Extracting an attribute: from geometry to rock
Learning objectives
- Extract an RMS amplitude along a mapped horizon and choose a window
- Explain why a window that is too thin or too thick both degrade the result
- Describe how an error in the horizon propagates into a quantitative product
- Recognise why a poorer surface favours a wider extraction window
Everything so far has been geometry: where surfaces are. An attribute map is the first thing an interpretation says about the ROCK.
The idea is simple. Follow the mapped horizon through the volume and, at every bin, reduce the samples near it to one number; root-mean-square amplitude in a window is the usual choice. If the reservoir reflection strength varies with its quality, an amplitude map is a prospect-ranking tool. If it does not, the map is decoration. Deciding which you have is quantitative interpretation; here, you at least get to make the map honestly.
The window is not a detail
Open the Map viewport in AMPLITUDE and sweep the window. At zero thickness you read the single sample the surface happens to land on, so the map carries the sample rate as much as the geology: move the surface half a sample and the value changes. Open the window too far and you average in the reflectors above and below; the map gets smoother, more convincing, and steadily less about your reservoir. The usable middle sits near a wavelet period.
Now remember whose surface is positioning that window: yours. Every place your gridded horizon is wrong, from a sparse framework or a mis-tracked line, the window sits off the event and the attribute reads noise dressed as rock. A quantitative product is never better than the interpretation underneath it, and a wider window is more forgiving of a poorer surface, which is why, on real data where you cannot know how wrong your surface is, the wider window is often the safer engineering choice even though it is the worse one in principle.