Sedimentary Rock Classification via X-ray Tomography
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Solution Overview
Problem
Carbonate reservoirs pose challenges in classification due to their complex pore structures and varying permeability, which are not well-defined by existing methods, making it difficult to predict their producibility and requiring improved methods for interpreting log and seismic data.
Innovation Solution
The method involves generating 1, 2, or 3D images and data from micro-CT tomograms to distinguish and quantify different components within sedimentary rocks, including carbonate rocks, based on x-ray density differences, enabling classification into Reservoir Rock Types and improving the accuracy of log and seismic data interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional classification schemes (e.g., Dunham) are used for carbonate rocks, then classification based on depositional texture is achieved, but the complex pore structures and varying permeability cannot be adequately characterized
Solution Approach 1:
The invention transitions from qualitative textural classification to quantitative classification by introducing new measurement parameters (T2 distribution characteristics, porosity, permeability) that directly characterize pore structure and fluid flow properties, resolving the inability of conventional schemes to adequately describe complex carbonate pore systems
Solution Approach 2:
The invention replaces visual/textural classification methods with NMR-based physical measurement systems that objectively quantify pore structure and permeability, eliminating subjectivity and improving measurement precision for carbonate rock classification
2Reliability
If NMR measurements are used for formation evaluation, then pore space characterization is obtained, but the relationship between T2 distribution and permeability is inconsistent in carbonates compared to sandstones
Solution Approach 1:
The invention applies different classification approaches and T2 distribution analysis methods tailored to specific carbonate lithofacies and pore structure types, recognizing that a single universal relationship does not exist and that local variations in rock composition and pore architecture require customized interpretation frameworks
Solution Approach 2:
The invention segments carbonate rocks into distinct classification categories (e.g., grainstone, packstone, wackestone, mudstone) with characteristic T2 distribution patterns, allowing for more reliable permeability prediction within each category while acknowledging inter-category variability
3Measurement precision
If long echo trains with large number of echoes and long pre-polarization time are used, then carbonate pore structure is better characterized, but measurement time and complexity increase
Solution Approach 1:
The invention uses sufficient but not excessive echo train lengths and pre-polarization times optimized for carbonate-specific pore structure characteristics, achieving adequate measurement precision without the full extent of measurement parameters required for all rock types, thus reducing measurement time while maintaining necessary accuracy
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for precise classification of rock types and enhances the accuracy of permeability estimation by distinguishing components with the same mineralogy but different porosity distributions, thereby improving the evaluation of reservoir properties.
Implementation Method 1
the use of x-ray attenuation or absorption to derive a classification of samples into reservoir rock types
Implementation Method 2
Generating x-ray tomograms of one or more rock samples
Data Source
AI summary
A method of determining a parameter of interest of reservoir rock formation is described using the steps of measuring an x-ray attenuation or absorption distribution of a sample of said rock formation, identifying the mineral phase part of said distribution, and subdividing the mineral phase part of said distribution to derive classification or rock type information of said sample.