bluuebunny/crossref_metadata_2025_split huggingface dataset:
Aquifers Vulnerability: A New Mapping Method Based on Hydrogeological and Seismic Data – Case Study: Mio-Pliocene Aquifer in Hassi Messaoud - Algeria
Authors: Bensenane F. K. , Dhina O.. , Abdallahelhadj M. A. , Boukraba M.. , Cherfaoui A.. 
Published: February 2011

Abstract
Non renewable groundwater resources in some arid regions are the unique source of water and constitute strategic reserves for more than ever with the expected water shortage in the near future. Thus, contamination due to the infiltration of pollutants coming from oil and gas industries is one of the major risks that are jeopardizing these valuable resources.
The main purpose of this paper is to present the widespread aquifers vulnerability evaluation methods prior to introduce and discuss the new mapping method we propose for oil and gas upstream activities.
The rationale for this mapping method is linked to time of travel and the quantity of contaminants that move from the surface to the aquifer. Three key parameters are considered: ■Depth to water table (D);■Consolidation of the upper geologic layers in the unsaturated zone (C);■Aquifer Media (A).
Therefore, hydrogeological Data from almost a hundred water wells in Hassi Messaoud field in Algeria have been used to determine the hydraulic head, the static level and the local underground flow of the mio-pliocene water table. Seismic data have been used to measure the consolidation rate of the unsaturated (vadose) zone, as well.
Thus, processed data were used for the purpose of drawing the vulnerability maps allowing a better understanding and assessment of the aquifer sensitivity to potential pollution. This simple yet useful method is developed to be used as a decision making tool when dealing with groundwater protection in oil and gas activities.
Addressing MAH (Major Accident Hazards) – From Assessment to Actions
Authors: Peball Ulrich , Dragan Sorin 
Published: February 2011

Abstract
An international Oil and Gas Company took over a state owned oil company in 2005 with about 22.000 employees, operating in more than 300 fields all over the country with significant offshore operations in the black Sea. The Original Company had been in operation for 150 years with 50 years under communist management and had become available for buy out after several restructuring efforts by the state.
In the early years after take over the focus was very much on inventorying the existing infrastructure and an assessment of technical safety aspects.
Multilateral-Complex Well Optimization
Authors: Crumpton P. I. , Habiballah W. A. , Wardell-Yerburgh P. G. , Nasser K. A. , Faleh A. A. 
Published: February 2011

Abstract
Reservoir simulation is used to optimize the design of multilateral wells, where the parameters include the well path, well position, the completions design (e.g. ICD configuration), the production constraints and uncertainty from multiple realizations of a geological model.
The target application is the design of new multi-lateral complex wells within an established large study. The optimized well is included in the prediction scenario of a history matched model. The target full field models are very large (millions of cells) and have large amounts of wells (hundreds).
The parameterization of the well path and position is performed by constraining the well path to surfaces, and geometric translation. Laterals are independently parameterized allowing the tie-in depth, angle and length to be parameterized. Completion design is parameterized by compartment length, number of valves per compartment and valve inflow diameter. The complex completion design relies on advanced well modeling features of the reservoir simulator. Production constraints, such as target oil rates and water limits are considered within the optimization. Finally, the uncertainty from multiple geological realizations is accounted for; this is achieved by introduction of a risk aversion factor which penalizes the objective function by a scaled standard deviation.
Examples of optimization demonstrate long multi-lateral wells being optimized within a Full field study. The sensitivity of each parameter is studied, i.e. the effect of oil production on the well position, effect of modifying ICD inflow area, the ICD compartment length, the water handling limits, and the geological uncertainty.
The major significance of this work is the provision of a system to enable engineers to produce an optimal well configuration given geological, production, well placement and completion (ICD) uncertainties in a timely fashion. The use of optimization assists the engineer in designing an optimal solution given many variables.
Modeling Multi-tubing Wells in Reservoir Simulation
Authors: Browning David J. , Morton Alison 
Published: February 2011

Abstract
This paper describes the modeling of single and dual tubing wells in a full-field reservoir simulator. The tubings associated with each well are modeled in terms of one primary and one or more secondary wells. The multi-segment well modeling techniques incorporated in the simulator have been extended to model multi-tubing systems thereby avoiding the requirement to model special grid blocks representing a wellbore. Wells with two or more tubings present a significant challenge to reservoir simulation where traditionally the coupling of the well with the reservoir, however complex, is solved in terms of a single surface inlet or outlet.
The case studies have been selected to demonstrate that injectors and producers can both be modeled in terms of primary and secondary wells both with targets and constraints. Comparisons are presented showing the differences between single and dual tubing injection and production schemes. An example of a multi-tubing production schemes is also included. These demonstrate that modeling wells with two or more tubings using the multi-segment well approach is practicable for the simulation of hydrocarbon recovery processes.
Application of Operator Splitting Technique in Numerical Simulation of Gas Hydrate Reservoirs
Authors: Shahbazi A. , Pooladi-Darvish M. 
Published: February 2011

Abstract
Modeling of hydrate reservoirs has revealed large timescale discrepancies between the involved mechanisms. Compared to the fluid and heat flow terms, the timescale of the kinetics term is orders of magnitude smaller, especially when the intrinsic reaction rate is large. Previous studies have shown that simulation of hydrates may require very small time steps, due to a convergence problem. Further investigations have shown that, for sharp decomposition cases where dissociation occurs in a narrow region, non-physical oscillation becomes a simulation issue, unless very small time steps are chosen. A three-dimensional numerical model incorporating heat/fluid flow, with kinetics of decomposition and (re)formation of hydrates has been developed. In this paper, a methodology for the use of larger time steps is proposed without loss of accuracy. The focus of this work is on the decoupling of the reaction and flow operators. The decoupling, or so-called operator splitting, helps to select different time steps for different mechanisms. The success of the splitting methodology in saving computational time is demonstrated for two cases. The first case shows oscillatory solutions, and the application of operator splitting allows for an oscillation free solution at a smaller run time. In the second case, a problem with a range of reaction constants is studied. Obtaining a stable solution requires adjusting the overall time step, such that the problem with the larger reaction rate requires very small time steps. The application of operator splitting in this case allows for a stable solution with much larger time steps. The contribution of this work is the computational timesaving in large-scale simulations of gas hydrate reservoirs without losing accuracy. Furthermore, using the splitting technique does not require a change of physically determined parameters, including the reaction constants.
Exploiting Capabilities of Many Core Platforms in Reservoir Simulation
Authors: Klie Hector , Sudan Hari , Li Ruipeng , Saad Yousef 
Published: February 2011

Abstract
The forthcoming generation of many-core architectures suggests a strong paradigm shift in the way algorithms have been designed to achieve maximum performance in reservoir simulations. In this work, we propose a novel poly-algorithmic solver approach to develop hybrid CPU multicore and GPU computations for solving large sparse linear systems arising in realistic black oil and compositional flow scenarios. The GPU implementation exploits data parallelism through the simultaneous deployment of thousands of threads while reducing memory overhead per floating point operations involved in most BLAS kernels and in a suite of preconditioner options such as BILU(k), BILUT and multicoloring SSOR. On the other hand, multicore CPU computations are used to exploit functional parallelism to perform system partitioning and reordering, algebraic multigrid preconditioning, sparsification and model reduction operations in order to accelerate and reduce the number of GCR iterations. The efficient orchestration of these operations relies on carefully designing the heuristics depending on the timestep evolution, degree of nonlinearity and current system properties. Hence, we also propose several criteria to automatically decide the type of solver configuration to be employed at every time step of the simulation. To illustrate the potentials of the proposed solver approach, we perform numerical computations on state-of-the-art multicore CPU and GPU platforms. Computational experiments on a wide range of highly complex reservoir cases reveal that the solver approach yields significant speedups with respect to conventional CPU multicore solver implementations. The solver performance gain is of the order of 3x which impacts in about 2x the overall compositional simulation turnaround time. These results demonstrate the potential that many core solvers have to offer in improving the performance of near future reservoir simulations.
Mixed Multiscale Finite Volume Method for Reservoir Simulation in Porous Media with Non Local Features
Authors: Mishev Ilya , Jiang Lijian 
Published: February 2011

Abstract
Multiscale finite volume (MsFV) methods have been successfully applied to solving reservoir simulation problems with localized high heterogeneity (i.e., separable scales), but the accuracy decreases when this is not possible (non-separable scales and long-range features). We develop a mixed multiscale finite volume method (MMsFV) on a uniform mesh that can use global information in order to improve the accuracy and the robustness of the multiscale simulations of fluid flows in porous media with non-local features.
Our development starts with the observation that multi-point flux approximation (MPFA) methods implicitly approximate the velocity and therefore any multiscale generalization also has to do the same. MsFV uses multiscale approximation of the pressure and piecewise constant approximation of the velocity. The novelty of the mixed MsFV method is the explicit approximation of the velocity. We construct a new multiscale basis for the velocity and approximate the pressure with piecewise constants. The velocity basis functions can be calculated with either local information (local mixed MsFV) or global information (global mixed MsFV). We demonstrate the improved accuracy of the global mixed MsFV compared to the local version on several problems including the SPE 10 comparative solution problem. The error of the global mixed MsFV is usually much smaller than the error of the local method when porous media exhibit non-local features.
Using the same framework and the extra flexibility of the two approximation spaces other mixed multiscale finite volume methods can be derived including extensions to unstructured meshes.
Enhancement of the CPR Preconditioner for Efficient Solution of the Adjoint Equation
Authors: Han Choongyong , Wallis John , Sarma Pallav , Li Gary , Schrader Mark L. , Chen Wen 
Published: February 2011

Abstract
It is well known that the adjoint approach is the most efficient approach for gradient calculation, which can be used with gradient-based optimization techniques to solve various optimization problems such as the production optimization problem, history-matching problem, etc. The adjoint equation to be solved in the approach is a linear equation formed with the "transpose" of the Jacobian matrix from a fully implicit reservoir simulator. For a large and/or complex reservoir model, generalized preconditioners often prove impractical for solving the adjoint equation. Preconditioned specialized for reservoir simulation such as Constrained Pressure Residual (CPR) exploit properties of the Jacobian matrix in order to accelerate convergence, so they cannot be directly applied to the adjoint equation. To overcome this challenge, we have developed a new two-stage preconditioner for efficient solution of the adjoint equation (named CPRA).
The CPRA preconditioner has been coupled with an Algebraic Multi Grid (AMG) linear solver and implemented in Chevron's in-house reservoir simulator. The AMG solver is well known for its outstanding capability to solve the pressure equation of complex reservoir models; and solving the linear system with the "transpose" of pressure matrix is one of the two-stages of construction of the CPRA preconditioner.
Through test cases, we have confirmed that the CPRA/AMG solver with GMRES acceleration solves the adjoint equation very efficiently with reasonable number of linear solver iterations. Adjoint simulations to calculate the gradients with the CPRA/AMG solver take about the same time at most as corresponding CPR/AMG forward simulations. Accuracy of the solutions has also been confirmed by verifying the gradients against solutions with a direct solver. A production optimization case study for a real field using the CPRA/AMG solver has further validated its accuracy, efficiency, and the capability to perform long term optimization for large, complex reservoir models at low computational cost.
Numerical Studies on Two-way Coupled Fluid Flow and Geomechanics in Hydrate Deposits
Authors: Kim J. , Yang D. , Moridis G. J. , Rutqvist J. 
Published: February 2011

Abstract
Coupled flow and geomechanics play an important role in gas hydrate reservoirs because the stiffness of the rock skeleton, porosity and permeability are directly influenced by changes of the fluid (water and gas) and solid (hydrate and water) phase saturations, and the deformation of the reservoir. The fluid and solid phases coexist, yielding a high nonlinearity for flow and mechanics, so the coupled problem for hydrates is exceptionally complicated.
In previous study, the stability of hydrate-bearing sediments was assessed by one-way coupled analysis, where the change of fluid properties affects mechanics, but there is no feedback from mechanics to flow. In this paper, we develop and test rigorous two-way coupling between fluid flow and mechanics, where the solutions from mechanics are used to solve the flow problem. We employ the fixed-stress split for a convergent sequential implicit scheme.
We have found noticeable differences between one- and two-way couplings for several cases. The nature of the elliptic boundary problem of quasi-static mechanics results in instantaneous compaction or dilation over the domain by loading from reservoir fluid production. This yields the pressure rise-up or drop at early time (low pressure diffusion), which changes the effective stress instantaneously and geological instability may occur. Also, the change of pressure or temperature affects the solid saturation, rock stiffness, porosity, and permeability fields, changing the fluid flow regime. These behaviors cannot be captured by one-way coupling.
In conclusion, we developed, verified, and demonstrated the applicability of the tightly coupled sequential approach. It pro- vides a rigorous two-way coupled simulator which is ready to be applied to large scale problems to hydrate-bearing sediments.
Robust Constrained Optimization of Short and Long-Term NPV for Closed-Loop Reservoir Management
Authors: Chen Chaohui , Li Gaoming , Reynolds A. C. 
Published: February 2011

Abstract
In this paper, we develop an efficient algorithm for production optimization under linear and nonlinear constraints and an uncertain reservoir description. The linear and nonlinear constraints are incorporated into the objective function using the the augmented Lagrangian method and the bound constraints are enforced using a gradient-projection trust-region method. Robust long-term optimization maximizes the expected life-cycle net-present value (NPV) over a set of geological models, which represent the uncertainty of reservoir description. As the life-cycle optimal controls may be in conflict with the operator's objective of maximizing short-time production, the method is adapted to maximize the expectation of short-term NPV over the next one or two years subject to the constraint that the life-cycle NPV will not be substantially decreased. The technique is applied to synthetic reservoir problems to demonstrate its efficiency and robustness. Experiments show that the field can not always achieve the optimal NPV using the optimal well controls obtained based on a single, but uncertain reservoir model, whereas the application of robust optimization significantly reduces this risk. Experimental results also show robust sequential optimization on each short-term period is not able to achieve an expected life-cycle NPV as high as the one obtained with robust long-term optimization.
Accelerating Reservoir Simulators using GPU Technology
Authors: Appleyard John R. , Appleyard Jeremy D. , Wakefield Mark A. , Desitter Arnaud L. 
Published: February 2011

Abstract
The recent advances in graphics processing units (GPU) and associated development environments suitable for scientific modeling has generated significant interest in the high performance computing arena. In this paper we investigate strategies to incorporate this new technology into an existing commercial reservoir simulator. The use of the GPU for solving linear systems is demonstrated and the algorithmic considerations required in order to exploit the hardware are discussed. The paper describes a massively parallel incomplete factorization which is used as a preconditioner in conjunction with the GMRES algorithm. This factorization balances parallelism with accuracy resulting in a method that is significantly faster than the current serial solver when both are implemented on current hardware, despite in general requiring more iterations to converge. The performance of the implementation is shown to be dependent on problem size and indicates that when fully loaded the GPU is capable of producing a factor of 10 speed-up in the linear solver compared with the CPU based serial solver. The algorithm can also be applied on cluster systems, using domain decomposition, and although no numerical results are yet available, there are grounds for anticipating that performance will scale well for sufficiently large problems. The paper also discusses the benefits of migrating other simulator components of the simulator, such as the Jacobian matrix assembly, to the GPU. It is shown that this improves the overall performance of the simulator considerably.
A Comparison Between Wavelet and Renormalization Upscaling Methods and Iterative Upscaling-Downscaling Scheme
Authors: Babaei Masoud , King Peter R. 
Published: February 2011

Abstract
For all the advances in computational power of modern computers the problem of upscaling the properties of geological reservoir models is an ongoing challenge for reservoir simulation. Unfortunately, the upscaling of parameters like absolute permeability in single phase flow produces errors due to discarding the subgrid complexities. For tackling this problem, reconstruction techniques have been developed to downscale the local dynamic regions of the reservoir. Nonetheless, the upscaling part of these methods still has a significant effect on the final results since a poor upscaling can lead to errors even if coarse scale solution is downscaled or refined. In this work we focus on application of a range of conventional upscaling methods including pressure-solver, Renormalization, renormalization based modifications and wavelet transmissibility upscaling.
We simulate incompressible flooding scenarios in two and three dimensions. The results are subsequently compared to a modified local-global upscaling method coupled with modified nested-gridding downscaling. CPU runtime, error in breakthrough time and production values are the parameters considered in the comparative analysis.
We show improvements by iteration in providing better coarse scale solutions compared to conventional upscaling methods. Finally, we hope that this study can present a reliable ranking of renormalization and wavelet methods compared to iterative reconstruction method in terms of the trade-off between precision and speed.
Two-Stage Algebraic Multiscale Linear Solver for Highly Heterogeneous Reservoir Models
Authors: Zhou H. , Tchelepi H. A. 
Published: February 2011

Abstract
Previous research on multiscale methods for subsurface flow aims to obtain an efficient multiscale solution to the fine-scale problems. Such multiscale solution is usually a good approximation to the fine-scale problem. However, it has been reported that the multiscale solution deteriorates for high aspect ratios and channelized structures of permeability. Moreover, the multiscale solution does not converge to the fine-scale solution unless some special techniques are used.
In this paper, we propose an efficient two-stage algebraic multiscale (TAMS) method that converges to the fine-scale solution. The TAMS method consists of two stages, namely global and local stages. In the global stage, a multiscale solution is obtained purely algebraically from the fine-scale matrix. In the local stage, a local solution is constructed from a local preconditioner such as Block ILU(0) (BILU) or Additive Schwarz (AS) method. Spectral analysis shows that the multiscale solution step captures the low-frequency spectra in the original matrix very well and when combined with a local preconditioner that represents the high-frequency spectra, the full spectra can be well approximated. Thus the TAMS is guaranteed to converge to the fine-scale solution. Moreover, the spectra of the TAMS method tend to cluster together, which is a favorable property for Krylov subspace methods to converge fast. We also show that local mass conservation can be preserved if a multiscale solution step with the finite-volume restriction operator is applied before the iterative procedure exits. This allows for the TAMS method to be used to build efficient approximate solutions for multiphase flow problems that need to solve transport equations.
We test the numerical performance of the TAMS method using several challenging large-scale problems (fine-scale grid in the magnitude of one million) with complex heterogeneous structures and high aspect ratios. Different choices in the TAMS algorithm are employed, including the Galerkin or finite-volume type of restriction operator, BILU or AS preconditioner for the second stage, and the size of blocks for BILU and AS. The performance of TAMS is comparable or superior to the state-of-the-art algebraic multigrid (AMG) preconditioner when some optimal choice in the TAMS method is adopted. Moreover, the convergence of the TAMS method is insensitive to problem sizes, and the CPU time is almost linear to problem sizes. These indicate the TAMS method is efficient and robust for large-scale problems.