Wellhead Component Life Prediction for Fracturing Wear Planning
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Solution Overview
Problem
Hydraulic fracturing system components in wellheads have short operational lives due to operational parameters, leading to safety issues and increased costs and downtime, as existing systems lack effective methods for predicting component lifespan and optimizing configuration.
Innovation Solution
A method and apparatus using a computer to predict the remaining operational life of wellhead system components by receiving data on their location, operational parameters, and historical wear patterns, allowing for improved safety, reduced downtime, and cost optimization through RFID tagging and data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If components are operated under high operational parameters to maintain production, then productivity is improved, but component operational life decreases and reliability deteriorates
Solution Approach 1:
The system performs preliminary assessment of component conditions and predicts remaining operational life before actual failure occurs. By using sensors to monitor operational parameters and comparing them against historical data and wear models, the system proactively identifies components that are approaching failure thresholds, allowing for planned maintenance before reliability deteriorates to critical levels.
Solution Approach 2:
The system implements continuous feedback loops where operational parameters are monitored in real-time, component conditions are assessed, and this information feeds back into the prediction models. The system adjusts maintenance schedules and operational parameters based on actual component performance feedback, creating a closed-loop system that balances productivity demands with component reliability preservation.
2Reliability
If components are replaced frequently to ensure safety and reliability, then reliability is improved, but downtime and operational costs increase
Solution Approach 1:
The system enables self-service through automated monitoring and assessment capabilities. Sensors continuously collect operational data, the system automatically assesses component conditions using embedded algorithms, and generates maintenance recommendations without requiring constant human intervention. This autonomous operation reduces the frequency of unnecessary maintenance interventions while ensuring safety-critical components are identified and replaced only when truly needed.
Solution Approach 2:
The system performs preliminary identification of components requiring replacement before actual failure occurs. By predicting remaining operational life and comparing it against safety thresholds, the system schedules replacements in advance during planned maintenance windows rather than performing emergency replacements during unplanned downtime, thereby improving reliability while minimizing operational disruption.
3Reliability
If comprehensive monitoring and assessment systems are implemented to predict component life, then reliability is improved, but device complexity and initial costs increase
Solution Approach 1:
The system applies segmentation by dividing the monitoring and assessment functionality into modular components. Different sensor types monitor specific operational parameters independently, assessment algorithms analyze specific component types separately, and prediction models are developed for individual component categories. This modular architecture improves reliability through comprehensive monitoring while managing complexity by allowing incremental implementation and easier maintenance of discrete functional modules.
Data Source
AI summary
According to one aspect, data identifying a component is received, wherein the component is part of a system associated with a wellhead. A location at which the component is positioned relative to one or more other components is identified. The useful remaining operational life of the component is predicted based on at least an operational parameter specific to the location, and the operational history of the component or one or more components equivalent thereto. According to another aspect, a model representing at least a portion of a proposed system associated with a wellhead is generated, the model comprising a plurality of objects, each of which has a proposed location and represents an existing component. The useful remaining operational life for each object is predicted based on an operational parameter at the corresponding proposed location, and data associated with the respective operational history of the existing component.