Bottom hole pressure estimation and adaptive control in managed pressure drilling system

Abstract

In the past decade, managed pressure drilling, a technology aiming at precise well pressure control, has been gaining increasing popularity and been a key enabler for some of the most challenging well drilling cases such as the offshore deep water well drilling. This paper attempts to solve two of the main challenges involved in the managed pressure drilling systems: first, the bottom-hole states measurements are updated at a low rate, which can be practically viewed as unmeasured and thus need to be estimated in real time for both monitoring and control purposes and second, the drilling process is subject to uncertainties including unknown system parameters (e.g., frictions and densities), unmodeled actuator dynamics, and noise, which require a robust adaptive controller for control of the bottom-hole pressure. Towards this objective, an integrated estimator and L 1 adaptive control scheme is proposed. The estimator provides estimation of the bottom-hole pressure and flow rate, based on the available measurements from the topside. The L 1 adaptive controller drives the bottom-hole pressure to the desired value following a reference model, which also handles the time delays in the input signal. The design is based on a recently developed nonlinear drilling model. The results demonstrate that the L 1 adaptive controller has guaranteed performance bounds for both the input and the output signals of the system while using the estimation of the regulated outputs. Simulation results covering different operational conditions verify the theoretical findings.

A new approach to analyzing wellbore pressure dynamically in well-killing

“…Aarsnes, Ulf Jakob F et al (Aarsnes, 2016;Aarsnes et al, 2016) proposed a simple gas-liquid two-phase flow model and carried out a steady-state analysis of the system through the drift flux model. Li Zhiyuan (Li et al, 2017) estimate the bottom hole pressure based on the non-linear drilling model. Seyhmus Guner et al (Guner et al, 2017) analyzed the effect of gas well surge on wellbore pressure.…”

L1 adaptive output-feedback fault-tolerant control for uncertain nonlinear systems subject to unmodeled actuator dynamics and faults

“…The L1 adaptive controller or hybrid controller combined with other control methods has been verified to deal with the uncertain nonlinear systems well. Li et al (2017b) designed a L1 adaptive controller to deal with the uncertainties of managed pressure drilling system, including unknown system parameters, unmodeled actuator dynamics, and noise. For uncertain systems with input delays, Nguyen and Dankowicz (2019) proposed a modified L1 adaptive control framework, which introduces a time delay in the control input of the state predictor to compensate for the decrease in stability caused by input delays.…”

“…Compared with the projection operator-based adaptive law in most L1 adaptive controllers (Ahmadi et al, 2019; Alyazidi and Mahmoud, 2018; Li et al, 2017b; Nguyen and Dankowicz, 2019), the piecewise-constant adaptive law is adopted in this paper. The former reduces the estimation error by choosing a high adaptive gain, but the high adaptive gain will bring undesirable high-frequency oscillations and the choice of gain is restricted by hardware conditions, which needs a trade-off scheme.…”

Dynamic behavior of coalbed methane flow along the annulus of single-phase production

“…The distributions of temperature and pressure on the bottom of the hole during the SC-CO 2 jet drilling were simulated experimentally and numerically, and the impacts of the nozzle diameter, the jet length, and the inlet pressure of the SC-CO 2 jet were analyzed. Artificial neural networks (ANN) (Osman and Aggour 2002;Mohammadpoor et al 2010;Li et al 2017) create models that can recognize highly complex and non-straight-forward problems. ANN provides an integrated approach for the prediction of bottom-hole pressures in multiphase flow.…”