Dynamic Data Frequency Optimization for Drilling Predictive Modeling
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Solution Overview
Problem
Current predictive modeling in well drilling operations is computationally intensive and costly due to a fixed data input frequency, which does not adapt to changing operational and environmental parameters, leading to inefficient resource usage and increased costs.
Innovation Solution
A dynamic data frequency optimizer system that selectively inputs data at varying frequencies into predictive models, using algorithms like Bayesian optimization to determine the optimum frequency based on current parameters, thereby reducing computational resources while maintaining accurate rate of penetration (ROP) predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is input into the predictive model at a fixed high frequency, then the accuracy of ROP predictions is maintained, but the computational processing requirements and costs increase
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed data frequency to a dynamic, variable data frequency that adapts to changing drilling conditions. The system continuously monitors drilling parameters and adjusts the data input frequency to the predictive model based on current operational needs, allowing high frequency when conditions change rapidly and low frequency when conditions are stable, thus optimizing the balance between prediction accuracy and computational efficiency
Solution Approach 2:
The patent implements parameter changes by modifying the data frequency parameter based on drilling conditions. The system changes the sampling frequency of drilling data input to the predictive model according to the variability of operational parameters, enabling the system to maintain accurate predictions while reducing computational load during periods of stable drilling conditions
2Productivity
If data frequency is reduced to lower computational costs, then processing efficiency improves, but the accuracy of ROP predictions may deteriorate
Solution Approach 1:
The patent applies feedback by continuously monitoring drilling operational parameters and using this information to adjust the data frequency fed into the predictive model. The system evaluates changes in drilling conditions in real-time and dynamically modifies the data sampling frequency, ensuring that prediction accuracy is maintained during critical periods while improving processing efficiency during stable periods through adaptive feedback control
3Ease of operation
If a fixed data frequency is used for all drilling conditions, then the system is simple to operate, but it cannot adapt to changing operational and environmental parameters
Solution Approach 1:
The patent implements self-service by enabling the system to automatically adjust its own data processing parameters without external intervention. The predictive modeling system autonomously monitors drilling conditions and self-regulates the data input frequency based on detected changes in operational parameters, eliminating the need for manual configuration while maintaining both simplicity and adaptability
Data Source
AI summary
Systems and methods can automatically and dynamically determine an optimum frequency for data being input into a drilling optimization tool in order to provide predictive modeling for well drilling operations. The methods and systems selectively input sets of data having different frequencies into the drilling optimization tool to build different predictive models at different frequencies. An optimization algorithm such as Bayesian optimization is then applied to the models to identify in real time an optimum frequency for the data sets being input into the drilling optimization tool based on current operational and environmental parameters.