Asphaltene study is now becoming a regular menu as a part of gas-injection studies (Kokal et al. Moghadasi et al. 2006). The asphaltene onset pressure (AOP) is one of the most important factors in understanding asphaltene precipitating behavior. The solid detection system (SDS) based on light-scattering technique has been quite popular and widely used in all over the world (Kokal et al. 2003(Kokal et al. , 2004Jamaluddin et al. 2000;Negahban et al. 2005;Gholoum et al. 2003;Garcia et al. 2001;Oskui et al. 2006;Gonzalez et al. 2007) to measure AOP. The simple experiments to measure AOP are usually conducted using a mixture of reservoir fluid and injection gas, and various gas-mixing volumes are assumed to be investigated. These various experimental specifications of gas-mixing volume are useful in understanding asphaltene risks during gas-injection projects. However, this type of investigation can show only a static asphaltene behavior, and sometimes might overlook true asphaltene risks.In the gas-injection pilot (GIP) project in an offshore carbonate oil field in the Arabian Gulf, the static asphaltene behavior was studied by the SDS using near-infrared (NIR) light-scattering technique. For this study, a single-phase bottomhole sample was collected from the same producing zone, but the sampling location was 90 ft shallower than the GIP area. Various combinations of mixtures were examined to measure AOP (i.e., reservoir fluid mixed with 0, 25, 37.5, 43.5, and 50 mol% injection gas). Furthermore, the numerical models were generated and calibrated with the experimental findings. To evaluate the asphaltene risks at the GIP area, the models were adjusted to the target oil composition by considering existing oil compositional gradient in the field. However, the modeling analyses showed that the operating conditions of producing wells are outside the estimated asphaltene-precipitation envelope (APE). This result was inconsistent with the field fact in which actual asphaltene deposits were observed and collected from the bottomhole of some wells in the GIP area. Thus we were obliged to recognize that our current experimental results of static asphaltene behavior overlooked the actual asphaltene risks. What is insufficient for realistic modeling? Our hypothesis is the dynamic asphaltene behavior.During a gas-injection process, the injected-gas composition is changed because of a vaporizing-gas-drive (VGD) mechanism, in which gas was enriched with the intermediate-molecular-weight hydrocarbons from reservoir oil. Our latest experiments investigated a static asphaltene behavior only; that is, it did not include this process. Therefore, the sensitivity analyses were motivated to realistically evaluate the actual APE, counting the VGD effects with the calibrated model. Various enriched-gas compositions were investigated in terms of how these enriched gases would affect APE. Consequently, it was found that the enrichment of intermediate components expanded the APE, and the operating conditions of asphaltene-problematic wells co...

Asphaltene study is now becoming a regular menu as a part of gas injection studies 1–11. The asphaltene onset pressure (AOP) is one of the most important factors to understand asphaltene precipitating behavior. The SDS (solid detection system) based on light scattering technique has been quite popular and widely used in all over the world 1,7–9,12–15. The simple experiments to measure AOP are usually conducted using mixture of reservoir fluid and injection gas, and various gas mixing volume are assumed to be investigated. These various experimental specification of gas mixing volume are useful to understand asphaltene risks during gas injection projects. However, what this investigation can show is just a static asphaltene behavior, and sometimes might overlook true asphaltene risks. In the gas injection pilot (GIP) project in an offshore carbonate oil field in the Arabian Gulf, the static asphaltene behavior was studied by the SDS using NIR (neear infrared) light scattering technique. For this study, a single phase bottomhole sample was collected from the same producing zone, but the sampling location was 90 ft shallower than the GIP area. Various combination of mixtures (sampled reservoir fluid mixed with 0, 25, 37.5, 43.5 and 50 mol% injection gas) were examined to measure AOP. Furthermore, the numerical models were generated and calibrated with the experimental findings. In order to evaluate the asphaltene risks at the GIP area, the models were adjusted to the target oil composition by considering existing oil compositional gradient in the field. However, the modeling analyses showed that the operating conditions of producing wells are outside the estimated asphaltene precipitation envelope (APE). This result was inconsistent with the field fact, in which actual asphaltene deposits were observed and collected from bottomhole of some wells in the GIP area. Namely, we were obliged to judge that our current experimental results of static asphaltene behavior overlooked at the actual asphaltene risks. What is insufficient for a realistic modeling ? Our hypothesis is the dynamic asphaltene behavior. During gas injection process, the injected gas composition is changed due to vaporizing gas drive (VGD) mechanism, in which gas was enriched with the intermediate molecular weight hydrocarbons from reservoir oil. Our latest experimental investigation of static asphaltene behavior did not include this process. Therefore, the sensitivity analyses of the VGD effects were carried out with the calibrated model to realistically evaluate the actual APE. Various enriched gas composition were assumed, and the affects of these enriched gas on APE were investigated. Consequently, it was found that the enrichment of intermediate components expanded APE, and the operating condition of asphaltene problematic wells could be explained to be inside APE. Therefore, we concluded that the dynamic asphaltene behavior must be understood for a realistic risk evaluation in the gas injection project. Introduction Background and Histories The target field was discovered in 1963 and started production in 1967. It is currently operated by ADMA-OPCO. It produces from two carbonate reservoirs (A and B) and its oil is transported and processed at the plant near an island. To maintain reservoir pressure, the dump flood water injection started in 1972, followed by powered water injection in 1978 and the crestal gas injection in 2003. In addition to this project, gas injection pilot project at the flank area has been carried out at western flank area of the field.

Intelligent completion technology is designed to optimize well production and reservoir management processes by enabling operators to remotely monitor and control well inflow or injection downhole, at the reservoir, without physical intervention. Intelligent completions consist of some combination of flow control and zonal isolation devices (typically interval control valves and packers), permanent downhole gauges, downhole control systems and digital infrastructure. The production and reservoir data acquired with downhole sensors can improve the understanding of reservoir behavior and assist in the appropriate selection of infill drilling locations and well designs. Intelligent-well technology can enable a single well to do the job of several wells, whether through controlled commingling of zones or monitoring and control of multiple laterals. This paper presents the first intelligent well installation in UAE offshore field. The well was equipped with three Interval Control Valves (ICVs) and four permanent downhole gauges (PDHG) with remote control ability to control the flow from each zone monitoring the real time gauge data. This completion enabled commingled production from three reservoirs while balancing flow contribution from each reservoir and avoiding cross-flows from one reservoir into another. Commingled production from stacked reservoirs in the same field, or a single reservoir with multiple pay intervals, has many benefits during the development of a field – higher production rates per well, cost savings from reduction in the total number of development wells, flexibility in locating surface facilities as a result of minimal footprint, etc. Wide variation in reservoir properties, coupled with the existence of natural fractures within an active water flood environment in this case, prompted the adoption of intelligent well technology to control fluid withdrawal and enhance water flood front conformance. The new completion uses a highly debris tolerant interval control valve (ICV), dual real time gauge system (PDG) and feed-through packers. This paper describes the well selection process, equipment selection, planning, and deployment procedures of the intelligent well completion. Economic drivers for this robust completion as well as the reservoir management implications of successful deployment in this pilot case well and future expansion across the entire field are also discussed.