ogst
Accueil Tous les numéros Volume 74 (2019) Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles, 74 (2019) 73 Références
Open Access
Numéro
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 74, 2019
Numéro d'article 73
Nombre de pages 18
DOI https://doi.org/10.2516/ogst/2019045
Publié en ligne 18 septembre 2019
  • Abdollahzadeh A., Reynolds A., Christie M.A., Corne D. (2012) A parallel GA-EDA hybrid algorithm for history-matching (SPE-153750). SPE Oil and Gas India Conference and Exhibition, 28–30 March, Mumbai, India. [Google Scholar]
  • Abdollahzadeh A., Christie M., Corne D., Davies B., Elliott M. (2013) An adaptive evolutionary algorithm for history-matching (SPE-164824). EAGE Annual Conference & Exhibition Incorporating SPE Europec, 10–13 June, London, United Kingdom. [Google Scholar]
  • Avansi G.D., Schiozer D.J. (2015) UNISIM-I: Synthetic model for reservoir development and management applications, Int. J. Model. Simul. Petrol. Ind. 9, 1, 21–30. [Google Scholar]
  • Avansi G.D., Maschio C., Schiozer D.J. (2016) Simultaneous history-matching approach by use of reservoir-characterization and reservoir-simulation studies (SPE-179740-PA), SPE Reserv. Evalu. Eng. 19, 4, 69–712. [Google Scholar]
  • Bennett F., Graf T. (2002) Use of geostatistical modeling and automatic history matching to estimate production forecast uncertainty – A case study (SPE 74389). International Petroleum Conference and Exhibition in Mexico, 10–12 February, Villahermosa, Mexico. [Google Scholar]
  • Chakra N.C.C., Saraf D.N. (2016) History matching of petroleum reservoirs employing adaptive genetic algorithm, J. Petrol. Explor. Prod. Technol. 6, 4, 653–674. [CrossRef] [Google Scholar]
  • Chen P.H., Shahandashti S.M. (2009) Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints, Autom. Constr. 18, 434–443. [CrossRef] [Google Scholar]
  • Chen C., Jin L., Gao G., Weber D., Vink J.C., Hohl D., Alpak F.O., Pirmez C. (2012) Assisted history matching using three derivative-free optimization algorithms (SPE-154112). SPE Europec/EAGE Annual Conference, 4–7 June, Copenhagen, Denmark. [Google Scholar]
  • CMG – Computer Modelling Group Ltd. (2012) IMEX User’s Guide, Calgary, Canada. [Google Scholar]
  • Davolio A., Schiozer D.J. (2018) Probabilistic seismic history matching using binary images, J. Geophys. Eng. 15, 261–274. [CrossRef] [Google Scholar]
  • Floris F.J.T., Bush M.D., Cuypers M., Roggero F., Syversveen A.R. (2001) Methods for quantifying the uncertainty of production forecasts – a comparative study, Petrol. Geosci. 7, S87–S96. [CrossRef] [Google Scholar]
  • Gautier Y., Noetinger B. (2004) Geostatistical parameters estimation using well test data, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 59, 2, 167–183. [CrossRef] [Google Scholar]
  • Guérillot D., Roggero F. (1995) Matching the future for the evaluation of extreme reservoir development scenarios. 8th European Symposium on Improved Oil Recovery, 15–17 May, Vienna, Austria. [Google Scholar]
  • Holland J.H. (1975) Adaptation in natural and artificial system, The University of Michigan Press, Ann Arbor, MI. [Google Scholar]
  • Hu L.Y., Blanc G., Noetinger B. (2001) Gradual deformation and iterative calibration of sequential stochastic simulations, Math. Geol. 33, 4, 475–489. [Google Scholar]
  • Junghans L., Darde N. (2015) Hybrid single objective genetic algorithm coupled with the simulated annealing optimization method for building optimization, Energy Build. 86, 651–662. [Google Scholar]
  • Kirkpatrick S., Gelatt C.D. Jr, Vecchi M.P. (1983) Optimization by simulated annealing, Science 220, 671–680. [Google Scholar]
  • Li X.G., Wei X. (2008) An improved genetic algorithm-simulated annealing hybrid algorithm for the optimization of multiple reservoirs, Water Res. Manag. 22, 8, 1031–1049. [CrossRef] [Google Scholar]
  • Long J., van den Hoek P.J., Alpak F.O., Pirmez C., Fehintola T., Tendo F., Olaniyan E.E. (2012) A comparison of stochastic data-integration algorithms for the joint history matching of production and time-lapse-seismic data (SPE-146418), SPE Reserv. Evalu. Eng. 15, 4, 498–512. [CrossRef] [Google Scholar]
  • Maschio C., Vidal A.C., Schiozer D.J. (2008) A framework to integrate history matching and geostatistical modeling using genetic algorithm and direct search methods, J. Petrol. Sci. Eng. 63, 34–42. [CrossRef] [Google Scholar]
  • Maschio C., Davolio A., Correia M.G., Schiozer D.J. (2015) A new framework for geostatistics-based history matching using genetic algorithm with adaptive bounds, J. Pet. Sci. Eng. 127, 387–397. [Google Scholar]
  • Maschio C., Schiozer D.J. (2016) Probabilistic history matching using discrete Latin Hypercube sampling and nonparametric density estimation, J. Petrol. Sci. Eng. 147, 98–115. [CrossRef] [Google Scholar]
  • Maschio C., Schiozer D.J. (2018) A new methodology for history matching combining iterative discrete Latin Hypercube with multi-start simulated annealing, J. Petrol. Sci. Eng. 169, 560–577. [CrossRef] [Google Scholar]
  • Oliveira G.S., Schiozer D.J., Maschio C. (2017) History matching by integrating regional multi-property image perturbation methods with a multivariate sensitivity analysis, J. Petrol. Sci. Eng. 153, 111–122. [CrossRef] [Google Scholar]
  • Ouenes A., Bhagavan S., Bunge P.H., Travis B.J. (1994) Application of simulated annealing and other global optimization methods to reservoir description: Myths and realities (SPE-28415). SPE Annual Technical Conference and Exhibition, 25–28 September, New Orleans, Louisiana. [Google Scholar]
  • Roggero F., Guérillot D. (1996) Gradient method and Bayesian formalism application to petrophysical parameter characterization. 5th European Conference on the Mathematics of Oil Recovery, 3–6 September, Leoben, Austria. [Google Scholar]
  • Romero C.E., Carter J.N., Zimmerman R.W., Gringarten A.C. (2000) Improved reservoir characterization through evolutionary computation (SPE-62942). Annual Technical Conference and Exhibition, 1–4 October, Dallas, Texas. [Google Scholar]
  • Romero C.E., Carter J.N. (2001) Using genetic algorithms for reservoir characterization, J. Petrol. Sci. Eng. 31, 2–4, 113–123. [CrossRef] [Google Scholar]
  • Sanghyun L., Stephen K.D. (2018) Optimizing automatic history matching for field application using genetic algorithm and particle swarm optimization (OTC-28401-MS). Offshore Technology Conference Asia, 20–23 March, Kuala Lumpur, Malaysia. [Google Scholar]
  • Sayyafzadeh M., Haghighi M., Carter J.N. (2012) Regularization in history matching using multi-objective genetic algorithm and Bayesian framework (SPE-154544). SPE Europec/EAGE Annual Conference, 4–7 June, Copenhagen, Denmark. [Google Scholar]
  • Schulze-Riegert R.W., Axmann J.K., Haase O., Rian D.T., You Y.L. (2002) Evolutionary algorithms applied to history matching of complex reservoirs (SPE-77301), SPE Reserv. Evalu. Eng. 5, 2, 163–173. [CrossRef] [Google Scholar]
  • Schulze-Riegert R.W., Haase O. (2003) Combined global and local optimization techniques applied to history matching (SPE-79668). Reservoir Simulation Symposium, 3–5 February, Houston, Texas. [Google Scholar]
  • Sultan A.J., Ouenes A., Weiss W.W. (1994) Automatic history matching for an integrated reservoir description and improving oil recovery (SPE-27712). Permian Basin Oil and Gas Recovery Conference, 16–18 March, Midland, Texas. [Google Scholar]
  • UNISIM (2015) Research group on Reservoir Simulation and Management. UNISIM-I-H: Case Study for History Matching. https://www.unisim.cepetro.unicamp.br/benchmarks/en/unisim-i/unisim-i-h. [Google Scholar]
  • Xavier C.R., dos Santos E.P., da Fonseca Vieira V., dos Santos R.W. (2013) Genetic algorithm for the history matching problem, Procedia Comput. Sci. 18, 946–955. [Google Scholar]

Les statistiques affichées correspondent au cumul d'une part des vues des résumés de l'article et d'autre part des vues et téléchargements de l'article plein-texte (PDF, Full-HTML, ePub... selon les formats disponibles) sur la platefome Vision4Press.

Les statistiques sont disponibles avec un délai de 48 à 96 heures et sont mises à jour quotidiennement en semaine.

Le chargement des statistiques peut être long.