Pipeline Risk Modeling Using Multi-Source Inspection Data
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Solution Overview
Problem
Current pipeline risk models face challenges in accurately identifying and managing uncertainties and ambiguities in pipeline failure risks due to insufficient or uncertain data, leading to inefficiencies in risk assessment and decision-making for pipeline operators.
Innovation Solution
A computational risk modeling system that integrates data from in-line inspection vehicles, external companion devices, and geo-data to predict failure modes and initiate risk monitoring, utilizing a Cluster Machine with transition functions and machine learning to process large datasets and reduce uncertainties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional risk models are used with limited data, then the system is simpler to operate, but the measurement precision and reliability of risk assessment deteriorates
Solution Approach 1:
The patent combines multiple data sources including in-line inspection data, external sensor data, geo-data, and operational data into a unified risk modeling system. This integration of diverse data streams enhances measurement precision by providing comprehensive inputs for risk assessment while managing complexity through systematic data fusion approaches
Solution Approach 2:
The risk modeling system is designed to process multiple types of data (inspection data, sensor data, geo-data, operational data) through a unified framework that can handle various failure modes and risk scenarios. This multi-functional approach improves assessment accuracy across different pipeline conditions without requiring separate specialized models for each data type
2Reliability
If comprehensive data integration is implemented, then the reliability of risk assessment improves, but the device complexity and data processing requirements worsen
Solution Approach 1:
The patent segments the risk modeling system into distinct functional modules: data acquisition from multiple sources, data processing and integration, risk calculation engines, and output generation. This segmentation improves reliability by ensuring each component can be independently validated and maintained, while managing overall system complexity through modular architecture
Solution Approach 2:
The system employs intermediate data processing layers that transform raw data from various sources into standardized formats suitable for risk analysis. These intermediary processing steps ensure data quality and consistency, improving reliability while preventing complexity from propagating through the entire system by containing transformation logic in dedicated intermediate layers
3Productivity
If advanced computational modeling is used, then the productivity of risk assessment improves, but the loss of information and data requirements worsen
Solution Approach 1:
The system performs preliminary data collection and validation from multiple sources before initiating the main risk assessment process. In-line inspection data, external sensor data, and geo-data are gathered and pre-processed in advance, ensuring that all necessary information is available before computational modeling begins, thereby improving productivity without risking information loss
Data Source
AI summary
A system for operation and integrity management of a pipeline stores field data obtained from an operational system, and in-line data obtained from an in-line inspection vehicle and external data from a video camera on an external companion device. The system performs data processing on the in-line data and field data to generate input for risk modeling and performs risk modeling of the pipeline using the input to predict a risk of one of a plurality of failure mode states at a portion of the pipeline. The system may initiate risk monitoring for the portion of the pipeline by the operational system and the in-line inspection vehicle.