Physiological Sensing for Hydrocarbon Decision Certainty
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Solution Overview
Problem
Current hydrocarbon management decision-making processes in the industry face uncertainty due to overlooked human factors, poor data quality, and user bias, leading to sub-optimal understanding and increased time for decision-making.
Innovation Solution
A method and apparatus that utilize human physiological responses, such as brainwave activity, eye movement, and other physiological signals, to provide real-time feedback and associate these responses with hydrocarbon-related data, enhancing the accuracy and efficiency of decision-making by visualizing certainty levels and data uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data analysis methods are used without capturing human factors, then the decision-making process is simpler and faster, but the quality and level of certainty of decisions deteriorate due to overlooked human factors and user bias
Solution Approach 1:
The system implements feedback by capturing human physiological responses (brainwaves, eye movements, muscle activity) during data analysis and feeding this information back into the decision-making process. This allows the system to account for human factors and uncertainty levels without requiring complex manual documentation, as the physiological data automatically provides insight into the analyst's confidence and cognitive state.
Solution Approach 2:
The system employs self-service by using automated physiological sensing technology to capture human factors without requiring the analyst to manually document their thought process or uncertainty levels. The physiological sensors automatically record brainwave patterns, eye movements, and muscle activity, eliminating the need for time-consuming manual commentary while still capturing essential human factors.
2Loss of information
If manual documentation of human factors is implemented, then the understanding of uncertainty improves, but the time required for data evaluation increases significantly
Solution Approach 1:
The system replaces the mechanical process of manual documentation with automated physiological sensing. Instead of requiring analysts to manually write comments or fill out forms about their uncertainty and thought processes, the system uses sensors to automatically detect brainwave patterns, eye movements, and muscle activity, which are then processed to infer human factors and confidence levels.
Solution Approach 2:
The system introduces an intermediary layer between the analyst and the decision-making process. Physiological sensors act as intermediaries that automatically capture subtle human factors without requiring direct analyst intervention. The sensors translate physiological signals into actionable data about uncertainty and cognitive state, bridging the gap between human thought processes and formal decision documentation.
3Measurement precision
If physiological sensing technology is integrated into the system, then the accuracy of uncertainty measurement improves, but the device complexity and initial time investment increase
Solution Approach 1:
The system achieves universality by using a multi-functional sensing platform that simultaneously captures multiple types of physiological data (brainwaves, eye movements, muscle activity) through a single integrated system. This allows the technology to measure various aspects of human factors and uncertainty through one unified apparatus, reducing the need for multiple separate devices and simplifying the overall system architecture.
Data Source
AI summary
A method of analyzing hydrocarbon-related data is disclosed. Data representative of a hydrocarbon entity is presented. A physiological response of a viewer of the data is sensed. The physiological response is associated with the data. The data and a representation of the associated physiological response is outputted.