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SUMMARY:Spatial categorical inversion: Seismic inversion into lithology/fl
 uid classes - Omre\, H (University of Science and Technology\, Trondheim)
DTSTART:20111213T153000Z
DTEND:20111213T160000Z
UID:TALK34937@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Modeling of discrete variables in a three-dimensional referenc
 e space is a challenging problem. Constraints on the model expressed as in
 valid local combinations and as indirect measurements of spatial averages 
 add even more complexity.\n \nEvaluation of offshore petroleum reservoirs 
 covering many square kilometers and buried at several kilometers depth con
 tain problems of this type. Foc us is on identification of hydrocarbon (ga
 s or oil) pockets in the subsurface - these appear as rare events. The res
 ervoir is classified into lithology (rock) cla sses - shale and sandstone 
 - and the latter contains fluids - either gas\, oil or brine (salt water).
  It is known that these classes are vertically thin with large horizontal 
 continuity. The reservoir is considered to be in equilibrium - hence fixed
  vertical sequences of fluids - gas/oil/brine - occur due to gravitational
  sorting.  Seismic surveys covering the reservoir is made and through proc
 essing of the data\, angle-dependent amplitudes of reflections are availab
 le. Moreover\, a few wells are drilled through the reservoir and exact obs
 e rvations of the reservoir properties are collected along the well trace.
   \n\nThe inversion is phrased in a hierarchical Bayesian inversion framew
 ork. The prior model\, capturing the geometry and ordering of the classes\
 , is of Markov random field type. A particular parameterization coined Pro
 file Markov random field is def ined. The likelihood model linking litholo
 gy/fluids and seismic data captures maj or characteristics of rock physics
  models and the wave equation. Several parameters in this likelihood model
  are considered to be stochastic and they are inferred from seismic data a
 nd observations along the well trace. The posterior model is explored by a
 n extremely efficient MCMC-algorithm.\n\nThe methodology is defined and de
 monstrated on observations from a real North Sea reservoir.\n
LOCATION:Seminar Room 1\, Newton Institute
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