3D transdimensional Markov-chain Monte Carlo seismic inversion with uncertainty analysis

Cho, Yongchae (Texas A&M University) | Zhu, Dehan (Texas A&M University) | Gibson, Richard (Texas A&M University)


The Markov chain Monte Carlo (McMC) stochastic approach is widely used to estimate subsurface properties. However, estimating uncertainty quantitatively is also very important when performing stochastic inversion. Therefore, the goal of this paper is to apply the transdimensional, or reversible jump, McMC (rjMcMC) method to obtain a 3-D seismic impedance model and to determine a corresponding uncertainty cube by estimating the standard deviation of the models that are included in the Markov chains. By combining the uncertainty volume and impedance models, we can estimate the acoustic impedance and the uncertainty of the layer boundary location. The uncertainty can also be related to the magnitude of velocity discontinuity. To demonstrate the performance and reliability of the rjMcMC inversion, we used the seismic data from the E-segment of Norne field in Norwegian Sea. The results of transdimensional McMC inversion show high velocity contrasts nearby gas-oil contacts and high uncertainty near discontinuities.

Presentation Date: Wednesday, September 27, 2017

Start Time: 8:55 AM

Location: 370D

Presentation Type: ORAL