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Abstract

Summary

This study presents a novel approach to handle structural uncertainties in the interpretation of stratigraphic structures using a transdimensional sampler, the reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. Moving beyond conventional reservoir simulation models which use a fixed number of layers, the layering size is treated as variable. Hence, the number and position of model layers are unknowns to be determined by the inverse problem. The proposed method extends the application of one-dimensional RJMCMC by introducing a dip parameter, enabling the simulation of sedimentary wedges and erosional features around a well, which is not typically accounted for in standard layer-cake models used for instance in well test interpretation. To keep the model dimension low, a two-dimensional, piecewise constant permeability field is determined for each layering configuration. This inverse method, tested on a synthetic well log, facilitates robust interpretations of geological reservoir geometries and could improve the prediction of permeability spatial distribution in sedimentary settings. Demonstrating successful recovery of target models, the study opens interesting perspectives both for near-well subsurface models and full field reservoir models, and identifies the need for future work on allowing interface intersections to simulate more complex stratigraphic structures.

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/content/papers/10.3997/2214-4609.202335016
2023-11-27
2025-07-08

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References

  1. Bodin, T. [2010] Transdimensional approaches to geophysical inverse problems. Australian National University.
     [Google Scholar]
  2. Bodin, T., Sambridge, M. and Gallagher, K. [2009] A self-parametrizing partition model approach to tomographic inverse problems.Inverse Problems, 25(5), 055009.
     [Google Scholar]
  3. Bodin, T., Sambridge, M., Tkalčić, H., Arroucau, P., Gallagher, K. and Rawlinson, N. [2012] Transdimensional inversion of receiver functions and surface wave dispersion.Journal of Geophysical Research: Solid Earth, 117(B2).
     [Google Scholar]
  4. Carrera, J., Alcolea, A., Medina, A., Hidalgo, J. and Slooten, L. J. [2005] Inverse problem in hydrogeology.Hydrogeology journal, 206–222.
     [Google Scholar]
  5. Dettmer, J., Dosso, S. E. and Holland, C. W. [2010] Trans-dimensional geoacoustic inversion.The Journal of the Acoustical Society of America, 128(6), 3393–3405.
     [Google Scholar]
  6. Geyer, C. J. and Møller, J. [1994] Simulation procedures and likelihood inference for spatial point processes.Scandinavian journal of statistics, 21(4), 359–373.
     [Google Scholar]
  7. Green, P. J. [1995] Reversible jump Markov chain Monte Carlo computation and Bayesian model determination.Biometrika, 82(4), 711–732.
     [Google Scholar]
  8. Hastings, W. K. [1970] Monte Carlo sampling methods using Markov chains and their applications.Biometrika, 57(1), 97–109.
     [Google Scholar]
  9. Lallier, F., Caumon, G., Borgomano, J., Viseur, S., Royer, JJ. and Antoine, C. [2016] Uncertainty assessment in the stratigraphic well correlation of a carbonate ramp: Method and application to the Beausset Basin, SE France.Comptes Rendus Geoscience, 348(7), 499–509.
     [Google Scholar]
  10. Malinverno, A. [2002] Parsimonious Bayesian Markov chain Monte Carlo inversion in a nonlinear geophysical problem.Geophysical Journal International, 151(3), 675–688.
     [Google Scholar]
  11. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. and Teller, E. [1953] Equation of state calculations by fast computing machines.The journal of chemical physics, 21(6), 1087–1092.
     [Google Scholar]
  12. Sambridge, M., Bodin, T., Gallagher, K. and Tkalčić, H. [2013] Transdimensional inference in the geosciences.Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1984), 20110547.
     [Google Scholar]
  13. Wellmann, F. and Caumon, G. [2018] 3-D Structural geological models: Concepts, methods, and uncertainties.Advances in Geophysics, 1–121.
     [Google Scholar]
/content/papers/10.3997/2214-4609.202335016
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Transdimensional Sampling of Two-Dimensional Layered Geological Models with Variable Slope: a Proof of Concept
Conference Proceedings 2023, 1 (2023); https://doi.org/10.3997/2214-4609.202335016
/content/papers/10.3997/2214-4609.202335016
/content/papers/10.3997/2214-4609.202335016
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