One of Society of Exploration Geophysicists (SEG) great traditions is the special recognition of individuals and organizations for their contributions to geophysics and to the Society. Recently notified by the SEG, we are proud to announce that the paper 'Probalistic seismic inversion using pseudo-wells', authored by Pat Connolly, PCA Ltd and Mark O'Brien, Cegal Ltd, has been selected as the Best Paper Presented at the 2017 SEG Annual Meeting. We are proud of this great achievement, the industry recognition and the excellent contribution to the the SEG organization and technical programme given by Pat Connolly and Mark O'Brien.
There is generally a high degree of uncertainty in any attempt to characterize the subsurface. Bayesian methods provide a framework to account for the uncertainty of the prior knowledge and the data and to estimate the uncertainty of the result.
Connolly and Hughes (2016) describe a probalistic seismic inversion method based on matching large numbers of pseudo-wells. From a Bayesian perspective the pseudo-wells are samples from the prior distribition. The samples are uncorrelated so, in essence, this is a simple Monte Carlo method. High efficiency is achieved by restricting dimensionality.
The method allows for the inclusion of a wide range of prior data types and for the uncertainty of these data to be specified. This optimizes the balance between the prior data and the seismic. Here we provide further commentary on the method, describe some recent improvements and show more results.