
TY  - JOUR
A1  - Lewis, Edward 
A1  - Martin, Tony 
A1  - Bell, Tony 
A1  - Burg, Dennis van der 
A1  - Russo, Lorenzo 
T1  - A method for assessing earth model uncertainty in the Taranaki Basin, New Zealand
JF  - ASEG Extended Abstracts
JO  - ASEG Extended Abstracts
Y1  - 2018
VL  - 2018
IS  - 1
SP  - 1
EP  - 5
KW  - 
UR  - https://doi.org/10.1071/ASEG2018ABT6_1A 
N2  - As exploration moves into areas of increasing geological complexity and limited legacy data, reservoir evaluation is often based on the interpretation of one seismic image. Building a suitable velocity model followed by pre-stack depth migration plays an important role in the creation of this image on which economic evaluations are often based. In many cases drilling commitments are planned long in advance. Geologists have a good idea about the geometry and size of a potential reservoir but require accurate interpretation and positioning in the depth domain. Typically, the amount of uncertainty associated with an image is poorly quantified. During a depth migration velocity model building project, such as shown here, we deliver a single final velocity model and its associated seismic products. The only quantitative measure of the reliability of the data would be through comparison with available auxiliary data or from analysis of volumetric residual move-out. This may provide an indication of how well the model converges to a solution which satisfies the observations on the data. The high non-linearity inherent within the tomography used to generate the model yield multiple solution realizations. These honour the constraining data and yield the same convergence criterion. In isolation such data provides little useful evidence of the reliability of any one individual model. We aim to rectify this by employing a workflow which assesses the uncertainty in our tomography process. This initially establishes both the resolution, and the degree to which the tomography fails to recover an implied perturbation. Using these criteria we generate a volume of models which equally conform to the observed data and derive confidence attributes assigned to the target model governing the image. Here we present data from Taranaki Basin, Offshore New Zealand, and show how a model uncertainty workflow could de-risk exploration and development decisions. 
ER  -

