Pipeline Corrosion Prediction Using Flow Simulation and Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing pipeline corrosion prediction methods are inaccurate and resource-intensive, failing to account for the non-deterministic nature of corrosion development and requiring extensive manual inspections that are not pipeline-agnostic.
Innovation Solution
A computer-implemented method using a combination of physical simulation models (CFD and FEA) and machine learning models to predict pipeline corrosion by integrating pipeline-specific simulation and real inspection data, including geometric, historical, and chemical composition data, to generate a detailed physical flow profile and future corrosion profile.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deterministic or statistical models are used for corrosion prediction, then the model structure is simple, but the prediction accuracy is insufficient due to inability to capture complex non-deterministic corrosion mechanisms
Solution Approach 1:
The patent transforms the corrosion prediction approach by changing the fundamental parameters from deterministic statistical variables to neural network parameters that can capture non-deterministic patterns. The system uses multiple neural networks with different architectures (CNN, LSTM, Transformer) to process various input parameters including flow velocity, temperature, pressure, and corrosion measurements, enabling accurate prediction of complex corrosion mechanisms that traditional models cannot capture
Solution Approach 2:
The patent creates a composite prediction system by integrating multiple neural network models (CNN for spatial features, LSTM for temporal sequences, Transformer for attention-based relationships) into a unified framework. This composite approach combines the strengths of different model types to accurately predict corrosion profiles while maintaining the ability to handle complex non-deterministic relationships in the data
2Measurement precision
If comprehensive physical and chemical parameters are integrated into the machine learning model, then the corrosion prediction accuracy is improved, but the data processing complexity increases
Solution Approach 1:
The patent segments the complex data processing task by dividing it into distinct processing stages handled by different neural network components. The CNN processes spatial patterns in corrosion data, the LSTM handles temporal sequences of measurements, and the Transformer captures attention-based relationships between different parameters. This segmentation allows comprehensive parameter integration while managing complexity through modular architecture
Solution Approach 2:
The patent introduces intermediary processing layers that transform raw physical and chemical parameters into meaningful features before final prediction. The neural networks act as intermediaries that automatically extract relevant patterns from complex input data including flow velocity, temperature, pressure, and corrosion measurements, reducing the burden of manual feature engineering while maintaining high prediction accuracy
3Area of stationary object
If traditional wall thickness measurement techniques (RT, X-rays, ultrasonic) are used, then measurement coverage can be comprehensive, but the cost and time requirements become prohibitive
Solution Approach 1:
The patent performs preliminary corrosion prediction using neural networks trained on historical data and physical parameters before actual inspection occurs. By predicting which pipeline segments are most susceptible to corrosion based on flow conditions, temperature, pressure, and previous measurements, the system pre-identifies areas requiring detailed inspection, allowing comprehensive coverage to be achieved efficiently by focusing resources on high-risk areas
Solution Approach 2:
The patent replaces traditional mechanical measurement techniques (ultrasonic, radiographic testing) with a data-driven neural network prediction system. Instead of physically measuring every pipeline segment with expensive and time-consuming equipment, the system uses machine learning models to predict corrosion profiles based on operational parameters and historical data, dramatically reducing inspection time and cost while maintaining comprehensive coverage through targeted physical verification of predicted high-risk areas
4Reliability
If ILI smart pigging tools are deployed periodically to detect pipeline defects, then pipeline integrity can be monitored, but resource usage becomes inefficient due to limited ILI availability
Solution Approach 1:
The patent implements a feedback-driven inspection scheduling system where neural networks continuously analyze operational data, flow conditions, and previous inspection results to predict corrosion development. This feedback loop identifies pipelines that require urgent ILI inspection versus those that can wait, optimizing the allocation of limited ILI resources to maintain pipeline integrity while maximizing resource utilization efficiency
Solution Approach 2:
The patent performs preliminary risk assessment using neural network predictions before ILI deployment decisions are made. By pre-identifying pipelines with high corrosion risk based on operational parameters and historical trends, the system prepares prioritized inspection lists that enable efficient scheduling of limited ILI resources, ensuring critical pipelines are inspected first while maintaining overall pipeline integrity
Data Source
AI summary
A computer-implemented method for predicting corrosion of a pipeline comprises the steps of: generating, using a physical simulation model, a physical flow profile of the pipeline based on at least a part of an inspection profile of the pipeline; predicting, using a machine learning model, a future corrosion profile of the pipeline including at least one future corrosion feature based on at least a part of the inspection profile of the pipeline and the physical flow profile of the pipeline; wherein the inspection profile includes operating data of the pipeline comprising inspection data from at least one physical inspection of the pipeline. In addition, a corresponding training method, model, computer program as well as data processing devices are disclosed.