Steering Behavior Model for Automatic Well Trajectory Control
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Solution Overview
Problem
Current directional drilling methods rely heavily on human expertise and experience, making the control of well trajectories non-systematic, inconsistent, and prone to deviations, which increases the demand for skilled directional drillers and complicates the drilling process.
Innovation Solution
A method utilizing a steering behavior model with build rate and turn rate equations, calibrated to minimize variance between actual and estimated drill string behavior, allowing for automatic control of the drill string trajectory by comparing estimated positions and orientations to a well plan and determining corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automatic control using a steering behavior model is implemented, then consistency and predictability of trajectory control is improved, but system complexity increases due to the need for calibrated build rate and turn rate equations
Solution Approach 1:
The patent creates a virtual copy of the drilling system through a steering behavior model that replicates the complex physics of drill string behavior. This virtual model allows automatic control algorithms to operate on simplified representations rather than directly managing the physical complexity, thereby improving reliability while containing system complexity through simulation-based abstraction
Solution Approach 2:
The system performs preliminary calibration of the steering behavior model using historical drilling data before actual automatic control operations. This pre-calibration establishes the build rate and turn rate equations in advance, so that during operation, the system can rely on pre-computed parameters rather than calculating complex physics in real-time, improving reliability without proportionally increasing operational complexity
2Measurement precision
If a calibrated steering behavior model is used to predict drill string behavior, then trajectory prediction accuracy is improved, but the time required for calibration and computation increases
Solution Approach 1:
The steering behavior model is calibrated in advance using historical drilling data from previous operations. This preliminary calibration establishes the build rate and turn rate equations before actual trajectory prediction is needed, allowing the system to achieve high prediction accuracy without performing time-consuming calibration during critical drilling operations
Solution Approach 2:
The patent replaces complex mechanical calculations and iterative physics simulations with pre-calibrated mathematical equations for build rate and turn rate. This substitution of mechanical computation with algebraic equations significantly reduces computation time while maintaining trajectory prediction accuracy, resolving the contradiction between precision and speed
3Extent of automation
If automatic control determines corrective actions by comparing estimated position to well plan, then reduction of reliance on skilled drillers is achieved, but the system becomes more sensitive to deviations requiring frequent adjustments
Solution Approach 1:
The system implements continuous feedback by comparing the estimated drill string position (derived from calibrated build rate and turn rate equations) against the planned well trajectory. This automated feedback loop detects deviations and triggers corrective actions automatically, achieving high extent of automation while using the mathematical model to smooth out minor variations and avoid excessive adjustments
Data Source
AI summary
Steering behavior model can include build rate and/or turn rate equations to model bottom-hole assembly behavior. Build and/or turn rate equations can be calibrated by adjusting model parameters thereof to minimize any variance between actual response 118 and estimated response produced for an interval of the well. Estimated position and orientation 104 of a bottom-hole assembly along a subsequent interval can be generated by inputting subsequent tool settings into the calibrated steering behavior model. Estimated position and orientation 104 can be compared to a well plan 106 with a controller 108 which determines a corrective action 110. Corrective action 110 can be converted from a build and/or turn rate to a set of recommended tool settings 114 by using an inverse application 112 of the steering behavior model. As additional data 118 becomes available, steering behavior model can be further calibrated 102 through iteration.


