Summary
This paper considers the use of extended Kalman filtering as a soft-sensing
technique for gas lift wells. This technique is deployed for the estimation of
dynamic variables that are not directly measured. Possible applications are the
estimation of flow rates from surface and downhole pressure measurements or the
estimation of parameters of a drift-flux model. By means of simulation
examples, different configurations of sensor systems are analyzed. Finally, the
estimation of drift-flux model parameters is demonstrated on real data from a
laboratory setup.
Introduction
During the last 10 years, the industry has seen different downhole actuation
technologies (commonly known as intelligent completions or under different
trademarks) coming into existence. The goal of these technologies is ultimately
to maximize the value of an asset by applying “right-time” optimization
concepts borrowed from control engineering. Depending on the specific economics
of the asset, this can be translated into more specific objectives such as
speeding up of production, stabilization of unstable production, deferment of
production of unwanted fluids, maximizing ultimate recovery, or a combination
of some of the aforementioned short- and long-term objectives.
Control theory concepts of optimization by means of a feedback loop require
means for determining the deviation between the actual response and the desired
response of the system. In wells, this often boils down to some sort of
multiphase flow measurement. Different accurate multiphase-measurement
technologies have been matured during the last decade, and the industry seems
to be crossing the chasm between the early-adopter and the early-follower
stages. Often for control purposes, direct measurements with high absolute
accuracy are not required, as long as the measurements give a good indication
of the relative change in the property that needs to be optimized.
In different process industries, soft-sensing techniques were developed to
determine variables where it is either impossible to directly measure the
variables of interest or where it is economically not justifiable. In this
paper, the concept of soft sensing is used; unmeasured dynamic variables (such
as flow rates) are estimated from measured ones (i.e., pressures) by fitting a
sufficiently accurate numerical model to the available measurements. We have
looked at the gas lifted well application, where the lift gas rate may be
controlled. Ideally this control would be based on directly measured multiphase
flow rates, but in reality one often finds that this information is not
available. Other measurements, such as surface and downhole pressure and
temperature measurements, are more readily available and may be used for soft
sensing.
The paper is organized in the following manner: first, the model of the gas
lifted well is described; then, the soft-sensing concepts are explained; and,
finally, different examples and configurations are shown in which this
technology is applied for estimating multiphase flows.
© 2006. Society of Petroleum Engineers
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History
- Original manuscript received:
9 May 2005
- Revised manuscript received:
21 July 2006
- Manuscript approved:
24 July 2006
- Version of record:
20 December 2006
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