measuring acoustic velocities as the flooding experiment is conducted, these experiments can be used to establish a link between pore scale CO2
injection and time-lapse seismic on the field scale. Figure 4.17 shows an example of how CO2
influenced the seismic data over time. By combining 4D traveltime and amplitude changes, we have developed methods of estimating the thickness of CO2
Another key parameter is CO2
be estimated using rock physics measurements and models. Although the precision of both 4D seismic
-
layers, making it possible to estimate volumes. saturation, which can
methods and rock physics is increasing, there is no doubt that precise estimates are hard to achieve, and therefore we need to improve existing methods and learn how to combine several methods in order to decrease the uncertainties associated with these monitoring methods. A more recent example of time-lapse seismic monitoring storage is from the Snøhvit field. Snøhvit is a gas field
of CO2
in the western Barents Sea, offshore Norway, where the gas contains approximately 5–8% CO2
, which is separated from
the gas and re-injected into sand layers close to the reservoir. From 2008 to 2011 approximately 1.6 million tons of CO2
were
injected into the Tubåen Formation. An increase in the pore pressure in this formation led to the decision to inject into the Stø Formation by drilling a new injection well in 2016. An example of pressure-saturation discrimination using
time-lapse seismic data acquired in 2003 and 2009 (see Grude et al., 2013) is shown in Figure 4.18. From this figure we clearly see the increased pore pressure within the Tubåen Formation, and relatively confined saturation changes that are visible closer to the injection well. Te figure also demonstrates that the discrimination method improves when the offset span is increased, since the lower panel shows clearer images of pressure and saturation and probably less cross-talk between saturation and pressure.
4.1.9 Future Aspects
Te most important issue for further improvement of the 4D seismic method is to improve repeatability. Improvements in both seismic acquisition and 4D processing will contribute to this process. As sketched in Figure 4.11, it is expected that this improvement will be less pronounced than it was in the
Figure 4.16: (left) Long core showing the location for the X-ray cross-section (red arrow). Water injection is 50 g/l. (right) X-ray density maps of a core slice: 6 time steps during the injection process (from Marsala and Landrø, EAGE extended abstracts, 2005).
past decade. However, it is still a crucial issue, and even minor improvements might mean a lot for the value of a 4D seismic study. Further improvements in repeatability will probably involve issues like source stability, source positioning, shot time interval and improved handling of various noise sources. Maybe in future we will see vessels towing a super- dense grid of sensors behind them in order to obtain perfect repositioning of the receiver positions. Another direction to improve 4D studies could be to
constrain the time-lapse seismic information by other types of information, such as geomechanical modelling, time-lapse EM or gravimetric data or innovative rock physics measurements. Our understanding of the relation between changes in
the subsurface stress field and the seismic parameters is still limited, and research within this specific area will be crucial to advance 4D in future. New analysis methods like long offset 4D might be a complementary technique, or an alternative method where conventional 4D analysis has limited success. However, this technique is limited to reservoirs where the velocity increases from the cap rock to the reservoir rock. Te link between reservoir simulation (fluid-flow
simulation) and time-lapse seismic will continue to be developed. As computer resources increase, the feasibility of a joint inversion exploiting both reservoir simulation and time- lapse seismic data in the same subsurface model will increase. Despite extra computer power, it is reasonable to expect that the non-uniqueness problem (several scenarios fitting the same data sets) requires that the number of earth models is constrained by geology.
Figure 4.17: Time- lapse seismic data showing monitoring of the CO2
injection
at Sleipner. The strong amplitude increase (shown in blue) is interpreted as a thin CO2
layer.
The dashed red lines indicate top and base of the Utsira sand layer (figure modified from Ghaderi and Landrø, 2009).
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