Te obvious way to fix the first challenge in this list is to
trench the receiver cables into the seabed, which is often referred to as PRM (Permanent Reservoir Monitoring). Tis is costly, but has a high reward at the other end – high quality and very precise 4D seismic data. PRM systems have been installed at four fields offshore Norway and some other fields elsewhere, but it is not a very common practice so far. Since the upfront costs are extensive, PRM needs a large field where 4D seismic is expected to increase hydrocarbon recovery significantly. A common way to quantify seismic repeatability is to use the normalised RMS (root-mean-square)-level:
RMS (monitor – base) NRMS = 2 RMS (monitor) + RMS (base)
(7)
where the RMS-levels of the monitor and base traces are measured within a given time window (on a sample by sample basis). Te factor 2 in equation 7 arises from taking the average of the monitor and base RMS-values. Normally, NRMS is measured in a time window where no production changes are expected. Figure 4.9 shows two seismic traces from a VSP (Vertical Seismic Profile) experiment where the receiver is fixed (in the well at approximately 2 km depth), and the source coordinates are changed by 5m in the horizontal direction. We notice that the normalised RMS-error (NRMS) in this case is low, only 8%. In 1995 Norsk Hydro (now Statoil) acquired a 3D VSP data
set over the Oseberg field in the North Sea. Tis data set consists of 10,000 shots acquired in a circular shooting pattern and recorded by a 5-level receiver string in the well. By comparing shot-pairs with different source positions (and the same receiver), it is possible to estimate the NRMS-level as a function of the horizontal distance between the shot locations. Tis is shown
Figure 4.9: Two VSP-traces measured at exactly the same position in the well, but with a slight difference in the source location (5m in the horizontal direction). The distance between two timelines is 50 ms.
in Figure 4.10, where approximately 70,000 shot pairs with varying horizontal mis-positioning is shown. Te main message from this figure is clear: it is important to repeat the horizontal positions both for sources and receivers as accurately as possible, as even a misalignment of 20–30m might lead to a significant increase in the NRMS-level. Such a plot can serve as a variogram, since it shows the spread for each separation distance. Detailed studies have shown that the NRMS-level increases significantly in areas where the geology between the source and receiver is complex (along the straight line between the source and receiver). From Figure 4.10 we can see that the NRMS-value for a shot separation distance of 40m might vary between 20 and 80% and a significant portion of this spread is attributed to variation in geology. Tis means that comparing NRMS-levels between various fields is not straightforward, since the geological setting might be very different. NRMS-levels are frequently used, however, since it is a simple, quantitative measure. It is also important to note that the NRMS-level is frequency dependent, so the frequency band used in the data analysis should be given. Over the last two decades the focus on source and receiver
positioning accuracy has led to a significant increase in the repeatability of 4D seismic data. Some of this improvement is also attributed to better processing of time-lapse seismic data. Tis trend is sketched in Figure 4.11. Today the global average NRMS-level is around 20–30%. It is expected that this trend will continue, but not at the same rate as previously. Te main reason for this expectation is that the non-repeatable factors, especially rough weather conditions, that need to be attacked to get beyond 20–30% are more difficult and will represent a major hurdle on the way to increased repeatability.
4.1.5 Geomechanical Changes Caused by Hydrocarbon Production
Geomechanics has traditionally been an important discipline in both exploration for and production of hydrocarbons. However, its importance in time-lapse seismic was not fully realised until
Figure 4.10: NRMS as a function of the source separation distance between approximately 70,000 shot pairs for the Oseberg 3D VSP data set.
100
10 20 30 40 50 60 70 80 90
0 0 10 20 30 40 50 60 Shot separation distance (m) 160 70 80 90 100
Landrø, 1999 RMS error (%)
Landrø, 1999
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