Including "smartness" in your field does not necessarily add additional expenditures. ADNOC Offshore piloted a new well completion design combining Interval Control Valves (ICVs) in the shallow reservoir and Inflow Control Devices (ICDs) in the deeper reservoir, both deployed in a water injector well for the first time in the company. The objectives were to improve reservoir management, reduce well construction complexity and achieve one of the main business targets of cost optimization. This paper covers the subsurface study, detailed well construction design, completion deployment, well intervention and overall well performance in commingled injection mode. A multi-disciplinary study was conducted based on updated reservoir data available after the first two years of production in a heterogeneous multi reservoir field. This study showed the possibility of replacing the upper horizontal drain by a deviated perforated section. The authors identified the need of completion compartmentalization to overcome challenges such as high reservoir heterogeneity and uneven pressure depletion enforced by non selective acid stimulation. As part of the evaluation, a simulation was performed to evaluate the expected injection performance across the four zones with different combinations of ICVs and ICDs in order to cater for different injection scenarios. As a result of the integrated analysis, a new well completion design was deployed to optimize a Dual Horizontal Water Injector into a Single Smart Completion with 3 Inflow Control Valves (ICVs) covering the upper perforated zones and 14 Inflow Control Devices (ICDs) with sliding sleeves across lower lateral reservoir. Cost savings and reduction of rig time was achieved with this new completion design demonstrating very pro-active participation from all involved teams, ADNOC Offshore and Service Companies. The requirements to complete high and low permeability zones in one single well can be successfully accomplished. Firstly, mitigation of early water breakthrough is achieved by incorporating surface water injection control in high permeable zones and secondly, the injection target for the low permeable reservoir is also delivered. Building on the successful results and captured lesson learnt, this new well completion design provided the capabilities to optimize the water injection plan while reducing costs. Therefore, the project has passed the trial phase and the team proposed its implementation.
Objectives/Scope Recently Abu Dhabi National Oil Company has called Whitson (PERA), a world leading PVT modelling consultancy company, to develop a best practice methodology/tool to quantify the condensate liquid production originating from the gas cap that is produced through oil rim producers' wells. This practice is integrating simulation work with field measured data and provided for the first time a solution to an oil and gas industry challenge, which is causing a conflict of interest between shareholders especially when oil rim and the associated gas cap are belonging to different concessions. Methods, Procedures, Process The work has been done for a giant oil field with large gas cap (rich in condensate) where only the oil is being developed since the 1960s. Initially the production GOR was limited to RS, but in 2010 the development strategy changed, and the field was being produced at GOR higher than RS allowing free gas from Gas Cap (rich with condensate) to be produced with oil. The question then arised of how much condensate is being produced through the oil rim producers. The condensate allocation method makes use of all measured well test data (Qo, GOR and API) and compositional reservoir simulation results. The used EOS (equation of state) model has been tuned to all available laboratory PVT data. This method uses a history-matched, reservoir simulation model run with a "dual-EOS" that is constructed by duplicating the tuned EOS model into two identical EOS models - one for the initial gas cap, and the other one for the initial oil zone. The dual- EOS run gives identical performance to single EOS model run. The generated dual-EOS compositional wellstreams are adjusted (1) to honor exactly the historical well test GOR data for each well, and (2) to honor as best possible the historical well test APIs for each well. The resulting wellstream will honor exactly the simulation model oil rates of each well throughout history, exactly the measured well test GOR, and close-to-exact APIs for each well. The final altered well streams are processed through a 4-stage field separator, yielding the well total stock-tank oil and condensate volumes. Results, Observations, Conclusions Historical gas cap condensate volumes produced from wells completed in the oil rim has been achieved during the field history. This was made possible by using (1) well production test data (GORs and APIs), (2) results from a history-matched compositional model, (3) tracking of components originally found in gas cap and in oil rim, and (4) application of a tuned EOS model. The conclusion is that such an integrated approach will result in a consistent and quantitatively accurate volume of condensate production volumes. Novel/Additive Information An innovative quantitative approach to the accurate estimation of condensate volumes originating in the gas cap - but produced from wells completed in the oil rim zone - has been developed and validated and could be applied for other fields, in addition it is fully flexible for future enhancements if needed. This methodology will definitely save time and unnecessary discussion and will provide more consistent results that will lead to more consensus from different parties.
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.
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