Sun. Sep 24th, 2023

In the ever-evolving field of petroleum engineering, understanding reservoir properties is crucial for optimizing oil and gas recovery. One such property that plays a vital role in this process is the saturation height function (SHF). The SHF describes the relationship between capillary pressure and fluid saturation within a porous rock reservoir. In this article, we will explore diverse methods for deriving saturation height functions from capillary pressure data, shedding light on the techniques used by petroleum engineers to unlock the secrets hidden beneath the earth’s surface.


The accurate estimation of saturation height functions is fundamental in reservoir characterization. These functions are essential for predicting fluid distribution, flow behavior, and, ultimately, optimizing hydrocarbon recovery. Let’s delve into petroleum engineers’ various methods to derive saturation height functions from capillary pressure data.

Importance of Saturation Height Functions

Saturation height functions provide insights into how fluids flow within a reservoir. They are indispensable for predicting and managing reservoir performance. Understanding capillary pressure-saturation relationships aids in identifying sweet spots for drilling, enhancing production strategies, and minimizing risks associated with fluid displacement.

Traditional Laboratory Methods

Core Flooding

One of the classical methods involves core flooding experiments. Porous rock samples are subjected to varying pressures and fluid injections, allowing engineers to observe how capillary pressure affects fluid saturation. This method provides valuable data but is time-consuming and limited to small core samples.

Centrifuge Capillary Pressure

Centrifuge capillary pressure tests involve spinning a rock core sample at high speeds to simulate capillary pressure effects. It offers quicker results than core flooding but has limitations in replicating complex reservoir conditions.

Petrophysical Models

Leverett J-function

The Leverett J-function is a commonly used model for SHF estimation. It relates saturation and capillary pressure using an empirical equation, making it suitable for quick estimations but less accurate for heterogeneous reservoirs.

Brooks-Corey Model

The Brooks-Corey model is another empirical model that provides insights into capillary pressure-saturation relationships. It is versatile but may require calibration for specific reservoirs.

Digital Rock Physics

X-ray CT Scanning

X-ray computed tomography (CT) scanning is a non-destructive method that provides 3D images of rock samples. These images are used to analyze pore structures and estimate saturation height functions accurately.

Pore-scale Modeling

Pore-scale modeling employs numerical simulations to replicate fluid behavior at the microscopic level. This advanced technique enables engineers to derive SHFs with high precision.

Machine Learning Approaches

Support Vector Machines

Machine learning algorithms, such as Support Vector Machines (SVM), can learn complex patterns from capillary pressure data. SVMs offer the advantage of adaptability to different reservoir conditions.

Neural Networks

With their ability to handle vast datasets, Neural networks have shown promise in SHF derivation. They excel in capturing non-linear relationships between capillary pressure and saturation.

Integration of Field Data

Well Logs

Well, logs provide valuable in-situ data for SHF estimation. Integrating well-log information with laboratory data enhances the accuracy of saturation height functions.

Production History Matching

Production history matching involves comparing actual production data with simulated reservoir models. This iterative process helps refine SHFs over time.

Challenges and Considerations

Deriving saturation height functions is challenging. Engineers must contend with data quality issues, non-uniqueness in modeling, and uncertainties associated with reservoir heterogeneity.

Applications in Reservoir Management

Saturation height functions find applications in reservoir management, including reservoir simulation, well placement, enhanced oil recovery, and decision-making processes crucial to maximizing hydrocarbon extraction.


In conclusion, the diverse methods for deriving saturation height functions from capillary pressure data provide valuable tools for petroleum engineers. These methods range from traditional laboratory techniques to cutting-edge machine-learning algorithms and digital rock physics. Each approach has advantages and limitations, but when combined and carefully integrated, they contribute to a more comprehensive understanding of reservoir behavior and improved reservoir management.

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