This paper proposes an integrated approach to model High Permeability Streaks (HPS) using the case study of heterogeneous carbonate Reservoir B, utilizing static and dynamic data. Modelling the HPS is critical as they play an important role in fluid dynamics within the reservoir. The impact is observed from 60 years of development, where flood front movement is captured by rich density of Pulsed Neutron and recently drilled open hole logs. Injection water is overriding from tighter lower subzones (injected zones) to permeable upper subzones of the reservoir, thereby leaving the tighter lower subzones unswept. Gas cusping down to the oil zone occurs through the HPS resulting in non-uniform gas cap expansion, which leads to early gas breakthrough in producers near the gas cap. The problem with characterizing HPS is associated with their thickness- in Reservoir B it ranges from 0.5 to 2.5ft and occur in multiple subzones in the upper part of the reservoir. The standard triple combo suite of logs does not have the resolution to detect these thin HPS. In addition, the cored interval of the HPS is mainly disintegrated which is attributed majorly to well sorted grain-supported lithofacies. Therefore, sampling for porosity & permeability via Routine Core Analysis (RCA) and Capillary pressure as well as pore throat distribution using Mercury Injection Capillary Pressure (MICP) method is extremely difficult. This results in a gap in the input dataset for the static models, where the higher permeability samples are not captured in logs or cores and are therefore under-represented. Current approach to unify this gap is to use permeability multipliers, which does not honor geological trends. The HPS in Reservoir B has added complexities when compared to other regional HPS. Not only are they multiple and distributed across subzones, there is also preferential movement of water through the HPS within the same area. Of the 3 upper subzones that have HPS, in some areas, water injected in lower subzone will override the HPS in the middle and move right to the HPS in the top subzone, thereby ignoring the hierarchical flood front movement from bottom to the top. A robust workflow was developed in order to address and resolve the above mentioned uncertainties related to High Permeability Streaks. The proposed integrated workflow consisted of five stages: Developing a robust geological conceptual model Mapping spatial distribution & continuity Capturing the vertical presence in cored & uncored wells (depth & thickness) Permeability Quantification of HPS using Well Test Measurements Modelling High Permeability Streaks The paper highlights the utilization of a range of static (core, Routine Core Analysis (RCA), image logs, OH logs) and dynamic data (Pulse Neutron Logs (PNL's), later drilled Open Hole Logs, Production Logging Tools (PLTs) and well test data). Quantitative (HPS depth indicated by water saturation profile indicated by waterflood movement) and Qualitative (Flooding observed but HPS depth is uncertain) depth indicators/flags were generated from the data set and became the foundation of the modelling the HPS. The first step in the workflow is to establish a robust geological conceptual model. For Reservoir B, certain facies contribute to HPS, which are mainly leached Rudist Rudstones and Coated grain Algal Floatstones as well as well sorted Skeletal Grainstones. Based on core observations, they have confirmed vertical stratigraphic presence in each subzone (top, mid, base) which is attributed to storm events. These were consequently mapped using average thickness from core descriptions and revised using contributing facies trend maps and qualitative dynamic observations. These maps served as basis for probability trend distribution for static rock type models. The vertical presence of HPS was increased from 10% to 30% by re-introducing them in the missing core intervals using quantitative dynamic flags and thickness from isochores. Consequently, permeability were assigned in the missing section using the proposed permeability enhancement technique that honors the verified well test measurements. Based on the above improvements, the HPS intervals were mapped to the static rock type with best reservoir quality (SRT 1), which is also linked to certain geological attributes (i.e. lithofacies, diagenetic overprint & depositional environment). The enhanced permeability in the identified HPS intervals is also reflected as upgraded SRT (from lower SRT 2 to best SRT 1). The overall impact is observed by improvement of poro-perm cloud, with added control points for HPS SRT (1), which is vital for permeability modelling. The updated permeability model, captures high perm streaks in terms of vertical presence and magnitude. By introducing higher permeability in the upper subzones of the reservoir, the water overriding/gas cusping phenomena could then be mimicked in the dynamic model. The proposed methodology is an integrated workflow that maximizes the input from each disciplines (G&G, Petrophysics and Reservoir Engineering) to create a robust static model through incorporation of high permeability streaks. The use static and dynamic data, has helped to establish HPS existence/preference, which then could be used to upgrade the permeability/SRT. This will in turn lead to a better static model and a better history match in the dynamic model. It will also led to better remaining in place prediction and enable accurate prediction for future field development, especially where EOR is involved.

One of the reservoirs in a giant field in onshore Abu Dhabi has been producing for six decades. The reservoir was already saturated at the time of production commencement, with a large oil rim and a gas cap. Both water injection and lean gas injection have been relied upon to sustain production, and will play an even more prominent role for the future development of oil rim and gas cap. Due to the stakeholders’ different entitlements / equity interests in the hydrocarbons originally existed in oil rim area versus gas cap area, it is important to be able to allocate liquid hydrocarbon production and injection gas utilization among the stakeholders, based on a systematic framework. This paper presents a comprehensive comparison of two modeling-based approaches of fluid tracking for condensate allocation and gas utilization – a tracer modeling option in a commercial reservoir simulator, and a full component fluid tracking approach implemented for this reservoir. The component tracking approach is based on the idea that if individual components represented in a fully compositional reservoir model are tracked separately starting from model initialization, one can trace back the source of hydrocarbon production from both gas cap and oil rim. This approach is implemented through the doubling of the number of components in the equation of state fluid characterization – one set of components for the gas cap, and another set for the oil rim. In order to track the net utilization of the injected lean gas, additional components are needed – in this case one more component representing the lean gas, as the injected gas is a dry gas. The results of the comprehensive comparison demonstrate very clearly that these two approaches yield consistent condensate allocation and gas utilization results over the entire life of field (including history match and prediction). For condensate allocation, the hydrocarbon liquid production split depends on how the injected lean gas is tracked. For gas utilization, the injected lean gas must be tracked as a distinct component separate from both oil rim and gas cap components. The comparison also shows that although the tracer-based approach is numerically more efficient with less runtime, the full component tracking approach is simulator agnostic, and therefore can be implemented in any reservoir simulator. In addition, the full component tracking method can be used for cases where injection gas is a known mixture of oil rim and gas cap gas – something the tracer-based option cannot handle. In summary, this paper presents a first comprehensive comparison of the two (2) different fluid tracking modeling approaches, with practical recommendations on modeling-based hydrocarbon liquid production and injection gas utilization allocation in cases where the commercial framework makes such allocation necessary.