A TORQUE ESTIMATION METHOD TO AID AN INTELLIGENT
MANAGEMENT SYSTEM FOR OIL WELLS AUTOMATED
Alberto S. Rebouc¸as, Fl
´
avia N. Serafim, Milena de A. Moreira, Ven
´
ıcio R. V. Rodeiro,
Amauri Oliveira and J
´
es J. F. Cerqueira
Departamento de Engenharia El
´
etrica of the Escola Polit
´
ecnica
of the Universidade Federal da Bahia
Rua Aristides Novis, 02, Federac¸
˜
ao, CEP:40210-630 Salvador, Bahia, Brasil
Keywords:
Intelligence, Supervision, Estimation, Induction Motors.
Abstract:
This article presents a contribution for an oil wells intelligent management system called SGPA that nowadays
manages about 700 oil wells using the rod pumping lift method at Bahia State, Brazil. The intelligent man-
agement system will be applied on oil wells using the gradual pumping method. In this oil pumping method,
the torque on the rod is very important for detection of operational problems. It will be considered that the
well is driven by an induction motor. A torque estimation method on rod and some results from laboratory are
presented.
1 INTRODUCTION
In oil reservoirs, usually the pressure of bottom is not
enough to move oil to surface, either for its natural
characteristic or the end of its productive life. In this
case, artificial lift systems are used to move oil to sur-
face. To select the fittest lift method, it is necessary to
know the conditions of the wells and to take in con-
siderate several factors:
Characteristics of the well such as porosity, perme-
ability, sand rate, gas rate, pressure, temperature,
production forecast, diameter of the covering and
depth;
Recovery methods for the well due to alterations of
behavior during the operational life;
Fluid properties such as density, viscosity, paraffin
rate and sand rate;
Energy available such as electric energy, gas or
fuel;
Localization, number of wells and resources from
the field;
Legal restrictions such as ambient standards;
Economic evaluation of the project;
The main artificial lift methods are:
Continuous and Intermittent Gas-lift: The gas-lift
(CGL or IGL) uses the energy of gas compressed
to move the oil to surface. The gas is used to gasify
the oil (GLC) or to throw it (GLI) of the deep until
the surface.
Submerged Centrifugal Pump: The Submerged
Centrifugal Pump consists of a centrifugal pump
of several stages in serie, in accordance with
the pressure of the bottom of the well. Motors
projected to work under high temperature and high
pressure are used into this lift system;
Rod Pumping: In the Rod Pumping (RP), an elec-
tric motor or a combustion motor drives an appara-
tus to convert the rotational movement into linear
movement to a rod. This rod drives a piston pump
at the bottom of the well.
Gradual Pump: The Gradual Pump (GP) uses a
pump with a steel screw rotor and a stator of soft
material, usually an elastomer. The pump is used
in the bottom of the well working submerged into
oil. The rotational movement of the pump dislo-
cates the oil gradually in the direction of the sur-
face. Without necessity of valves, the pump can be
driven from the surface with the rotational move-
ment transmitted by a rod. Electric motors can be
used to drive the pump.
An oil extraction field can have a lot of wells. For
example, the Bahia Extraction Field, at Brazil, has
about 4000 wells. Such numbers of well produces a
lot of information such that its analysis by engineers
is very difficult. Some times, this analysis are nec-
essary to intervene in the well operation. Specialists
270
Rebouças A., Serafim F., Moreira M., Rodeiro V., Oliveira A. and Cerqueira J. (2004).
A TORQUE ESTIMATION METHOD TO AID AN INTELLIGENT MANAGEMENT SYSTEM FOR OIL WELLS AUTOMATED.
In Proceedings of the First International Conference on Informatics in Control, Automation and Robotics, pages 270-275
DOI: 10.5220/0001137802700275
Copyright
c
SciTePress
motor
head
rod
pump
Figure 1: Gradual pump system (GP).
have proposed the use of intelligent system approach
to aid the management of oil wells (Alegre et al.,
1993; Hosn et al., 2001; Nikravesh and Aminzadeh,
2001; Cerqueira et al., 2002b; Cerqueira et al., 2002c;
Cerqueira et al., 2002a; Cerqueira et al., 2003).
The torque variable is very important to supervise
the wells. The torque analysis supplies information
about the load of the electric motor. This makes pos-
sible preventive actions to avoid that the system oper-
ates in a dangerous situation.
The goal of this paper is to present a torque esti-
mation method to aid the supervision of GP in oil
well. The torque estimated at head of the well can
warn operational problems. The GP system is driven
by induction motor in the surface that transmits the
rotating movement for the pump at the bottom of the
well by a rod coupled with a gear train (see figure 1).
Currently, a frequency inverter can be used to control
the motor speed.
The paper is organized this way. In section 2 are
presented operational problems in GP. In section 3
is presented an intelligent distributed system used to
manage oil wells, the SGPA. In section 4 the torque
estimation method for induction motor is described
in mathematical format. The hardware/software ar-
chitecture used to estimate the torque is presented in
section 5. In sections 6 and 7 are presented some ex-
periment results and the conclusions of the paper re-
spectively.
2 OPERATIONAL PROBLEMS
Excess of torque on the rod that moves the pump in
the bottom of the well can break the rod or the gear
train coupled with motor and to stop the oil pumping.
For repair a broken pump/rod set is necessary to re-
move a rod with hundreds of meters and to capture
the pump fallen at bottom of the well using special
equipment and specialized people. Sometimes, the
well recovery can delay weeks and to cause losses of
production and of cash.
On the other hand, a very small torque on the rod
causes low power factor of power on the electric sys-
tem and can indicate that the pump is not pumping
the fluid. This situation could damage the elastomer
of the stator hindering the perfect rotation of the rod
and leading the system to the previous situation, with
high torque. Additionally, when the fluid is pumped,
it lubricates and refrigeration of the mechanical sub-
system of bottom of the well.
A previous fault detention allows the planning of
the maintenance of the lift system, in order to attenu-
ate the impacts provoked for the operation stops and
to reduce the maintenance costs.
It is necessary accompaniment of other variable of
the system such as the noise level in the head, the
pressure in the top of the well and the electric current
on the motor during the pumping of the oil. These
variable are also important for fault detection in lift
system, but they are not approached in this paper.
3 THE INTELLIGENT
MANAGEMENT SYSTEM
Researchers of Universidade Federal da Bahia in
Brazil joint with engineers of Brazilian petroleum
company (PETROBRAS) have applied concept of ex-
pert systems into an automated lift well management
system, called SGPA - Sistema de Gerenciamento
de Poc¸os Automatizados (Cerqueira et al., 2002b;
Cerqueira et al., 2002c; Cerqueira et al., 2002a;
Cerqueira et al., 2003). The SGPA consists of three
levels of management:
Level 1: Where a programmable logical controller
(PLC), sensors and actuators work directly in the
supervision and control of the well. The electric
motor is supplied by electronic inverter. This level
of management and automation is local and the
well become automated;
Level 2: Where intelligent supervisor system re-
ceives the information from level 1 by radio com-
munication link. The intelligent supervisor man-
ages group of wells of an extraction field from a
control room;
Level 3: Where are made analysis, diagnostics and
recovery actions are chosen for the lift systems.
In this level is added information about production
costs, economic information, operational historical
of the wells, and information from previous levels.
In this management model, Artificial Intelligence
techniques such as Symbolic Neural Nets and Fuzzy
Logical are used for application of knowledge get
from specialists in petroleum engineering.
A TORQUE ESTIMATION METHOD TO AID AN INTELLIGENT MANAGEMENT SYSTEM FOR OIL WELLS
AUTOMATED
271
τ
τ
n
torque
rotation
n
n
n
n
s
Figure 2: Curve torque × rotation for induction motor.
For torque measurement from rod in GP method,
a torquimeter could be installed in the rod. However,
a torquimeter is not a robust equipment and has high
cost, mainly considering that must be installed in the
well surface and submitted to weather action. The
same inconvenience is observed when is analyzed the
possibility of insertion of a torquimeter in the gear
train or in the electrical motor or in the bottom of
the well. The insertion of a torquimeter in the mo-
tor would had advantage the torquimeter works with
lesser torques.
A torque estimation method developed for the in-
duction motor from the spectrum analysis of the elec-
tric current is presented as option for torque estima-
tion in the rod and to supervise the GP method. This
estimation method does not modify the mechanical
structure of the wells. Therefore, the method is not
intrusive. Petroleum engineers can use the torque es-
timated to study problemas in the wells.
The torque estimation from the current spectrum
offers satisfactory accuracy, practically without intru-
sion in the system, but it requires sophisticated anal-
ysis. The identification of a specific frequency har-
monic component of the electrical current on the elec-
tric motor is necessary. This frequencies are function
of the rotor speed. With this, the slip of the induction
motor can be obtained. The slip has an almost linear
relation with the torque into operational region. From
this linear relation, the torque can be estimated.
4 THE TORQUE ESTIMATION
METHOD
In curve torque × rotation for induction motor is ver-
ified that near to the synchronous speed the curve can
be approached by a straight line with negative angu-
lar coefficient, as shown in figure 2, where: n
n
is the
nominal speed; τ
n
is the nominal torque; n
s
is the
synchronous speed; n is the speed; and τ is the torque.
From nameplate data, the torque can be estimated
by the expression
τ =
(n
s
n)
(n
s
n
n
)
P
n
0.10466 n
n
(1)
where P
n
is nominal power and the speeds are in rpm
(rotation per minute).
This expression is gotten from a other expression
used for estimation of the power output of the induc-
tion motor and indicated by technical standards (IEEE
Standard, 1991; Damasceno, 2002; Damasceno et al.,
2002a; Damasceno et al., 2002b). For torque esti-
mation, the information about electric motor speed is
necessary.
4.1 SPEED ESTIMATION
The speed measurement in induction motor is as dif-
ficult as torque measurement in the GP system. Me-
chanical considerations and noise make the use of
tachometer or encoders connected to the axle of the
motor a solution does not appropriate. An alterna-
tive is the speed estimation from spectrum of the elec-
tric current signal of any electrical phases that supply
the motor, as described for (Hurst and Habetler, 1996;
Blasco-Gimenez et al., 1996; Benbouzid, 2000).
Because interactions between magnetic fields into
the induction motor and constructive imperfections -
as the slots in the rotor where the squirrel cage is built
- the spectrum of the electrical current is rich in har-
monic components. Some of these components are
related to the slip in the form
f
sh
= f
s
(kR± n
d
)
1 s
P
2
± n
w
(2)
where f
s
is electrical fundamental frequency, f
sh
is
frequency multiple of the fundamental frequency, R
is the number of rotor slots, n
d
it is the eccentricity,
P it is the pole number of the stator, n
w
is a harmonic
order and k is a any multiple.
The method works as follows. For s =0, values for
k, n
d
and n
w
are attributed. With this, a frequency
called f
sh
0
is determined. A maximum slip value
s
max
is attributed and applied in equation 2. This de-
termines a frequency f
sh
min
. So, a spectral window
from f
sh
min
to f
sh
0
can specified where f
sh
will be
always the maximum amplitude component. The mo-
tor slip can be determined from expression
s =1
f
sh
f
s
n
w
p
2
kR± n
d
. (3)
The motor speed can be determined in rpm from
expression
n =(1 s)
120 f
s
p
. (4)
ICINCO 2004 - INTELLIGENT CONTROL SYSTEMS AND OPTIMIZATION
272
Current
Sensor
Current
Sensor
Data
Acquisition
Detector
of f
s
Filter 1
Detector
of f
sh
Equations
1e2
Figure 3: System Blocks Diagram.
This estimation method has accuracy enough to be
used in torque estimation. Details about implementa-
tion of speed estimation method can be found in (San-
tos, 2002; Santos and Oliveira, 2002).
5 THE SYSTEM ARCHITECTURE
The systems built from three subsystem: one of data
acquisition; one of hardware for data processing; and
one third of software.
The data acquisition subsystem is composed of cur-
rent sensors and a data acquisition hardware with
sample-hold and analog-to-digital converter.
The processing subsystem is constituted of a per-
sonal computer. The software subsystem was imple-
mented using resources of the LABVIEW
package.
Figure 3 illustrates the organization of the system.
Two current sensors are used. A sensors is used for
current signal acquisition and subsequent detention
of electrical signal fundamental frequency, f
s
. The
other sensor is used for current signal acquisition and
subsequent detention of the frequency that carries the
information about the induction motor slip, f
sh
For detention of f
sh
, the current signal is filtered
by an pass-band analog filter, represented in the figure
for filter 1. This filter objectives to eliminate in order
the fundamental component of the electrical signal,
f
s
and the high frequencies. In sequence, the data ac-
quisition is made. The digital signal is processed for
detection of the component f
sh
. After the detention
of f
s
and of f
sh
, the information about R, P
n
, n
n
and P
n
are used to estimate the speed and the torque
in the induction motor attributing values for k, n
w
and
n
d
into expressions (1)-(4).
From the speed reduction on the gear train the
torque in well head is gotten. Petroleum engineers
use this torque to estimate the torque at some points
of the rod. The SGPA can be implemented adding in
order these information with other about the working
of the wells.
6 EXPERIMENTAL RESULTS
Laboratory tests was done for validation of the the-
oretical developments presented in this text. In the
test system, a continuous current generator simulates
a mechanical load in the axle of the three-phase in-
duction motor corresponding to mechanical load on
rod in the GP system. For the torque estimation sys-
tem illustrated in figure 3 and described in section 5,
a tachometer and a torquimeter to compare its indica-
tions with estimated values were added.
The torque estimation system was tested with an
induction motor. A 2 cv, 220/360 V (/Y), 4 pole,
1725 rpm, and 44 rotor slots. For this motor, the ex-
perimental results are presented in table 1.
The first measure was carried out with the contin-
uous current generator working without field current
and with this the induction motor worked with very
small load. The last measure was carried out at over-
load condition. Analyzing tables 1 is observed that
inside normal band operation of induction motor the
error in the torque estimation is very small. The errors
were only significant in order on overload and very
small load condition. Additionally, at overload con-
dition, the error can be attributed to linear approach
made for torque, once the true curve is not a straight
line. Therefore, the torque estimation method is effi-
cient inside motors operation band.
The efficiency of the estimation method depends on
accuracy of data from manufacturer of the induction
motors. The nameplate data have their accuracy stan-
dardized, but this does not avoid that any induction
motor works outside the nameplate data. Some times,
nameplate data and manufactures book data are differ-
ent. So, part of error for the motor can be attributed
A TORQUE ESTIMATION METHOD TO AID AN INTELLIGENT MANAGEMENT SYSTEM FOR OIL WELLS
AUTOMATED
273
Table 1: Experimental Results for 2 cv Motor.
f
s
n measure (rpm) n estimated (rpm) τ measured(Nm) τ estimated (Nm) error (%)
59.992 1792 1790 0.82 1.01 23
60.005 1782 1782 2.02 1.94 3.96
59.978 1764 1763 4.03 3.95 1.99
59.978 1744 1744 6.03 6.04 0.17
60.031 1724 1723 8.08 8.28 2.48
60.005 1696 1696 10.05 11.23 11.74
60.024 1667 1666 12.01 14.44 20.23
to accuracy of manufacturer data.
7 CONCLUSION
The estimation methods of torque and speed are suf-
ficiently accurate inside operation nominal band of
three-phase induction motors. The use of these meth-
ods makes possible the GP supervision without the
installation of encoders, tachometers or torquimeters.
This means cost reduction of systems like SGPA in
GP wells.
A next step is the test of the system using frequency
inversor and its evaluation on the wells. It is intended
also improvement of hardware using a dedicated sub-
system. This subsystem must be built with microcon-
troller and digital signal processor (DSP). The com-
plete system will be used in the supervision of ap-
proximately 200 oil wells installed in Bahia State, in
Brazil.
ACKNOWLEDGEMENTS
The authors would like to thank in order the Finan-
ciadora de Estudos e Projetos (FINEP), the PETRO-
BRAS S. A., and the Conselho Nacional de Desen-
volvimento Cient
´
ıfico e Tecnol
´
ogico, all of Brazil, by
their support for this work.
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