ogst
Accueil Tous les numéros Volume 76 (2021) Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles, 76 (2021) 60 Références
Open Access
Numéro
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 76, 2021
Numéro d'article 60
Nombre de pages 19
DOI https://doi.org/10.2516/ogst/2021039
Publié en ligne 17 septembre 2021
  • Sarma P., Aziz K., Drlofsky L.J., (2005) Implementation of adjoint solution for optimal control of smart wells, in: SPE Reservoir Simulation Symposium Houston, Texas, USA. Paper SPE 92864. [Google Scholar]
  • Jansen J.D., Douma S.D., Brouwer D.R., Van den Hof P.M.J., Bosgra O.H., Heemink A.W. (2009) Closed Loop Reservoir Management, in: SPE Reservoir Simulation Symposium, 2–4 February, The Woodlands, Texas, USA. https://doi.org/10.2118/119098-MS. [Google Scholar]
  • Chen Y., Oliver D.S. (2010) Ensemble-Based Closed-Loop Optimization Applied to Brugge Field, SPE Reserv. Evaluation Eng. 13, 1, 56–71. https://doi.org/10.2118/118926-PA. [Google Scholar]
  • Jansen J.D. (2011) Adjoint-based optimization of multiphase flow through porous media-a review, Comput. Fluids. 46, 1, 40–51. [Google Scholar]
  • Isebor O.J., Durlofsky L.J. (2014) Biobjective optimization for general oil field development, J. Pet. Sci. Eng. 119, 123–138. https://doi.org/10.1016/j.petrol.2014.04.021. [CrossRef] [Google Scholar]
  • da Cruz Schaefer B., Sampaio M.A. (2020) Efficient workflow for optimizing intelligent well completion using production parameters in real-time, Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 75, 69. [CrossRef] [Google Scholar]
  • Van Essen G.M., Van den Hof P.M.J., Jansen J.D. (2011) Hierarchical long-term and short-term production optimization, SPE J. 1, 191–199. https://doi.org/10.2118/124332-PA. [CrossRef] [Google Scholar]
  • Siraj M.M., Van den Hof P.M.J., Jansen J.D. (2015) Handling risk of uncertainty in model-based production optimization: A robust hierarchical approach, 2nd IFAC Workshop Autom. Control Offshore Oil Gas Prod. Florianpolis, Brazil 48, 6, 248–253. [Google Scholar]
  • Siraj M.M., Van den Hof P.M., Jansen J.D. (2016) Robust optimization of waterflooding in oil reservoirs using risk management tools, in: Proceedings of the 11th IFAC Symposium on Dynamics and Control of Process Systems, pp. 133–138. [Google Scholar]
  • Fu J., Wen X.H. (2017) Model-based MOO methods for efficient management of subsurface flow, SPE J. 22, 6, 1984–1998. https://doi.org/10.2118/182598-PA. [CrossRef] [Google Scholar]
  • von Hohendorff Filho J.C., Schiozer D.J. (2020) Influence of well management in the development of multiple reservoir sharing production facilities, Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 75, 70. [CrossRef] [Google Scholar]
  • Bagherinezhad A., Boozarjomehry Bozorgmehry R., Pishvaie M.R. (2016) Multi-criterion based well placement and control in the water-flooding of naturally fractured reservoir, J. Pet. Sci. Eng. 149, 675–685. https://doi.org/10.1016/j.petrol.2016.11.013. [Google Scholar]
  • Schiozer D.J., dos Santos A.A.S., Santos S.M.G., Hohendorff Filho J.C. (2019) Model-based decision analysis applied to petroleum field development and management, Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 74, 46. https://doi.org/10.2516/ogst/2019019. [CrossRef] [Google Scholar]
  • Gallardo E., Deutsch C.V. (2019) Decision making in the presence of geological uncertainty with the mean-variance criterion and stochastic dominance rules, SPE Reserv. Evaluation Eng. 23, 1, 1094–6470. [Google Scholar]
  • Vincent P., Schaaf T. (2019) Reservoir and economic-uncertainties assessment for recovery-strategy selection using stochastic decision trees, SPE Reserv. Evaluation Eng. 22, 4, 1094–6470. [Google Scholar]
  • Dubos-Sallée N., Fourno A., Zarate-Rada J., Gervais V., Rasolofosaon P.N.J., Lerat O. (2020) A complete workflow applied on an oil reservoir analogue to evaluate the ability of 4D seismics to anticipate the success of a chemical enhanced oil recovery process, Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 75, 18. [CrossRef] [Google Scholar]
  • Siraj M.M., Van den Hof P.M.J., Jansen J.D. (2017) Handling geological and economic uncertainties in balancing short-term and long-term objectives in waterflooding optimization, SPE J. 22, 4, 1313–1325. https://doi.org/10.2118/185954-PA. [CrossRef] [Google Scholar]
  • Van Essen G., Zandvliet M., Vanden Hof P.M.J., Bosgra O., Jansen J.D. (2009) Robust water-flooding optimization of multiple geological scenarios, SPE J. 14, 01, 202–210. [Google Scholar]
  • Chen C., Li G., Reynolds A.C. (2012) Robust constrained optimization of short- and long-term net present value for closed-loop reservoir management, SPE J. 17, 3, 849–864. [Google Scholar]
  • Beiranvand H., Ghazanfari M., Sahebi H., Pishvaee M.S. (2018) A robust crude oil supply chain design under uncertain demand and market price: A case study, Oil Gas Sci. Technol.– Revue d’IFP Energies nouvelles 73, 66. [CrossRef] [Google Scholar]
  • Fonseca R. (2015) A modified gradient formulation for ensemble optimization under geological uncertainty. PhD thesis, Delft University of Technology. [Google Scholar]
  • Isebor O.J. (2013) Derivative-free optimization for generalized oil field development, Unpublished PhD thesis, Stanford University. [Google Scholar]
  • Zitzler E., Deb K., Thiele L. (2000) Comparison of multi-objective evolutionary algorithms: Empirical results, Evol. Comput. 8, 2, 173–195. [Google Scholar]
  • Das I., Dennis J.E. (1997) A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems, Struct. Optim. 14, 1, 63–69. [CrossRef] [Google Scholar]
  • Coello Coello C.A., Toscano-Pulido G., Salazar-Lechuga M. (2004) Handling multiple objectives with particle swarm optimization, IEEE Trans. Evol. Comput. 8, 3, 256–279. [CrossRef] [Google Scholar]
  • Mohamed L., Christie M., Demyanov V. (2011) History matching and uncertainty quantification: Multiobjective particle swarm optimisation approach, Society of Petroleum Engineers, Vienna, Austria. https://doi.org/10.2118/143067-MS. [Google Scholar]
  • Alalimi A., Pan L., Al-qaness M.A.A., Ewees A.A., Wang X., Abd Elaziz M. (2021) Optimized random vector functional link network to predict oil production from Tahe oil field in China, Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 76, 3. [CrossRef] [Google Scholar]
  • Fonseca R., Leeuwenburgh O., den Hof P.V., Jansen J. (2014) Ensemble-based hierarchical multi-objective production optimization of smart wells, Comput. Geosci.: Model. Simul. Data Anal. 18, 3–4, 449–461. [Google Scholar]
  • Bouzarkouna Z., Ding D.Y., Auger A. (2012) Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models, Comput Geosci. 16, 75–92. https://doi.org/10.1007/s10596-011-9254-2. [CrossRef] [Google Scholar]
  • Fonseca R.M., Leeuwenburgh O., Della Rossa E., Van den Hof P.M.J., Jansen J.D. (2015) Ensemble-based multiobjective optimization of on-off control devices under geological uncertainty, SPE Reserv. Evaluation Eng. 18, 4, 1094–6470. [Google Scholar]
  • Yasari E., Reza M., Khorasheh F., Salahshoor K. (2013) Application of multi-criterion robust optimization in waterflooding of oil reservoir, J. Pet. Sci. Eng. 109, 1–11. https://doi.org/10.1016/j.petrol.2013.07.008. [CrossRef] [Google Scholar]
  • Yasari E., Pishvaie M.R. (2015) Pareto-based robust optimization of water-flooding using multiple realizations, J. Pet. Sci. Eng. 132, 18–27. https://doi.org/10.1016j.petrol.2015.04.038. [Google Scholar]
  • Abellan A., Noetinger B. (2010) Optimizing Subsurface Field Data Acquisition Using Information Theory, Math Geosci. 42, 603–630. https://doi.org/10.1007/s11004-010-9285-6. [CrossRef] [Google Scholar]
  • Wen T., Ciaurri D.E., Thiele M., Ye Y., Aziz K. (2014) How much is an oil price forecast worth in reservoir management?, in ECMOR XIV-14th European Conference on the Mathematics of Oil Recovery, Catania, Sicily, Italy. https://doi.org/10.3997/2214-4609.20141900. [Google Scholar]
  • Li H., Dang C., Mirbozorg A., Yang C., Nghiem L. (2019) Robust Optimization of ASP Flooding Under Oil Price Uncertainty, in: SPE Reservoir Simulation Conference, 10–11 April, Galveston, Texas, USA. [Google Scholar]
  • Yu P.L. (1974) Cone convexity, cone extreme points, and nondominated solutions in decision problems with multiobjectives, J. Optim. Theory Appl. 14, 319–377. https://doi.org/10.1007/BF00932614. [CrossRef] [Google Scholar]
  • Engelbrecht A. (2005) Fundamentals of Computational Swarm Intelligence, John Wiley & Sons, Chichester, England, UK. [Google Scholar]
  • Reyes M., Coello C. (2006) Multi-objective particle swarm optimisers: A survey of the state-of-the-art, Int. J. Comput. Intell. Res. 2, 3, 287–308. [Google Scholar]
  • Deb K. (2009) Multi-objective optimisation using evolutionary algorithms (reprinted version), John Wiley & Sons, Chichester, England, UK. [Google Scholar]
  • Moore J., Chapman R. (1999) Application of particle swarm to multiobjective optimization, Department of Computer Science and Software Engineering, Auburn University. [Google Scholar]
  • Coello Coello C.A., Lechuga M.S. (2002) MOPSO: A proposal for multiple objective particle swarm optimization, in: Proc. Congr. Evolutionary Computation (CEC’2002), May, Honolulu, HI vol. 1, pp. 1051–1056. [Google Scholar]
  • Xue B., Zhang M., Browne W.N. (2013) Particle swarm optimization for feature selection in classification: A multi-objective approach, IEEE Trans. Cybern. 43, 6, 1656–1671. [PubMed] [Google Scholar]
  • Zheng Y.J., Ling H.F., Xue J.Y. (2014) Population classification in fire evacuation: A multiobjective particle swarm optimization approach, IEEE Trans. Evol. Comput. 18, 1, 70–81. [CrossRef] [Google Scholar]
  • Zain M.Z.B.M., Kanesan J., Chuah J.H., Dhanapal S., Kendall G. (2018) A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization, Appl. Soft Comput. 70, 680–700. [CrossRef] [Google Scholar]
  • Shirangi M.G., Mukerji T. (2012) Retrospective optimization of well controls under uncertainty using kernel clustering, Monterey, California, USA. [Google Scholar]
  • Wang H., Echeverria Ciaurri D., Durlofsky L.J., Cominelli A. (2012) Optimal well placement under uncertainty using a retrospective optimization framework, SPE J. 17, 1, 112–121. [CrossRef] [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]
  • Celebi M.E., Kingravi H.A., Vela P.A. (2013) A comparative study of efficient initialization methods for the k-means clustering algorithm, Expert Syst. Appl. 40, 200–210. [CrossRef] [Google Scholar]
  • Adeniran A., Adebayo A., Salami H., Yahaya M., Abdulraheem A. (2019) A competitive ensemble model for permeability prediction in heterogeneous oil and gas reservoirs, Appl. Comput. Geosci. 1, 100004. https://doi.org/10.1016/j.acags.2019.100004. [CrossRef] [Google Scholar]
  • Meira L.A., Coelho G.P., Santos A.A.S., Schiozer D.J. (2015) Selection of representative models for decision analysis under uncertainty, Comput. Geosci. 88, 67–82. [CrossRef] [Google Scholar]
  • Rahim S., Li Z., Trivedi J. (2015) Reservoir geological uncertainty reduction: An optimization-based method using multiple static measures, Math. Geosci. 47, 4, 373–396. [Google Scholar]
  • Anitha P., Patil M.M. (2019) RFM model for customer purchase behavior using K-means algorithm, J. King Saud Univ. – Comput. Inf. Sci. https://doi.org/10.1016/j.jksuci.2019.12.011 [Google Scholar]
  • Jansen J.D., Fonseca R.M., Kahrobaei S., Siraj M.M., Van Essen G.M., Van den Hof P.M.J. (2014) The Egg model – a geological ensemble for reservoir simulation, Geosci. Data J. 1, 192–195. [CrossRef] [Google Scholar]
  • Fonseca R., Reynolds A.C., Jansen J.D. (2016) Generation of a Pareto front for a biobjective water flooding optimization problem using approximate ensemble gradients, J. Petrol. Sci. Eng. 147, 249–260. [CrossRef] [Google Scholar]
  • Soares J., Vale Z., Borges N., Lezama F., Kagan N. (2017) Multi-objective robust optimization to solve energy scheduling in buildings under uncertainty, in: International Conference on Intelligent System Application to Power Systems, September 17–21, San Antonio, Texas, USA, IEEE [Google Scholar]
  • Criqui P. (2001) POLES: Prospective outlook on long-term energy systems. Information document, LEPII-EPE, Grenoble, France. http://web.upmfgrenoble.fr/lepii-epe/textes/POLES8p01.pdf. [Google Scholar]
  • Lapillonne B., Chateau B., Criqui P., Kitous A., Menanteau P., Mima S., Gusbin D., Gilis S., Soria A., Russ P., Szabo L., Suwa W. (2007) World energy technology outlook – 2050 – WETO-H2, Post-Print halshs-00121063, HAL. [Google Scholar]
  • Ljung L. (1999) System identification – theory for the user, Prentice-Hall. [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.