Drilling State Filtering With Neural Kalman Models
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Solution Overview
Problem
Current drilling operations face challenges in efficiently managing drilling dynamics and optimizing well construction processes due to the lack of real-time, systematic analysis and control, often relying on manual methods and laborious analysis.
Innovation Solution
A system utilizing a deep neural network-based Kalman filter that learns from real data to characterize drilling responses and conditions, enabling real-time monitoring and control of drilling operations by comparing actual operations with pre-defined procedures, and adjusting well plans accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods and laborious analysis are used for drilling operations, then operational simplicity is maintained, but productivity and analysis accuracy deteriorate
Solution Approach 1:
The patent replaces manual analysis methods with an automated deep neural network-based Kalman filter system. The neural network learns optimal filter parameters from historical drilling data, substituting human expertise with an intelligent algorithm that automatically characterizes drilling responses and provides real-time well plan recommendations, thereby improving productivity without requiring complex manual intervention
Solution Approach 2:
The system employs self-learning through the deep neural network that automatically trains on drilling data to improve filter characterization over time. The Kalman filter continuously updates drilling state estimates based on real-time data, and the system autonomously generates well plan recommendations without requiring constant human input, enabling the system to serve itself and improve continuously
2Measurement precision
If real-time monitoring and control systems are implemented, then drilling operations accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The deep neural network performs preliminary learning during offline training phases, where it studies historical drilling data to understand optimal filter behavior. This pre-computed knowledge is then applied during real-time operations, allowing the Kalman filter to quickly characterize drilling responses without performing complex computations during critical drilling operations, thus maintaining accuracy while reducing real-time processing time
Solution Approach 2:
The system dynamically adjusts Kalman filter parameters based on drilling conditions. The neural network learns optimal filter parameters for different drilling scenarios and automatically selects or adjusts them in real-time, allowing the system to maintain high measurement precision while adapting computational complexity to match the actual drilling situation, reducing unnecessary processing time
3Measurement precision
If deep neural network-based Kalman filter is used, then characterization accuracy of drilling responses is improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The deep neural network-based Kalman filter system serves multiple functions: it characterizes drilling responses, predicts drilling behavior, generates well plan recommendations, and continuously learns from new data. By consolidating these functions into a single integrated system, the patent improves characterization accuracy while avoiding the need for multiple separate complex systems, thereby managing overall implementation complexity
Data Source
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AI summary
A method can include training a deep neural network to generate a trained deep neural network where the trained deep neural network represents functions of a non-linear Kalman filter that represents a dynamic system of equipment and environment via an internal state vector of the dynamic system; generating a base internal state vector, that corresponds to a pre-defined operational procedure, using the trained deep neural network; receiving operation data from the equipment responsive to operation in the environment; generating an internal state vector using the operation data and the trained deep neural network; and comparing at least the internal state vector to at least the base internal state vector.