Adaptive Drilling Control via Objective Map Optimization
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Solution Overview
Problem
Current drilling technologies face challenges in efficiently managing drilling time and rate of penetration (ROP) due to variations in subsurface strata, leading to ineffective control of drilling operations, especially when variations occur over short length scales.
Innovation Solution
The development of advanced drilling control systems that utilize an objective map to calculate and update critical points based on independent operational parameters such as weight-on-bit and revolutions per minute, allowing for iterative improvement of drilling performance indicators and adaptive control of drilling operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional drilling control methods are used with single parameter adjustment, then equipment simplicity is maintained, but drilling performance cannot be optimized under highly variable subsurface conditions
Solution Approach 1:
The control system dynamically adjusts multiple operational parameters (weight on bit, revolutions per minute, pump rate) based on real-time drilling conditions and objective map updates, transitioning from static single-parameter control to dynamic multi-parameter optimization. This enables the system to adapt to highly variable subsurface conditions while maintaining manageable complexity through automated feedback loops.
Solution Approach 2:
The system implements continuous feedback by monitoring actual drilling performance indicators, updating the objective map with new data, and using this updated information to recalculate and adjust optimal parameter settings. This closed-loop feedback mechanism enables progressive optimization of drilling performance without requiring overly complex manual intervention.
2Loss of time
If drilling operations are controlled without iterative optimization, then operational simplicity is maintained, but drilling time cannot be reduced under varying subsurface conditions
Solution Approach 1:
The system performs preliminary calculations of optimal drilling parameters by generating an objective map that identifies critical points and optimal settings before actual drilling operations begin. This advance preparation enables faster drilling by having optimization strategies ready in advance, reducing the need for time-consuming adjustments during drilling while maintaining manageable operational complexity.
Solution Approach 2:
The control system transitions from static parameter settings to dynamic iterative optimization, continuously updating the objective map and recalculating optimal parameters as drilling progresses and subsurface conditions change. This dynamic approach reduces drilling time by adapting to real-time conditions while maintaining operational simplicity through automated control algorithms.
3Productivity
If single parameter control (weight-on-bit) is used, then control simplicity is maintained, but ROP cannot be maximized when drilling conditions are highly variable
Solution Approach 1:
The system merges control of multiple operational parameters (weight on bit, revolutions per minute, pump rate) into a unified control framework that optimizes their interactions simultaneously. By combining these parameters and analyzing their joint effects on drilling performance through the objective map, the system achieves higher ROP in variable conditions while maintaining control simplicity through integrated automated decision-making.
Solution Approach 2:
The system employs comprehensive parameter changes by simultaneously adjusting multiple drilling parameters (weight on bit, revolutions per minute, pump rate) rather than relying on single-parameter control. This multi-parameter optimization approach maximizes ROP under highly variable subsurface conditions while the automated objective map framework maintains control simplicity by coordinating all parameter changes systematically.
Data Source
AI summary
Methods of drilling a wellbore within a subsurface region and drilling control systems that perform the methods. The methods include accessing an objective map and calculating a plurality of critical points of the objective map. The methods also include scoring each critical point and selecting a selected critical point of the plurality of critical points. The selected critical point describes an estimated value of at least one drilling performance indicator for a selected value of at least one independent operational parameter. The methods further include operating the drilling rig at the selected value of the at least one independent operational parameter and, during the operating, determining an actual value of the at least one drilling performance indicator. The methods also include updating the objective map to generate an updated objective map and repeating at least a portion of the methods.