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Abstract

Summary

Assessing risk and volumetric estimates of hydrocarbon prospects in a probabilistic manner has become industry-standard in the last twenty years. This means that the G&G teams are asked to translate their expertise and their data in a number of probability distributions for volumetric, fluid and even economical parameters.

In recent years, it is becoming more and more common to introduce linear correlations and other more complex dependencies among some of these input parameters’ distributions, in order to get meaningful results representing the local geology of the prospect. For this reason, classical diagnostic tools focusing on the relative importance of the input parameters, such as tornado diagrams, are becoming too simplistic.

In the present work, we investigate a number of alternative approaches for performing a more accurate pre-drill diagnosis of importance factors. The first one is based on Global Sensitivity Analysis methodology and includes the computation of first, second and total order indices to quantify contribution from uncertain input parameters to uncertainty of the model prediction. The second one is based on an analysis of variance approach via a randomization over all the possible orderings of the input variables. Both these approaches allow a precise computation of the relative importance of the different factors, without losing the dependency structure existing among the variables.

We illustrate and contrast these approaches using a few simplified yet realistic case studies focusing on volumetric estimates of hydrocarbon prospects.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.20141870
2014-09-08
2024-04-26

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References

  1. Archer, G. E. B., Saltelli, A., and Sobol’, I.M.
    [1997] Sensitivity measures, ANOVA-like techniques and the use of bootstrap, Journal of Statistical Computation and Simulation, 58, 120–.
     [Google Scholar]
  2. Darlington, R. B.
    [1968] Multiple regression in psychological research and practice, Psychological Bulletin, 69, 182–.
     [Google Scholar]
  3. Ditlevsen, O. and Madsen, H.
    [1996] Structural Reliability Methods. J. Wiley and Sons, Chichester.
     [Google Scholar]
  4. GrompingU.
    [2007] Estimators of relative importance in linear regression based on variance decomposition, The American Statistician, 61(2).
     [Google Scholar]
  5. JohnsonJ.W. and LeBretonJ. M.
    [2004] History and use of relative importance indices in organizational research, Organizational Research Methods, 7(3).
     [Google Scholar]
  6. Li, L., Lu, Z., and Hao, W.
    [2013] Importance analysis for models with correlated input variables using state dependent parameters approach, Mechanical Systems and Signal Processing, 41(1–2), 86–97.
     [Google Scholar]
  7. LindemanR., MerendaP. F., and GoldR.Z.
    [1980] Introduction to Bivariate and Multivariate Analysis. Foresman Scott, 444 pp.
     [Google Scholar]
  8. Kruskal, W. and Majors, R.
    [1989] Concepts of relative importance in recent scientific literature, The American Statistician, 43, 6–.
     [Google Scholar]
  9. MaraT.A. and Tarantola, S.
    [2012] Variance-based sensitivity indices for models with dependent inputs, Reliability Engineering & System Safety, Volume 107, November 2012, 115–121.
     [Google Scholar]
  10. Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S.
    [2008] Global Sensitivity Analysis: The Primer. Wiley-Interscience.
     [Google Scholar]
  11. Sobol’, I.M.
    [1993] Sensitivity estimates for nonlinear mathematical models, Mathematical Modeling and Computational Experiment, 1, 407-414.
     [Google Scholar]
  12. Storlie, C.B., Swiler, L.P., Helton, J.C., and Sallaberry, C.J.
    [2009], Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models, Reliability Engineering and System Safety, 94(11), 1735–1763.
     [Google Scholar]
  13. Sudret, B.
    [2008] Global sensitivity analysis using polynomial chaos expansion, Reliability Engineering & System Safety, 93(7), 964–979.
     [Google Scholar]
  14. Liu, P-L. P.-L. and Der Kiureghian, A.
    [1986] Multivariate distribution models with prescribed marginals and covariance, Probabilistic Engineering Mechanics, 1(2): 105–112.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20141870
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Sensitivity Analysis with Correlated Inputs for Volumetric Analysis of Hydrocarbon Prospects
Conference Proceedings 2014, 1 (2014); https://doi.org/10.3997/2214-4609.20141870
/content/papers/10.3997/2214-4609.20141870
/content/papers/10.3997/2214-4609.20141870
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