Computational Geosciences (COMPUTAT GEOSCI)

Publisher: Springer Verlag

Journal description

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.

Current impact factor: 1.87

Impact Factor Rankings

2016 Impact Factor Available summer 2017
2014 / 2015 Impact Factor 1.868
2013 Impact Factor 1.612
2012 Impact Factor 1.422
2011 Impact Factor 1.348
2010 Impact Factor 1.056
2009 Impact Factor 1.306
2008 Impact Factor 1.222
2007 Impact Factor 0.742
2006 Impact Factor 1
2005 Impact Factor 0.806
2004 Impact Factor 0.744
2003 Impact Factor 0.175
2002 Impact Factor 0.655
2001 Impact Factor 0.533
2000 Impact Factor 0.344

Impact factor over time

Impact factor

Additional details

5-year impact 1.88
Cited half-life 5.30
Immediacy index 0.19
Eigenfactor 0.00
Article influence 1.03
Website Computational Geosciences website
Other titles Computational geosciences (Online), CG
ISSN 1420-0597
OCLC 40420652
Material type Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Springer Verlag

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    • Must link to publisher version
    • Set phrase to accompany link to published version (see policy)
    • Articles in some journals can be made Open Access on payment of additional charge
  • Classification

Publications in this journal

  • No preview · Article · Feb 2016 · Computational Geosciences
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    ABSTRACT: Waveform modelling is essential for seismic imaging and inversion. Because including more physical characteristics can potentially yield more accurate Earth models, we analyse strategies for elastic seismic wave propagation modelling including topography. We focus on using finite differences on modified staggered grids. Computational grids can be curved to fit the topography using distribution functions. With the chain rule, the elasto-dynamic formulation is adapted to be solved directly on curved staggered grids. The chain-rule approach is computationally less expensive than the tensorial approach for finite differences below the 6th order, but more expensive than the classical approach for flat topography (i.e. rectangular staggered grids). Free-surface conditions are evaluated and implemented according to the stress image method. Non-reflective boundary conditions are simulated via a Convolutional Perfect Matching Layer. This implementation does not generate spurious diffractions when the free-surface topography is not horizontal, as long as the topography is smoothly curved. Optimal results are obtained when the angle between grid lines at the free surface is orthogonal. The chain-rule implementation shows high accuracy when compared to the analytical solution in the case of the Lamb’s problem, Garvin’s problem and elastic interface.
    No preview · Article · Feb 2016 · Computational Geosciences

  • No preview · Article · Jan 2016 · Computational Geosciences
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    ABSTRACT: The ensemble Kalman filter (EnKF) has been successfully applied to data assimilation in steam-assisted gravity drainage (SAGD) process, but applications of localization for the EnKF in the SAGD process have not been studied. Distance-based localization has been reported to be very efficient for assimilation of large amounts of independent data with a small ensemble in water flooding process, but it is not applicable to the SAGD process, since in the SAGD process, oil is produced mainly from the transition zone steam chamber to cold oil instead of the regions around the producer. As the oil production rate is mainly affected by the temperature distribution in the transition zone, temperature-based localization was proposed for automatic history matching of the SAGD process. The regions of the localization function were determined through sensitivity analysis by using a large ensemble with 1000 members. The sensitivity analysis indicated that the regions of cross-correlations between oil production and state variables are much wider than the correlations between production data and model variables. To choose localization regions that are large enough to include the true regions of non-zero cross-covariance, the localization function is defined based on the regions of non-zero covariances of production data to state variables. The non-zero covariances between production data and state variables are distributed in accordance with the steam chamber. This makes the definition of a universal localization function for different state variables easier. Based on the cross-correlation analysis, the temperature range in which oil production is contributed is determined, and beyond or below this range, the localization function reduces from one, and at the critical temperature or steam temperature, the localization function reduces to zero. The temperature-based localization function was obtained through modifying the distance-based localization function. Localization is applied to covariance of data with permeability, saturation, and temperature, as well as the covariance of data with data. A small ensemble (10 ensemble members) was employed in several case studies. Without localization, the variability in the ensemble collapsed very quickly and lost the ability to assimilate later data. The mean variance of model variables dropped dramatically by 95 %, and there was almost no variability in ensemble forecasts, while the prediction was far from the reference with data mismatch keeping up at a high level. At least 50 ensemble members are needed to keep the qualities of matches and forecasts, which significantly increases the computation time. The EnKF with temperature-based localization is able to avoid the collapse of ensemble variability with a small ensemble (10 members), which saves the computation time and gives better history match and prediction results.
    No preview · Article · Jan 2016 · Computational Geosciences
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    ABSTRACT: A generalized cubic equation of state is given. The Peng-Robinson and the Soave-Redlich-Kwong equations are special cases of this equation. The generalized equation of state is precisely as simple and computationally efficient as these classical equations. Through comparison with the Span-Wagner equation for CO 2, we obtain an improved density accuracy in predefined temperature-pressure domains. The generalized equation is then verified through two relevant examples of CO 2 injection and migration. Comparisons are made with other standard cubic EOS in order to show the range of solutions obtained with less accurate EOS.
    No preview · Article · Dec 2015 · Computational Geosciences
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    ABSTRACT: We present a high-order method for miscible displacement simulation in porous media. The method is based on discontinuous Galerkin discretization with weighted average stabilization technique and flux reconstruction post processing. The mathematical model is decoupled and solved sequentially. We apply domain decomposition and algebraic multigrid preconditioner for the linear system resulting from the high-order discretization. The accuracy and robustness of the method are demonstrated in the convergence study with analytical solutions and heterogeneous porous media, respectively. We also investigate the effect of grid orientation and anisotropic permeability using high-order discontinuous Galerkin method in contrast with cell-centered finite volume method. The study of the parallel implementation shows the scalability and efficiency of the method on parallel architecture. We also verify the simulation result on highly heterogeneous permeability field from the SPE10 model.
    No preview · Article · Oct 2015 · Computational Geosciences
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    ABSTRACT: We present a new Bayesian framework for the validation of models for subsurface flows. We use a compositional model to simulate CO2 storage in saline aquifers, comparing simulated saturations to observed saturations, together with a Bayesian analysis, to refine the permeability field. At the laboratory scale, we consider a core that is initially fully saturated with brine in a drainage experiment performed at aquifer conditions. Two types of data are incorporated in the framework: the porosity field in the entire core and CO2 saturation values at equally spaced core slices for several values of time. These parameters are directly measured with a computed tomography scanner. We then find permeability fields that (1) are consistent with the measured parameters and, at the same time, (2) allow one to predict future fluid flow. We combine high performance computing, Bayesian inference, and a Markov chain Monte Carlo (McMC) method for characterizing the posterior distribution of the permeability field conditioned on the available dynamic measurements (saturation values at slices). We assess the quality of our characterization procedure by Monte Carlo predictive simulations, using permeability fields sampled from the posterior distribution. In our characterization step, we solve a compositional two-phase flow model for each permeability proposal and compare the solution of the model with the measured data. To establish the feasibility of the proposed framework, we present computational experiments involving a synthetic permeability field known in detail. The experiments show that the framework captures almost all the information about the heterogeneity of the permeability field of the core. We then apply the framework to real cores, using data measured in the laboratory.
    No preview · Article · Oct 2015 · Computational Geosciences