DATA PROCESSING & INTERPRETATION 

Seismic modelling: deeper insight into the subsurface

 The rising speed and lowering cost of processing power means that generating a complete synthetic seismic data set is now affordable for individual companies and many times cheaper than an actual seismic survey. Keith Forward reports.

 Zunehmend schnellere und kostengünstigere Rechenleistungen bedeuten, dass die Erstellung eines vollständigen synthetischen seismischen Datensatzes jetzt auch für einzelne Unternehmen erschwinglich und um ein Vielfaches preiswerter als die eigentliche seismische Vermessung ist. Keith Forward berichtet.

 El aumento de la velocidad y la disminución del coste del procesamiento energético hacen que la generación de conjuntos de datos sísmicos sintéticos completos tenga un coste mucho menor que el de un estudio sísmico y que sea asequible para las empresas individuales. Comenta Keith Forward.

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enerating synthetic seismic data from the earth model and comparing it to real seismic data

can give a deeper insight into the actual geometry of the subsurface – and tell you where a real seismic survey would be worth the money. Usually you need to shoot the seismic

in order to find out what it could tell you about the subsurface, but with seismic modelling you can get this information more quickly and at a fraction of the cost. Seismic modelling or synthetic seismic

is the process of using the wave equation which governs the passage of acoustic waves through the subsurface to generate a prediction of what a real seismic survey would look like. Starting with an approximate earth model, built up from existing information about the subsurface, the seismic model takes into account all the imperfections of a real survey, including the noise and multiple reflections that can make seismic data hard to interpret.

Although the earth model is not perfect

and therefore the seismic data generated from it will not be perfect, it can still provide valuable information about what a real seismic survey would reveal. If it shows that seismic data would not be very useful, there is no need to actually go out and get it. Or maybe there are certain regions that would benefit from a wide azimuth survey but there is no reason to shoot the entire prospect.

Tis is what BP did in 2003, when it was considering doing the first ever wide azimuth survey for sub-salt imaging on fields that could not be imaged well using standard seismic techniques, which was going to be very expensive. Management decided it was too great a risk to spend tens of millions of dollars on the acquisition, which at that time was still a theoretical proposition, so first they spent a few million on a seismic model. With the model, the geoscientists could show the

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benefits of the wide azimuth survey, justify the expense of acquiring the extra data, and pinpoint which areas would benefit the most.

Te result is history and the technique of wide azimuth acquisition was born and is now being used by many other companies around the world.

Managing risk Synthetic seismic is basically a tool to quantify and minimise seismic risk. Te information that can be gained about what real seismic would look like, or what existing seismic data is telling you, makes it possible to decide how confident you can be in pursuing a particular course of action. For example, some parts of a reservoir could be in a shadow zone which is not susceptible to acoustic imaging. Te synthetic data can show you which parts would be best illuminated by a seismic survey and which would benefit from a different method such a gravity or magnetic. But it is not just before a seismic acquisition project that it can be useful; it can also be used with existing seismic data to give an indication of how much it can be trusted. Since seismic always contains a significant amount of noise and artefacts, from multiple reflections between rock boundaries or ground roll from surface waves, part of the work of a geoscientist is to decide how reliable the data is and to quantify the risk that an interpretation could be wrong. Having a synthetic model to compare with the real seismic can highlight the areas where the noise makes interpretation more risky.

Migration result In Fig. 1, the fact that the migration result is so different from the seismic impendence reflectivity in the original model shows how imperfect seismic data is. If seismic imaging were perfect the two images would be identical. Knowing these imperfections can

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