Model-Based Predictive Control for Bottom Hole Assembly Trajectory
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Solution Overview
Problem
The challenge in accurately controlling the trajectory of a wellbore during hydrocarbon drilling is exacerbated by remote downhole equipment, unpredictable operating conditions, and nearby wellbores, leading to inaccuracies and delays in sensor measurements, which limit the effectiveness of human operator adjustments.
Innovation Solution
Implementing a model-based predictive control (MPC) system that generates relational information to control the bottom hole assembly (BHA) using a secondary system, which pre-computes control inputs based on sensor measurements and updates the model of BHA dynamics to adapt to changing conditions, allowing for proactive adjustments to maintain the wellbore trajectory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor measurements are used to control wellbore trajectory, then position and angle detection is improved, but measurement accuracy and timeliness deteriorate due to delays and inaccuracies in data transmission
Solution Approach 1:
The system pre-computes control inputs based on current sensor measurements and stores them in a lookup table before they are needed. This preliminary computation eliminates the need for real-time calculations during critical control moments, ensuring that control decisions can be made immediately based on the most recent measurements without waiting for computational results.
Solution Approach 2:
A secondary system acts as an intermediary between the BHA sensors and the control actuators. This secondary system receives sensor measurements, performs model-based predictive control computations, and generates control inputs. This intermediary layer enables sophisticated control algorithms while maintaining real-time responsiveness by handling the computational burden separately from the time-critical measurement-to-action loop.
2Adaptability or versatility
If model-based predictive control computations are performed in real-time, then adaptability to changing conditions is improved, but computational burden on the BHA increases
Solution Approach 1:
The system pre-computes control inputs for various operating conditions and stores them in a lookup table. When the BHA encounters a specific operating condition, it can quickly retrieve the pre-computed control input without performing complex real-time calculations. This approach maintains adaptability to changing downhole conditions while significantly reducing the computational burden on the BHA hardware.
Solution Approach 2:
Instead of performing complex predictive control computations directly on the BHA, the system creates a simplified representation (lookup table) of the control strategy that can be easily stored and retrieved. The lookup table contains pre-computed control inputs that replicate the效果 of full predictive control computations, enabling the BHA to adapt to changing conditions with minimal computational resources.
3Ease of operation
If human operators manually adjust drilling equipment based on sensor data, then operational flexibility is improved, but control accuracy deteriorates due to best-guess estimates
Solution Approach 1:
The system implements a closed-loop feedback control mechanism where sensor measurements of wellbore position and angle are continuously fed into the model-based predictive control algorithm. The controller compares the actual trajectory with the desired trajectory and automatically adjusts control inputs to minimize deviations. This automated feedback loop eliminates the need for human operators to make best-guess estimates, significantly improving trajectory control accuracy while maintaining operational flexibility through programmable control strategies.
Data Source
AI summary
Techniques for controlling a bottom hole assembly (BHA) include determining a model of BHA dynamics based on sensor measurements from the BHA; determining, based on the model of BHA dynamics, an objective function including a predicted future deviation from a planned wellbore path; determining a control input to the BHA that satisfies the objective function for a set of operating conditions of the BHA; generating, at a secondary system, relational information that relates the control input to the set of operating conditions; and transmitting the relational information from the secondary system to the BHA.