Multi-Modality Imaging System for Rock Core Hydrocarbon Estimation
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Solution Overview
Problem
Current X-ray Computed Tomography (CT) and Multi-Energy Computed Tomography (MECT) imaging modalities face challenges in accurately and efficiently differentiating materials, particularly in rock cores, due to limited contrast between hydrocarbons and solid materials, and are unable to provide fast volumetric estimation of hydrocarbon distribution in large rock cores, which is essential for operational decisions in the petroleum industry.
Innovation Solution
A system and method combining X-ray CT, X-Ray Diffraction (XRD), and Electromagnetic (EM)-based Tomography, such as Electrical Impedance Tomography (EIT) or Magnetic Induction Tomography (MIT), with machine learning to estimate material properties, enabling accurate and fast volumetric imaging of objects with multiple materials by training a prediction model using data from multiple imaging modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If X-ray CT or micro-CT scanning is used to image rock cores, then volumetric data can be obtained, but the ability to differentiate hydrocarbons from water and solid materials is limited due to small attenuation differences
Solution Approach 1:
The patent combines multiple imaging modalities (X-ray CT, neutron CT, and electromagnetic tomography) into a unified multi-modality system. Each modality provides complementary information: X-ray CT for density and atomic number, neutron CT for hydrogen content, and electromagnetic tomography for electrical properties. By merging these modalities, the system achieves superior material differentiation capability that cannot be obtained by any single modality alone, directly resolving the limitation of small attenuation differences between hydrocarbons and other materials.
Solution Approach 2:
The patent creates a composite information space by integrating data from multiple imaging modalities. The machine learning model processes composite features derived from X-ray attenuation, neutron scattering, and electromagnetic properties to distinguish hydrocarbons from water and solid materials. This composite approach enables accurate differentiation by leveraging the complementary strengths of each modality, overcoming the limitation of individual modalities.
2Measurement precision
If high-resolution scanning is performed to accurately estimate hydrocarbon distribution, then measurement accuracy improves, but scanning speed decreases making it unsuitable for fast wellsite operations
Solution Approach 1:
The patent implements a two-stage scanning approach: a fast low-resolution screening scan followed by targeted high-resolution scanning only in regions of interest. The machine learning model identifies areas with potential hydrocarbon presence from the rapid screening data, and then directs detailed high-resolution scanning only to those specific regions. This partial action strategy achieves accurate hydrocarbon distribution estimation while maintaining high overall scanning throughput, making the system suitable for fast wellsite operations.
Solution Approach 2:
The patent performs preliminary low-resolution scanning and machine learning-based analysis before conducting high-resolution scanning. The preliminary scan quickly identifies regions containing hydrocarbons, allowing the system to pre-select areas of interest. This preliminary action enables the subsequent high-resolution scanning to be focused only where needed, significantly improving overall productivity while maintaining measurement precision in critical regions.
3Productivity
If single-modality imaging is used for fast scanning, then productivity increases, but measurement precision for hydrocarbon differentiation is insufficient
Solution Approach 1:
The patent segments the imaging task into multiple functional components, each handled by a different modality. X-ray CT provides rapid density-based imaging, neutron CT delivers fast hydrogen content mapping, and electromagnetic tomography offers quick electrical property assessment. By segmenting the imaging function across multiple modalities, the system achieves both high productivity through parallel data acquisition and superior measurement precision through complementary information fusion in the machine learning model.
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 rapid estimation of hydrocarbon content in large rock cores, improving operational decision-making by combining the strengths of different imaging modalities and leveraging machine learning for enhanced resolution and accuracy.
Implementation Method 1
X-ray Computed Tomography (CT) is a common and versatile imaging modality used to differentiate materials based on their density
Implementation Method 2
Electromagnetic (EM)-based Tomography (EMT), such as Electrical Impedance Tomography (EIT)
Data Source
AI summary
In various embodiments, the present invention provides a multi-modality imaging system in combination with a novel imaging method comprising complex modeling of the modalities and machine learning. In a particular embodiment, multi-modality imaging of a large rock core is described.