Bayesian RNN Position Estimation for Downhole Cementing Plugs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for determining the position of downhole components like cementing plugs in wellbores are inaccurate due to complex physics, parameter variations, and noisy data, often relying on Gaussian assumptions and explicit system dynamics, which are ineffective in high-dimensional and non-linear systems, and lack uncertainty quantification.
Innovation Solution
Utilizing a Bayesian Recurrent Neural Network (RNN) to predict the position of downhole components by capturing complex, non-linear time-domain relationships without explicit system dynamics, accounting for noise and uncertainty, and enabling real-time online training for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to determine downhole component position, then the system is simpler to implement, but the measurement precision deteriorates due to complex physics, parameter variations, and noisy data
Solution Approach 1:
The patent replaces conventional mechanical/mathematical modeling methods with a neural network-based data-driven system. The neural network learns complex non-linear relationships from historical pressure data without requiring explicit physics-based models, thereby improving position estimation accuracy while avoiding the complexity of detailed system modeling.
Solution Approach 2:
The patent introduces a neural network as an intermediary between raw pressure data and position estimation. This intermediary component processes noisy pressure measurements and extracts meaningful position information, effectively decoupling the complexity of data processing from the overall system architecture.
2Reliability
If explicit system dynamics models are used, then the model structure is simpler, but the reliability deteriorates in high-dimensional and non-linear systems
Solution Approach 1:
The patent substitutes explicit physics-based dynamic models with a data-driven neural network approach. The neural network automatically captures complex non-linear system dynamics from training data, providing more reliable predictions in high-dimensional scenarios without requiring manual model formulation.
Solution Approach 2:
The patent transforms the modeling approach by changing from fixed physics-based parameters to adaptive learned parameters. The neural network learns optimal parameters from data, allowing the model to adapt to varying operating conditions and improve reliability across different scenarios.
3Measurement precision
If conventional position determination methods are used, then the computational resources required are fewer, but the measurement precision deteriorates due to inability to account for uncertainty
Solution Approach 1:
The patent replaces uncertainty-aware probabilistic models with a neural network that outputs both mean position estimates and uncertainty quantification. This approach achieves precise measurements with better computational efficiency by leveraging the neural network's ability to process information in parallel.
4Adaptability or versatility
If real-time online training is implemented, then the adaptability improves, but the computational resources and time required increase
Solution Approach 1:
The patent implements incremental online training that updates the neural network with small batches of new data rather than complete retraining. This partial action approach maintains adaptability while significantly reducing the time and computational resources required compared to full retraining cycles.
Data Source
AI summary
A computer-implemented method for determining the position of a downhole component with a neural network model is provided. The computer-implemented method can include acquiring real-time or characteristic data including values for one or more input variables associated with one or more time steps in a cementing operation, training the neural network to minimize a loss function and estimate a value for the position of the downhole component and an uncertainty at one or more time steps, estimating the value at the one or more time steps, estimating an uncertainty in the value at the one or more time steps, determining an operation position of the downhole component when an operation is to be performed, and determining the time step when the operation is to be performed.