A Model Predictive Control Method for Autonomous Directional Drilling
- Nazli Demirer (Halliburton) | Umut Zalluhoglu (Halliburton) | Julien Marck (Halliburton) | Hossam Gharib (Former Halliburton) | Robert Darbe (Halliburton)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 30 September - 2 October, Calgary, Alberta, Canada
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Optimal Control, Autonomous Drilling, RSS, Model Predictive Control, Directional Drilling
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- 277 since 2007
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Directional drilling for hydrocarbon exploration has been challenged to become more cost-effective and consistent with fast-growing drilling operations for both offshore and onshore production areas. Autonomous directional drilling provides a solution to these challenges by providing repeatable drilling decisions for accurate well placement, improved borehole quality, and flexibility to adapt smoothly to new technologies for drilling tools and sensors. This work proposes a model predictive control (MPC)-based approach for trajectory tracking in autonomous drilling. Given a well plan, bottomhole assembly (BHA) configuration, and operational drilling parameters, the optimal control problem is formulated to determine steering commands (i.e., tool face and steering ratio) necessary to achieve drilling objectives while satisfying operational constraints.
The proposed control method was recently tested and validated during multiple field trials in various drilling basins on two- and three-dimensional (2D and 3D) well plans for both rotary steerable systems (RSS) and mud motors. Multiple curve sections were drilled successfully with automated steering decisions, generating smooth wellbores and maintaining proximity with the given well plan.
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