An automated cross-validation method to assess seismic time-to-depth conversion accuracy: a case study on the Cooper and Eromanga basins, Australia
D. Kulikowski, C. Hochwald and K. Amrouch
Journal name: Geophysical Prospecting
Issue: Vol 66, No 8, October 2018 pp. 1521 - 1534
Info: Article, PDF ( 16.42Mb )
Selecting a seismic time-to-depth conversion method can be a subjective choice that is made by geophysicists, and is particularly difficult if the accuracy of these methods is unknown. This study presents an automated statistical approach for assessing seismic time-to-depth conversion accuracy by integrating the cross-validation method with four commonly used seismic time-to-depth conversion methods. To showcase this automated approach, we use a regional dataset from the Cooper and Eromanga basins, Australia, consisting of 13 three-dimensional (3D) seismic surveys, 73 two-way-time surface grids and 729 wells. Approximately 10,000 error values (predicted depth vs. measured well depth) and associated variables were calculated. The average velocity method was the most accurate overall (7.6 m mean error); however, the most accurate method and the expected error changed by several metres depending on the combination and value of the most significant variables. Cluster analysis tested the significance of the associated variables to find that the seismic survey location (potentially related to local geology (i.e. sedimentology, structural geology, cementation, pore pressure, etc.), processing workflow, or seismic vintage), formation (potentially associated with reduced signal-to-noise with increasing depth or the changes in lithology), distance to the nearest well control, and the spatial location of the predicted well relative to the existing well data envelope had the largest impact on accuracy. Importantly, the effect of these significant variables on accuracy were found to be more important than choosing between the four methods, highlighting the importance of better understanding seismic time-to-depth conversions, which can be achieved by applying this automated cross-validation method.