A method for assessing earth model uncertainty in the Taranaki Basin, New Zealand. Issue 1 (1st December 2018)

• Record Type: Journal Article
• Authors: Lewis, Edward; Martin, Tony; Bell, Tony; van der Burg, Dennis; Russo, Lorenzo
• Is Part Of: ASEG Extended Abstracts (Online). Volume 2018:Issue 1(2018)
• Page Start: 1
• Page End: 5
• Publication Date: 2018-12-01
• Subjects: velocity -- uncertainty -- big data -- tomography -- New Zealand; Prospecting -- Geophysical methods -- Periodicals; Prospecting -- Geophysical methods; Periodicals
• DOI: 10.1071/ASEG2018abT6_1A
• Languages: English
• ISSNs: 2202-0586

Abstract

Abstract : 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.