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.

Integrated Asset Management (IAM) solutions provides an excellent platform for combining data from multiple sources (subsurface to markets). The level of detail incorporated in such models determines the usefulness of such tools in reservoir management. The integrated modelling tools can be as simple as combining decline profiles to very detailed 3D numerical simulation models coupled to thermodynamic pipelines and facility network with risk management logic represented externally. This study makes an effort to address the issue of differentiating flow assurance models from forecasting tools. The advantages of using IAM tools with optimal detail for questions faced by an engineer are discussed through a real field application.IAM solutions are becoming the norm in the industry to assist engineers and planners formulate sound investment decisions. Modern IAM tools have been documented to increase annual revenues in excess of 100 million USD. In this paper, we discuss the inputs required to build such IAM tools and the types of analysis typically conducted in such tools.The role of flow assurance is inevitable in field development but increasingly we see the misuse of the technology under different circumstances. Here a case study is conducted on a real field Integrated Asset Model comprising of more than 20 gas/gas condensate reservoirs in Oceania. A typical network modelling tool is utilized to construct the entire field model with fields represented through pressure based type curves/ material balance tanks connecting platforms to facilities through pipelines.The IAM model is used to conduct flow assurance study, field development optimization, short-term and long term forecasting. In each case, the effect of pipeline flow formulations, temperature tracking, velocity constraints etc., are discussed and its implications on model runtime and accuracy is presented. The understanding is important to make IAM more efficient and effective to carry out the different scenario analysis.The results show the flow assurance model comprised of detailed representation of flow behavior is useful in field development planning but they tend to be very slow due to large amount of details required. The forecasting model with necessary inputs tends to produce similar results and proves to be effective in conducting large number of scenario analysis.Recommendations are made to an engineer when looking to employ a detailed IAM model aimed at flow assurance as compared to forecasting studies. Guidelines are presented to planners/engineers on awareness of computational overhead when considering an IAM study.

This paper presents a complete integrated asset model for gas planning and, in particular, the handling of stranded gas during exploration. Uniquely, it models a gas network from the reservoir, right through to product allocation based on contractual obligations. It back-allocates through the network from the market, to ensure accurate supply and product tracking. We show how this type of model provides unprecedented confidence in field evaluation and gas planning. The market-based approach to integrated asset modelling that we used includes an optimal amount of information to deliver a fast, accurate solution. Oil and gas exploration activities do not usually involve planning for stranded or associated gas. Many discovered gas reservoirs are left undeveloped due to a lack of scenario analysis during the exploration phase. A fully integrated gas planning approach is required for effective field development planning in a timely manner. This has been a subject of intense research and development in recent years. Stranded gas reserves can be effectively developed using proactive market planning and strategic gas exploration guided by an integrated gas planning approach. As such, our model includes reservoirs, platforms, facilities, pipelines and markets to effectively understand the network deliverability. In this paper we defined multiple scenarios for bringing up gas from reservoirs, through the facilities and delivery point, right up to allocation of resources to meet the market demand. The case study deals with a market-based approach for modeling 40 gas reservoirs and associated gas products from nearby platforms. The practical constraints such as condensate handling capacities, compressor systems, and contract gas quality are integrated into the model analysis. The unique, fully integrated reservoir to market approach to IAM we demonstrate in this study provides a real breakthrough in E&P companies’' ability to model and run scenarios against their complete gas network in a relatively very short time. In particular the back-allocation from the market provides confidence around their ability to meet their contractual sales obligations and enables product tracking through the whole network.

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The case study presented in this paper will demonstrate the efficiency of an integrated subsurface and surface model running numerous what-if scenarios in a timely manner. It will also compare the results and the time taken to run the same scenarios with current conventional methods of having separate subsurface and surface modelling packages. Surface network configuration of a field depends on the field development strategies. As fields mature and reservoir conditions alter, these strategies change. This leads to changes in the configuration of the surface facilities, such as re-adjusting the facilities target rate, rerouting gas and products to different branches etc. Most of the current commercial Integrated Asset Modeling (IAM) tools, model reservoirs as a material balance tank. However, in cases of complex reservoirs, the material balance solution may not be able to model the full complexity in the behavior of the reservoirs. In such cases using a reservoir simulator to model the reservoir is a better option. Performance of the reservoirs depends on the pressure and capacity constraints of the facilities and also the configuration of the surface network. This demonstrates the importance of coupling a reservoir simulator and a network modeler to accommodate the changes to the surface network while accurately meeting the criteria of reservoir management. An integrated subsurface – surface model enables the user to evaluate the impact of changes in production policies while honoring all reservoir management constraints. In this paper we present the results of a fully integrated gas planning system coupled with a reservoir simulator, for guiding field development. We defined multiple scenarios for bringing up gas from reservoirs, through the facilities and delivery point to meet the market demand. The case study deals with a market-based approach for modelling 3 gas-condensate reservoirs. The unique, fully integrated reservoir to market approach we demonstrate in this study provides the ability to model and run scenarios against their complete gas network in a relatively very short time. In particular, the back-allocation from the market provides confidence in the ability to meet contractual sales obligations.