Downhole Tool Health Prognostics via Time-to-Failure Modeling
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Solution Overview
Problem
Downhole exploration and production operations are hindered by tool malfunctions, which require costly halts for repair or replacement due to the lack of effective predictive maintenance for tool health prognostics.
Innovation Solution
A system and method that utilize a database to store life cycle information, statistical equations, and a processor to build and validate a time-to-failure model for tool health prognostics, enabling informed selection and management of tools for deployment, including calibration and validation based on environmental and operational parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tools are deployed in downhole environments without predictive maintenance, then operational simplicity is maintained, but tool reliability deteriorates due to unexpected malfunctions requiring costly halts for repair or replacement
Solution Approach 1:
The system performs preliminary actions by building time-to-failure models using historical life cycle information and environmental parameters before tools are deployed. These models predict potential failures in advance, allowing maintenance to be scheduled proactively rather than reactively, thus improving reliability without requiring complex real-time monitoring systems during operation
Solution Approach 2:
The system segments the tool lifecycle into distinct phases (design, deployment, operation, retirement) and creates separate time-to-failure models for different tool components based on their specific failure modes. This segmentation allows the complexity to be distributed across multiple manageable models rather than requiring a single comprehensive complex system
2Measurement precision
If statistical models are calibrated using historical life cycle information, then measurement precision improves for failure prediction, but device complexity increases due to model calibration and validation requirements
Solution Approach 1:
The system changes parameters by identifying and focusing on the most critical environmental and operational parameters that influence tool failure, rather than attempting to model all possible variables. This parameter selection and calibration process improves prediction precision by concentrating computational resources on the most significant factors affecting tool reliability
Data Source
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AI summary
A system and method to determine health prognostics for selection and management of a tool for deployment in a downhole environment are described. The system includes a database to store life cycle information of the tool, the life cycle information including environmental and operational parameters associated with use of the tool. The system also includes a memory device to store statistical equations to determine the health prognostics of the tool, and a processor to calibrate the statistical equations and build a time-to-failure model of the tool based on a first portion of the life cycle information in the database.