Wellbore Digital Twin Adaptation Using Ensemble Kalman Filter
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Solution Overview
Problem
Oil and gas recovery processes face challenges in adapting to rapidly changing environments due to constant spatial and temporal variations in process parameters, such as reservoir pressure depletion and changing rock properties, which hinder optimal production and increase costs.
Innovation Solution
The implementation of a self-adapting digital twin system using the Ensemble Kalman Filter (EnKF) allows for real-time adjustment of model parameters based on sensor data, enabling dynamic adaptation without altering the algorithmic or numerical structure of existing digital twins, thus optimizing nonlinear systems and improving predictive accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed-parameter models are used to predict process evolution, then model structure remains simple and manageable, but the models cannot adapt to constant spatial and temporal variations in process parameters
Solution Approach 1:
The patent transforms fixed-parameter models into dynamic models by implementing the Ensemble Kalman Filter (EnKF) that continuously updates model parameters in real-time based on incoming sensor measurements. The filter dynamically adjusts permeability, porosity, and other reservoir parameters as the system evolves, allowing the model to adapt to changing conditions without requiring a completely new model structure.
Solution Approach 2:
The EnKF implementation establishes a feedback loop where model predictions are continuously compared against actual sensor measurements, and parameter updates are generated based on the discrepancies. This feedback mechanism enables the model to self-correct and adapt to varying process conditions while maintaining computational efficiency through the modular filter architecture.
2Measurement precision
If numerical models are adapted based on data from multiple sensors, then process accuracy improves, but the complexity of adapting models in rapidly changing environments increases
Solution Approach 1:
The patent introduces the Ensemble Kalman Filter as an intermediary layer between multiple sensors and the numerical model. The EnKF systematically integrates measurements from various sensors (pressure, temperature, flow rate) and transforms them into updated model parameters, simplifying the adaptation process by providing a structured framework for handling multi-sensor data without requiring complex custom integration logic.
Solution Approach 2:
The system achieves improved measurement precision by continuously updating model parameters (permeability, porosity, viscosity) based on sensor data through the EnKF. The filter dynamically adjusts these parameters to reflect current reservoir conditions, transforming static model parameters into time-varying quantities that accurately represent the evolving system state.
3Productivity
If constant parameter models are used, then computational efficiency is maintained, but production optimization is hindered due to inability to capture rapidly changing reservoir conditions
Solution Approach 1:
The patent applies partial updates to model parameters rather than complete re-modeling. The EnKF selectively updates only the necessary parameters (permeability, porosity, viscosity) based on available sensor data, performing sufficient adaptation to capture changing reservoir conditions without the computational overhead of complete model re-calibration, thus optimizing production responses in a timely manner.
Data Source
AI summary
A self-adapting digital twin of a wellbore environment can be created. The self-adapting digital twin incorporates a standalone Ensemble Kalman Filter (EnKF) module with a constant parameter digital twin developed for a fixed environment. The standalone EnKF module receives streaming measurement data from multiple sensors and prediction data from the digital twin and executes the standalone EnKF module using the streaming measurement data and the prediction data from the digital twin. The results of executing the standalone EnKF module are input parameter corrections for the digital twin that are communicated to the digital twin. Output predictions of the digital twin are used to modify operational parameters of an oil or gas recovery process.