External-Sensor AI Control for Hydraulic Fracturing Equipment
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Solution Overview
Problem
Hydraulic fracturing operations face logistical challenges including equipment complexity, inefficiency, and environmental impact, necessitating improvements in monitoring and control systems to optimize operations and reduce downtime.
Innovation Solution
An AI system is employed to monitor and control hydraulic fracturing equipment using machine learning models trained on sensor data to detect predetermined states and perform automated functions, such as shutting down equipment or alerting operators, thereby preventing damage and reducing downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring systems are used for hydraulic fracturing equipment, then system complexity is reduced, but detection precision and operational efficiency deteriorate
Solution Approach 1:
The patent replaces traditional mechanical monitoring systems with an AI-based system that uses machine learning models to analyze sensor data. The AI model processes data from multiple sensors (temperature, pressure, vibration) to detect equipment states, substituting complex mechanical monitoring infrastructure with a more efficient AI-driven approach that achieves higher detection precision without proportionally increasing system complexity.
Solution Approach 2:
The patent introduces an intermediary AI model layer between raw sensor data and operational decisions. This intermediary component processes and interprets complex sensor patterns, translating them into actionable insights about equipment health. The AI model acts as a mediator that simplifies the relationship between numerous sensors and operational control, enabling precise detection without requiring direct complex interconnections between all system components.
2Productivity
If manual monitoring of hydraulic fracturing equipment is used, then operational simplicity is maintained, but productivity and response time worsen
Solution Approach 1:
The patent implements self-service monitoring where the AI system automatically detects equipment states and triggers appropriate responses without requiring continuous human intervention. The system monitors itself and its components, automatically identifying anomalies and initiating corrective actions. This self-service capability significantly improves productivity by eliminating delays associated with manual checking while maintaining operational simplicity through automated decision-making.
Solution Approach 2:
The patent establishes a closed-loop feedback system where sensor data continuously feeds into the AI model, which then provides real-time insights and recommendations back to the operational system. This feedback mechanism enables dynamic adjustment of equipment operation based on actual performance data, improving productivity through continuous optimization while keeping the operation simple through automated information flow.
3Reliability
If extensive monitoring data is collected from hydraulic fracturing equipment, then detection precision improves, but information processing complexity increases
Solution Approach 1:
The patent substitutes manual data processing with AI-based automated analysis. The machine learning model automatically processes extensive sensor data, filtering relevant information and identifying patterns without human intervention. This substitution maintains high detection reliability through comprehensive data analysis while reducing processing complexity by using AI algorithms that efficiently handle large datasets through learned patterns and features.
Solution Approach 2:
The patent transforms the approach to data processing by changing from raw data volume to processed information quality. The AI model converts extensive raw sensor data into meaningful parameters and insights about equipment state. This parameter transformation approach maintains high reliability through comprehensive analysis while managing complexity by focusing on extracting key diagnostic parameters rather than processing all raw data uniformly.
Data Source
AI summary
A system monitors operation of a component in a hydraulic fracturing fleet. A sensor exposed to an external environment of the component is configured to detect external indicia of the operation of the component. Memory stores an artificial intelligence (AI) model, the AI model being trained to monitor the operation of the component in the system. One or more processors are operatively coupled to the memory and the sensor. The one or more processors are configured to obtain data of the external indicia detected with the sensor; input the obtained data into the AI model; detect, with the AI model and based on the input data of the external indicia, one of a plurality of predetermined states corresponding to the operation of the component; and perform a predetermined function based on the detected one of the plurality of predetermined states.