Bayesian lithology and fluid prediction from seismic prestack data

Abstract

A fast Bayesian inversion method for 3D lithology and fluid prediction from prestack seismic data, and a corresponding feasibility analysis were developed and tested on a real data set. The objective of the inversion is to find the probabilities for different lithology-fluid classes from seismic data and geologic knowledge. The method combines stochastic rock physics relations between the elastic parameters and the different lithology-fluid classes with the results from a fast Bayesian seismic simultaneous inversion from seismic data to elastic parameters. A method for feasibility analysis predicts the expected modification of the prior probabilities to posterior probabilities for the different lithology-fluid classes. The feasibility analysis can be carried out before the seismic data are analyzed. Both the feasibility method and the seismic lithology-fluid probability inversion were applied to a prospect offshore Norway. The analysis improves the probability for gas sand from 0.1 to about 0.2–0.4 with seismic data.

Neuro-Bayesian facies inversion of prestack seismic data from a carbonate reservoir in Iran

“…According to Buland et al (2008), W is a block diagonal matrix containing one wavelet for each angle, A is the angle dependent weak-contrast coefficients introduced by Aki and Richards (1980) and D is the differential matrix giving the contrast of the elastic properties m. 2. Bayesian inversion of petrophysical parameters (R) from elastic parameters.…”

“…Bayesian inversion (Buland et al, 2008;Grana and Della Rossa, 2010;Karimpouli et al, 2013aKarimpouli et al, , 2013b) is a natural choice for geophysical inverse problems such as for facies inversion, where it is possible to combine a priori knowledge with the information contained in measured data to predict facies type (Ulrych et al, 2001;Buland and Omre, 2003;Tarantola, 2005). In seismic studies, the analytical forms of solutions are usually computationally superior to iterative search/approach and simulation-based solutions (Buland and Omre, 2003).…”

Bayesian lithology/fluid inversion—comparison of two algorithms

“…The posterior model contains the complete solution in the Bayesian setting. For Bayesian LF inversion approaches, see Eidsvik et al [5], Avseth et al [2], Larsen et al [8], Hammer and Tjelmeland [7], González et al [6], Buland et al [3], Ulvmoen and Omre [10], and Ulvmoen et al [11]. In the current study, we focus on the approaches in Larsen et al [8] and Hammer and Tjelmeland [7].…”

Sparse Bayesian linearized amplitude‐versus‐angle inversion

“…In statistical approaches, some of the constraints or parameters are related to random variables. They include the use of Bayesian approaches (Buland and Omre ; Downton ; Buland and El Ouair ; Rabben, Tjelmeland and Ursin ; Wang and Zhang 2010; Grana and Della Rossa ; Alemie and Sacchi ; Grana et al . ; Lehochi, Avseth and Hadziavidic ; Pan et al .…”