EXPLICACIÓN DE LA ACTIVIDAD 5 Tema: Dibujar el paisaje

Watkins et al. (1992) highlight the importance of user interaction in the history matching process. In their context, the reservoir engineer’s decision in updating the reservoir model may need to be added as an updated too. These strategic changes in updating the model are usually caused by the advances in understanding the fundamental geology, geophysical or reservoir engineering data. Driven by the 3D seismic data, the interpretation and modelling of this information is traditionally implemented in an integration workflow called 3D seismic-to-simulator modelling (3D seis2sim). The prototype of 3D seis2sim can be seen in Figure 1.8, in which the 3D seismic data is processed, interpreted, and inverted into petro-physical parameters for reservoir modelling. The 3D seis2sim modelling had become a standard exercise in the industry by the mid 2000’s with the maturation of quantitative 3D seismic interpretation techniques, while most of the case studies were carried out under names such as “seismic reservoir characterizations” or “integrated reservoir modelling”. However, one major drawback of the 3D seis2sim process is the lack of ability to validate the resultant model in terms of the correctness in replicating the original seismic data. The lack of

15

this feedback mechanism leads to a loophole, which cannot be closed without introducing a 3D simulator-to-seismic modelling workflow (3D sim2seis). Practically, the 3D sim2seis can generate synthetic 3D seismic responses of the reservoir model, based on its initial set-ups. The calculation of sim2seis includes the rock-physics modelling via the Gassmann substitution, stress modelling and the seismic modelling. The synthetic results from sim2seis can be compared to the seismic observation. Moreover,t he discrepancy highlighted in this comparison can lead to efficient 3D CtL updates of the reservoir model, since the observed 3D seismic data have captured the realistic distributions of reservoir properties such as porosity, NTG (net-to-gross) and possibly the permeability estimations.

Nonetheless, a natural expansion of the 3D CtL with sim2seis and 3D seis2sim is to incorporate the 4D data (Figure 1.9). Unlike the 3D seismic, 4D data capture the dynamic changes of the reservoir which mainly lead to the update of dynamic- performance-related parameters, such as flow barriers and permeability, in the reservoir model. The 4D loop is jointly closed by 4D sim2seis and 4D seis2sim workflows, where 4D sim2seis forward models the synthetic 4D elastic and seismic responses according to the simulated pressure and saturation variations, while the 4D seis2sim aims to invert the 4D seismic into more meaningful elastic differences. 4D CtL is established only if the inversion and forward modelling are designed in a consistent scheme, otherwise their results are not compatible. Nevertheless, 4D CtL is particularly robust in detecting the fundamental discrepancy across the domains, leading to a more target-orientated model-updating strategy.

16

The potential of the seis2sim exercise has been applied to many fields. In the literature, most of the relevant case studies are not directly under the name of CtL, but in fact they all tend to close some of the selected loops by pre- or post-processing the 3D and 4D seismic data. Zachariassen et al. (2006) conducted the 3D and 4D elastic inversion for the Oseberg field, the results of which led to a probabilistic classification of the reservoir sand and various facies according to the 3D and 4D data (Figure 1.10). Model realisations generated from the sand probability cubes are ranked according to the visual comparison between the synthetic and observed 4D seismic responses. Nonetheless, their 4D seis2sim work was only validated by checking the 4D and production matches, leaving the static loop loosely closed. Ingrid et al. (2009) extended the 3D inversion work of Wijngaarden et al. (2007) to 4D and applied it to the Troll West field. The porosity and clay distribution of the geomodel were updated by the 3D inversion results, while the initial and produced oil-water contacts were inferred by the inversion of 3D and 4D seismic. They also updated the depth location of the model accordingly (Gjerding et al., 2010). However, their 4D seis2sim work did not show the “feedback loop”, therefore the update was not verified. Leguijt (2001, 2009) introduced a probabilistic Bayesian approach to invert for 3D static reservoir properties. Floricich et al. (2010, 2011) applied the method to the Schiehallion field and one other North Sea field to update the NTG and facies distributions in the reservoir models, which led to better static seismic responses. They carried out the inversion of the Schiehallion 4D data, in which the eight time-lapse vintages were simultaneously inverted into pressure and saturations over time and compared with the model predictions. This series

Figure 1.10 Conceptual sketch of the 3D (a) and (4D) classification. In (a) low AI and low

VP/VS ratio is classified as sand while in (b) decrease in both AI and VP/VS reflect gas flooding sand. (c) shows the final groups of sand, according to the 3D and 4D classification (modified from Zachariassen,2006).

17

of advances have depicted the modern evolution of the seis2sim methodology. In addition, Kleemeyer et al. (2012) utilised the same technique for the Astokh field. The inverted pressure and saturation were thresholded to highlight the essential 4D changes which were to be used for the next level of the history matching process. In addition, the dynamic loop can also be closed by extracting unique geomechanical attributes (e.g. time-shifts, time-strain, or relative velocity changes) from 4D seismic. De Gennaro et al. (2008) and Dutry (2013) discuss the workflow through which the seismic geomechanical data can be incorporated into the reservoir model, detecting the fluid barriers and updating the fault transmissibilities.