Automated Hydrocarbon Field Monitoring via Self-Organizing Maps
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Solution Overview
Problem
Current hydrocarbon recovery operations lack a standardized system for data retrieval and analysis across large populations of field systems, leading to incomplete data acquisition, inability to accurately predict structural health, and increased economic costs.
Innovation Solution
A method and apparatus for monitoring hydrocarbon recovery systems that involves acquiring data from each system, transferring it to a remote server, preprocessing, conducting feature selection, generating a self-organizing map, and using it for predictive maintenance and risk evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is collected from each field system individually using conventional methods, then data acquisition is simple and direct, but data completeness is poor and trends between different locations are lost
Solution Approach 1:
The patent combines data from multiple field systems into a centralized database, merging previously separate data streams into a unified structure. This allows comprehensive analysis of trends across different locations while maintaining data completeness, directly resolving the contradiction between information loss and system complexity.
Solution Approach 2:
The monitoring system is designed to handle multiple types of data from various field systems simultaneously, creating a universal platform that can process diverse data sources. This multi-functional approach enables complete data collection across different locations without requiring separate systems for each field, thereby reducing information loss while managing complexity through standardization.
2Reliability
If components are replaced only upon failure in hydrocarbon operations, then operational costs are reduced, but unplanned wellbore outages occur which severely impact recovery and economic operations
Solution Approach 1:
The system performs preliminary analysis of component conditions by monitoring data trends and applying machine learning algorithms to predict potential failures before they occur. This preliminary action enables maintenance to be scheduled in advance, preventing unplanned outages and improving system reliability without incurring the costs of premature component replacement.
Solution Approach 2:
The monitoring system continuously collects data from field systems, analyzes trends, and provides feedback on component health status. This feedback loop enables real-time assessment of component conditions, allowing operators to intervene before failures occur, thereby improving reliability while minimizing unplanned downtime through timely, data-driven maintenance decisions.
3Loss of energy
If bulk ordering of components is performed, then cost benefits are achieved, but inventory management complexity increases and may lead to premature retirement of components
Solution Approach 1:
The system provides continuous feedback on actual component performance and remaining useful life based on monitored data trends. This feedback enables precise inventory planning, ordering components in optimal quantities based on actual needs rather than bulk ordering, thereby reducing economic costs while simplifying inventory management through data-driven demand forecasting.
Solution Approach 2:
The system changes the parameter of component replacement timing from fixed schedules or bulk ordering to dynamic, condition-based timing determined by real-time data analysis. This parameter change optimizes inventory levels by ordering components based on actual degradation trends, reducing both economic costs and inventory management complexity through precise, needs-based ordering.
4Loss of information
If no standardized system is used for data retrieval, then ease of implementation is maintained, but data standardization is poor which complicates analysis and processing
Solution Approach 1:
The system implements a universal data retrieval standard that can handle multiple field systems and data formats through a single standardized interface. This universal approach improves data standardization for analysis while maintaining implementation ease by providing a unified method that simplifies the integration process compared to custom solutions for each system.
Solution Approach 2:
The system enforces homogeneous data structures and formats across all field systems through standardized retrieval protocols. This homogenization improves data quality and standardization for analysis while maintaining implementation ease by using consistent methods across all systems, reducing the complexity that would arise from handling diverse data formats.
Data Source
AI summary
Embodiments presented provide for a system and method for automating the monitoring of a large population of field systems. The recovery of data pertaining to each of the field systems may be used in structural health monitoring to increase project safety, improve maintenance of the facilities, and increase economic returns.