RESERVOIR

The growing need for sustainable petroleum production and greenhouse gas reduction has driven interest in carbon dioxide (CO₂)-based Enhanced Oil Recovery (EOR) as a dual-purpose technology for increasing oil recovery while enabling carbon sequestration. This study investigates the potential of CO₂ injection for enhanced oil recovery and geo-storage in tight sandstone reservoirs within the Niger Delta Basin, Nigeria. Laboratory-based simulations and analytical modeling were used to evaluate the influence of injection rate, permeability, wettability, and fracture dip angle on oil displacement efficiency and CO₂ storage performance. Results show that at lower injection rates (0.2 mL/min), delayed breakthrough (0.85 PV) was observed with a recovery factor of 88.2%, while moderate rates (0.5 mL/min) achieved optimal recovery (91.4%) and balanced CO₂ retention (58.1%). Higher rates (1.0 mL/min) led to early breakthrough (0.55 PV) and reduced storage efficiency (54.9%). Overall, findings demonstrate that CO₂ injection enhances oil recovery by 85–93% and achieves up to 65% storage efficiency, depending on reservoir conditions. The study confirms that CO₂-EOR is technically feasible for the Niger Delta Basin, offering both economic and environmental benefits by improving hydrocarbon recovery and reducing carbon emissions.

In the petroleum industry, the accurate determination of hydrocarbons initially in place is critical for effective reservoir management and the formulation of efficient production strategies. This research focuses on the evaluation of a saturated oil reservoir in an onshore Niger Delta field through the application of Material Balance Analysis (MBA). The objectives of the study include estimating the initial oil in place, evaluating the presence and strength of an underlying aquifer, identifying the dominant reservoir drive mechanism, and recommending an optimal production scheme capable of maximizing economic recovery. For this research, the majority of the reservoir and production data were obtained from Nigerian National Petroleum Company (NNPC), which also provided access to the MBAL simulation software. MBAL is a simplified yet powerful analytical tool that applies the concept of material balance to characterize reservoir behavior. The program models the reservoir using a zero-dimensional (tank) approach, which does not explicitly account for geometry, drainage area, or well orientation, but provides valuable insights into fluid distribution and drive mechanisms by analyzing production and pressure performance. The results from the MBAL analysis indicate that the reservoir contains an estimated 250.258 MMSTB of oil initially in place, and the predicted volume of water in place to be 99788.4 MMft3 . Furthermore, the aquifer’s influence on reservoir pressure performance demonstrates that water drive is the predominant drive mechanism sustaining production. These findings provide a reliable basis for planning production strategies that optimize reservoir performance while enhancing ultimate recovery. In carrying out this research, valuable hands-on experience was gained in data handling, diagnostic plotting, and MBAL simulation. The work strengthened descriptive, technical and analytical competence in reservoir evaluation and provided deeper insight into the importance of accurate data interpretation for sustainable hydrocarbon recovery in the Niger Delta basin.

The Inflow Performance Relationship (IPR) describes the behavior of a well’s pressure and quantifies its production rate. In the petroleum industry, various IPR correlations exist today, with the most commonly used models being those of Vogel and Fetkovich.In this work, a new model was developed to predict the IPR curve using a new correlation that accurately describes the behavior of a well’s flow rate as a function of the average reservoir pressure. This new correlation was obtained using actual field cases.After the development of the new model, its validity was tested by comparing its accuracy with that of the most common IPR models, such as Vogel, Fetkovich, and Wiggins. The results of this comparison showed that the newly developed model gave the best accuracy, with an absolute error of 5.54%. The other models were ranked according to their accuracy as follows: Fetkovich, Vogel, and Wiggins, with absolute errors of 6.73%, 23.18%, and 32.3%, respectively.

Gas condensate is very important reservoir fluid because it is made up of a mixture of low density mixture of hydrocarbon in which during processing it yields other products like associated dry gas and also creates condensate oil after being extracted. A condensate reservoir exhibits a unique characteristic which make it special and at the same time difficult to recover due to liquid banking which is formed as a result of pressure and temperature change during production. In other to optimize production in a gas condensate reservoir proper attention must be paid to its phase behavior.The objective of this study is to model a gas condensate reservoir. The approach used in this study is the compositional analysis (the use of software) to determine the components of the gas condensate and compared with experimental approach which involve constant composition expansion (CCE) test and constant volume depletion (CVD) test. CCE provides information about the dew point, relative volume of fluid and condensate liquid while CVD provides information about condensate oil saturation and condensate oil and gas recovery. Finally the empirical method which is the equation of state model (EOS) using a simulation software to match the results gotten from the different approaches. The result of the study from compositional analysis simulation shows that the P-T diagram of critical pressure, temperature can be constructed and its results shows that composition significantly varies as a function of fluid phase behavior and also affects the producing sequence of a condensate reservoir. It as well pointed out the need to conduct more studies on characterization in other to be able to make the best recovery choice for optimal production from a gas condensate reservoir