Lithology and fluid prediction from prestack seismic data using a Bayesian model with Markov process prior

Abstract

We invert prestack seismic amplitude data to nd rock properties of a vertical prole of the earth. In particular we focus on lithology, porosity and uid. Our model includes vertical dependencies of the rock properties. This allows us to compute quantities valid for the full prole such as the probability that the vertical prole 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. Litho-uid prediction from seismic 2The relation between rock properties and elastic parameters is established through a stochastic rock model, and a convolutional model links the reectivity 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 dierent data sets oshore Norway, among these one prole has well control. For all test cases the MCMC algorithm provides reliable estimates with uncertainty quantication 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 signicant 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 uid classes in our model.The vertical correlation in elastic parameters mainly inuences the upside potential of the volume distribution.The approach is best suited to evaluate a few selected vertical proles since the MCMC algorithm is computer demanding.

Multi‐trace basis‐pursuit seismic inversion for resolution enhancement

“…In the aspect of lithology and fluid identification, Hammer et al. () and Kolbjørnsen et al. () realized the simultaneous prediction of lithology and fluid in the Bayesian framework; Yin and Zhang () utilized the effective bulk modulus of pore fluid as a fluid indicator and realized the fluid identification (Yin, Zong and Wu ; Zong and Yin ).…”

“…Some scholars (Rabben, Tjelmeland and Ursin 2008;Ulvmoen and Hammer, 2010;Purnomo and Ghosh, 2013) used the prior information as constraints through Bayesian theory to improve the resolution of inversion and reduce the uncertainty. In the aspect of lithology and fluid identification, Hammer et al (2012) and Kolbjørnsen et al (2016) realized the simultaneous prediction of lithology and fluid in the Bayesian framework; Yin and Zhang (2014) utilized the effective bulk modulus of pore fluid as a fluid indicator and realized the fluid identification (Yin, Zong and Wu 2015;Zong and Yin 2017). According to the sedimentology principle, the subsurface medium is commonly dominated by layered structures and seismic reflection events generally show good lateral coherence and continuity.…”

A nonlinear method for multiparameter inversion of pre‐stack seismic data based on anisotropic Markov random field

“…(), Rimstad and Omre () and Hammer et al . () determined lithology and fluid content from pre‐stack seismic data by using a Bayesian model with Markovian priors. In the existing literature, the isotropic MRF (or the isotropic smoothing operator) has usually been used, and little attention has been paid to the anisotropic features of MRF (Zhang et al .…”

Seismic to Facies Inversion Using Convolved Hidden Markov Model

“…Later publications (30,31,32) use MCMC sampling in order to assess the exact posterior distribution, but with a Gibbs sampling strategy while the present thesis uses independent proposal Metropolis-Hastings sampling.…”

“…On the other hand, these publications' algorithms take longer time per facies sampling than the present thesis. In (32), 20000 samplings take 30 hours, while section 6.4 shows that with the algorithms used in this thesis 20000 samplings take 50 min, plus a small overhead time for the first forward recursion, which, in the worst case scenario, is approximately 15 min (for k = 15). (33,34,35,36) presents and analyses an extension of (8) model to invert 2D seismic sections.…”