Load Data Bucketing for Well Equipment Lifetime Prediction
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Solution Overview
Problem
Existing methods for storing load data for lifetime prediction of equipment used in well operations are inefficient, leading to increased memory usage and costs, and do not accurately account for the varying loads and failure mechanisms of different equipment types.
Innovation Solution
The method involves discretizing load data into optimized buckets based on a lifetime model and load data distribution, using application-specific integrated circuits (ASICs) to store and process data, and applying algorithms like the Arrhenius Curve and Gauss-Distribution to minimize memory usage while maintaining accurate lifetime estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If load data is stored with high resolution and detail for accurate lifetime prediction, then measurement precision is improved, but memory usage increases
Solution Approach 1:
The patent segments the continuous load data into discrete buckets based on load magnitude ranges. Instead of storing every individual load measurement, the system divides the load spectrum into multiple buckets and stores only the count of occurrences in each bucket, significantly reducing memory requirements while preserving the distribution characteristics needed for lifetime prediction.
Solution Approach 2:
The patent transforms the load data from its original continuous form into a discretized representation by changing the parameter from individual load values to bucket counts. This parameter transformation maintains the essential information for lifetime prediction (the distribution of loads) while dramatically reducing the data volume that needs to be stored.
2Reliability
If all load data is stored in memory for comprehensive analysis, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential information needed for lifetime prediction from the complete load data set. By taking out and storing only the bucket count distribution rather than all individual measurements, the system maintains prediction reliability while simplifying the storage and processing architecture.
Solution Approach 2:
The patent performs preliminary discretization and aggregation of load data into buckets before storage. This preliminary action organizes the data in a way that simplifies subsequent lifetime prediction calculations, reducing the complexity of the overall system while maintaining reliability.
3Loss of substance
If load data is discretized into buckets to reduce memory usage, then loss of substance is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent applies local quality by using finer bucket resolutions in load ranges that are more critical for lifetime prediction (typically higher load ranges where failure is more likely) and coarser resolutions in less critical ranges. This localized adaptation of bucket granularity maintains precision where it matters most while maximizing storage efficiency overall.
Data Source
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AI summary
Examples of techniques for optimal storage of load data for lifetime prediction for a piece of equipment used in a well operation are disclosed. In one example implementation according to aspects of the present disclosure, a method may include: using a lifetime model for the piece of equipment used in the well operation; discretizing, by a processing device, a load data spectrum into one or more buckets, the one or more buckets having a bucket size, wherein the bucket size of at least one bucket is based on one of the lifetime model and a distribution of load data; collecting load data of the piece of equipment; assigning, by the processing device, the collected load data to the one or more buckets of the load data spectrum; and storing, by the processing device, the collected load data assigned to the one or more buckets to a memory.