PECULIARIDADES DEL NEGOCIO ASEGURADOR

A reservoir simulation is launched and the predicted fluid saturation and pressure fields are converted into elastic parameters by the calibrated PEM and sim2seis calculation. By subtracting the predicted P-impedance with the one at the baseline time, the 4D P- impedance is revealed, from which a map is averaged over the Fangst group layers and plotted in Figure 3.15 (a). In the area of interest, the OOWC moves to the north, and the water flooded area is represented by the impedance “hardening” (water replacing oil) zone. In contrast, re-injection of gas from the crescent has pushed the OGOC towards south, resulting in individual lanes of impedance “softening”. The gas signals are well

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Figure 4.15 (a) The 4D P-impedance map averaged over the Fangst group layers from the

reservoir model; (b) The inversion derived map of 4D P-impedance. The gas cap is highlighted. (c) The constrained inversion results, in which the gas cap extends over the C and D Segments.

confined in the B, C and D Segments, which reflects the initial set-ups of the transmissibility and NNC values. Figure 4.15 (b) and (c) compare the 4D seis2sim results with and without sim2seis constraints. The fundamental difference between them has been discussed in the previous chapter, but it is still worth pointing out that the constrained seis2sim indicates the leakage of gas between Segments B and C in the south, in addition to Segments C and D in the north. However, the lateral extension of the gas signal from the model prediction does not replicate this observation. In order to update the model, it is vital to have insight into the 4D volumes to figure out where the leakage exactly takes place. To assess this, a vertical cross-section through the problematic segments is created. Figure 4.16 (a) and (b) show the comparison between

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Figure 4.16 (a) The 4D P-impedance from the sim2seis prediction on the model grid; (b) The

seis2sim inverted 4D P-impedance on the model grid; (c) 4D P-impedance prediction in TWT; (d) Inverted 4D P-impedance in TWT.

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Figure 4.17 (a) The initial NNC values at the fault locations; (b) The updated NNC values,

which opened Segments B,C and D; (c) Updated 4D P-impedance prediction in the reservoir model; (d) Updated 4D P-impedance prediction in TWT.

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the prediction and inversion results on the reservoir model grid, in which the black lines indicate the NOT shale layer. In the G Segment, the water flood is represented by a 4- 6% impedance “hardening”, and there are locally some “softening” signals, indicating the gas presence, particularly in the compartments B, C and D. By comparing the sim2seis prediction with the seis2sim inversion result, a prominent discrepancy is found in Segment C, where seis2sim suggests continuous extension of a gas cap over the B, C and D segments, while the prediction isolates the C Segment from its neighbours. Such a disagreement exists, too, in the time domain when converting the model prediction to TWT, as shown in Figure 4.16 (c) and (d). The time-domain comparison shows a visually similar behaviour in both the model and the 4D seis2sim, except for the presence of gas in the C Segment. This sharp contrast reflects the non-permeable fault defined initially in the model, which needs an update. The initial NNC model is shown in Figure 4.17 (a), in which the cells beside the faults have zero NNC definitions. According to the seis2sim results, it is decided to open the barriers between the B, C and D Segments by adding non-zero NNC values, as shown in Figure 4.17 (b). Therefore, the fluids are now capable of migrating through the faults at these particular points. The prediction of the 4D P-impedance of the updated model is shown in

Figure 4.18 (a) The average map of the 4D P-impedance; (b) The average map of the 4D P-

impedance prediction from the updated reservoir model, in which the missing gas in Segment C has appeared.

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Figure 4.17 (c) and (d). The previous discrepancy has been improved, as the expected gas signal in Segment C appears, being represented by a consistent increase in the P- impedance with the inversion result.

Laterally, the updated model shows a consistent dynamic behaviour with the seis2sim results, as shown in Figure 4.18 (a) and (b), which is now capable of predicting the correct gas cap extension over the B, C and D Segments in the centre crescent. Thus an improvement leads to a closed dynamic loop in matching the seismic behaviour of the reservoir model to the observed in the P-impedance domain. However, since the gas signal is the main driver in this update, Well P-4 in Segment C and Well P-6 in Segment B are chosen to test the local impact of the NCC values introduced into the history match loop. The predicted GOR ratios shown in Figure 4.19 (a) and (b) suggest additional improvements in both wells, whose magnitudes are greater than those introduced during the static loop. This proves that the model updating is more sensitive for the Heidrun field in terms fault communication. Moreover, the reduced GOR

Figure 4.19 (a) The simulated GOR profiles of well P-04 before and after closing the dynamic

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at the wells is caused by the opening of new reservoir volume that was closed by the faults. The fault updating strategy here is relatively straightforward according to the 4D observation, in contrast to any other form of artificial revision required by the numerical optimisation. With only the dynamic insight into the 4D seismic, such an engineering consistent update can be obtained. Further improvement can be possibly achieved by looking at other areas of the field.

4.5 Summary

Regardless of what history matching techniques have been applied to the Heidrun reservoir model, its original mismatch to the observed 3D and 4D seismic data indicates the potential problem it contained. To improve its reliability, the reservoir model has to be updated to simultaneously to honour the observation of time-lapse seismic and the engineering production histories. The proposed workflow to close the static and dynamic loops becomes applicable by the virtue of its attempts to update the model according to the mismatches between the sim2seis predictions and seis2sim observations. The seis2sim-derived 3D and 4D P-impedance, are considered as “hard” data in the model updating practice. It is also noticed that the static parameter, effective porosity, dominates the calculation of effective reservoir volume, but is less efficient in altering the production loop. However, its impact on the static seismic response is vital. This implies a situation where the initial reservoir model has reached the material balance by utilising an unrealistic porosity distribution. By closing the static loop, the reservoir model turns out to retain the history match, while reproducing the observed 3D seismic.

However, due to the uncertainty in the seis2sim prediction, especially the lack of dynamic calibration, the comparison between the predictions and observations is limited to a qualitative or visual level for the Heidrun field. Despite this, the comparison is still powerful in detecting the field-scale patterns of production changes by analysing the seis2sim inversion results in a map-based approach. Moreover, because of the volumetric representation of the 4D changes, decisions could be made to open the faults by adding positive NNC cells in specified locations. This is effective because the 4D impedance represents passages of fluid migration. Overall, the allocation of different tasks to different stages seems to be a successful approach in addressing the static and dynamic problems of the given Heidrun model.

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