A new field in offshore Abu Dhabi is currently being developed by ADMA-OPCO by combining the production from six distinct carbonate reservoirs, each of which has different characteristics. The production and injection streams from all the reservoirs will be mixed and processed using common surface facilities (Offshore Super Complex).The development scheme was optimized based on the integration of the available geological, seismic, petrophysical, and dynamic reservoir data into six separate reservoir models. The optimized development plan was defined for each reservoir using its corresponding individual simulation model, where all reservoir models are compositional with similar pseudo-components.Because the field will be developed as a gas self-sufficient field with no gas export line, all the produced gas from the six reservoirs has to be managed within the field. After removing the fuel gas, part of the total produced gas is used for gas lift; whereas, the remaining gas is compressed and re-injected into reservoirs D, F, and G to ensure full gas balance.The facilities are shared by all reservoirs, so to further enhance field-development optimization modeling, especially with gas-recycling requirements, an integrated, multi-reservoir model with surface network was constructed. In this model, the six reservoirs were fully coupled with a single surface network, including wellhead towers, production/injection pipelines, and the Super Complex layout. This was accomplished by using a next-generation simulator that allows both surface and subsurface equations to be solved simultaneously.This paper addresses the steps followed and the challenges encountered and then summarizes the main outcomes. In addition, it provides a comparison with the results obtained from the single-reservoir models when run individually.The results obtained from the integrated model were slightly different when compared to those of the individual models because of the newly developed surface EOS model and the impact of the network on the individual reservoir's performance. The integrated reservoir model (IRM) proved its advantages, especially for gas recycling, by eliminating the previous iterative runs performed to achieve the field gas balance and by its ability to make the most effective usage of both water and gas plans to maximize recovery.

ADMA-OPCO is currently in progress of optimizing the development plans for several offshore undeveloped Abu Dhabi oil fields. A common basis in these developments is the incorporation of Intelligent Oil Field Concepts to improve recovery, safety and operating costs. This requires the application of several new technologies amongst which Smart Completions is seen as a challenging opportunity. These completions incorporate a combination of Permanent Downhole Gauges (PDHG), Inflow Control Devices (ICDs) and Multi Lateral Tie Back Systems (MLTBS) in various completion configurations. One of these reservoirs addressed in this paper is targeted to be developed via a 5-Spot water injection pattern. Due to the high heterogeneity of carbonate reservoirs, premature water breakthrough is a major concern. The planned well configuration will add significantly to the development Drilling CAPEX, hence understanding and quantifying the benefits of utilizing ICDs and MLTBS technology is required. Throughout this paper, the work flow used to assess the added values of ICDs and MLTBS has been presented based on sector models carefully extracted from the full field static model. These sector models target the key areas of the field where these smart wells are planned to be drilled. A history match process has been performed for model validation and to preserve the fine scale heterogeneity across the reservoirs. Approaches used for modeling these completion components using simulation software are discussed in this paper. The results obtained from this study have shown a positive impact of MLTBS whereas the ICDs implementation has shown no significant improvement in the ultimate recovery compared to the conventional open hole completion except the establishment of uniform flow distribution form heel to toe. Additionally, several different realizations have been undertaken to investigate the key uncertainties associated with such results and these realizations were compared to results obtained from a similar study performed on an adjacent field being developed by ADMA-OPCO. Lessons learnt were captured and summarized.

This paper presents the successful use of LWD NMR and LWD resistivity image log technology to meet the challenge of placing wells in a thin reservoir with lateral facies variation without the use of radioactive sources and with simultaneous data acquisition to evaluate the wells and design their downhole inflow control devices (ICDs). A series of horizontal producer wells were planned in a thin reservoir with lateral facies variation. After drilling the wells they were completed with downhole ICDs. Optimum placement of the wells within the reservoir and data acquisition to evaluate the wells and design their completions was achieved without the use of radioactive sources, as these created an unacceptable drilling risk. Rapid and accurate processing of the data in real time and subsequent design of the ICDs was required to enable the completions to run in a timely fashion. The NMR permeability was normalized using the new calibration parameters that were developed by integrating NMR results with core data, and the same relationship has been tested in other lateral wells. Real-time NMR total porosity played a significant part in facilitating effective geosteering and well placement without the drilling risks associated with radioactive sources. In addition, the NMR provided a porosity distribution that was used to estimate a permeability index. This index was normalized using core permeability available from offset appraisal wells. The core and NMR log data in the offset wells were combined to derive the parameters for an NMR permeability relationship. The standard volumetric analysis results and the permeability index were used for identifying reservoir flow units using crossplots of normalized flow capacity versus normalized storage capacity (modified Lorenz Plots). These results were then used to develop the parameters for the ICD completions. High resolution LWD image log data was incorporated to select the best possible sections in the wells for isolation of the ICD segments. Following completion and stimulation, multiphase PLTs were run across the ICD compartments to evaluate the wells. These results were then compared against expectations and used in subsequent well completion designs. The results of the wells presented show that the chosen methodology enables the successful placement and completion of horizontal wells in this reservoir. Decisions about ICD completion design can be made in a timely fashion just after the drilling phase is complete, avoiding rig downtime. This approach has become the default procedure for the field and will be used for the bulk of the remaining producer wells in the reservoir.

Integrated reservoir simulation model (IRSM) is a common working tool in multidisciplinary teams nowadays. Usually such models constructed and used before field production startup, on the pre-project stage. But authors are commonly missing part about how good is the prediction ability of the model after first production and pressure data acquired? History matching, calibration of all parts of integrated model (reservoir, well, surface pipelines) requires constant efforts of multidisciplinary team or even building new model from scratch. In the scope of the paper the preparation of dynamic integrated reservoir simulation model and calibration based on 2 years of Phase 1 field production is covered. Commercially available reservoir simulator with fully coupled network infrastructure was used for modeling purposes. Multi-reservoir static model grid was linked with networks (oil production, gas and water injection). Real time data from well downhole gauges (including smart wells) and MPFMs were used for reservoir models HM. Additional data from surface gauges and export meter were used to calibrate pressure drop in wellbore and surface pipelines. Initially created IRSM was not able without HM stage and calibration to accurately predict the reservoir performance and pressure in the surface production system. Exceeding complexity of the model was decreasing the accuracy of the forecast even more. Only after history matching step for all wells using daily pressure/rate data and implementation of real pipeline layouts, the model was used for different purposes such as well placement and business planning. Highly efficient prediction tool was constructed in the scope of the study.

Conventional way of building PVT-models of reservoir fluids consists of creating the simplistic so-called black-oil model. Results from PVT laboratory (fluid densities, bubble-point pressure, GOR, etc…) are using as input data for PVT correlations. All other properties of the reservoir fluids are calculated from widely known PVT correlations. But simplistic black-oil models were not designed for the modelling of change of fluid state (oil to gas and vice versa). Also black-oil models are not capable to simulate effects on the oil-gas contact. In order to overcome above mentioned issues, compositional PVT-models have been introduced. These models solve equation of state (EOS) for each component. They add number of equations to the numerical simulator (lumping procedure could be introduced), but allow to accurately simulate effects on the gas-oil contact and transfet of one hydraucarbon phase to another. But usually a few results of fluid analysis are availbale and the question is: how to use all those results to build a non contradictive PVT model which is in line with all PVT experiments results? In order to overcome this challenge, the workflow for quality control (QC) and analysis of fluid PVT data is proposed. That workflow was used to analyse data from reservoirs an Abu Dhabi Offshore field. After selection and cleaning of data set, the lumping of initially broad composition (C36+) was executed. Then regression algorithms from commercially available software have been used to match lumped composition properties with results of PVT experiments. Results of PVT experiments from other wells were used as a blind test for derived composition.