ML Well Optimization via Physics Emulation
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Solution Overview
Problem
Current machine learning approaches for optimizing well operations are vulnerable to noise and uncertainty, lack adaptive learning, and require extensive manual processes for model calibration and simulation, making real-time optimization challenging and inefficient.
Innovation Solution
An automated process that cycles through data gathering, simulation, inverse modeling, and recommendation, utilizing supervised machine learning models and neural networks for high-resolution, physically consistent simulations, and transfer learning to adapt to changing well conditions, enabling real-time optimization and set-point adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning approaches are used for well operation optimization, then model training can be performed with available data, but the system is vulnerable to noise and uncertainty and lacks adaptive learning capability
Solution Approach 1:
The system dynamically adapts to changing well conditions through continuous data collection and iterative optimization cycles. The optimization process is not static but evolves over time as new field data becomes available, allowing the system to adjust to declining production and changing reservoir conditions automatically
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring actual well performance against predicted performance, using the discrepancy to refine probabilistic estimates and update optimization recommendations. This feedback mechanism enables the system to learn from real-world outcomes and improve its predictions and recommendations over time
2Manufacturing precision
If manual model calibration and simulation processes are used, then physically consistent models can be developed, but extensive manual effort and time are required
Solution Approach 1:
The system performs preliminary probabilistic estimation of uncertain parameters using available field data before conducting full optimization simulations. This preliminary action narrows the search space and provides initial guesses that accelerate the subsequent calibration and optimization processes, reducing overall computation time
Solution Approach 2:
The system automates the model calibration process by using field data to automatically update probabilistic estimates of reservoir parameters and well performance characteristics. The system serves itself by continuously refining its own models without requiring manual intervention for each calibration cycle
3Measurement precision
If high-resolution simulations are performed for well optimization, then accurate predictions can be obtained, but computational complexity and processing time increase
Solution Approach 1:
The optimization problem is segmented into multiple components: probabilistic estimation of uncertain parameters, deterministic simulation of well performance, and optimization of operational parameters. This segmentation allows each component to be solved with appropriate methods and enables parallel processing, reducing overall computational complexity while maintaining accuracy
Data Source
AI summary
A methodology for providing set-point recommendations in an automated manner to optimize the operation of a well-producing fluid, by establishing a live synergy between physics-based simulation and real-time field data, through the employment of machine learning models. The machine learning models serve two distinct purposes in this approach: 1. Accelerate emulation of the numerical physics-based simulation to enable real-time solutions 2. Provide a probabilistic estimate of the unknown operating conditions of a well and updating the estimate based on the response to the set-point changes made, thus improving with each iteration.