MRI-Based Capillary Pressure Modeling for Rock Samples
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Solution Overview
Problem
Conventional centrifuge methods for capillary pressure determination are time-consuming, require expensive and specialized equipment, and often result in significant errors due to assumptions that cannot be simultaneously satisfied, especially for unconsolidated or friable rock samples, and do not accurately represent the wettability of reservoir systems.
Innovation Solution
A method for modeling capillary pressure curves using measurements obtained with a single centrifuge speed, employing magnetic resonance imaging (MRI) for direct water saturation measurements and applying the Brooks-Corey or van Genuchten models with an optimized error function to generate capillary pressure curves, which is up to 5 times faster than conventional methods and reduces measurement errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple centrifuge speeds are used to measure the full capillary pressure curve, then measurement accuracy is improved, but experimental time increases significantly (15 different speeds require one day to several days)
Solution Approach 1:
The patent uses MRI to create a detailed copy or map of the saturation distribution throughout the core sample. Instead of physically measuring at multiple centrifuge speeds, the MRI provides a complete spatial snapshot of fluid saturation that can be used to derive the capillary pressure curve, effectively copying the information that would otherwise require multiple physical measurements to obtain.
Solution Approach 2:
The patent replaces the mechanical centrifuge measurement system with an MRI-based measurement system. Instead of relying on mechanical centrifugal force and physical fluid collection at multiple speeds, the MRI uses magnetic resonance to directly image and quantify fluid saturation, substituting a non-mechanical field-based measurement approach for the traditional mechanical system.
2Measurement precision
If high centrifugal forces are applied to obtain high capillary pressure measurements, then capillary pressure range is improved, but rock pore structure changes in unconsolidated or friable samples
Solution Approach 1:
The patent replaces the mechanical centrifugal force system with an MRI-based measurement system that does not require applying high mechanical stresses to the rock sample. The MRI can measure saturation distribution under various conditions without physically altering the rock pore structure, thus obtaining capillary pressure information without compromising sample integrity.
Solution Approach 2:
The patent changes the measurement parameter from direct mechanical centrifugal force application to magnetic resonance signal detection. By using MRI to detect hydrogen nucleus signals from fluids in the rock pores, the system obtains saturation information through a different physical parameter (magnetic resonance properties) rather than mechanical stress, avoiding structural damage to friable samples.
3Measurement precision
If conventional centrifuge methods are used, then capillary pressure data can be obtained, but expensive and specialized equipment is required with precise speed control over a wide range of speeds
Solution Approach 1:
The patent replaces the complex mechanical centrifuge system with an MRI scanner, which is a different type of equipment that uses magnetic fields rather than mechanical rotation. The MRI system eliminates the need for precise mechanical speed control over wide ranges, special core holders for centrifugation, and stroboscopes for fluid collection, substituting a more versatile imaging platform.
Solution Approach 2:
The patent leverages the universality of MRI technology, which can be applied to various types of samples and measurement scenarios. The MRI scanner is a multi-functional device that can image different materials and provide various types of information, making it more versatile than the specialized centrifuge equipment designed solely for capillary pressure measurements.
4Device complexity
If assumptions are made in conventional centrifuge interpretation (nonlinearity of centrifugal field not significant, gravity has no effect, capillary pressure is zero at outlet), then data processing is simplified, but significant errors are introduced in measurement
Solution Approach 1:
The MRI provides a complete spatial copy of the saturation distribution, allowing direct observation and measurement without relying on simplifying assumptions. The full three-dimensional saturation map captured by MRI enables accurate calculation of capillary pressure without needing to assume that centrifugal field nonlinearity is insignificant or that gravity has no effect, as the actual saturation state is directly measured rather than inferred.
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 accurate and efficient modeling of capillary pressure curves with reduced experimental time and equipment costs, providing more reliable data for reservoir analysis by minimizing errors and eliminating the need for complex centrifuge speed variations.
Implementation Method 1
obtaining a spatially resolved measurement of fluid saturation in the sample using an external force
Implementation Method 2
Sample rotation yields a centrifugal force which will empty pores with matching capillary forces
Implementation Method 3
Capillary pressure curves provide critical information frequently used in the assessment of the economic viability of oil reservoir development
Data Source
AI summary
A method for modelling a saturation dependant property in a sample by obtaining a spatially resolved measurement of fluid saturation in the sample; determining a measured value of a saturation dependant property from the measurement; fitting a model to the measured value to obtain a model value for the measured value; and optimising the fit of the model to the measured value by minimizing an error between the model and the measured value where the error is a distance between the measured value and the model value.