Hydrocarbon exploration risk evaluation through uncertainty and sensitivity analyses techniques

Ruffo, Paolo; Bazzana, Livia; Consonni, Alberto; Corradi, Anna; Saltelli, Andrea; Tarantola, Stefano

doi:10.1016/j.ress.2005.11.056

Cited by 20 publications

(16 citation statements)

References 5 publications

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“…A second option is to substitute each random function realization for a discrete number, which can correspond to the scenario parameter of Ruffo et al [18] (where the number of geostatistical realizations is finite and fixed, and where each different value of the discrete parameter corresponds to a different realization). Then, a metamodel is fitted using this dicrete parameter as a qualitative input variable.…”

Section: The Joint Modeling Approachmentioning

confidence: 99%

Global sensitivity analysis of computer models with functional inputs

Iooss

Ribatet

2009

Reliability Engineering & System Safety

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Global sensitivity analysis is used to quantify the influence of uncertain input parameters on the response variability of a numerical model. The common quantitative methods are appropriate with computer codes having scalar input variables. This paper aims at illustrating different variance-based sensitivity analysis techniques, based on the so-called Sobol's indices, when some input variables are functional, such as stochastic processes or random spatial fields. In this work, we focus on large cpu time computer codes which need a preliminary metamodeling step before performing the sensitivity analysis. We propose the use of the joint modeling approach, i.e., modeling simultaneously the mean and the dispersion of the code outputs using two interlinked Generalized Linear Models (GLM) or Generalized Additive Models (GAM). The "mean model" allows to estimate the sensitivity indices of each scalar input variables, while the "dispersion model" allows to derive the total sensitivity index of the functional input variables. The proposed approach is compared to some classical sensitivity analysis methodologies on an analytical function.Lastly, the new methodology is applied to an industrial computer code that simulates the nuclear fuel irradiation.

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Section: The Joint Modeling Approachmentioning

confidence: 99%

Global sensitivity analysis of computer models with functional inputs

Iooss

Ribatet

2009

Reliability Engineering & System Safety

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“…Uncertainty based on reservoir simulation for given uncertain inputs is our main concern, and this relies on an understanding of uncertainties arising from the geomodelling process, and also from operational and platform considerations. This essential topic is covered elsewhere (Charles, Guemene, Corre, Vincent, & Dubrule, 2001) (Elsheikh, Hoteit, & Wheeler., 2013) (Wolff, 2010) (Singh, Yemez, & Sotomayor, 2013) (Massonnat, 2000) (Ruffo, et al, 2006) (Saputelli, et al, 2007) (Bu & Damsleth, 1996).…”

Section: Approaches To History Matching and Uncertainty Quantificationmentioning

confidence: 99%

Bridging the Gap Between Deterministic and Probabilistic Uncertainty Quantification Using Advanced Proxy Based Methods

Goodwin

2015

SPE Reservoir Simulation Symposium

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The use of proxy models for optimisation of expensive functions has demonstrated its value since the 1990's in many industries. Within reservoir engineering, similar techniques have been used for over a decade for history matching, both in commercial tools and in-house software.In addition to efficient history matching, proxy models have a distinct advantage when performing uncertainty quantification of probabilistic forecasts. Markov Chain Monte Carlo (MCMC) methods cannot realistically be applied directly with reservoir simulations, and even fast proxy models can fail dramatically to adequately represent the range of uncertainty if implemented without due care.A pitfall of the use of proxy models is that they are considered 'black box' and their quality is difficult to measure. Engineers prefer to deal with deterministic simulation models which they can evaluate and understand.The main pitfall of simple random walk MCMC techniques, which have begun to appear within reservoir engineering workflows, is a focus on theoretical properties which are not observed in practical implementations. This gives rise to potential gross errors, which are not generally appreciated by practitioners. Advances in recent years within the field of Bayesian statistics have significantly improved this situation, but have not yet been disseminated within the oil and gas industry. This paper describes the limitations of random walk MCMC techniques which are currently used for reservoir prediction studies, and shows how Hamiltonian MCMC techniques, together with an efficient implementation of proxy models, can lead to a more reliable and validated probabilistic uncertainty quantification, whilst also generating a suitable ensemble of deterministic reservoir models. Scientific comparison studies are performed for both an analytical case and a realistic reservoir simulation case to demonstrate the validity of the approach.The benefit of this methodology is to allow asset teams to effectively manage reservoir decisions using a robust and validated understanding of uncertainty. It lays the scientific foundations for the next generation of uncertainty tools and workflows.

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“…Some authors have suggested solutions to handle spatially distributed inputs as well (Volkova et al, 2008 ;Iooss & Ribatet, 2009;Ruffo et al, 2006;Lilburne & Tarantola, 2009). We describe in this section how to estimate sensitivity indices in model M by associating randomly generated realizations of uncertain 2D-field Z(u) to scalar values, according to the approach developed by Lilburne and Tarantola.…”

Section: Estimating Sensitivity Indices Using Geostatistical Simulationsmentioning

confidence: 99%

“…Then, random realizations of X i can be generated through geostatistical simulation (Journel and Huijbregts, 1978). These random realizations can be used to propagate uncertainty through model f and discuss the resulting uncertainty on model output Y (Aerts et al, 2003 -on a problem of optimal location of a ski run; Ruffo et al, 2006 -on hydrocarbon exploration risk evaluation). Within variance-based global sensitivity analysis (GSA) framework, these random realizations can also be sampled alongside with other scalar model inputs to estimate sensitivity indices for each model input (Lilburne & Tarantola, 2009).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of spatial models using geostatistical simulation

Saint-Geours¹,

Lavergne²,

Bailly³

et al. 2011

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Geostatistical simulations are used to perform a global sensitivity analysis on a model Y = f(X 1 ... X k ) where one of the model inputs X i is a continuous 2D-field. Geostatistics allow specifying uncertainty on X i with a spatial covariance model and generating random realizations of X i . These random realizations are used to propagate uncertainty through model f and estimate global sensitivity indices. Focusing on variance-based global sensitivity analysis (GSA), we assess in this paper how sensitivity indices vary with covariance parameters (range, sill, nugget). Results give a better understanding on how and when to use geostatistical simulations for sensitivity analysis of spatially distributed models.

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Hydrocarbon exploration risk evaluation through uncertainty and sensitivity analyses techniques

Cited by 20 publications

References 5 publications

Global sensitivity analysis of computer models with functional inputs

Global sensitivity analysis of computer models with functional inputs

Bridging the Gap Between Deterministic and Probabilistic Uncertainty Quantification Using Advanced Proxy Based Methods

Sensitivity analysis of spatial models using geostatistical simulation

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