On the Pore Geometry and Structure Rock Typing
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View abstract on PubMed
Summary
This summary is machine-generated.This study provides the first physical interpretations of pore geometry and structure (PGS) rock typing, enhancing reservoir characterization and enabling accurate permeability predictions using resistivity and rock type. This advances oil and gas exploration and CO2 storage.
Area Of Science
- Petroleum Geoscience
- Reservoir Engineering
- Geophysics
Background
- Accurate rock typing is crucial for reservoir development, impacting hydrocarbon reserve predictions and CO2/hydrogen storage capacity.
- The pore geometry and structure (PGS) method offers improved predictions of connate water saturation and capillary pressure grouping compared to traditional methods.
- Existing PGS rock typing lacks comprehensive physical interpretations, hindering its full application.
Purpose Of The Study
- To provide the first holistic physical interpretations of the pore geometry and structure (PGS) rock typing method.
- To establish a physics-inspired, data-driven framework for understanding PGS rock typing using extensive hydraulic property data.
- To develop a novel method for permeability prediction based on electrical resistivity and rock type.
Main Methods
- Compilation and analysis of thousands of experimentally measured hydraulic properties (permeability, porosity, specific surface area, pore size).
- Application of analytical theory and the Kozeny-Carman equation for physical interpretation of PGS rock types.
- Comparative analysis of PGS with hydraulic flow unit rock typing.
Main Results
- Demonstrated why the PGS method surpasses hydraulic flow unit rock typing.
- Clarified causal versus indirect relationships between hydraulic properties, rock type, and electrical resistivity.
- Proposed a novel method for predicting permeability from resistivity and rock type number.
- Offered insights into avoiding recursive predictions in permeability.
Conclusions
- The physics-inspired data-driven interpretation of PGS rock typing provides significant advancements in reservoir characterization.
- The developed methods enhance the accuracy of hydrocarbon reserve estimation and underground storage capacity predictions.
- This work lays the foundation for more reliable reservoir modeling and management.
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