Abstract

Acoustic wave performance modelling is of considerable interest to seismic interpreters. Conventional interpretative models employ continuous density log data to compute theoretical acoustic data. Their dependence on high quality density log data limits the application of these techniques to areas where good logs have been acquired. Such techniques are therefore commonly used in field studies, but are generally not used in exploration.This paper details a newly developed method of predicting acoustic compression wave performance, based on sequence compaction theory, elastic wave theory and established petrophysical relationships. This allows extension of the existing density log based p-wave velocity prediction methods into areas where log data are minimal and log quality is marginal.Applications of the technique include:improvements to the quality of acquired sonic data;improved velocity control, resulting in better quality synthetic seismograms and more precise control of time-depth conversion; andimproved seismic attribute mapping, offering possibilities of better drilling target selection.With application of the technique, many wells can now be used in which poor log data quality or absence of key logs previously prevented their use as well-to-seismic control points in basin modelling. The technique can be allied to MWD resistivity logging technology to allow continuous computation of acoustic properties and realtime correlation of drilling data to seismic. This results in better location of casing and logging points, and the design of more efficient logging programs, with benefits to both drilling safety and economics.The technique allows improvement in the quality of acquired data and acquisition economics, better utilisation of old data, and better engineering and geologic management practices in current exploration drilling. It has considerable potential for overcoming sonic log acquisition problems associated with poorly compacted rocks, and has application in many areas of sequence and basin studies.

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