Annular Pressure Build-up (APB) is caused by heating of the trapped drilling fluids (during production) which may lead to burst/collapse of the casing or axial ballooning, especially in subsea HP/HT wells. The objective of present paper is to apply machine-learning tools to increase precision of the APB estimation, and thereby improve the fluid and casing design for APB mitigation in a given well.
The APB estimation methods in literature involve theoretical and computational tools that accommodate two separate effects: volumetric expansion (PVT response) of the annulus drilling fluids and circumferential expansion (and corresponding mechanical equilibrium) of the well casings. In the present work, machine-learning algorithms were used to accurately model ‘fluid density=f(T,P)’ based on the experimental PVT data of a given fluid at a range of (T, P) conditions. Sensitivity analysis was carried out to demonstrate improvement in precision of APB estimation (for different subsea well scenarios employing different fluids) using the machine-learning based models.
The study demonstrated that, in several subsea scenarios, a relatively small error in the experimental fluid PVT data itself can lead to significant variation of in APB estimation. The machine-learning based models for ‘density = f(T,P)’ for the fluids ensure that the cumulative error during the modeling process is minimized. The use of certain machine-learning based density models was shown to improve the precision of APB estimation by several hundreds of psi. This advantage of the machine-learning based density models was employed to improve the casing/fluid design for APB mitigation; the case was demonstrated for a subsea well scenario. Accordingly, the work may be used to mitigate the APB issue in the subsea HP/HT wells.