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Abstract

Wavelet extraction is a fundamental step in linking imaged seismic data and well-logs. This step provides time-to-depth estimates that help locate seismic data more precisely in depth, and the wavelet that connects the low-resolution seismic data to the fine-scale properties typical of geocellular models. Noise estimates from well ties are the most important parameter controlling the extent to which seismic data constrain these fine-scale models.<br><br>In usual practice, many subjective judgements are made regarding issues like wavelet phase, span, rock--physics models, imaging quality etc, all of which make the results less objective than desirable. It is also widely under-appreciated that these subjective decisions have strong impacts on the most important outputs of the extraction process. They propagate far downstream into reservoir prediction or forecasting, and their influence can easily dominate development decisions.<br><br>We show that model selection choices relating to wavelet span, rock-physics models, segmentation etc, can be made more objectively using Bayesian model-selection criteria. We present some case studies showing how strongly parameter estimates and uncertainties are coupled to model choice, and thus why a more objective model-selection process is crucial to the wavelet extraction workflow.<br>

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/content/papers/10.3997/2214-4609.201403046
2007-09-10
2020-09-21
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201403046
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