Real-Time Multimodal Radiometry for Rock Classification During Laser Drilling
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Solution Overview
Problem
Existing drilling technologies lack effective real-time methods for rock characterization and classification during high-power laser operations, which are crucial for understanding fluid saturation and layered formation in gas and oil exploration.
Innovation Solution
A method involving irradiation with a process beam, signal beam analysis using polarization and non-polarization arms, and machine learning techniques to determine rock classification and drilling process status, incorporating features like boosting, K-means clustering, and Support Vector Machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high-power laser operations are performed during drilling, then drilling efficiency and speed are improved, but real-time rock characterization capability deteriorates due to lack of effective monitoring methods
Solution Approach 1:
The patent introduces an optical sensor system as an intermediary between the high-power laser drilling process and the rock characterization analysis. The sensor captures scattered and radiated light from the rock target, serving as a mediator that enables real-time monitoring without interfering with the primary drilling operation. This intermediary system bridges the gap between high-power laser processing and real-time rock classification.
2Measurement precision
If optical sensors are used to detect rock properties, then real-time rock characterization is enabled, but the system complexity increases due to multiple measurement arms and machine learning components
Solution Approach 1:
The optical measurement system is segmented into distinct functional arms: a polarization arm that performs polarization-dependent intensity and spectrum measurements, and a non-polarization arm that performs standard intensity and spectrum measurements. This segmentation allows each arm to specialize in specific measurement types, improving overall measurement precision while organizing the system complexity into manageable, independent modules that can be processed separately.
3Measurement precision
If multiple polarization-dependent measurements are performed, then rock composition analysis accuracy is improved, but measurement time and processing complexity increase
Solution Approach 1:
The system performs polarization-dependent intensity and spectrum measurements continuously during the drilling process rather than as discrete sequential steps. The optical sensor operates in real-time, capturing light scattered from and radiated by the rock target throughout the drilling operation. This continuous measurement approach enables real-time rock classification without adding significant measurement time, as the measurements occur concurrently with the drilling process.
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
Enables real-time and in-situ rock type classification and drilling process monitoring, providing insights into rock composition, fluid saturation, and mechanical properties, enhancing the efficiency and accuracy of drilling operations.
Implementation Method 1
receiving a signal beam that contains light scattered from the target surface
Implementation Method 2
receiving a signal beam that contains light radiating from the target surface
Implementation Method 3
splitting the signal beam into a first portion on a polarization arm and a second portion on a non-polarization arm
Data Source
AI summary
Some implementations of the present disclosure provide a method that includes: irradiating a target surface with a process beam during a drilling process; in response to irradiating with the process beam, receiving a signal beam that contains light scattered from the target surface as well as light radiating from the target surface; splitting the signal beam into a first portion on a polarization arm and a second portion on a non-polarization arm; performing, on the polarization arm, a first plurality of polarization-dependent intensity and spectrum measurements of the first portion; performing, on the non-polarization arm, a second plurality of intensity and spectrum measurements of the second portion; and based on applying one or more machine learning techniques to at least portions of (i) the first plurality of polarization-dependent intensity and spectrum measurements, and (ii) the second plurality of intensity and spectrum measurements, determining a classification of the target surface.