Computational Geosciences Journal Impact Factor & Information

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

2015 Impact Factor Available summer 2016
2014 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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    • Articles in some journals can be made Open Access on payment of additional charge
  • Classification
    ​ green

Publications in this journal

  • Computational Geosciences 10/2015; DOI:10.1007/s10596-015-9530-7
  • Computational Geosciences 09/2015; DOI:10.1007/s10596-015-9527-2
  • Computational Geosciences 08/2015; DOI:10.1007/s10596-015-9516-5
  • [Show abstract] [Hide abstract]
    ABSTRACT: Ensemble based data assimilation methods, such as the (sequential) ensemble Kalman Filter (EnKF) and the (non-sequential) ensemble smoother (ES), are widely used for history matching petroleum reservoir models. In a recent study (Fossum and Mannseth, Inverse Probl. 30(11):114002-3, [2014]), investigating the difference between sequential and non-sequential assimilation, it was shown that, for a series of weakly non-linear data, the sequential assimilation strategy outperformed the non-sequential approach, especially if the data were ordered according to ascending degree of non-linearity. In this paper, we assess, numerically, various assimilation strategies. Here, we consider numerous data types representing a large variation in the degree of non-linearity, and we consider both simple and complex forward models. The numerical study is divided into two parts. Firstly, the assimilation methods are assessed for problems that allow a controllable variation in the degree of data non-linearity. This investigation is conducted by toy models to ensure that a sufficiently large range of non-linear data is tested. Secondly, considering a 2D synthetic reservoir case, the assimilation methods are assessed for different production strategies and reference models. The numerical experiments show that for most models, considering data with a suitable degree of non-linearity, assimilating the data ordered after ascending degree of non-linearity produce the lowest approximation error. Two counter examples illustrate that the optimal assimilation strategy cannot be determined for all cases, especially if the degree of non-linearity depends greatly on the position in parameter space.
    Computational Geosciences 08/2015; 19(4). DOI:10.1007/s10596-015-9492-9
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    ABSTRACT: Computationally efficient updating of reservoir models with new production data has received considerable attention recently. In this paper however, we focus on the challenges of updating reservoir models prior to production, in particular when new exploration wells are drilled. At this stage, uncertainty in the depositional model is highly impactful in terms of risk and decision making. Mathematically, such uncertainty is often decomposed into uncertainty of lithological trends in facies proportions which is typically informed by seismic data, and sub-seismic variability often modeled geostatistically by means of training images. While uncertainty in the training image has received considerable attention, uncertainty in the trend/facies proportion receives little to no consideration. In many practical applications, with either poor geophysical data or little well information, the trend is often as uncertain as the training image, yet is often fixed, leading to unrealistic uncertainty models. The problem is addressed through a hierarchical model of probability. Total model uncertainty is divided into first uncertainty in the training image, then uncertainty in the trend given the uncertain training image. Our methodology relies on an efficient Bayesian updating of these model parameters (trend and training image) by modeling forward-simulated well facies profiles in low-dimensional metric space. We apply this methodology to a real field case study involving wells drilled sequentially in the subsurface, where as more data becomes available, uncertainty in both training image and trend require updating to improve characterization of the facies.
    Computational Geosciences 08/2015; 19(4). DOI:10.1007/s10596-015-9491-x
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    ABSTRACT: An efficient, robust, and flexible adjoint-based computational framework for performing closed-loop reservoir management is developed and applied. The methodology includes gradient-based production optimization and data assimilation (history matching). Flexibility is achieved through the use of automatic differentiation (AD) within the reservoir simulation, production optimization, and history matching modules. The use of AD will also facilitate the application of closed-loop reservoir management to physical models of higher complexity. A fast sequential convex programming (SCP) solver based on the method of moving asymptotes (MMA) is applied for the production optimization component of the closed-loop. This technique is shown to outperform the sequential quadratic programming (SQP) method, which is commonly used for production optimization computations. The history matching component of the workflow integrates both production data and proxy seismic measurements into a unified adjoint-based data assimilation framework. The effect of noisy data, and data of different types, on the accuracy of the history matching component is assessed. The overall closed-loop reservoir management methodology is tested using the well-documented Brugge model. Results demonstrate the efficient performance of the individual closed-loop components and the improvement in net present value that is achieved using these procedures.
    Computational Geosciences 08/2015; 19(4). DOI:10.1007/s10596-015-9496-5
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    ABSTRACT: The formation and development of patterns in the unstable interface between an injected fluid and hydrocarbons or saline aqueous phase in a porous medium can be driven by viscous effects and gravity. Numerical simulation of the so-called fingering is a challenge, which requires rigorous representation of the fluid flow and thermodynamics as well as highresolution discretization in order to minimize numerical artifacts. To achieve such a high resolution, we present higherorder 3D finite element methods for the simulation of fully compositional, three-phase and multi-component flow. This is based on a combination of the mixed hybrid finite element (MHFE) method for total fluid velocity and discontinuous Galerkin (DG) method for the species transport. The phase behavior is described by cubic or cubic-plus-association (CPA) equations of state. We present challenging numerical examples of compositionally triggered fingering at both the core and the large scale. Four additional test cases illustrate the robustness and efficiency of the proposed methods, which demonstrate their power for problems of this complexity. Results reveal three orders of magnitude improvement in CPU time in our method compared with the lowest-order finite difference method for some of the examples. Comparison between 3D and 2D results highlights the significance of dimensionality in the flow simulation.
    Computational Geosciences 08/2015; 19(4). DOI:10.1007/s10596-015-9501-z
  • Computational Geosciences 07/2015; DOI:10.1007/s10596-015-9514-7
  • Computational Geosciences 07/2015; DOI:10.1007/s10596-015-9512-9
  • Computational Geosciences 06/2015; DOI:10.1007/s10596-015-9508-5
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    ABSTRACT: A reactive transport benchmark problem set has been developed based on in situ uranium bio-immobilization experiments that have been performed at a former uranium mill tailing site in Rifle, CO, USA. Acetate-amended groundwater stimulates indigenous microorganisms to catalyze the reduction of U(VI) to a sparingly soluble U(IV) mineral. The interplay between the flow, acetate loading periods and rates, and microbially mediated and geochemical reactions leads to dynamic behavior in metal- and sulfate-reducing bacteria, pH, alkalinity, and reactive mineral surfaces. The benchmark is based on an 8.5 m long one-dimensional model domain with constant saturated flow and uniform porosity. The 159-day simulation introduces acetate and bromide through the upgradient boundary in 14- and 85-day pulses separated by a 10 day interruption. Acetate loading is tripled during the second pulse, which is followed by a 50 day recovery period. Terminal electron-accepting processes for goethite, phyllosilicate Fe(III), U(VI), and sulfate are modeled using Monod-type rate laws. Major ion geochemistry modeled includes mineral reactions as well as aqueous and surface complexation reactions for UO\(_{2}^{2+}\), Fe2+, and H+. In addition to the dynamics imparted by the transport of the acetate pulses, U(VI) behavior involves the interplay between bioreduction, which is dependent on acetate availability, and speciation-controlled surface complexation, which is dependent on pH, alkalinity, and available surface complexation sites. The general difficulty of this benchmark is the large number of reactions (74), multiple rate law formulations, a multisite uranium surface complexation model, and the strong interdependency and sensitivity of the reaction processes. Results are presented for three simulators: HYDROGEOCHEM, PHT3D, and PHREEQC.
    Computational Geosciences 06/2015; 19(3). DOI:10.1007/s10596-015-9474-y
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    ABSTRACT: Bentonite clay is considered as a potential buffer and backfill material in subsurface repositories for high-level nuclear waste. As a result of its low permeability, transport of water and solutes in compacted bentonite is driven primarily by diffusion. Developing models for species transport in bentonite is complicated, because of the interaction of charged species and the negative surface charge of clay mineral surfaces. The effective diffusion coefficient of an ion in bentonite depends on the ion’s polarity and valence, on the ionic strength of the solution, and on the bulk dry density of the bentonite. These dependencies need to be understood and incorporated into models if one wants to predict the effectiveness of bentonite as a barrier to radionuclides in a nuclear repository. In this work, we present a benchmark problem for reactive transport simulators based on a flow-through experiment carried out on a saturated bentonite core. The measured effluent composition shows the complex interplay of species transport in a charged medium in combination with sorption and mineral precipitation/dissolution reactions. The codes compared in this study are PHREEQC, CrunchFlow, FLOTRAN, and MIN3P. The benchmark problem is divided into four component problems of increasing complexity, leading up to the main problem which addresses the effects of advective and diffusive transport of ions through bentonite with explicit treatment of electrostatic effects. All codes show excellent agreement between results provided that the activity model, Debye-Hückel parameters, and thermodynamic data used in the simulations are consistent. A comparison of results using species-specific diffusion and uniform species diffusion reveals that simulated species concentrations in the effluent differ by less than 8 %, and that these differences vanish as the system approaches steady state.
    Computational Geosciences 06/2015; 19(3):535-550. DOI:10.1007/s10596-014-9451-x
  • Computational Geosciences 06/2015; 19(3). DOI:10.1007/s10596-015-9499-2