Risk quantification combining geostatistical realizations and discretized Latin Hypercube

Almeida FR, Gomes AD, Schiozer DJ (2014) A new approach to perform a probabilistic and multi-objective history matching. Paper SPE 170623. In: SPE Annual Technical Conference and Exhibition, Amsterdam, Netherlands, 27–29 October

Avansi GD, Schiozer DJ (2015) UNISIM-I: synthetic model for reservoir development and management applications. Int J Model Simul Pet Ind 9:21–30

Avansi GD, Schiozer DJ (2015) A new approach to history matching using reservoir characterization and reservoir simulation integrated studies. Paper OTC 26038. In: Offshore Technology Conference, Houston, Texas, 4–7 May

Belson WA (1959) Matching and prediction on the principle of biological classification. Appl Stat 8:65–75

Bertolini AC, Maschio C, Schiozer DJ (2015) A methodology to evaluate and reduce reservoir uncertainties using multivariate distribution. J Pet Sci Eng 128:1–14. doi: 10.1016/j.petrol.2015.02.003

Caers J (2011) Modeling Uncertainty in the Earth Sciences, 1st edn. Wiley-Blackwell, Hoboken, New Jersey, p 249

Cullick AS, Johnson WD, Shi G (2006) Improved and more rapid history matching with a nonlinear proxy and global optimization. In: SPE Annual Technical Conference and Exhibition. SPE 101933. Society of Petroleum Engineers, San Antonio, Texas, USA. doi: 10.2118/101933-MS

Dowd P (1991) A review of recent developments in geostatistics. Comp Geosci 17(10):1481–1500. doi: 10.1016/0098-3004(91)90009-3

Dubrule O (1998) Geostatistics in petroleum geology. AAPG Continuing Education Course Note Series #38. Tulsa, Oklahoma, USA: The American Association of Petroleum Geologists, 251

Fetel E, Caumon G (2008) Reservoir flow uncertainty assessment using response surface constrained by secondary information. J Pet Sci Eng 60:170–182. doi: 10.1016/j.petrol.2007.06.003

Goda T, Sato K (2014) History matching with iterative Latin hypercube samplings and parameterization of reservoir heterogeneity. J Pet Sci Eng 114:61–73. doi: 10.1016/j.petrol.2014.01.009

Gomez-Hernandez JJ, Journel AG (1993) Joint sequential simulation of multiGaussian fields. In: Fourth International Geostatistics Congress, Tróia, Portugal, 13–18 September

Guardado LR, Gamboa LAP, Lucchesi CF (1989) Petroleum Geology of the Campos Basin, Brazil, a Model for a producing atlantic type basin: part 1. AAPG Spec Vol A132:33

Guardado LR, Gamboa LAP, Lucchesi CF (1989) Petroleum geology of the campos basin, Brazil, a model for a producing atlantic type basin: part 2. AAPG Spec Vol A132:42

Isaaks EH (1990) The application of Monte Carlo methods to the analysis of spatially correlated data. Ph.D. thesis, Stanford University

Maschio C, Schiozer DJ (2014) Bayesian history matching using artificial neural network and Markov Chain Monte Carlo. J Petrol Sci Eng. doi: 10.1016/j.petrol.2014.05.016

Heltona JC, Davisb FJ (2003) Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliab Eng Syst Saf 81(2003):23–69

Hughes William T (1995) Risk analysis and asset valuation: a Monte Carlo Simulation using stochastic rents. J Real Estate Financ Econ 11(2):177–187. doi: 10.1007/BF01098661

Jensen TB (1998) Estimation of production forecast uncertainty for a mature production license. In: SPE Annual Technical Conference and Exhibition. SPE 49091. Society of Petroleum Engineers, New Orleans, Louisiana, USA

Kelkar M, Perez G (2002) Applied geostatistics for reservoir characterization. Society of Petroleum Engineers Inc, Richardson

Kwak, Hoon Y, Ingall L (2007) Exploring Monte Carlo simulation applications for project management. Risk Manag 9(1):44–57. doi: 10.1057/palgrave.rm.8250017

Li X, Chan CW (2010) Application of an enhanced decision tree learning approach for prediction of petroleum production. Eng Appl Artif Intell 23:102–109. doi: 10.1016/j.engappai.2009.06.003

Li B, Friedmann F (2005) Novel multiple resolutions design of experiment/response surface methodology for uncertainty analysis of reservoir simulation forecasts. In: SPE Reservoir Simulation Symposium. SPE 92853. Society of Petroleum Engineers. doi: 10.2118/92853-MS

McKay MD, Beckman RJ, Conover WJ (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21:239–245. doi: 10.2307/1268522

Panjalizadeh H, Alizadeh N, Mashhadi H (2014) A workflow for risk analysis and optimization of steam flooding scenario using static and dynamic proxy models. J Pet Sci Eng 121:78–86. doi: 10.1016/j.petrol.2014.06.010

Peng CY, Gupta R (2003) Experimental design in deterministic modelling: assessing significant uncertainties. In: SPE Asia Pacific Oil and Gas Conference and Exhibition. SPE 80537. Society of Petroleum Engineers, Jakarta, Indonesia

Perez HH, Datta-Gupta A, Mishra S (2005) The role of electrofacies, lithofacies, and hydraulic flow units in permeability prediction from well logs: a comparative analysis using classification trees. SPE Reserv Eval Eng 8:143–155

Pilger GG, Costa JF, Koppe J (2008) The benefits of Latin hypercube sampling in sequential simulation algorithms for geostatistical applications. Appl Earth Sci Trans Inst Min Metall Sect B 117:160–174

Platon V, Constantinescu A (2014) Monte Carlo method in risk analysis for investment projects. Proc Econ Financ 15:393–400. doi: 10.1016/S2212-5671(14)00463-8

Rajabi MM, Ataie-Ashtiani B, Janssen H (2014) Efficiency enhancement of optimized Latin hypercube sampling strategies: application to monte carlo uncertainty analysis and meta-modeling. Adv Water Resour. doi: 10.1016/j.advwatres.2014.12.008

Ravenne C, Galli A, Doligez B, Beucher H, Eschard R (2002) Quantification of facies relationships via proportion curves. In: Armstrong M, Bettini C, Champigny N, Galli A, Remacre A (eds) Geostatistics Rio 2000: Proceedings of the Geostatistics Sessions of the 31st International Geological Congress. Rio de Janeiro: Springer, Netherlands. doi: 10.1007/978-94-017-1701-4 19–39

Risso FVA, Risso VF, Schiozer DJ (2011) Risk analysis of petroleum fields using Latin hypercube, Monte Carlo and derivative tree techniques. J Pet Gas Explor Res 01:14–21

Rubinstein BY (1981) Simulation and the Monte Carlo method. Wiley, New York

Schiozer DJ, Ligero EL, Maschio C, Risso FVA (2008) Risk assessment of petroleum fields—use of numerical simulation and proxy models. Pet Sci Technol 26:1247–1266

Schiozer DJ, Ligero EL, Santos JAM (2004) Risk assessment for reservoir development under uncertainty. J Braz Soc Mech Eng 26(2):213–217

Seifert D, Jensen JL (1999) Using sequential indicator simulation as a tool in reservoir description: issues and uncertainties. Math Geol 31(5):527–550. doi: 10.1023/a:1007563907124

Silva FBM, Davolio A, Schiozer DJ (2015) A systematic approach to uncertainties reduction with a probabilistic and multi-objective history matching. Paper SPE 174359. In: Europec/EAGE Annual Conference and Exhibition, Madrid, Spain, 1–4 June

Stein M (1987) Large sample properties of simulations using Latin hypercube sampling. Technometrics 29:143–151. doi: 10.2307/1269769

Suslick SB, Schiozer DJ (2004) Risk analysis applied to petroleum exploration and production: an overview. J Pet Sci Eng 44(1–2):1–9

Vose D (2008) Risk analysis: a quantitative guide. 3rd edn Sussex: John Wiley and Sons, Ltd. Chichester, United Kingdom p 735

Yeten B, Castellini A, Guyaguler BA (2005) Comparison study on experimental design and response surface methodologies. In: SPE Reservoir Simulation Symposium. SPE 93347. Society of Petroleum Engineers, Houston, Texas, USA. doi: 10.2118/93347-MS

Zabalza-Mezghani I, Manceau E, Feraille M, Jourdan A (2004) Uncertainty management: from geological scenarios to production scheme optimization. J Petrol Sci Eng 44:11–25. doi: 10.1016/j.petrol.2004.02.002