Wellbore Image Feedback Control for Real-Time Drilling Deviations
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Solution Overview
Problem
Current wellbore drilling techniques rely on predictive models that often fail to account for real-time deviations in drilling conditions, leading to operational inefficiencies, safety hazards, and well failure due to inaccurate setting of drilling parameters.
Innovation Solution
Utilizing a Deep Neural Network (DNN) to analyze images of wellbore objects in real-time, comparing them with predicted and expected models to identify deviations, and adjusting drilling parameters and computer models accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If predictive models are used to control drilling parameters, then operational parameters can be set in advance, but the models fail to account for real-time deviations in drilling conditions
Solution Approach 1:
The system continuously monitors actual drilling conditions using sensors and compares them with predicted conditions from the computer model. When deviations are detected, the system automatically adjusts drilling parameters in real-time, creating a closed-loop feedback control system that maintains reliability while enabling rapid response to changing conditions.
Solution Approach 2:
The drilling control system transitions from static pre-set parameters to dynamic real-time adjustment. The system continuously adapts drilling parameters based on current wellbore conditions, allowing the operational parameters to change dynamically as drilling progresses and conditions evolve.
2Reliability
If real-time image analysis is implemented, then drilling parameters can be adjusted dynamically, but system complexity increases
Solution Approach 1:
The imaging system is designed to perform multiple functions: capturing wellbore images, identifying objects and features, analyzing drilling conditions, and triggering parameter adjustments. This multi-functionality reduces the need for separate specialized systems while maintaining comprehensive monitoring and control capabilities.
Solution Approach 2:
The system introduces an intelligent image analysis intermediary that processes complex visual data and translates it into actionable drilling parameter adjustments. This intermediary layer handles the complexity of image processing while presenting simplified control outputs to the drilling system.
3Productivity
If predictive models are used without real-time verification, then operational efficiency is maintained, but safety hazards arise from inaccurate parameter settings
Solution Approach 1:
The system proactively identifies potential wellbore instability conditions by continuously comparing actual drilling conditions with predicted conditions. When deviations indicating potential collapse or fracturing are detected, the system preemptively adjusts drilling parameters to prevent these harmful events before they occur, rather than responding after damage has happened.
Solution Approach 2:
Real-time monitoring and comparison of actual versus predicted drilling conditions creates a feedback mechanism that continuously verifies the safety and appropriateness of drilling parameters. This feedback loop ensures that productivity is maintained while preventing safety hazards through immediate parameter adjustments when conditions warrant.
Data Source
AI summary
Computer implemented methods and systems for testing one or more operational changes in a drill rig includes initiating the one or more operational changes and using, in part, image data of a mechanical mud separation machines (“MMSM”) to detect the impact of the one or more changes. The image data may be processed by a Deep Neural Network to identify objects in the object flow, operational parameters of the MMSM, and wellbore environmental conditions. Additional image data may be selected for additional processing based on the results of the analysis. The results of the test may be used to update the drilling operation or a drilling model.