. 6 fuel oil. Salinity had few clear effects on Orimulsion-400 toxicity, but aeration of test solutions greatly reduced toxicity by causing bitumen to coalesce and float. Aeration also removed toxic chemicals such as PAHs. The present study suggests that in the event of a spill, Orimulsion-400 could impair fish recruitment, but that strong wave action would reduce toxicity by accelerating the removal of emulsified bitumen from the water column.
A Multidomain Approach to Multiscale Compositional Reservoir Simulation
Authors: Al-Matrouk Mohammed F. , Al-Kobaisi Mohammed , Kazemi Hossein , Ozkan Erdal 
Published: February 2009

Abstract
This paper presents a multidomain (MD) computational approach to multiscale (MS) compositional reservoir simulation amenable to parallel processing. The main objective is to minimize the upscaling process and preserve the pertinent flow characteristics of the fine-scale geological features of the reservoir. The preservation of the fine-scale reservoir characteristics yields higher-accuracy solution compared to an upscaled model.
The computing algorithm involves breaking down the fine-grid (FG) computing mesh into an underlying coarse-grid (CG) subdomain. Unlike other MS techniques, the CG subdomain only serves as an assemblage of the FG cells and not as a computational CG overlaying the FG cells. The flow equations are then restructured into a global pressure equation which is solved on the FG using a large time step without solving for the compositions, saturations, etc. The convergence tolerance for the global pressure solution is soft (no iterations) to reduce computing time. The pressure solution is then used to calculate the molar flux of components only at the FG boundaries of the CG subdomain cells. Finally, each CG subdomain subject to its boundary conditions is solved independently using a smaller time step to compute cell pressures, compositions, and phase saturations. This procedure is amenable to parallel processing.
This MS scheme was applied to time domain, in which the global pressure solution is obtained with a larger time step than in the local FG solution. This means that the molar flux boundary condition at the CG subdomain boundaries is not updated every time step, thus, buying additional computational advantage. This new technique was tested on several volatile oil and gas-condensate systems and excellent agreement was obtained with FG simulation with about 60% reduction in computing time using a single processor. Much greater time reduction is expected in a multi processing environment.
Stochastic Inversion for Reservoir Properties Using Parallel Learning-Based VFSA and Pilot Point Parameterization
Authors: Jin Long , Stoffa Paul L. , Sen Mrinal K. 
Published: February 2009

Abstract
Joint inversion of flow and seismic data for reservoir parameters is a challenging task in that these disparate datasets are sensitive to different physics and model resolutions for the forward problem. The inverse problem is highly non-linear introducing additional complexity. To overcome some of these challenges, we developed a global optimization method based on very fast simulated annealing (VFSA) and a multi-resolution model parameterization scheme. The choice of proposal distribution for sampling the parameter space and the time consuming of reservoir modeling are two critical issues which need to be resolved. Here, we propose a new parallel learning-based VFSA to address these issues. The key idea behind this method is that at any iteration, the previously sampled models are saved and used as guides in subsequent search resulting in an increased effieciency in our search speed. Parallel computing is used to search a much wider model space without increasing the computing time.
Our new method works as follows: (1) the Cauchy distribution is used as the proposal distribution in the beginning; (2) parallel computing is used to sample the parameter space in multiple chains each running at a different temperature; (3) at certain iterations, we collect all the sampled models from the previous parallel runs and compute the binned probability distribution of the model parameters; (4) new proposal distribution is formed by combining the computed probabilityr distribution and Cauchy distribution for every parameter; (5) parallel sampling using the new proposal distribution; (6) collection of all the models and computation of posterior distribution.
The results from the synthetic examples reveal that our method is able to obtain reasonable descriptions of the reservoir by combining the global optimization and multi-resolution parameterization. Our new parallel learning-based VFSA shows better performance compared with a conventional method in terms of accuracy.
Parallel Numerical Reservoir Simulations of Non-Isothermal Compositional Flow and Chemistry
Authors: Delshad M. , Thomas S. G. , Wheeler M. F. 
Published: February 2009

Abstract
This paper describes an efficient numerical scheme for non-isothermal compositional flow coupled to chemistry. An iterative IMPEC method is applied to solve the flow problem using a volume balance convergence criterion. A backward Euler mixed FEM with lowest order RT0 elements is applied to solve the pressure equation and a component local mass preserving explicit scheme is used to update concentrations. Chemical reactions are solved using explicit Runge-Kutta ODE integration schemes. A higher order Godunov method and a backward Euler mixed FEM are applied for thermal advection and conduction, respectively, in a time-split scheme.
One of the major applications of the method is in the modeling of field scale CO2 sequestration as an EOR process or for containment in deep saline aquifers where chemical reactions and temperature variations may have an impact on the flow and transport of CO2. Leakage patterns when CO2 is injected near leaky abandoned wells, the displacement of methane from depleted gas reservoirs and accurate modeling of geochemical reactions involving injected CO2 are other applications of interest.
Results of a benchmark problem in multiphase flow with several hydrocarbon components in formations with highly heterogeneous permeability on very fine grids, as well as a large scale parallel implementation of modeling CO2 sequestration are presented to justify the practical use of the model. A parallel efficiency of about 80% was observed on upto 512 cores in the benchmark study. Results from a problem simulating injection of CO2 in deep aquifers including non-isothermal and chemical effects are also presented. The results indicate a good agreement of the solutions with published data where available.
Numerical modeling and simulation of CO2 sequestration plays a major role in future site selections and in designing storage facilities for effective CO2 containment. The main contribution of this paper lies in providing a parallel and efficient method of simulating challenging compositional flow problems such as in the study of CO2 sequestration as well as flow coupled to thermal and geochemical effects.
An Iterative Algorithm for Generating Constrained Voronoi Grids
Authors: Katzmayr Markus , Ganzer Leonhard 
Published: February 2009

Abstract
In general, the purpose of a simulation is to mimic a real system in order to study its behavior. Among the first steps in a simulation study is the proper definition of the spatial discretization. This abstraction process of the space domain produces the simulation model and is essential for the result of the simulation.
A new approach for the simulation of faulted hydrocarbon reservoirs is proposed. The described algorithm iteratively generates a constrained Voronoi grid suitable for two-point flux approximation schemes. The algorithm is based on a Delaunay triangulation and is characterized by its robustness, efficiency and the iterative modeling approach. Initially, a Delaunay triangulated mesh is calculated for a given point distribution. Using the triangulated mesh, the proper Voronoi grid is derived. Constraints (faults, pinch-outs, boundaries) are aligned sequentially on the derived Voronoi cell and integrated into the Delaunay mesh. The alignment is necessary to ensure that the use of two-point flux approximation methods on the generated reservoir grid model is correct. The higher the resolution of the point distribution in the area of the constraint, the better the aligned constraint matches the actual constraint. Finally, all Voronoi cells touching the integrated constraints are reconstructed using the Delaunay mesh. Similar to the insertion of new constraints, local grid refinements or model elements can be added without reconstructing the entire model. All modifications of the reservoir model result in local grid adjustments.
The runtime complexity of the algorithm depends on constructing the underlying Delaunay mesh and can be estimated as O(n log n) plus some overhead O(n) for deriving the Voronoi grid.
This new approach reduces the construction and updating times for reservoir grid models considerably and results in grid models ranging from structured, Cartesian-type of grid to high complex unstructured models depending on the original distribution of grid points.
Efficient and Accurate Reservoir Modeling Using Adaptive Gridding with Global Scale Up
Authors: Branets Larisa V. , Ghai Sartaj S. , Lyons Stephen L. , Wu Xiao-Hui 
Published: February 2009

Abstract
An accurate and efficient reservoir modeling process is essential for developing and producing hydrocarbon reserves, especially from unconventional resources. In this paper, we address some of the main challenges associated with modeling complex reservoir geometry and heterogeneous reservoir properties. We present recently developed techniques for adaptively constrained 2.5D Voronoi grid generation and for generic global flow-based scale-up. Our novel gridding approach is based on a new constrained Delaunay triangulation algorithm and a rigorous procedure of constructing a Voronoi grid that conforms to piecewise linear constraints. These gridding approaches allow us to generate 2.5D Voronoi grids that precisely honor small faults, intersections of multiple faults, and intersections of faults at sharp angles, as well as adapt the grid cell sizes to a specified density function. By precisely representing geologic structures in our simulation grid and by accurately scaling up fine-scale geologic properties, we improve the consistency between our geologic descriptions and reservoir simulation models, leading to more accurate simulation results.
Numerical examples are provided to demonstrate the techniques and the advantages (both in efficiency and accuracy) of using adaptive gridding with global scale-up.
Development of a Reservoir Simulator for Souring Predictions
Authors: Farhadinia M. A. , Bryant S. L. , Sepehrnoori K. , Delshad M. 
Published: February 2009

Abstract
During the last two decades, several simple models have been developed to predict the onset of reservoir souring in seawater injected reservoirs. The key mechanism to generate hydrogen sulfide is a biological reaction between sulfate in the injection water and volatile fatty acids in the formation water in the presence of sulfate reducing bacteria (SRB). The produced hydrogen sulfide interacts with rock and partitions between oil and water phases. Comparison of field data with reservoir souring model predictions often shows inconsistent results. We present the development of a comprehensive reservoir souring model in a chemical flooding simulator that accounts for these mechanisms.
Being able to estimate the likelihood and timing of the onset of H2S production would permit more realistic assessments of project economics. We incorporated mechanisms of generation and transportation of H2S in porous media to develop a reservoir souring simulator to predict the onset of souring in oil reservoirs.
We implemented a general souring model in a 3D finite difference compositional non-isothermal reservoir simulator. The results indicate that depending on the type of SRB, the temperature propagation in the formation determines the onset of biological reactions and consequently, the generation of different hydrogen sulfide concentrations. The lag in the temperature front with respect to the injection front can cause a delay in the observed souring if the temperature is not favorable for SRB activation.
A predictive model would enable operators to make better decisions for remedial actions to either prevent souring or to mitigate its impact. The developed finite difference simulator is 3D and accurately accounts for variation and impact of in-situ concentrations, temperature, pressure, and reservoir heterogeneity on H2S transport and production.
Feedback Controllers for the Simulation of Field Processes
Authors: Guyaguler B. , Papadopoulos A. T. , Philpot J. A. 
Published: February 2009

AbstractControl systems with feedback controllers are useful in reservoir simulation as they enable the maintenance of desired operating conditions of a field. This in turn helps establish the value of implementing automated mechanisms in the field, and also in determining long term field operating strategies. A generic controller framework is constructed within a reservoir simulator that enables the usage of different kinds of controller algorithms for managing a variety of field processes. In this study, three field processes are considered. First, average pressure within a reservoir region is maintained by adjusting the voidage replacement ratio between a group of injectors and producers. Second, control systems are used for the prevention of gas/water coning for single and multiple wells. Finally, the average temperature within a reservoir region is maintained at a critical value by controlling flow into the formation, so as to operate with the desired mobility of heavy-oil. Traditional Proportional, Integral, Derivative (PID) controllers, as well as linear and nonlinear fuzzy controllers are considered. The advantages and disadvantages of the approaches are discussed. Tuning control systems is a difficult process in practice. Several methods for tuning the parameters of these controllers are investigated, and rule-of-thumb values are suggested in this study. Synthetic and real reservoir models are used.
Experience With Numerical Stability, Formulation, and Parallel Efficiency of Adaptive Implicit Methods
Authors: Lu P. , Shaw J. S. , Eccles T. K. , Mishev I. D. , Beckner B. L. 
Published: February 2009

Abstract
The availability of multi-core CPUs for personal computers makes desktop parallel computing a reality. Parallel computing for reservoir simulation creates the opportunity for significant run time reduction but also introduces additional technical challenges, particularly in the areas of load balancing and general algorithmic robustness.
In this paper we will discuss our experience with the Adaptive Implicit Method (AIM) in parallel desktop computing environments, with ExxonMobil's numerical reservoir simulator, EMpowerTM, focusing on the following areas: A highly efficient and robust parallel partition of the fully unstructured grids for modeling large and complex reservoirsAdaptive Implicit Method (AIM) for a high-level of numerical stability with significant run-time speed-up and memory footprint reductionUnique load balancing challenges arising from integration between shared-memory based parallel computing, unstructured grids, and adaptive implicit methods
We will also describe experiences with real models and discuss mechanisms for addressing problems encountered and demonstrate that we can achieve a 3~4 time speedup over serial runs without AIM on real field cases using quad-core desktop machines.
Closed-Loop Reservoir Management
Authors: Jansen J. D. , Douma S. D. , Brouwer D. R. , Van den Hof P. M. J. , Bosgra O. H. , Heemink A. W. 
Published: February 2009

AbstractClosed-loop reservoir management is a combination of model-based optimization and data assimilation (computer-assisted history matching), also referred to as ‘real-time reservoir management’, ‘smart reservoir management’ or ‘closed-loop optimization’. The aim is to maximize reservoir performance, in terms of recovery or financial measures, over the life of the reservoir by changing reservoir management from a periodic to a near-continuous process. The key sources of inspiration for our work are measurement and control theory as used in the process industry and data assimilation techniques as used in meteorology and oceanography. We present results of a numerical example to illustrate the scope for closed-loop water flooding using real-time production data under uncertain reservoir conditions. The example concerns a 12-well water flood in a channelized reservoir. Optimization was performed using a reservoir simulator with functionality for adjoint-based life cycle optimization under rate and pressure constraints. Data assimilation was performed using the ensemble Kalman filter. Applying an optimization frequency of respectively once per 4 years, once per 2 years, once per year and once per 30 days resulted in an increase of net present value (NPV) with 6.68, 8.29, 8.30 and 8.71% compared to a conventional reactive control strategy. Moreover, the results for the 30-day cycle were very close (0.15% lower NPV) to those obtained by open-loop optimization using the ‘true’ reservoir model. We illustrate that for closed-loop reservoir management with a fixed well configuration, the use of considerably different reservoir models may lead to near-identical results in terms of NPV. This implies that in such cases the essential information may be represented with a much less complex model than suggested by the large number of grid blocks in typical reservoir models. We also illustrate that the optimal rates and pressures as obtained by open- or closed-loop optimization are often too irregular to be practically applicable. Fortunately, just as is the case for the data assimilation problem, the flooding optimization problem usually contains many more control variables than necessary, allowing for optimization of long-term reservoir performance while maintaining freedom to perform short-term production optimization.
Efficient Parallel Reservoir Simulation Using Multicore Architectures
Authors: Khait M. L. 
Published: February 2009

Abstract
Modern reservoir simulation of complex physical processes for highly detailed models needs a lot of computational power. Constrained Pressure Residual (CPR) – based preconditioner for linear solver are the best in terms of performance and robustness for reservoir simulation. From another side the most advanced processors are based on multi-core architectures, which allow for efficient parallel processing with extremely low cost of memory-processor communication. The work was focused on adaptation of CPR algorithms for multi-core architectures with help of OpenMP technology, as well as parallelization the rest of simulation code. This work was done based on Stanford's General Purpose Research Simulator (GPRS). The two-stage CPR preconditioner where the first stage involves formulating and solving a global pressure system, and the second stage requires application of a simple preconditioner, but on the full Jacobian, was completely parallelized. One of the most challenging issues was parallelization of ILU(0) which implies second stage of preconditioning. One of approaches of parallel ILU(0) based on domain decomposition was formulated and tested using challenging compositional simulation problems with highly detailed models. The results demonstrate good scalability of this parallel ILU(0) approach for SPE 10 compositional model.
Automatic Well Placement Optimization in a Channelized Turbidite Reservoir Using Adjoint Based Sensitivities
Authors: Castiñeira David , Alpak Faruk O. , Hohl Detlef 
Published: February 2009

Abstract
Economic constraints impose stringent limits on the number of wells that can be drilled in deepwater developments. Thus, optimal placement and operation of wells have a major impact on the project rewards. Well-placement in deepwater developments is a challenging optimization problem. Manual approaches to its solution can be cumbersome even with good use of engineering judgment: (a) There often exist many combinations of well locations subject to investigation; (b) There is need to optimize operational constraints for every well-placement scenario; (c) The optimization process has to be repeated for a variety of geologic realizations; (d) Presence of complex sub-seismic geologic architecture may render workflows that solely rely on seismic data obsolete. We developed an adjoint-based optimization algorithm that rapidly identifies alternative optimal well-placement scenarios for a given geologic realization. Adjoint-based gradients approximate the sensitivities of a suitable objective function with respect to well locations. These sensitivities guide the iterative search with improving directions that progressively maximize the ultimate recovery. The main advantage of the adjoint method is that it provides sensitivities with only one forward (reservoir) and one backward (adjoint) simulation, rendering computational costs affordable for field applications. The adjoint method in our implementation operates in local search mode.
Our algorithm is applied to two different geologically complex channelized turbidite reservoirs to identify alternative optimal locations for a new injector-producer pair given existing operational and well constraints. Ensuing results demonstrate that the adjoint-based sensitivities can be effectively used to find optimal well locations. In the field simulation models, the optimization derived well locations lead to a higher ultimate oil recovery compared to a manual optimization approach. The algorithm yields multiple alternative well-placement scenarios within a shorter amount of time compared to a single optimal location found through manual optimization.