Errors due to the tuning effect in calculating NTG for the Seis2perm

Another important aspect of the permeability-estimation transform (Equation 2.1) is calculating the effective porosity. The process of permeability assessment is a practice in which the 4D seismic is used to filter the active sands (sand-bodies responding to production activities). These sand-bodies are defined by the base-line seismic through the effective porosity estimation in the Seis2perm technique. This means that the channels are indicated by the effective porosity, and that the 4D-seismic signature manifests the active channels over time. Effective porosity is the product of sand porosity by net-to-gross (NTG). In the Schiehallion field, the variation of porosity is very small (for example, in segment 1 it is 0.27 to 0.29), so that it is generally considered as a constant value. Thus, the NTG effect is the influential parameter determining the shape of the channels and affecting the permeability result.

The conventional NTG calculation in this field has been using the arithmetic average or RMS average of amplitudes extracted from the coloured inversion volume (pre- production 1996 seismic survey), which is considered to be a good indicator of channels. For example, Soldo (2004) proposed a linear transform to scale the seismic- attribute range to NTG range for this field:

(4.5)

where NTG(x,y) is the vertically averaged NTG map; RMS(x,y) is the average

seismic attribute; and NTGmax and NTGmin are the maximum and minimum of vertically

averaged NTG obtained from the operator‟s reservoir model. This transform could provide a good approximation when the appropriate attribute discriminating sand channels from shale is selected for NTG calculation. However, discounting the tuning effect may play an important role in introducing errors into the calculation.

The modelling performed for the Schiehallion field revealed that the RMS attribute used for calculating the NTG shows a tuning effect and cannot be directly transformed to NTG estimates (see Appendix E for more details). Therefore, the method proposed by Connolly (2007) is employed to calculate the detuned net pay for Schiehallion (refer to





Therefore, the estimated net pay is divided by the reservoir thickness extracted from the reservoir-simulation model. The final estimated NTG is shown in Figure 4.8(a). In fact, the NTG calculated based on the described procedure is a detuned and calibrated revision of the seismic attribute in Figure 4.8(c). The result appears to be more consistent with the NTG of the simulation model (Figure 4.8(b)). However, the NTG calculated from seismic data may preferentially reveal the reservoir reality between the wells, in contrast to the interpolated NTG values from the simulation model.

Measuring the uncertainty attached to the algorithm used for NTG estimation is also a desirable objective here. Connolly‟s algorithm is accurate for arbitrarily small true thicknesses (although the signal-to-noise will decrease for thin reservoirs). The rule-of- thumb is that the separation between picks should not be greater than the half-cycle of the lowest frequency component of the wavelet. Results for greater thicknesses should be used with caution. The reservoir must be isolated, in other words the wedge model must apply. The degree of reliability of the algorithm applied depends on the validity of the assumptions, thus, the uncertainties attached to the assumptions have to be measured. The main assumption of this net pay estimation procedure is that, for the band-limited data, the average of the band-limited data over the apparent thickness between zero crossings is proportional to the seismic net-to-gross. However, the accuracy of this relationship decreases as the gross interval increases. Departures from proportionality are largely caused by variations of the internal layering within the reservoir. The details of the uncertainty quantification for NTG calculation using the specific algorithm described above were addressed by Connolly and Kemper (2007). In their method, layering patterns are simulated using power-law exponentials for sand and shale bed-thickness distributions. Thousands of pseudo well-logs are generated to synthesize the average band-limited impedance responses between zero crossings. This is performed for a range of gross thicknesses and net-to-gross in order to span a wide

range of net-to-gross. Total net from the Vshale log is measured and divided by apparent

thickness to yield seismic net-to-gross. Average band-limited impedance is cross- plotted against seismic net-to-gross. From these cross-plots, maps of apparent thickness, and seismic net-to-gross, a seismic net-to-gross standard-deviation map is generated. (For details of this procedure, refer to Connolly and Kemper, 2007.) The calculated standard deviation is displayed in Figure 4.9.

NTG from simulation NTG Connoly

NTG from simulation

NTG Connoly

Figure 4.8: (a) The NTG calculated from the coloured inversion base-line seismic (1996 seismic data), using a model adapted from Connolly‟s method; (b) the NTG from the simulation model; (c) the NTG estimated from the base-line seismic attribute without removing the tuning effect.

100 200 300 400 500 20 40 60 80 100 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 50 100 150 200 250 300 350 400 450 500 10 20 30 40 50 60 70 80 90 100 110

Figure 4.9: Map of the standard deviations, indicating the spatial uncertainty of the seismic net-to-gross estimates.

Removing the tuning effect also appears to be essential for calculating difference maps. The tuning effect may not be cancelled out in making difference maps, as it is not a linear event. The non-linearity is caused because the effect of fluid replacement changes the amplitude. As a result, the tuning thickness may shift as the top and base picks come together at a different location. Also, fluid contacts (the oil–water contact, the gas–oil contact and the water–gas contact) may show a tuning effect. The issue of a changing

tuning effect over time requires a technique to overcome the various challenges described above. In fact, this technique is very sensitive, as applying a method which does not consider all of these influencing artefacts may end up with added uncertainties instead of removing the uncertainty.

4.4 Validation of the assumptions of pressure-controlled seismic, and the