ogst
Accueil Tous les numéros Volume 76 (2021) Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles, 76 (2021) 41 Références
Open Access
Numéro
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 76, 2021
Numéro d'article 41
Nombre de pages 23
DOI https://doi.org/10.2516/ogst/2021014
Publié en ligne 14 juin 2021
  • Akin S., Kok M.V., Uraz I. (2010) Optimization of well placement geothermal reservoirs using artificial intelligence, Comput. Geosci. 36, 776–785. [CrossRef] [Google Scholar]
  • Artus V., Durlofsky L.J., Onwunalu J., Aziz K. (2006) Optimization of nonconventional wells under uncertainty using statistical proxies, Comput. Geosci. 10, 389–404. [CrossRef] [Google Scholar]
  • Bangerth W., Klie H., Wheeler M., Stoffa P., Sen M. (2006) On optimization algorithms for the reservoir oil well placement problem, Comput. Geosci. 10, 303–319. [CrossRef] [Google Scholar]
  • Beckner B., Song X. (1995) Field development planning using simulated annealing-optimal economic well scheduling and placement, in: SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 1995. [Google Scholar]
  • Bittencourt A.C., Horne R.N. (1997) Reservoir development and design optimization, in: SPE Annual Technical Conference and Exhibition, San Antonio, Texas, October 1997. [Google Scholar]
  • Da Cruz P.S., Horne R.N., Deutsch C.V. (1999) The quality map: a tool for reservoir uncertainty quantification and decision making, SPE Reserv. Eval. Eng. 7, 6–14. [CrossRef] [Google Scholar]
  • Emerick A.A., Silva E., Messer B., Almeida L.F., Szwarcman D., Pacheco M.A.C. (2009) Well placement optimization using a genetic algorithm with nonlinear constraints, in: SPE Reservoir Simulation Symposium, The Woodlands, Texas, February 2009. [Google Scholar]
  • Forouzanfar F., Li G., Reynolds A.C. (2010) A two-stage well placement optimization method based on adjoint gradient, in: SPE Annual Technical Conference and Exhibition, Florence, Italy, September 2010. [Google Scholar]
  • Ghazali A.K., Razib A.R. (2011) Optimizing development strategy and maximizing field economic recovery through simulation opportunity index, in: SPE Reservoir Characterisation and Simulation Conference and Exhibition, Abu Dhabi, UAE, October 2011. [Google Scholar]
  • Guyaguler B., Horne R.N., Rogers L., Rosenzweig J.J. (2002) Optimization of well placement in a Gulf of Mexico waterflooding project, SPE Reser. Eval. Eng. 5, 229–236. [CrossRef] [Google Scholar]
  • Insuasty E., Van den Hof P.M., Weiland S., Jansen J.-D. (2017) Flow-based dissimilarity measures for reservoir models: a spatial-temporal tensor approach, Comput. Geosci. 21, 645–663. [CrossRef] [Google Scholar]
  • Liu N., Jalali Y. (2006) Closing the loop between reservoir modeling and well placement and positioning, in: Intelligent Energy Conference and Exhibition, Amsterdam, The Netherlands, April 2006. [Google Scholar]
  • Lyons J., Nasrabadi H. (2013) Well placement optimization under time-dependent uncertainty using an ensemble Kalman filter and a genetic algorithm, J. Pet. Sci. Eng. 109, 70–79. [CrossRef] [Google Scholar]
  • Meira L., Coelho G., Santos A.A.S., Schiozer D. (2015) Selection of representative models for decision analysis under uncertainty, Comput. Geosci. 88, 67–82. [CrossRef] [Google Scholar]
  • Molina A.R., Rincon A.A. (2009) Exploitation plan design based on opportunity index analysis in numerical simulation models, in: Latin American and Caribbean Petroleum Engineering Conference, Cartagena de Indias, Colombia, May 2009. [Google Scholar]
  • Onwunalu J.E., Durlofsky L.J. (2010) Application of a particle swarm optimization algorithm for determining optimum well location and type, Comput. Geosci. 14, 183–198. [CrossRef] [Google Scholar]
  • Saputra W., Ariaji T. (2015) A new simulation opportunity index based software to optimize vertical well placements, public hearing of final project presentation, Department of Petroleum Engineering ITB. [Google Scholar]
  • Sarma P., Durlofsky L.J., Aziz K. (2008) Computational techniques for closed–loop reservoir modeling with application to a realistic reservoir, Petrol. Sci. Technol. 26, 1120–1140. 10th European Conference on the Mathematics of Oil Recovery. [CrossRef] [Google Scholar]
  • Pan Y., Horne R.N. (1998) Improved methods for multivariate optimization of field development scheduling and well placement design, in: SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, September 1998. [Google Scholar]
  • Shirangi M.G., Durlofsky L.J. (2016) A general method to select representative models for decision making and optimization under uncertainty, Comput. Geosci. 96, 109–123. [Google Scholar]
  • Taware S.V., Park H.-Y., Datta-Gupta A., Bhattacharya S., Tomar A., Kumar M. (2012) Well placement optimization in a mature carbonate waterflood using streamline-based quality maps, in: SPE Oil and Gas India Conference and Exhibition, Mumbai, India, March 2012. [Google Scholar]
  • Varela-Pineda A., Hutheli A.H., Mutairi S.M. (2014) Development of mature fields using reservoir opportunity index: a case study from a Saudi field, in: SPE Saudi Arabia Section Technical Symposium and Exhibition, Al-Khobar, Saudi Arabia, April 2014. [Google Scholar]
  • Wang H., Echeverría-Ciaurri D., Durlofsky L., Cominelli A. (2012) Optimal well placement under uncertainty using a retrospective optimization framework, SPE J. 17, 112–121. [CrossRef] [Google Scholar]
  • Zandvliet M., Handels M., Essen G., Brouwer R., Jansen J. (2008) Adjoint-based well-placement optimization under production constraints, SPE J. 13, 392–399. [CrossRef] [Google Scholar]
  • Zubarev D.I. (2009) Pros and cons of applying proxy-models as a substitute for full reservoir simulations, in: SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, October 2009. Journal of Petroleum Technology. 62, 41-42. [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.