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Lithology and fluid prediction from prestack seismic data using a Bayesian model with Markov process prior
Author(s) -
Hammer Hugo,
Kolbjørnsen Odd,
Tjelmeland Håkon,
Buland Arild
Publication year - 2012
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1111/j.1365-2478.2011.01012.x
Subject(s) - markov chain monte carlo , geology , seismic inversion , bayesian probability , algorithm , inversion (geology) , bayesian inference , inverse problem , amplitude , computer science , seismology , data assimilation , mathematics , artificial intelligence , mathematical analysis , physics , quantum mechanics , meteorology , tectonics
We invert prestack seismic amplitude data to find rock properties of a vertical profile of the earth. In particular we focus on lithology, porosity and fluid. Our model includes vertical dependencies of the rock properties. This allows us to compute quantities valid for the full profile such as the probability that the vertical profile contains hydrocarbons and volume distributions of hydrocarbons. In a standard point wise approach, these quantities can not be assessed. We formulate the problem in a Bayesian framework, and model the vertical dependency using spatial statistics. The relation between rock properties and elastic parameters is established through a stochastic rock model, and a convolutional model links the reflectivity to the seismic. A Markov chain Monte Carlo (MCMC) algorithm is used to generate multiple realizations that honours both the seismic data and the prior beliefs and respects the additional constraints imposed by the vertical dependencies. Convergence plots are used to provide quality check of the algorithm and to compare it with a similar method. The implementation has been tested on three different data sets offshore Norway, among these one profile has well control. For all test cases the MCMC algorithm provides reliable estimates with uncertainty quantification within three hours. The inversion result is consistent with the observed well data. In the case example we show that the seismic amplitudes make a significant impact on the inversion result even if the data have a moderate well tie, and that this is due to the vertical dependency imposed on the lithology fluid classes in our model. The vertical correlation in elastic parameters mainly influences the upside potential of the volume distribution. The approach is best suited to evaluate a few selected vertical profiles since the MCMC algorithm is computer demanding.