Rock Sample Thermal Conductivity Prediction via Distributed Sensing
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Solution Overview
Problem
Current methods for measuring thermal conductivity in rocks are inefficient and lack precision, particularly in determining thermal properties essential for understanding heat flow and hydrocarbon formation, which affects the generation and preservation of oil and gas in subterranean formations.
Innovation Solution
A system utilizing distributed temperature sensors and thermal sources, coupled with a machine-learning model, to determine thermal property data of rock samples by analyzing temperature changes induced by controlled heat emissions, enabling precise characterization of thermal conductivity and geological properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional thermal conductivity measurement methods are used, then the measurement process is simple, but the measurement precision and efficiency are insufficient
Solution Approach 1:
The system divides the rock sample into multiple measurement zones by placing distributed temperature sensors at different locations and depths. Each sensor measures temperature in its specific zone, allowing the system to reconstruct thermal conductivity properties throughout the entire sample through computational processing of distributed measurements.
Solution Approach 2:
The patent replaces traditional mechanical thermal conduction measurement methods with a thermal sensing system that uses controlled heat emissions and distributed temperature sensors to non-contact measure thermal properties. The machine-learning model processes temperature data to predict thermal conductivity, substituting mechanical measurement approaches with thermal field-based detection.
2Productivity
If distributed temperature sensors and machine-learning models are used, then measurement precision and speed are improved, but device complexity increases
Solution Approach 1:
The system performs preliminary thermal field generation by activating multiple thermal sources at controlled positions before measurement. Temperature sensors are pre-positioned at distributed locations, and the system pre-processes the thermal response data through machine-learning models to quickly determine thermal conductivity properties without requiring complex real-time calculations during measurement.
Solution Approach 2:
The distributed temperature sensing system serves multiple functions simultaneously: it measures temperature at multiple locations, determines thermal conductivity, and provides data for machine-learning-based property prediction. The same sensor network supports both immediate thermal characterization and long-term geological analysis, reducing the need for separate specialized measurement systems.
3Reliability
If multiple thermal sources and distributed sensors are deployed, then measurement accuracy improves, but the cost and complexity of the system increase
Solution Approach 1:
The system uses feedback from temperature sensors to continuously monitor and adjust thermal field conditions. The machine-learning model processes temperature data feedback to refine predictions of thermal conductivity properties, allowing the system to verify measurement reliability and correct anomalies through iterative analysis of the thermal response data.
Solution Approach 2:
The patent combines multiple thermal sources, distributed temperature sensors, and machine-learning processing into an integrated measurement system. The thermal sources and sensors work together as a unified sensing network, with data from all components merged and processed simultaneously to determine thermal properties, reducing the need for separate independent measurement systems.
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 rapid and accurate characterization of thermal properties and geological data, improving the understanding of rock samples and enhancing hydrocarbon exploration by predicting heat transfer rates and geological formations with high precision.
Implementation Method 1
thermal conductivity (TC) of a material may describe the rate of heat transfer through a materials' thickness
Implementation Method 2
thermal conductivity may define one of the basic transport properties that significantly affects heat transfer in a heterogenous medium
Data Source
AI summary
A method may include determining a thermal signal for a thermal analysis of a rock sample. The method may further include transmitting various commands to various thermal sources to produce various heat emissions. A respective command among the commands may cause a respective thermal source among the thermal sources to produce a respective heat emission based on the thermal signal. The method further includes determining distributed temperature data of the rock sample using various distributed temperature sensors in response to producing the heat emissions. The distributed temperature sensors may be coupled to the rock sample on a first rock surface and a second rock surface. The first rock surface may be on an opposite side of the rock sample from the second rock surface. The method may further include determining predicted thermal property data of the rock sample using the distributed temperature data and a machine-learning model.