A first principle model for multiphase slugging flow in vertical risers

Meglio, Florent Di; Kaasa, Glenn-Ole; Petit, Nicolas

doi:10.1109/cdc.2009.5400680

Cited by 30 publications

(43 citation statements)

References 17 publications

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“…This observer was designed based on a three-state simplified model of the system [9]. It was found that the peak of the sensitivity transfer function of the topside pressure for their system was not large, and it did not impose limitations on the controllability [8].…”

Section: Introductionmentioning

confidence: 99%

Anti-slug control experiments using nonlinear observers

Jahanshahi

Skogestad

Grøtli

2013

2013 American Control Conference

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Abstract-To prevent slug-flow on offshore oil production units, controlling a subsea pressure is the recommended solution. However, the subsea pressure is not often available as a measurement. The top-side pressure is usually measured but it is difficult to use for stabilizing control, because of its Right Half-Plane zero dynamics combined with nonlinearity. We have used the top-side pressure as measurement for different observers to estimate state variables of the system, and then used the estimated states for control. This scheme was tested in experiments using three types of observers. A simple Luenberger observer with a large gain was found to be more robust than the standard Unscented Kalman Filter (UKF). We modified the UKF to a Fast UKF for time-scale separation of the closed-loop system. The resulting observer was robust, also less sensitive to measurement noise compared to the simple Luenberger observer when measuring the top-side pressure. Surprisingly, the nonlinear observers were not able to work in closed-loop when the subsea pressure was used as the measurement. On the other hand, a linear observer worked very well for this case with a larger operating range compared to that of the top-side pressure.

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Section: Introductionmentioning

confidence: 99%

Anti-slug control experiments using nonlinear observers

Jahanshahi

Skogestad

Grøtli

2013

2013 American Control Conference

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“…The dataset used to train the RC network was generated in Matlab by simulating the ordinary differential equations (ODEs) of the vertical riser model described by [11] We generated n = 24, 000 seconds (about six and a half hours) of simulation using the ODE equations to collect the pairs (u[n],ŷ[n]) using a randomly created, squared-shaped, input signal u[n].…”

Section: Methodsmentioning

confidence: 99%

“…The model in [11] has three states which are the mass of liquid in the riser (m l,r ), the mass of gas flowing with liquid phase (m g,r ), and the mass of gas stuck in the bubbles (m g,eb ). These state variables are related by the following mass balance equations:ṁ…”

Section: Appendix A2 Error Measuresmentioning

confidence: 99%

“…The challenge of identifying the vertical riser model from [11] with a single network is that it presents two distinct regions: one stable, and another area characterized by oscillations (see Fig. 3(a)).…”

Section: Introductionmentioning

confidence: 99%

“…In this first part, RC is employed for identifying a model [11] which displays the complex phenomena involved in multiphase flow dynamics observed in vertical risers. A scheme of the riser can be seen in Fig.…”

Section: Introductionmentioning

confidence: 99%

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Echo State Networks for data-driven downhole pressure estimation in gas-lift oil wells

Antonelo¹,

Camponogara²,

Foss

2017

Neural Networks

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Process measurements are of vital importance for monitoring and control of industrial plants. When we consider offshore oil production platforms, wells that require gas-lift technology to yield oil production from low pressure oil reservoirs can become unstable under some conditions. This undesirable phenomenon is usually called slugging flow, and can be identified by an oscillatory behavior of the downhole pressure measurement. Given the importance of this measurement and the unreliability of the related sensor, this work aims at designing data-driven soft-sensors for downhole pressure estimation in two contexts: one for speeding up first-principle model simulation of a vertical riser model; and another for estimating the downhole pressure using real-world data from an oil well from Petrobras based only on topside platform measurements. Both tasks are tackled by employing Echo State Networks (ESN) as an efficient technique for training Recurrent Neural Networks. We show that a single ESN is capable of robustly modeling both the slugging flow behavior and a steady state based only on a square wave input signal representing the production choke opening in the vertical riser. Besides, we compare the performance of a standard network to the performance of a multiple timescale hierarchical architecture in the second task and show that the latter architecture performs better in modeling both large irregular transients and more commonly occurring small oscillations.

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