Control of managed pressure drilling systems using nonlinear predictive generalized minimum variance approach based...

Area of Science:

• Petroleum Engineering
• Control Systems Engineering
• Automation Technology

Background:

• Managed pressure drilling (MPD) systems require precise control to manage downhole conditions and prevent influxes.
• Accurate system modeling is challenging due to uncertainties and disturbances in real-world drilling operations.

Purpose of the Study:

• To develop and evaluate a robust nonlinear predictive generalized minimum variance (NPGMV) control scheme for MPD systems.
• To enhance bottom-hole pressure regulation and improve kick handling capabilities during drilling operations.

Main Methods:

• Identification of the MPD hydraulic flow model using an autoregressive second-order Volterra model via least-squares method.
• Implementation of the NPGMV control scheme for automatic control and disturbance rejection.
• Development of a switching mechanism for automatic transition to flow control mode during reservoir kick events.

Main Results:

• The NPGMV controller successfully regulates bottom-hole pressure and prevents reservoir fluid influx.
• The controller maintains bottom-hole pressure above reservoir pressure during critical scenarios like pipe connections.
• Comparative analysis shows NPGMV control outperforms switching PI controllers in steady-state performance and robustness against heave and uncertainty.

Conclusions:

• The proposed NPGMV control scheme offers a significant advancement in MPD system automation and safety.
• The controller demonstrates superior performance in handling disturbances and ensuring wellbore integrity.
• NPGMV control provides a robust and effective solution for complex drilling challenges.