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05/09/2026

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23/08/2026

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23/08/2026

23/08/2026

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23/08/2026

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03/08/2026

Importance of Saturation Height Model in 3D Reservoir Modeling

In 3D reservoir modeling, accurately representing fluid distributions within the reservoir is crucial for predicting hydrocarbon volumes, planning production strategies, and optimizing recovery. The saturation height model (SHM) plays a vital role in this by linking fluid saturations (especially water saturation, Sw) to the vertical position within the reservoir's pore space, i.e., the height above the free water level (FWL) or oil-water contact (OWC).

Why Saturation Height Modeling is Important:

Captures Vertical Fluid Distribution:

Reservoirs often exhibit vertical gradients in fluid saturations due to capillary forces and buoyancy.

Water saturation tends to increase closer to the free water level, while hydrocarbons dominate higher up. SHM models this gradient explicitly, enabling realistic vertical saturation profiles rather than uniform or arbitrary values.

🟢Reflects Capillary Pressure Effects:

The saturation distribution is governed by capillary pressure, which varies with height due to pore size distribution and rock wettability.

🔵Improves Static Model Realism:

Incorporating SHM helps create a more geologically and physically consistent 3D static reservoir model, where fluid saturations vary logically within different layers and facies.

🟠Supports Integration of Multiple Data Types:

SHM allows integration of well log saturation data, core measurements, capillary pressure curves, and seismic data, improving the reservoir characterization.

🟤Defines Fluid Distribution:

Water saturation defines the fraction of pore space occupied by water vs hydrocarbons, which directly affects the fluid contacts and reservoir quality.

🔴Supports Accurate Resource Estimation:

Estimating hydrocarbon volumes requires knowing how much pore space is filled with water versus hydrocarbons. Sw controls the movable hydrocarbon saturation, affecting reserves calculation.

🟣Controls Relative Permeability and Fluid Flow:

Water saturation influences relative permeability curves, which are critical inputs for dynamic simulation models predicting production performance.

Improves History Matching and Dynamic

Simulation:

A static model incorporating realistic Sw distributions provides a better starting point for dynamic reservoir simulation and history matching, reducing uncertainty.

03/08/2026

Petrophysical Interpretation

Key Petrophysical Properties

1. Porosity (Φ) - The fraction of a rock's volume occupied by pores, usually expressed as a percentage. It determines the storage capacity of the reservoir.

2. Permeability (k) - A measure of a rock's ability to transmit fluids, typically in millidarcies (mD).

3. Water Saturation (Sw) - The fraction of pore space filled with water, influencing hydrocarbon saturation (Sh = 1 - Sw) .

4. Hydrocarbon Saturation (Shc) - The proportion of pore space filled with oil or gas.

5. Formation Resistivity (Rt) - Used in log analysis to differentiate between water-bearing and hydrocarbon-bearing formations.

Methods of Petrophysical Interpretation

1. Well Logging

Well logs are the primary data source for petrophysical interpretation. Common logging tools include:

Gamma Ray (GR) Logs - Distinguish between shale (high GR) and clean reservoir rocks (low GR).

Resistivity Logs - Identify hydrocarbon-bearing zones by measuring electrical resistance. High resistivity suggests oil or gas presence.

Density Logs - Estimate porosity by measuring electron density, which correlates with rock matrix and pore space.

Neutron Logs - Provide additional porosity estimates by measuring hydrogen concentration in the formation.

Sonic Logs - Measure acoustic velocity, helping to determine porosity and mechanical properties.

2. Core Analysis

Core samples retrieved from wells provide direct measurements of rock properties. Key analyses include:

Routine Core Analysis (RCA) - Measures porosity, permeability, and grain density.

Special Core Analysis (SCAL) - Provides data on relative permeability, capillary pressure, and wettability.

3. Formation Testing

Tools like the Repeat Formation Tester (RFT) and Modular Formation Dynamics Tester (MDT) help measure in-situ fluid pressures and obtain fluid samples for further analysis.

4. Petrophysical Modeling

Advanced software and computational models integrate log and core data to improve reservoir characterization. Common techniques include:

Multimineral Models - Solve for rock and fluid components using multiple log measurements.

Artificial Intelligence (AI) and Machine Learning Enhance petrophysical analysis by identifying patterns in large datasets. -

Applications of Petrophysical Interpretation

1. Reservoir Characterization - Helps define the spatial distribution of reservoir properties.

2. Hydrocarbon Reserve Estimation – Provides input for volumetric calculations and reserve classification.

3. Drilling Optimization – Guides well placement and completion strategies.

4. Enhanced Oil Recovery (EOR) - Assists in selecting the best recovery techniques based on rock and fluid properties.