Slugging in multiphase flow as a mixed initial-boundary value problem for a hyperbolic system

This paper studies the multiphasis slugging flow phenomenon occurring in oil wells and flow lines. The main contribution is a low-dimensional distributed parameters model, comprising as states the gas mass fraction, the pressure, and gas velocity. Along with appropriate boundary conditions, on the one-dimensional space domain, it constitutes a well-posed mixed initial-boundary value problem for a quasilinear hyperbolic system. Numerical simulation results obtained with a presented characteristics method solver stress the validity of the approach and the fair representativeness of the model. In particular, the period of simulated oscillations and their overall shape is in accordance with reference results from the literature. Controllability and observability open problems are exposed for future works

Model-based control of slugging: advances and challenges

We review recent advances in the suppression of the slugging phenomenon by model-based control. We focus on three aspects of recent contributions: models, observers and control laws. For each category, we evaluate and compare existing solutions, and propose directions for improvement

Reproducing slugging oscillations of an oil well

This paper addresses the problem of reproducing oscillations generated by the well-known slugging phenomenon in multiphase flow. Reported investigations show how to determine the parameters of a recently proposed ordinary dierential equations system, so that it captures the characteristics of actually observed slugging oscillations. A tuning procedure based on the mathematical properties of the model is presented. It is then applied to a test case consisting of a real oil well located in the North Sea. An observer using only topside measurements allows asymptotic reconstruction of critical variables such as the downhole pressure

Control-Oriented Modeling for Managed Pressure Drilling Automation Using Model Order Reduction

Automation of Managed Pressure Drilling (MPD) enables fast and accurate pressure control in drilling operations. The performance that can be achieved by automated MPD is determined by, firstly, the controller design and, secondly, the hydraulics model that is used as a basis for controller design. On the one hand, such hydraulics model should be able to accurately capture essential flow dynamics, e.g., wave propagation effects, for which typically complex models are needed. On the other hand, a suitable model should be simple enough to allow for extensive simulation studies supporting well scenario analysis and high-performance controller design. In this paper, we develop a model order reduction approach for the derivation of such a control-oriented model for {single-phase flow} MPD {operations}. In particular, a nonlinear model order reduction procedure is presented that preserves key system properties such as stability and provides guaranteed (accuracy) bounds on the reduction error. To demonstrate the quality of the derived control-oriented model, {comparisons with field data and} both open-loop and closed-loop simulation-based case studies are presented

Nonlinear Model-Based Control of Unstable Wells

This paper illustrates the potential of nonlinear model-based control applied for stabilization of unstable flow in oil wells. A simple empirical model is developed that describes the qualitative behavior of the downhole pressure during severe riser slugging. A nonlinear controller is designed by an integrator backstepping approach, and stabilization for open-loop unstable pressure setpoints is demonstrated. The proposed backstepping controller is shown in simulations to perform better than PI and PD controllers for low pressure setpoints, and is in addition easier to tune. Operation at a low pressure setpoint is desirable since it corresponds to a high production flow rate. The simulation results are presented to illustrate the effectiveness of proposed control scheme

Adaptive approximation-based estimation of downhole pressure in managed pressure drilling

Low-complexity multi-model algorithms for adaptive bottomhole pressure estimation in drilling is derived, and theoretical and practical conditions for convergence (persistency of excitation) discussed, assuming a) topside measurements only, b) topside measurements and bottomhole pressure measurement, and c) topside measurements and delayed bottomhole pressure measurement

Reservoir characterization in Under-balanced Drilling using Low-Order Lumped Model

Estimation of the production index of oil and gas from the reservoir into the well during under-balanced drilling (UBD) is studied. This paper compares a Lyapunov-based adaptive observer and a joint unscented Kalman filter (UKF) based on a low order lumped (LOL) model and the joint UKF based on the distributed drift-flux model by using real-time measurements of the choke and the bottom-hole pressures. Using the OLGA simulator, it is found that all adaptive observers are capable of identifying the production constants of gas and liquid from the reservoir into the well, with some differences in performance. The results show that the LOL model is sufficient for the purpose of reservoir characterization during UBD operations. Robustness of the adaptive observers is investigated in case of uncertainties and errors in the reservoir and well parameters of the models

Design and comparison of adaptive estimators for Under-balanced Drilling

Real time knowledge of total mass of gas and liquid in the annulus and geological properties of the reservoir is very useful in many active controllers, fault detection systems and safety applications in the well during petroleum exploration and production drilling. Sensors and instrumentation can not measure the total mass of gas and liquid in the well directly and they are computed by solving a series of nonlinear algebraic equations with measuring the choke pressure and the bottom-hole pressure. This paper presents different estimator algorithms for estimation of the annular mass of gas and liquid, and production constants of gas and liquid from the reservoir into the well during Under Balanced Drilling. The results show that all estimators are capable of identifying the production constants of gas and liquid from the reservoir into the well, while the Lyapunov based adaptive observer gives the best performance comparing with other methods when there is a significant amount of noise

Evaluation of Lyapunov-based adaptive observer using low-order lumped model for estimation of production index in Under-Balanced Drilling

A distributed drift-flux model and a low-order lumped model describing a multiphase (gas-liquid) flow in the well during Under-Balanced Drilling (UBD) has been presented. This paper presents a novel nonlinear adaptive observer to estimate the total mass of gas and liquid in the annulus and production constant of gas and liquid from the reservoir into the well during UBD operations. Furthermore, it describes a joint unscented Kalman filter to estimate parameters and states for both the distributed drift-flux and lumped model by using real-time measurements of the choke and the bottom-hole pressures. The performance of the adaptive observers are evaluated for typical drilling scenarios. The results show that all adaptive observers are capable of identifying the production index, although the adaptive observers based on the low-order lumped model achieves better convergence rate than adaptive observer based on the drift-flux model. The results show that the LOL model is sufficient for the purpose of estimating the production parameters