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.

The aim of this study is to demonstrate the value of an integrated ensemble-based modeling approach for multiple reservoirs of varying complexity. Three different carbonate reservoirs are selected with varying challenges to showcase the flexibility of the approach to subsurface teams. Modeling uncertainties are included in both static and dynamic domains and valuable insights are attained in a short reservoir modeling cycle time. Integrated workflows are established with guidance from multi-disciplinary teams to incorporate recommended static and dynamic modeling processes in parallel to overcome the modeling challenges of the individual reservoirs. Challenges such as zonal communication, presence of baffles, high permeability streaks, communication from neighboring fields, water saturation modeling uncertainties, relative permeability with hysteresis, fluid contact depth shift etc. are considered when accounting for uncertainties. All the uncertainties in sedimentology, structure and dynamic reservoir parameters are set through common dialogue and collaboration between subsurface teams to ensure that modeling best practices are adhered to. Adaptive pluri-Gaussian simulation is used for facies modeling and uncertainties are propagated in the dynamic response of the geologically plausible ensembles. These equiprobable models are then history-matched simultaneously using an ensemble-based conditioning tool to match the available observed field production data within a specified tolerance; with each reservoir ranging in number of wells, number of grid cells and production history. This approach results in a significantly reduced modeling cycle time compared to the traditional approach, regardless of the inherent complexity of the reservoir, while giving better history-matched models that are honoring the geology and correlations in input data. These models are created with only enough detail level as per the modeling objectives, leaving more time to extract insights from the ensemble of models. Uncertainties in data, from various domains, are not isolated there, but rather propagated throughout, as these might have an important role in another domain, or in the total response uncertainty. Similarly, the approach encourages a collaborative effort in reservoir modeling and fosters trust between geo-scientists and engineers, ascertaining that models remain consistent across all subsurface domains. It allows for the flexibility to incorporate modeling practices fit for individual reservoirs. Moreover, analysis of the history-matched ensemble shows added insights to the reservoirs such as the location and possible extent of features like high permeability streaks and baffles that are not explicitly modeled in the process initially. Forecast strategies further run on these ensembles of equiprobable models, capture realistic uncertainties in dynamic responses which can help make informed reservoir management decisions. The integrated ensemble-based modeling approach is successfully applied on three different reservoir cases, with different levels of complexity. The fast-tracked process from model building to decision making enabled rapid insights for all domains involved.

The aim of this study is to demonstrate the value of a fully integrated ensemble-based modeling approach for an onshore field in Abu Dhabi. Model uncertainties are included in both static and dynamic domains and valuable insights are achieved in record time of nine-weeks with very promising results. Workflows are established to honor the recommended static and dynamic modeling processes suited to the complexity of the field. Realistic sedimentological, structural and dynamic reservoir parameter uncertainties are identified and propagated to obtain realistic variability in the reservoir simulator response. These integrated workflows are used to generate an ensemble of equi-probable reservoir models. All realizations in the ensemble are then history-matched simultaneously before carrying out the production predictions using the entire ensemble. Analysis of the updates made during the history-matching process demonstrates valuable insights to the reservoir such as the presence of enhanced permeability streaks. These represent a challenge in the explicit modeling process due to the complex responses on the well log profiles. However, results analysis of the history matched ensemble shows that the location of high permeability updates generated by the history matching process is consistent with geological observations of enhanced permeability streaks in cores and the sequence stratigraphic framework. Additionally, post processing of available PLT data as a blind test show trends of fluid flow along horizontal wells are well captured, increasing confidence in the geologic consistency of the ensemble of models. This modeling approach provides an ensemble of history- matched reservoir models having an excellent match for both field and individual wells’ observed field production data. Furthermore, with the recommended modeling workflows, the generated models are geologically consistent and honor inherent correlations in the input data. Forecast of this ensemble of models enables realistic uncertainties in dynamic responses to be quantified, providing insights for informed reservoir management decisions and risk mitigation. Analysis of forecasted ensemble dynamic responses help evaluating performance of existing infill targets and delineate new infill targets while understanding the associated risks under both static and dynamic uncertainty. Repeatable workflows allow incorporation of new data in a robust manner and accelerates time from model building to decision making.

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.