Carbonate reservoirs are notoriously complex and difficult to characterize, due to their inherit homogeneity. The ability to understand and model such homogeneity accurately, leads to better reservoir management and improved field development strategy in terms of well placement and optimized well patterns. Dynamic reservoir model was built from the static geological model i.e. using MDT, pressure volume temperature (PVT), routine core analysis (RCA) and special core analysis (SCAL) etc. As part of the reservoir model validation process, history matching of the model was conducted to match the observed data. After history matching was completed, tracers injection analysis and streamlines modelling was conducted in other validate the reservoir model, the well patterns and improved the full field development strategy. The full field development scheme includes water alternating gas (WAG) miscible hydrocarbon gas injection, with 5 observers covering both the flank and crestal locations. The tracer analysis included the injection of 11 different tracers in 11 different injectors, and the monthly monitoring of the producers within the well pattern. Streamline model was built simultaneously from the convention compositional model, in order to analysis and predict the different tracers break through times i.e. both observed from the field and simulated. In addition to this, streamline time of flight (TOF) analysis, the effect of tracking of different tracer components, geological and geophysical impact was evaluated, in order to improve the breakthrough time match between observed and simulated time. As a result of this analysis reservoir management improved, as the source of increasing higher GOR in specific wells were discovered. Remedial actions were recommended to help reduced the increasing high GOR in the respective wells. Also the field development strategy improved, as injector's contribution per well pattern well was quantified. As a result injection could be redistributed. In producer wells with little or no support from their respective injectors, a plan was made to ensure that such patterns could be close appropriately. This improved and maintains the voidage replacement ratio (VRR) in the field, according to reservoir guidelines. This paper describes how by using gas tracer injection and streamline modelling reservoir management and field development can be improved. In addition to the improvements in reservoir management and field development strategy, several lessons learnt and best practice were suggested from the tracer and streamline study conducted. They include but are not limited to; Different tracers can show different concentration level at breakthrough wells.Wells further away from injectors can show earlier breakthrough than well close to injectors, as it is dependent on reservoir connectivity.In analyzing model breakthrough times, the measurable tracer in the field needs to be used, as opposed to the actually first breakthrough seen in the simulator. As below such concentration cannot be measured.Operational changes in wells need to be captured properly in the simulator, in order ensure improve prediction of tracer breakthrough times.

Carbonate reservoirs are notoriously complex and difficult to characterise, due to their inherit homogeneity. The ability to understand and model such homogeneity accurately, leads to better reservoir management and improved field development strategy in terms of well placement and optimised well patterns. Dynamic reservoir model was built from the static geological model i.e. using MDT, pressure volume temperature (PVT), routine core analysis (RCA) and special core analysis (SCAL) etc. As part of the reservoir model validation process, history matching of the model was conducted to match the observed data. After history matching was completed, tracers injection analysis and streamlines modelling was conducted in other validate the reservoir model, the well patterns and improved the full field development strategy. The full field development scheme includes water alternating gas (WAG) miscible hydrocarbon gas injection, with 5 observers covering both the flank and crestal locations. The tracer analysis included the injection of 11 different tracers in 11 different injectors, and the monthly monitoring of the producers within the well pattern. Streamline model was built simultaneously from the convention compositional model, in order to analysis and predict the different tracers break through times i.e. both observed from the field and simulated. In addition to this, streamline time of flight (TOF) analysis, the effect of tracking of different tracer components, geological and geophysical impact was evaluated, in order to improve the breakthrough time match between observed and simulated time. As a result of this analysis reservoir management improved, as the source of increasing higher GOR in specific wells were discovered. Remedial actions were recommended to help reduced the increasing high GOR in the respective wells. Also the field development strategy improved, as injector's contribution per well pattern well was quantified. As a result injection could be redistributed. In producer wells with little or no support from their respective injectors, a plan was made to ensure that such patterns could be close appropriately. This improved and maintains the viodage replacement ratio (VRR) in the field, according to reservoir guidelines. This paper describes how by using gas tracer injection and streamline modelling reservoir management and field development can be improved. In addition to the improvements in reservoir management and field development strategy, several lessons learnt and best practice were suggested from the tracer and streamline study conducted. They include but are not limited to; Different tracers can show different concentration level at breakthrough wells.Wells further away from injectors can show earlier breakthrough than well close to injectors, as it is dependent on reservoir connectivity.In analysing model breakthrough times, the measurable tracer in the field needs to be used, as opposed to the actually first breakthrough seen in the simulator. As below such concentration cannot be measured.Operational changes in wells need to be captured properly in the simulator, in order ensure improve prediction of tracer breakthrough times.

Uncertainty analysis is usually conducted during field development, in order to quantify the uncertainty within the field, its impact on the recovery and the development strategy used in developing the field. However, uncertainty analysis usually focuses on field level, with little or no analysis done on the direct impact of well placement within uncertain region and it's impact on the field recovery i.e. well level uncertainty analysis. In order to investigate this and as a part the field phase development strategy, a thorough uncertainty analysis was conducted both on the field and well level. The field level uncertainty was done in order to quantify the reservoir uncertainty, and its effect on the production plateau length. The well level uncertainty was done, in order to optimize well placement and its effect of future recovery. In order quantify the uncertainty in the field, the main uncertain parameters and their respective ranges were first identified using the data available. Once defined their respective impact on stock tank oil initially in place (STOIIP) and cumulative production was calculated by sensitivity analysis. Monte Carlo simulation was then used to combine the different parameters, in order to obtain a pessimistic, base and optimistic case. Well level uncertainty analysis was conducted using FSCAT analysis, to ensure that based on the current well location, future well location and cumulative production to date, each well was capable of delivering the predicted rate forecasted. Wells within high uncertain regions or with lower potential in meet production plateau were highlighted for relocation in the field development strategy. The main uncertainty parameters identified in the field based on existing data were, oil water contact (OWC), Pressure Volume Temperature (PVT) oil formation volume factor (Bo), saturation height (rock type), structure and reservoir connectivity (poro/perm). Sensitivity analysis showed that structure has the highest impact on the STOIIP, while reservoir connectivity showed the smallest impact. The well level uncertainty analysis used a combination of conventional and streamline simulator, in order to produce a more robust form of decline curve analysis (DCA) using FSCAT analysis. This analysis allowed the calculation of the remaining oil per well that can be produced. This paper describes how the uncertainty analysis was conducted on a field and well level, by combining the conventional simulator (compositional) and streamline simulator. As a result of this uncertainty analysis, the P10, P50 and P90 cases were created. The base case (P50) showed it is possible to maintain plateau to 2043. The well level analysis showed that, each well could produce the forecasted production based on its current location in the base case (P50). Wells potential map could be used in optimizing well placement. Novel/Additive Information: Combined conventional and streamline simulation, in order to conduct a more robust uncertainty analysis and improved well placement strategy in field development.

In a volatile market such as the oil and gas industry, the ability to produce effectively and economically reservoir reserves is crucial. One of the most complex areas of a reservoir is the transition zone, especially in carbonates reservoirs. Transition zone refers to a certain height above the free water level (FWL) where both oil and water usually flow together. Reserves within transition zones vary from one reservoir to the other, in our reservoir we estimate that the transition zone contains about 20% of the total reservoir reserve. As such a study was conducted, to characterise and the defined the best development strategy, in order to produce the transition zone effectively. In characterising the transition zone various core were acquired, for routine core analysis (RCA) and special core analysis (SCAL). Cores collected were acquired to represent both the crest and flank of the reservoir structure. Using the mercury injection capillary pressure (MICP) and the reservoir quality index (RQI) from the RCA and SCAL, 5 reservoir rock types (RRT) were defined. With reservoir rock type 1 (RRT1) representing the best rock type and reservoir rock type 5 (RRT5) representing the worst rock type. Reservoir rock type 3 (RRT3) occurred mostly in the transition zone. The reservoir contains a very complex fluid, as the pressure volume and temperature (PVT) properties varies both vertically and areally, with a variation of bubble point pressure (Pb) between 1400psi and 4000psi. Flank location showed lower bubble point pressure, while crest location showed higher bubble point pressure. However, there are also certain cases where flanks show higher bubble point pressure, and crest show lower bubble point pressure. In order model this complicated behaviour in the reservoir simulator, one equation of state (EOS) i.e. the Peng Robinson (PR), with two regions were used in dynamic reservoir model. Various development strategies were investigated in order to obtain the best development strategy, including but not limited to water alternating gas (WAG), gas injection, water injection, different well spacing, different completion strategy, injection rates, WAG cycles, etc. This paper describes how the transition zone was defined in terms of static and dynamic properties, the various development strategy used and the respective recovery obtained from the most likely development strategy. The best development strategy for this field was found to be water alternating gas (WAG), with wells placed higher up in the transition zone using 6months WAG cycles.