GNGTS 2016 - Atti del 35° Convegno Nazionale

494 GNGTS 2016 S essione 3.1 Fig. 3 – a) From left to right 2D probability maps representing the maximum a posteriori solution for water saturation, porosity and shaliness, respectively, estimated along the interpreted top of the reservoir. b) Leftmost part: the maximum a posteriori solution for the litho-fluid facies distribution. Black, yellow and red correspond to shale, brine sand and gas sand, respectively. In b) the other plots represent the probability of occurrence of each facies at each CMP location. In a) and b) the white dashed crosses indicate the well location. setting with many interconnected and isolated sand channels, surrounded by shale sequences. As a final remark I point out that the high computational cost of my MCMC algorithm (5 hours in the field data test using a i5 CPU at 2.67 GHz) can be drastically reduced by an accurate parallel implementation. Acknowledgments. The author wish to thank EDISON for making the seismic and the well log data available and for the permission to publish this work. References Aleardi M., Ciabarri F., Peruzzo F., Garcea B. and Mazzotti A. 2016: Bayesian Estimation of Reservoir Properties by Means of Wide-angle AVA Inversion and a Petrophysical Zoeppritz Equation . In 78th EAGE Conference and Exhibition. doi: 10.3997/2214-4609.201601551. Sambridge M. and Mosegaard K. 2002: Monte Carlo methods in geophysical inverse problems . Reviews of Geophysics, 40(3), 1-29.