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Production of blended cements in which Portland cement is combined with supplementary cementitious materials (SCM) is an effective strategy for reducing the CO2 emissions during cement manufacturing and achieving sustainable concrete production. However, the high Al2O3 and SiO2 contents of SCM change the chemical composition of the main hydration product, calcium aluminate silicate hydrate (C–A–S–H). Herein, spectroscopic and structural data for C–A–S–H gels are reported in a large range of equilibration times from 3 months up to 2 years and Al/Si molar ratios from 0.001 to 0.2. The 27Al MAS NMR spectroscopy and thermogravimetric analysis indicate that in addition to the C–A–S–H phase, secondary phases such as strätlingite, katoite, Al(OH)3 and calcium aluminate hydrate are present at Al/Si ≥ 0.03 limiting the uptake of Al in C–A–S–H. More secondary phases are present at higher Al concentrations; their content decreases with equilibration time while more Al is taken up in the C–A–S–H phase. At low Al contents, Al concentrations decrease strongly with time indicating a slow equilibration, in contrast to high Al contents where a clear change in Al concentrations over time was not observed indicating that the equilibrium has been reached faster. The 27Al NMR studies show that tetrahedrally coordinated Al is incorporated in C–A–S–H and its amount increases with the amount of Al present in the solution.

When dealing with a tight reservoir of basinal settings, depositional facies can show a lot of overlap in their properties. This onshore tight carbonate reservoir, is almost exclusively composed of wackestones to mudstones. The main reservoir quality influencer is the diagenetic overprint reflected in the cementation and dissolution phases, and directly influencing the porosity variation. This made the classical generation of facies trend maps to constrain the properties less reliable, due to poor property discrimination. It demanded the utilization of seismic data to attempt to constrain the properties as much as possible. With the static rock types being sensitive to porosity variation, and porosity having a good correlation to the acoustic impedance, the seismic data may provide key information to obtain lateral trends guiding the properties distribution. A supervised neural network approach was used to provide a link between the lateral variation of reservoir quality and seismic data. The workflow relied on combining deterministic inversion and volume attributes to extract the information. This was followed by multi-attribute analysis to find the best combination of attributes to input into the neural network. Four main challenges had to be tackled. Firstly, an acquisition footprint masking the signal in the seismic data had to be reduced. Secondly, the integration of the seismic inversion output and well information in the modeling workflow. Thirdly, generating and finding the best combination of seismic attributes to fit the given logs. Lastly, adapting the low resolution of the seismic data to the subzones of the reservoir in order to capture the vertical variation. The resulting predicted impedance cube is validated with actual porosity data from wells. The result has proven to be beneficial in building an integrated 3D property model, giving more confidence in the predictability of the properties in horizontal wells and volume estimates. This is seen in the blind tests applied, which show a good alignment between actual and predicted properties.

The scope of this publication is to capture the main learnings from the application of ensemble-based modelling of three giant geologically complex carbonate reservoirs onshore and offshore Abu Dhabi, simultaneously considering static and dynamic uncertainties. The ability to consider these uncertainties in prediction studies is highlighted, leading to optimal economic decisions to be taken in the subsequent stages of development of these critical assets. For each oilfield, an integrated static-to-simulation modelling workflow was built in collaboration with the relevant asset teams, capturing their knowledge and expertise in the generation of an ensemble of cases, equiprobable and plausible from the point-of-view of geology and dynamic characteristics. Each of them has specific geological and hydrodynamic challenges to be taken into account, from the spatial distribution of the static rock types and of their heterogeneous petrophysical properties, the impact on the flow of high permeability streaks and stylolites, to the behavior of the aquifers. Each ensemble of cases is subsequently utilized to assimilate the production data using an iterative ensemble Kalman method yielding ensembles with the ability to reproduce the observed reservoir dynamics. These calibrated ensembles can subsequently be used for predictions and economic evaluations considering all remaining static and dynamic uncertainties. After data assimilation, the ensembles showed reasonable match to field and well historical data for the three different studies. There were significant learnings in the static and dynamic updates and uncertainty reduction that occurred during the data assimilation. They provided statistical insights with respect to the reservoir characterization, such as increased high permeability streaks probability in specific zones or reduced uncertainty surrounding the porosity/permeability transforms for each rock type, and fluid dynamic, such as fault behavior. These learnings will benefit the team to further their understanding and improve future modelling activities. Multiple development scenarios were considered for each asset and the simulated ensemble results were brought for economic evaluation under static and dynamic uncertainties. This provided representative estimates of the net present value of each scenario and eventually, a complete understanding of the potential outcome, allowing for informed decisions. Finally, another important benefit of working with calibrated ensembles was shown in its ability to identify the most likely bypassed area from a probabilistic standpoint, allowing to take confident decisions for new target identifications to increase the ultimate field recovery. While addressing the future challenges of major carbonate oilfield developments and to ensure an optimal decision-making process, the asset team has to consider the complexity of the underlying geological environment, the dynamics of the fluid in the reservoir and their associated uncertainties. An integrated ensemble-based approach from static to simulation with fast data assimilation and economic evaluation of possible scenarios proved to be key to reach all of these objectives.