Pump Component Demand Forecasting With Probabilistic Life Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional R and M forecasting methods for pump components in oil and gas operations are inadequate due to the lack of realistic assumptions about component usage and life metrics, leading to unreliable predictions of component failures and resource allocation.
Innovation Solution
The implementation of a probabilistic analysis using power law non-linear life models and load metrics based on pressure and cycle counts to predict component failures, allowing for the calculation of cost at risk and the optimization of resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional R and M forecasting methods are used with steady-state assumptions, then the forecasting process is simple, but the prediction accuracy is poor
Solution Approach 1:
The patent transforms the forecasting approach by changing from steady-state parameter assumptions to dynamic parameter modeling. It introduces time-varying parameters including non-linear life consumption rates, changing failure rates, and variable usage patterns that reflect real-world conditions, thereby improving prediction accuracy without requiring overly complex systems
Solution Approach 2:
The patent replaces traditional mechanical forecasting methods with probabilistic modeling and data analytics. It uses probability distributions, statistical analysis, and computational algorithms to model component failures, substituting simple assumption-based mechanics with sophisticated but manageable analytical approaches
2Reliability
If common life metrics such as pump hours or hydraulic horsepower hours are used for all components, then the forecasting method is easy to apply, but the reliability of predictions deteriorates
Solution Approach 1:
The patent segments the forecasting approach by component type, recognizing that different components have different failure patterns and life metrics. It divides the pump system into discrete components (seals, valves, pistons, etc.) and applies component-specific failure rate models and life consumption patterns, improving reliability while maintaining manageable complexity through systematic categorization
Solution Approach 2:
The patent applies local quality by tailoring forecasting parameters to specific component locations and functions within the pump system. Each component type receives customized failure rate assumptions and life metrics appropriate to its operating conditions and failure modes, rather than applying a uniform approach across all components
3Adaptability or versatility
If steady-state conditions are assumed for component usage, then the forecasting model is simple, but the adaptability to real-world conditions deteriorates
Solution Approach 1:
The patent introduces dynamics into the forecasting model by allowing parameters to change over time and with usage conditions. It models time-varying failure rates, changing usage patterns, and evolving component conditions, enabling the system to adapt to real-world variability while maintaining a structured approach that prevents excessive complexity
Solution Approach 2:
The patent applies preliminary action by establishing baseline failure rate models and life consumption patterns before actual usage data becomes available. It uses historical data and engineering knowledge to pre-calibrate models, then updates and refines predictions as real-world data accumulates, allowing gradual adaptation without requiring complete model restructuring
Data Source
AI summary
Disclosed is generating an acquisition request of a component, using a probabilistic model based on current and planned life consumption, to maintain a usable component inventory, by: determining planned consumption of the component; retrieving historical lifetime data associated with the component; determining the associated normalized current life consumed for historical specimens at failure based on a power law equivalency model; determining a probability of survival distribution associated with the component; determine a probability of future failure of each component in inventory given planned consumption of the component; determine a projected number of component failures prior to completion of the job associated with the component; generate the at least one acquisition request to acquire a quantity of new components at least equal to the projected number of specific component failures prior to completion of the job; wherein the usable inventory of the component includes at least the quantity of new components.