What are the advantages and disadvantages of using black oil or compositional models?

In black-oil models, reservoir hydrocarbon fluids are assumed to be comprised of two components, namely oil and gas. The model allows for certain amount of gas to be in solution with the oil at reservoir conditions. The amount of dissolved gas increases with, and is a sole function of, pressure for conditions below the bubble point. Above the bubble point pressure, the oil-component carries all the available gas in the reservoir, and a variable bubble point algorithm is usually implemented to predict conditions for the release of the dissolved gas. For this black-oil simplified model to be valid, actual oil and gas phases should maintain a fixed-composition throughout the process simulated in the reservoir.

Black oil models are fundamental in reservoir simulation due to their simplicity and efficiency, making them a popular choice for many engineers. However, their limitations in accurately representing complex fluid behaviors, such as phase changes and chemical interactions, can lead to significant inaccuracies in volatile or near-critical reservoirs. While they require less data and computational resources, the trade-off is a potential loss of detail in fluid characterization. Therefore, careful consideration is needed when applying these models to ensure they align with the specific reservoir conditions being studied.

In reservoirs containing light oil, e.g., gas condensate or volatile oils, the hydrocarbon composition as well as pressures affect fluid properties and vapor/liquid equilibrium depends on composition as well as pressure. Due to depletion there is a change in composition of the fluid due to liquid drop out. More complex fluid behavior requires treating all hydrocarbon phases as n-component mixtures and thus performing a compositional simulation. K values or and equation of state (EOS) must be used to determine hydrocarbon phase compositions. A compositional reservoir simulator calculates the PVT properties of oil and gas phases once they have been fitted to an equation of state (EOS), as a mixture of components.

Compositional models offer a sophisticated approach to reservoir fluid representation by accounting for individual components like methane and propane, allowing for accurate phase behavior predictions under varying conditions. They utilize equations of state, such as Peng-Robinson, to model complex phenomena like retrograde condensation and gas hydrates, which are critical for enhanced oil recovery strategies. However, the increased data requirements and computational demands pose significant challenges, making their implementation more complex compared to traditional black oil models. Despite these hurdles, their ability to capture intricate fluid dynamics makes them invaluable in modern reservoir engineering.

With compositional simulation—unnecessary in most cases—you gain a whole arsenal of technical jargon to throw around and seem smart. But... Got to keep the wheels turning and tick those boxes, right? Even when these complex and costly models offer no clear quantifiable edge, the alternative—simple, sound, analytical, even hand-written, methods—is just far too... straightforward. I mean, what kind of company are you if you don’t have large teams building big fancy models?

Detailed compositional models can also be important for process modelling in the surface facilities. Sometimes these parameters can be inferred from the outputs from a black oil model, but sometimes a compositional model is required to track the variation in composition. Another important consideration is how many components are required in the compositional model, often the full composition can be simplified to a handful of components and a plus fraction.

The method that describes and suits almost all fluids is the compositional, but keep in mind that you don't have to use hammer to kill a fly, if the fluid could be modelised in a simple way you should go for it.

After a quick review my production & pressure data, Pvt and other relevant data, if I ascertain the reservoir is under saturated with a high likelihood of pressure maintenance from a strong acquifer, I will default to a black model in the first instance.

In most scenarios, compositional models aren't needed. They add precision but not accuracy, and they come at a high computational cost and added complexity. Provided the black-oil PVT tables are properly generated using the same EOS you'd use in the comp. model, a BO model is perfectly suitable. In depletion fields, that is. In gas injection projects, on the other hand, there are material composition changes in the reservoir; therefore, a compositional model is justified.

When choosing between black oil and compositional models for reservoir simulation, it's essential to consider the specific objectives and characteristics of the reservoir fluid. Black oil models are generally more efficient for simpler, short-term simulations, while compositional models offer greater accuracy for complex, long-term scenarios. The decision should balance accuracy, complexity, and computational costs, and conducting a sensitivity analysis can provide valuable insights into how different models affect simulation outcomes. Ultimately, the choice hinges on the available data and the desired resolution of the simulation.

Phase behavior is relevant to every aspect of petroleum and natural gas engineering. According to the complexity of the reservoir fluid phase behavior, reservoir modeling is classified in black-oil simulation and compositional simulation. Often times, it is safe to assume that reservoir fluid behavior is only a function of pressure and independent of composition (black oil). In certain cases, the assumption of fixed oil and gas composition is no longer valid: for instance, depletion of gas-condensate reservoirs and volatile-oil reservoirs, and processes that aim at the vaporization or miscible flooding of the in-situ fluids by fluid injection (compositional models).

In addition to the technical considerations of fluid models, it's crucial to account for the evolving nature of reservoir conditions and the potential for unexpected behaviors over time. Real-world case studies can provide valuable lessons on the limitations and advantages of each model type. Furthermore, collaboration with multidisciplinary teams can enhance the understanding of reservoir dynamics, leading to more informed decisions. Lastly, continuous monitoring and updating of models based on new data can significantly improve simulation accuracy and reliability.