Accurate prediction of permeability is vital for maximizing hydrocarbon recovery and optimizing reservoir management strategies. However, conventional methods for permeability estimation often rely on complex laboratory analysis or empirical correlations, which can be time-consuming, expensive, and limited in their accuracy. Introducing Artificial Intelligence (AI): The rise of Artificial Intelligence (AI) provides a powerful new approach for overcoming these limitations. AI techniques, particularly machine learning and deep learning, can analyze vast datasets of geological and petrophysical data to identify complex patterns and relationships that influence permeability. This essay explores the potential of AI in predicting permeability within reservoirs. Key Points to be Addressed: The drawbacks of conventional methods for estimating permeability in reservoirs. The advantages of using AI for analyzing complex geological data. Different AI approaches suitable for permeability prediction (e.g., machine learning algorithms, neural networks). Applications of AI-based permeability prediction in reservoir characterization and management. Challenges and future directions in utilizing AI for reservoir permeability prediction.Core analysis is the traditional method, but can be costly or unavailable. Well logs are an alternative, used in correlations to infer permeability These correlations have limitations because the pore throats (which determine permeability) are not the same as what porosity or water saturation measure Core analysis is the traditional method, but can be costly or unavailable. Well logs are an alternative, used in correlations to infer permeability These correlations have limitations because the pore throats (which determine permeability) are not the same as what porosity or water saturation measure

1. PREDICTION OF
PREMEABILITY BY
USING AI
By: Ahmed said
ID:157511
SUPERVAISOR: PROF ATTIA ATTIA

2. What is Permeability
• A porous medium's permeability refers to how easily fluids
move through it.
• It's analogous to electrical conductivity.
• A crucial rock property for petroleum engineers, essential for
effective completion, production, and reservoir management.

3. Types of Permeability
• Absolute permeability: Intrinsic property of a rock, measured
when the medium is fully saturated with one fluid only. It
represents the ease of fluid flow through the rock itself.
• Effective permeability: The ability of a rock to allow the flow of
a specific fluid in the presence of other fluids. It considers the
saturation levels of different fluids within the rock.
• Relative permeability: Ratio between the effective permeability
of a specific fluid to the absolute permeability. It reflects the
relative ease of flow for a particular fluid compared to others,
considering saturation levels.

4. Traditional Permeability Assessment
• Core analysis is the traditional method, but can be costly or
unavailable.
• Well logs are an alternative, used in correlations to infer permeability
• These correlations have limitations because the pore throats (which
determine permeability) are not the same as what porosity or water
saturation measure
• Core analysis is the traditional method, but can be costly or
unavailable.
• Well logs are an alternative, used in correlations to infer permeability
• These correlations have limitations because the pore throats (which
determine permeability) are not the same as what porosity or water
saturation measure

5. Factors Affecting Permeability
• Rock properties:
• Porosity: Higher porosity generally leads to higher permeability.
• Pore size and distribution: Larger and well-connected pores allow for easier fluid
flow.
• Mineral composition: Different minerals can have varying effects on permeability.
• Fluid properties:
• Viscosity: Less viscous fluids flow more easily through smaller pores.
• Density: Differences in fluid density can affect flow dynamics.
• External factors:
• Temperature: Increased temperature can decrease fluid viscosity, enhancing
permeability.
• Pressure: Pressure gradients drive fluid flow through the medium.

6. Challenges in Permeability Prediction
• Darcy's Law describes permeability as a dynamic property,
requiring fluid flow for measurement.
• The complex pore geometries in rocks create challenges for
accurate upscaling.
• Current correlations based on well logs have inherent
limitations.

7. AI and Machine Learning to the
Rescue
• AI and Machine Learning (ML) have revolutionized many
industries, including petroleum engineering.
• ML can learn complex relationships between well log data and
permeability.
• Deep learning (a subset of ML) can analyze 3D micro-CT
scans, potentially revolutionizing predictions.

8. Pore-Scale Modeling Limitations
• Traditional pore network modeling (PNM) uses simplified pore
space representations.
• The Lattice Boltzmann Method (LBM) calculates directly on the
pore space for more accurate modeling, but it's computationally
expensive.

9. Machine Learning for Permeability
Prediction
• Well log data can be used as input for ML models
• ML models can outperform traditional correlations
• This study compares different ML methods (Random Forest,
ANN, Gradient Boosting, SVM) to optimize the prediction
process

10. Conclusion
• Summarize the advantages of using ML for permeability
prediction.
• Discuss potential for cost and time savings.
• Highlight areas for future research and improvements.