Deep Neural Network Kalman Filter for Drilling Deviation Detection
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Solution Overview
Problem
Current field operations in resource extraction, such as oil and gas drilling, face challenges in efficiently planning and executing drilling processes due to the complexity of subsurface environments and the lack of real-time, accurate feedback mechanisms, leading to inefficiencies and potential operational deviations from pre-defined procedures.
Innovation Solution
A method and system utilizing a deep neural network trained with time series data to model a non-linear Kalman filter, which compares actual operational sequences against pre-defined procedures in a latent space, providing a score function to quantify deviations and control electronic components for real-time adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional field operations are used without real-time monitoring, then operational simplicity is maintained, but operational accuracy and compliance with pre-defined procedures deteriorate
Solution Approach 1:
The system implements real-time feedback by continuously monitoring operational data from sensors and comparing it against pre-defined procedures stored in memory. The processor generates feedback signals when deviations are detected, enabling real-time corrections to maintain procedural compliance without requiring complex manual intervention.
Solution Approach 2:
The patent replaces manual monitoring and compliance verification with an automated electronic system consisting of sensors, processors, and control mechanisms. This substitution of mechanical/human monitoring with electronic automation improves measurement precision while managing system complexity through integrated software solutions.
2Productivity
If real-time monitoring and adjustment systems are implemented, then operational efficiency improves, but device complexity increases
Solution Approach 1:
The system achieves multi-functionality by integrating data acquisition, real-time analysis, procedural comparison, and control signal generation within a single integrated platform. The processor performs multiple functions including monitoring operational parameters, comparing against stored procedures, detecting deviations, and generating correction signals, thereby improving productivity without proportionally increasing complexity.
Solution Approach 2:
The system implements self-service through automated detection and correction mechanisms that operate without continuous human intervention. The processor automatically compares operational data against pre-defined procedures and generates control signals to correct deviations, enabling the system to self-regulate and maintain optimal performance efficiently.
3Measurement precision
If manual monitoring of operational sequences is used, then system complexity is reduced, but detection precision of operational deviations deteriorates
Solution Approach 1:
The system continuously feeds back operational data from sensors to the processor, which compares real-time measurements against pre-defined procedural parameters. This closed-loop feedback mechanism enables precise detection of deviations by constantly monitoring and comparing operational sequences, achieving high detection precision through automated electronic comparison rather than manual observation.
Data Source
AI summary
A system and method that can include training a deep neural network using time series data that represents functions of a non-linear Kalman filter that represents a dynamic system of equipment and environment and models a pre-defined operational procedure as a temporal sequence. The system and method can also include receiving operation data from the equipment responsive to operation in the environment and outputting an actual operation as an actual sequence of operational actions by the deep neural network. The system and method can additionally include performing an operation-level comparison to evaluate the temporal sequence against the actual sequence using a distance function in a latent space of the deep neural network and outputting a score function that quantifies the distance function in the latent space. The system and method can further include controlling an electronic component to execute an electronic operation based on the score function.