Computational Geosciences (COMPUTAT GEOSCI )

Publisher: Springer Verlag


Accurate and efficient imaging of subsurface structure and modeling of processes in the subsurface require multidisciplinary collaboration among mathematicians engineers chemists physicists and geoscientists. Presently there exists no journal whose main objective is to provide a platform for interaction among these diverse scientific groups. To remedy this we propose to establish a new journal Computational Geosciences . The aim of this international journal is to facilitate the exchange of ideas across the disciplines and among universities and industrial and governmental laboratories. Computational Geosciences will publish high quality papers on mathematical modeling simulation data analysis imaging inversion and interpretation with applications in the geosciences. The themes and application areas to be covered include reservoir and environmental engineering hydrology geochemistry geomechanics seismic and electromagnetic imaging geostatistics and reservoir/aquifer characterization and high performance parallel computing. More specifically Computational Geosciences welcomes contributions concerning for example bioremediation diffusion and dispersion geology and geostatistics scale up multiphase flow and reactive transport geophysical imaging and inversion methods seismic and electromagnetic modeling numerical methods and parallel computing. Both theoretical and applied scientists are invited to participate. Computational Geosciences focuses mainly on quantitative aspects of models describing transport processes in permeable media. It is targeted at petroleum engineers hydrologists quantitative environmental engineers soil physicists soil and geochemists applied mathematicians geologists and seismologists.

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    Computational Geosciences website
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    Computational geosciences (Online), CG
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    Internet Resource, Computer File, Journal / Magazine / Newspaper

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Springer Verlag

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Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: A benchmark problem set consisting of four problem levels was developed for the simulation of Cr isotope fractionation in 1D and 2D domains. The benchmark is based on a recent field study where Cr(VI) reduction and accompanying Cr isotope fractionation occurs abiotically by an aqueous reaction with dissolved Fe2+ (Wanner et al., 2012., Appl. Geochem., 27, 644–662). The problem set includes simulation of the major processes affecting the Cr isotopic composition such as the dissolution of various Cr(VI) bearing minerals, fractionation during abiotic aqueous Cr(VI) reduction, and non-fractionating precipitation of Cr(III) as sparingly soluble Cr-hydroxide. Accuracy of the presented solutions was ensured by running the problems with four well-established reactive transport modeling codes: TOUGHREACT, MIN3P, CRUNCHFLOW, and FLOTRAN. Results were also compared with an analytical Rayleigh-type fractionation model. An additional constraint on the correctness of the results was obtained by comparing output from the problem levels simulating Cr isotope fractionation with the corresponding ones only simulating bulk concentrations. For all problem levels, model to model comparisons showed excellent agreement, suggesting that for the tested geochemical processes any code is capable of accurately simulating the fate of individual Cr isotopes.
    Computational Geosciences 01/2015;
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    ABSTRACT: We discuss the construction of multi-level inexact linear solvers for control volume discretizations for porous media. The methodology forms a contrast to standard iterative solvers by utilizing an algebraic hierarchy of approximations which preserve the conservative structure of the underlying control volume. Our main result is the generalization of multiscale control volume methods as multi-level inexact linear solvers for conservative discretizations through the design of a particular class of preconditioners. This construction thereby bridges the gap between multiscale approximation and linear solvers. The resulting approximation sequence is referred to as inexact solvers. We seek a conservative solution, in the sense of control-volume discretizations, within a prescribed accuracy. To this end, we give an abstract guaranteed a posteriori error bound relating the accuracy of the linear solver to the underlying discretization. These error bounds are explicitly computable for the grids considered herein. The afore-mentioned hierarchy of conservative approximations can also be considered in the context of multi-level upscaling, and this perspective is highlighted in the text as appropriate. The new construction is supported by numerical examples highlighting the performance of the inexact linear solver realized in both a multi- and two-level context for two- and three-dimensional heterogeneous problems defined on structured and unstructured grids. The numerical examples assess the performance of the approach both as an inexact solver, as well in comparison to standard algebraic multigrid methods.
    Computational Geosciences 12/2014;
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    ABSTRACT: In this paper, the macroscopic representation of one-phase incompressible flow in fractured and cavity (or vuggy) porous media is studied from theoretical and numerical points of view. A single-domain (or equivalently a Darcy-Brinkman) type of approach is followed to describe the momentum transport at Darcy scale where the fracture or cavity region and porous matrix region are well identified. The Darcy scale model is upscaled yielding a macroscopic momentum equation operating on the equivalent homogeneous medium. Numerical solution to the associated closure problem is proposed in order to compute the effective permeability. Numerical results on some model fractured and cavity media are discussed and compared to some analytical results.
    Computational Geosciences 12/2014;
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    ABSTRACT: Changes of porosity, permeability, and tortuosity due to physical and geochemical processes are of vital importance for a variety of hydrogeological systems, including passive treatment facilities for contaminated groundwater, engineered barrier systems (EBS), and host rocks for high-level nuclear waste (HLW) repositories. Due to the nonlinear nature and chemical complexity of the problem, in most cases, it is impossible to verify reactive transport codes analytically, and code intercomparisons are the most suitable method to assess code capabilities and model performance. This paper summarizes model intercomparisons for six hypothetical scenarios with generally increasing geochemical or physical complexity using the reactive transport codes CrunchFlow, HP1, MIN3P, PFlotran, and TOUGHREACT. Benchmark problems include the enhancement of porosity and permeability through mineral dissolution, as well as near complete clogging due to localized mineral precipitation, leading to reduction of permeability and tortuosity. Processes considered in the benchmark simulations are advective-dispersive transport in saturated media, kinetically controlled mineral dissolution-precipitation, and aqueous complexation. Porosity changes are induced by mineral dissolution-precipitation reactions, and the Carman-Kozeny relationship is used to describe changes in permeability as a function of porosity. Archie’s law is used to update the tortuosity and the pore diffusion coefficient as a function of porosity. Results demonstrate that, generally, good agreement is reached amongst the computer models despite significant differences in model formulations. Some differences are observed, in particular for the more complex scenarios involving clogging; however, these differences do not affect the interpretation of system behavior and evolution.
    Computational Geosciences 11/2014;
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    ABSTRACT: Data assimilation in a reservoir with facies description using the ensemble Kalman filter (EnKF) is challenging. An important reason is that probability density functions for pixel-based representations of facies fields seldom follow the unimodal Gaussian assumption underlying traditional EnKF implementations. Different approaches for identification of facies fields, aiming to overcome this challenge, have been proposed within the EnKF framework. Level set (LS) representations of the facies field have been reported to alleviate the problems of multimodality. Several authors have, however, pointed out that the most commonly applied LS representation suffers from topological constraints that can create difficulties in an estimation setting. An alternative LS representation, that overcomes these topological constraints, leads to instabilities in the assimilated ensemble members. To overcome topological constraints, the recently proposed hierarchical LS representation is applied in an estimation setting for the first time in this paper. To improve stability and to alleviate challenges associated with model nonlinearities, we apply regularization by reduced representation of the LS functions and adjustable smoothing of the LS representation. The resolution of the reduced LS representation is selected based on the variability of the initial ensemble, aiming at preserving enough flexibility to disclose unexpected features. 2D and 3D estimation results demonstrate that the hierarchical LS representation does avoid topological constraints and that instabilities are avoided. The results suggest that the method is capable of handling estimation of facies fields while preserving geological plausibility.
    Computational Geosciences 10/2014; 18(5).
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    ABSTRACT: Geologic CO2 sequestration in deep saline aquifers is a promising technique to mitigate the effect of greenhouse gas emissions. Designing optimal CO2 injection strategy becomes a challenging problem in the presence of geological uncertainty. We propose a surrogate assisted optimisation technique for robust optimisation of CO2 injection strategies. The surrogate is built using Adaptive Sparse Grid Interpolation (ASGI) to accelerate the optimisation of CO2 injection rates. The surrogate model is adaptively built with different numbers of evaluation points (simulation runs) in different dimensions to allow automatic refinement in the dimension where added resolution is needed. This technique is referred to as dimensional adaptivity and provides a good balance between the accuracy of the surrogate model and the number of simulation runs to save computational costs. For a robust design, we propose a utility function which comprises the statistical moment of the objective function. Numerical testing of the proposed approach applied to benchmark functions and reservoir models shows the efficiency of the method for the robust optimisation of CO2 injection strategies under geological uncertainty.
    Computational Geosciences 10/2014; 18(5):763-778.
  • Computational Geosciences 09/2014;
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    ABSTRACT: In an earlier study two hierarchical multi-objective methods were suggested to include short-term targets in life-cycle production optimization. However this earlier study has two limitations: 1) the adjoint formulation is used to obtain gradient information, requiring simulator source code access and an extensive implementation effort, and 2) one of the two proposed methods relies on the Hessian matrix which is obtained by a computationally expensive method. In order to overcome the first of these limitations, we used ensemble-based optimization (EnOpt). EnOpt does not require source code access and is relatively easy to implement. To address the second limitation, we used the Broyden-Flecther-Goldfarb-Shanno (BFGS) algorithm to obtain an approximation of the Hessian matrix. We performed experiments in which a water flood was optimized in a geologically realistic multi-layer sector model. The controls were inflow control valve settings at pre-defined time intervals. Undiscounted Net Present Value (NPV) and highly discounted NPV were the long-term and short-term objective functions used. We obtained an increase of approximately 14% in the secondary objective for a decrease of only 0.2-0.5% in the primary objective. The study demonstrates that ensemble-based hierarchical multi-objective optimization can achieve results of practical value in a computationally efficient manner.
    Computational Geosciences 08/2014; 18(3-4):449-461.
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    ABSTRACT: Convective mixing of dissolved carbon dioxide (CO2) with formation brine has been shown to be a significant factor for the rate of dissolution of CO2 and thus for determining the viability of geological CO2 storage sites. In most previous convection investigations, a no-flow boundary condition was used to represent the interface between an upper region with CO2 and brine and the single-phase brine region beneath. However, due to interfacial tension between the phases, the water phase is partly mobile in the upper region and advection may occur. Based on linear stability analysis and numerical simulations, we show that advection across the interface leads to considerable destabilization of the system. In particular, the time of onset of instability is reduced by a factor of two and the rate of dissolution is enhanced by a factor of two for three of four formations we consider, and by 40 % for the fourth formation. It is found that exponential decay of the relative permeability away from the interface provides a useful approximation to the real system. In addition, the exponential decay also simplifies the linear stability analysis. Interestingly, formations with large absolute permeability and small porosity have the largest impact from the transition zone, despite the fact that the relative permeability decays quickly above the interface in these formations. This is because the length-scale of instability is smallest in these formations.
    Computational Geosciences 08/2014; 18:417-431.
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    ABSTRACT: We propose a workflow for decision making under uncertainty aiming at comparing different field development plan scenarios. The approach applies to mature fields where the residual uncertainty is estimated using a probabilistic inversion approach. Moreover, a robust optimization method is presented to optimize controllable parameters in the presence of uncertainty. The key element of this approach is the use of response surface model to reduce the very high number of simulator model evaluations that are classically needed to perform such workflows. The major issue is to be able to build an efficient and reliable response surface. This is achieved using a Gaussian process (kriging) statistical model and using a particular training set (experimental design) developed to take into account the variable correlation induced by the probabilistic inversion process. For the problem of optimization under uncertainty, an iterative training set is proposed, aiming at refining the response surface iteratively such as to effectively reduce approximation errors and converging faster to the true solution. The workflow is illustrated on a realistic test case of a mature field where the approach is used to compare two new development plan scenarios both in terms of expectation and of risk mitigation and to optimize well position parameters in the presence of uncertainty.
    Computational Geosciences 06/2014;
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    ABSTRACT: We present a formulation of the discontinuous Galerkin method aimed for simulations of gravity-driven viscous fingering instabilities occurring in porous media flow. Specifically, we are targeting applications characterized by complex geometrical features. Viscous fingering instabilities play a very important role in carbon sequestration in brine aquifers. The proposed method has the ability to preserve high order of accuracy on completely unstructured meshes, a feature that makes it ideal for high-fidelity computations of the challenging fingering flow patterns and very complex geometries of actual reservoirs and aquifers. An extensive set of numerical computations is also included, to confirm the stability, accuracy, and robustness of the method.
    Computational Geosciences 05/2014; 17(2).
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    ABSTRACT: In many applications in flows through porous media, one needs to determine the properties of subsurface to detect, monitor, or predict the actions of natural or induced forces. Here, we focus on two important subsurface properties: rock permeability and porosity. A Bayesian approach using a Markov Chain Monte Carlo (MCMC) algorithm is well suited for reconstructing the spatial distribution of permeability and porosity, and quantifying associated uncertainty in these properties. A crucial step in this approach is the computation of a likelihood function, which involves solving a possibly nonlinear system of partial differential equations. The computation time for the likelihood function limits the number of MCMC iterations that can be performed in a practical period of time. This affects the consistency of the posterior distribution of permeability and porosity obtained by MCMC exploration. To speed-up the posterior exploration, we can use a prefetching technique, which relies on the fact that multiple likelihoods of possible states into the future in an MCMC chain can be computed ahead of time. In this paper, we show that the prefetching technique implemented on multiple processors can make the Bayesian approach computationally tractable for subsurface characterization and prediction of porous media flows.
    Computational Geosciences 04/2014;
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    ABSTRACT: The adaptive Gaussian mixture filter (AGM) was introduced as a robust filter technique for large-scale applications and an alternative to the well-known ensemble Kalman filter (EnKF). It consists of two analysis steps, one linear update and one weighting/resampling step. The bias of AGM is determined by two parameters, one adaptive weight parameter (forcing the weights to be more uniform to avoid filter collapse) and one predetermined bandwidth parameter which decides the size of the linear update. It has been shown that if the adaptive parameter approaches one and the bandwidth parameter decreases, as an increasing function of the sample size, the filter can achieve asymptotic optimality. For large-scale applications with a limited sample size, the filter solution may be far from optimal as the adaptive parameter gets close to zero depending on how well the samples from the prior distribution match the data. The bandwidth parameter must often be selected significantly different from zero in order to make large enough linear updates to match the data, at the expense of bias in the estimates. In the iterative AGM we introduce here, we take advantage of the fact that the history matching problem is usually estimation of parameters and initial conditions. If the prior distribution of initial conditions and parameters is close to the posterior distribution, it is possible to match the historical data with a small bandwidth parameter and an adaptive weight parameter that gets close to one. Hence, the bias of the filter solution is small. In order to obtain this scenario, we iteratively run the AGM throughout the data history with a very small bandwidth to create a new prior distribution from the updated samples after each iteration. After a few iterations, nearly all samples from the previous iteration match the data, and the above scenario is achieved. A simple toy problem shows that it is possible to reconstruct the true posterior distribution using the iterative version of the AGM. Then a 2D synthetic reservoir is revisited to demonstrate the potential of the new method on large-scale problems.
    Computational Geosciences 03/2014;