Open Access
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 76, 2021
Article Number 60
Number of page(s) 19
Published online 17 September 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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [Google Scholar]
  • Abellan A., Noetinger B. (2010) Optimizing Subsurface Field Data Acquisition Using Information Theory, Math Geosci. 42, 603–630. [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. [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. [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. [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. [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. [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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.