Wellbore Stability Control via Real-Time Object Flow Imaging
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Solution Overview
Problem
Current methods for controlling wellbore operational parameters during drilling are inadequate, as they rely on predictive models that fail to account for real-time changes in rock type or formation pressure, leading to issues like well collapse, premature rock fracturing, and equipment failure.
Innovation Solution
The implementation of a computer-implemented method that uses automatic image analysis of wellbore objects to detect deviations in size, shape, color, and volume, which are then used to control wellbore operational parameters and update predictive models in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive models are used to control drilling operational parameters, then drilling operations can be planned and executed, but the models fail to account for real-time changes in rock type or formation pressure, leading to well collapse, premature rock fracturing, and equipment failure
Solution Approach 1:
The system implements feedback by continuously monitoring objects in the object flow using imaging devices and automatically adjusting drilling operational parameters based on detected deviations. The system compares detected object characteristics (size, shape, color, volume) with expected values and modifies parameters such as drill speed, fluid density, and flow rate in real-time to maintain wellbore stability
Solution Approach 2:
The system enables self-service by automatically detecting formation changes through object flow analysis and autonomously adjusting drilling parameters without requiring continuous human intervention. The automated control system processes imaging data and implements parameter adjustments independently, allowing the drilling operation to adapt to formation changes in real-time
2Measurement precision
If manual visual inspection of objects in object flow is performed, then wellbore issues can be identified, but it is expensive to staff rig personnel and the analysis is subject to human error and subjective judgment
Solution Approach 1:
The system replaces manual visual inspection with automated imaging devices and computer vision algorithms. The imaging devices capture images of objects in the object flow, and software automatically analyzes characteristics such as size, shape, color, and volume to detect wellbore issues, eliminating human subjectivity and error while providing continuous objective measurement
Solution Approach 2:
The system creates visual copies of objects in the object flow through imaging devices. These image copies are then analyzed by automated systems to detect deviations in object characteristics, allowing for precise measurement and detection without requiring direct human observation of the actual objects
3Productivity
If drilling operational parameters are controlled based on predictive models, then drilling can proceed according to plan, but unanticipated changes in rock type or formation pressure render the recommended parameters ineffective
Solution Approach 1:
The system implements dynamics by enabling drilling operational parameters to change dynamically in response to real-time formation conditions. Instead of using fixed parameters from predictive models, the system continuously adjusts drill speed, fluid density, and flow rate based on current object flow characteristics, allowing the drilling operation to adapt to unanticipated changes in rock type or formation pressure while maintaining productivity
Data Source
AI summary
An image system captures images and automatically identifies objects in an object flow at one or more mechanical mud separation machines. A control signal may be generated to change one or more drilling parameters in response to identifying the objects in an object flow. These parameters may be one of a drill speed, an equivalent circulating density, or a control valve.