Optimized Pressure Test Plan Generation for Pressurized Components
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Solution Overview
Problem
In the oil and gas industry, existing methods for pressure testing of pressurized components are inefficient, as they often require multiple tests and can expose equipment to excessive pressure, leading to wear and potential failures, without optimizing the test plans to minimize equipment exposure.
Innovation Solution
A software system utilizing machine learning algorithms, specifically a genetic algorithm, to generate optimal test plans that reduce the number of tests, minimize pressure exposure, and avoid configurations that could lead to over-pressurization or inefficiencies, such as open flow paths and isolated cavities, by tracking equipment data and optimizing test sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple pressure tests are performed to monitor component performance, then reliability of monitoring is improved, but equipment wear increases and equipment life decreases
Solution Approach 1:
The system performs preliminary identification of critical components and pre-calculates optimal test sequences before actual testing begins. By using historical data and machine learning algorithms to predict which components need testing and when, the system avoids unnecessary repeated tests while ensuring monitoring reliability is maintained through strategic test scheduling.
Solution Approach 2:
The system continuously monitors component performance data and uses this feedback to dynamically adjust test schedules. By analyzing real-time performance metrics and comparing them against established thresholds, the system determines when components are actually needing testing, thereby reducing unnecessary tests while maintaining reliable monitoring through data-driven decisions.
2Reliability
If pressure tests are performed to monitor component response, then monitoring capability is improved, but excessive pressure exposure causes wear and potential failures
Solution Approach 1:
The system dynamically adjusts test parameters including pressure levels, test durations, and sequencing based on component history and current conditions. By using machine learning models to predict optimal test parameters for each component, the system applies the minimum necessary pressure to achieve monitoring objectives while avoiding excessive pressure exposure that would cause wear or damage.
3Ease of operation
If traditional test plans are used, then simplicity of implementation is maintained, but test efficiency decreases and equipment is over-tested
Solution Approach 1:
The system automatically generates optimized test plans by processing historical data, component specifications, and performance thresholds without requiring manual intervention. The machine learning algorithms self-adjust test sequences and parameters based on actual component behavior, eliminating the need for complex manual planning while achieving high test efficiency through automated optimization.
4Reliability
If comprehensive testing of all components is performed, then monitoring completeness is improved, but time consumption increases
Solution Approach 1:
The system segments the testing process by identifying and prioritizing only the critical components that require monitoring based on their importance to system safety and functionality. By using data analysis to determine which components are most at risk or most critical, the system focuses testing resources on those specific components, thereby maintaining monitoring completeness for essential elements while reducing overall test time through selective testing.
Data Source
AI summary
Provided is a method for generating a pressure test plan comprising generating, by a computing system, a first set of tests for a set of pressurized components of a pressurized system; determining, by the computing system, a fitness scores for the tests of the first set; and generating an updated set of tests based on the fitness scores of the tests.