S
SPE 163704
B
Benefit Evaluation
n of Keep
ping an In
ntegrated
d Model D
During Re
eal-Time E
ESP
O
Operation
ns
T
T. Denney, SP
PE, B. Wolfe, SPE, D. Zhu
u, Baker Hugh
hes Inc.
C
Copyright 2013, Society
y of Petroleum Enginee
ers
T
This paper was prepare
ed for presentation at the 2013 SPE Digital Energy
E
Conference and
d Exhibition held in Thee Woodlands, Texas, U
USA, 5–7 March 2013..
mittee following review of information containned in an abstract subm
mitted by the author(s)). Contents of the pape
T
This paper was selected for presentation by an
a SPE program comm
er have not been
re
eviewed by the S ociety
y of Petroleum Engine
eers and are subject to
o correction by the a utthor(s). The material ddoes not necessarily re
eflect any position of t he Society of Petroleu
um Engineers, its
officers, or members. Electronic
E
reproduction
n, distribution, or st ora
age of any part of thiss paper without the wrritten consent of the S
Society of Pet roleum E
Engineers is pr ohibite
ed. Permission to
illustrations mayy not be copied. The abbstract must contain co
onspicuous acknowled
dgment of SPE copyrig
re
eproduce in print is res
stricted to an abstract of
o not more than 300 words;
w
ght.
Abstract
A
p
(ESP) ap
pplications, a great
g
deal of tim
me and effort iss spent upon thhe initial sizingg of the
IIn most electricc submersible pump
ESP – projectin
ng which equip
pment is most appropriate
a
forr the physical pproperties, reseervoir delivery,, and operator eeconomics
E
A
the ESP is
i designed, ordered, and insttalled, howeverr, the sizing is often placed inn a
oof the particulaar application. After
n referred to again until thee ESP has faileed and the nextt ESP is being ddesigned. By iintegrating thee design
rrepository and not
model into a real-time monito
oring environm
ment, more valu
ue can be demoonstrated. Inforrmation that is particularly vaaluable
m
oint, ESP diagnnostics alarms,, virtual monitooring and alarm
ms, and
inncludes the evaluation of opeerating point veersus design po
monitoring of sub-component
s
t design thresh
holds. In this paaper, we will reeview ESP dessign software inn general, the bbenefits of
m
mplementing a real-time mod
del comparison
n tool, demonsttrate several caases, and concllude with a disscussion of the challenges
im
h managing thee models. Addittionally, we wiill examine how
w sizing can be used to optim
mize productionn and
aassociated with
make adjustments for system enhancement.
m
IIntroduction
Electric submerrsible pumps (E
E
ESPs) have become one of th
he most populaar and widely ddeployed formss of artificial liift in the
world. ESPs offfer operators a large degree of
w
o durability an
nd reliability accross a host of applications, including slim--hole oil
wells, high-production water wells, and coal bed methane wells. As techhnology has im
w
mproved, ESPs hhave been pushhed to
eeven more challlenging enviro
onments, includ
ding steam assiisted gravity drrains (SAGD) and deepwaterr applications.
When reviewin
W
ng ESP failuress, it has been fo
ound that an alaarming rate off failures could have been prevented. Exam
mples of
ppreventable faillures include in
ncorrect sizing
g, improper setu
up, operator errror, and failuree to follow maiintenance proccedures.
The costs assocciated with prev
T
ventable failurres are significaant, and includde those from ddeferred producction, replacem
ment
eequipment, rig//work over costs, and significcant opportunitty costs from enngineering andd field personnnel.
All of the aforeementioned preesent strong inccentives for reaal-time operatioonal surveillannce. Operationaal surveillance is not new
A
ffor ESPs. Most operators hav
ve incorporated
d their ESPs into a Supervisoory Control andd Data Acquisittion, or SCAD
DA,
m ESP manu
nnetwork, and most
ufacturers have begun offering
g after sale moonitoring servicces. While tradditional SCADA
A
methodologies,, such as set po
m
oint alarming an
nd trending, caan offer signifiicant value to thhe operation, aadditional valuue can be
ggained by using
g some non-SC
CADA techniqu
ues. This paperr will explore tthe benefits of integrating a m
mathematical m
model into
thhe SCADA f an ESP operattion.
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ESP Design and Modeling
In most of today’s operations, a software model is used to approximate the physical conditions the ESP will experience. In
Table 1, Baker Hughes (Baker Hughes Centrilift, 2008) presents the data normally included in a typical ESP sizing model.
Table 1: ESP design considerations
Data Value
Static
Casing or liner size and weight
X
Tubing size, type and thread
X
Perforated or open hole interval
X
Pump setting depth
X
Dynamic
Wellhead tubing pressure
X
Wellhead casing pressure
X
Test production rate
X
Producing fluid level (Flow Pressure)
X
Static fluid level (Static Pressure)
X
Bottom hole temperature
X
Desired production rate
X
Gas-oil ratio
X
Water cut
X
Specific gravity of produced fluids
X
Bubble point pressure of gas
X
Viscosity of oil
X
PVT data
X
Power sources: primary voltage,
frequency, power source capabilities
Other considerations: sand, deposition,
corrosion, paraffin, emulsion, gas,
temperature
X
X
As shown above, the majority of the tags necessary are dynamic. During sizing, this is normally dealt with by developing
several different static ‘cases’. The cases produced generally represent the best and worst scenarios for production rates to
ensure the ESP is sized correctly. The sizing software gives the application engineer many tools to select the ‘right’ pump for
the application. The most powerful tools within the sizing software are the equipment ‘flags,’ which notify the application
SPE 163704
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engineer when equipment constraint is being encroached. Equipment constraint is typically a physical constraint such as
mechanical, hydraulic, or electrical issues.
One of the biggest challenges associated with ESP design is the level of uncertainty. For the average brownfield well, a deep
knowledge of the reservoir productivity and fluid characteristics is difficult to obtain at time zero, with further uncertainty
introduced as the well is produced.
However, by integrating the model with the real-time data, the operator can obtain the insight and care that was taken during
the design process, and carry that knowledge further to represent a complete operating envelope for the ESP (in addition to
the well and pump behavior changes). Furthermore, the design model can be used as a diagnostic tool to further our
understanding of adverse conditions.
ESP Diagnostics
An ESP is a complicated system to be mathematically modeled. It involves domain knowledge of mechanical, hydraulic, and
petroleum engineering, thermodynamics, and electrical engineering. Fuzzy logic is an innovative technology that can
circumvent the need for rigorous mathematical modeling. Before the advantages of fuzzy logic are presented, a typical
SCADA alarm system for ESP operation will be examined (Thornhill & Zhu).
To protect the ESP motor, it is important to monitor the motor’s operating temperature. The conventional SCADA system
defines an inflexible limit for a temperature alarm (for example, the ESP motor cannot operate above 300°F). There are two
flaws to this approach:
1.
2.
What if there is an issue with the temperature sensor? For example, the real temperature is 302°F, but the reading
from sensor is only 298°F because of inaccuracies in the sensor’s measurement. In this case, the conventional
SCADA system will not issue the alarm for an overheated motor. The traditional SCADA system is not robust
enough to handle signal noise or a false sensor reading.
Additionally, SCADA systems tend to treat alarms as black or white. Either the system is in a state of alarm or all
systems are clear. From a mechanical and electrical point of view, an ESP with a motor temperature of 298°F is not
in much better condition than an ESP operating at 300°F. Both situations require an ESP engineer be notified to take
proactive action to prevent a catastrophic failure. The SCADA system, however, will only issue an alarm when the
temperature reaches 300°F, ignoring the other situation. One could argue that the alarm condition could be made
looser. Overall, however, this only produces more alarms (often false ones) and still produces a black/white
boundary between alarms and all-clear conditions.
In contrast, fuzzy logic performs reasoning based on uncertain or imprecise information. Table 2 outlines the differences
between fuzzy logic and conventional crisp logic in an example of shutting down an ESP when high temperatures are
observed.
Table 2: Comparison of Fuzzy Logic vs. Crisp Logic for ESP shutdown
How to describe hot?
True or false
Proactive action support
Noise handling
Fuzzy Logic Approach
290°F will be close to hot, but to a
degree of 0.9; 299°F will be close to
hot to a degree of 1.
Partial true or false exists
Yes
Good
Conventional Crisp Logic Approach
300°F exactly is hot, 299.99°F is not
hot
Nothing between true and false
No
Poor
Another advantage of using fuzzy logic is its tendency to mimic human behavior. In the example above, a person knows to
shut down the motor when the temperature is too high. In fuzzy logic terms, we can set up an “IF—THEN” rule to declare “if
the motor temperature is too high, we need to shut down the motor.” The beauty of fuzzy logic lies in the linguistic term “too
high.” Note that with fuzzy logic, we do not say “if the motor temperature is above 300°F, we need to shut down the motor.”
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The linguistic term “too high” allow us to assign a degree of ‘hot’ beyond 0 and 1. In reality, “too high” will allow a system
to shut down a motor when temperatures reach 298°F, because from a fuzzy logic point of view, 298°F is close to “too high”
to a degree of 0.98.
The diagram illustrates the components that make up a fuzzy logic system:
Fig. 1: Components of a fuzzy logic system
Inputs
Inference Engine
Outputs
Rule base
During ESP operation, a SCADA reading such as motor temperature will be one of the inputs. The If-Then rule (“If motor
temperature is too high, we need to shut down motor”) will be one of the rules stored in the rule base. The inference engine of
the fuzzy logic system works much like the human brain. People use common sense and knowledge (inference engine) to
make decisions every second of the day. For example, a human being that feels the air temperature can tell if they need to
wear a coat or not. The air temperature does not have to drop below 50°F for a human to receive an alarm. The human can
feel degrees of cold and make a decision when a coat is necessary. An inference engine will first convert the linguistic term
“too high” to a numerical number that mathematical systems can understand. If the degree of truth is high enough, this rule
will trigger, and an action will be taken to shut down the motor.
The real challenge to build a robust fuzzy logic system is not the rule itself (since it is basically assembled together by
“expert knowledge”). In reality, the rule is not assigned a precise number. We would rather use linguistic terms like “too
high.” The difficult part of building a robust fuzzy logic engine is the inference engine, because computers cannot understand
a linguistic term such as “too high.” The expert must program the computer to know how to calculate the degree of truth for
“too high.” To discern this, an expert must define a “membership function” so the software can calculate the degree of truth.
A typical membership function is demonstrated as the following:
SPE 163704
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Fig. 2: Membership function example - degree of truth for “too high”
1
0
150F
300F
Temperature
In this example, when the temperature is below 150°F, the degree of truth for “too high” is zero. When the temperature is
above 300°F, the degree of truth for “too high” becomes 1. The strength of fuzzy logic is apparent when the temperature is
between 150°F and 300°F. The degree of truth will control how strong the rule will be applied.
In order for the computer to understand the linguistic term “too high,” we need to define:
1.
2.
3.
A normal value
A low value
A high value
In the example, the normal value could be 225°F, the low value 150°F, and the high value 300°F.
The sensible question at this point is how should the proper low/normal/high values be chosen? Once an ESP has been
designed, and its likely operating envelope is known, there is an idea about what the theoretical motor temperature will be. In
other words, there is strong reason to believe the real operational motor temperature will not be that far away from the
theoretical value (if the assumptions are valid). A reasonable approach for selecting a normal value includes studying the
sizing for theoretical motor temperature, then applying a factor to set the low and high values. This factor can be application
dependent (for example, a low flow application may have a higher expected motor temperature). Once the fuzzy logic system
has been deployed to field production, it is important for the engineer to review the low/normal/high value to overcome any
errors in the assumptions or model discrepancies. The strategy is to tune the fuzzy logic system to provide the correct
sensitivity level for the ESP application. A high-cost environment such as deep water may have tighter values than a
brownfield environment, because of the economic implications of a premature failure.
Below are several cases that demonstrate how fuzzy logic can be used for ESP operational troubleshooting.
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F
Fig. 3: Fuzzy logic
l
case histo
ory, tubing lea
ak
O
On 24Jan. 2009
9, the SCADA data showed pump
p
intake pressure (Pip) inncreasing and fl
flow (Qstk) deccreasing. Whenn the fuzzy
loogic inference engine processed the combin
nation of all SC
CADA signals against the tubbing leak rule iin the rule basee, the
ddegree of truth gradually rosee from 10% in January
J
to 51%
% in February, and then 61% in March.
T
This reveals an
nother advantag
ge of using fuzzzy logic. As Piip keeps increaasing, and flow
w keeps decreassing, the composited
ddegree of truth for the “if” sid
de of the tubing
g leak rule conttinues to get biigger. From ann operational pooint of view, thhe relative
cchange of the tu
ubing leak cou
uld be used as a trigger for an ESP engineer to evaluate a ssituation.
S
SPE 163704
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7
F
Fig. 4. Fuzzy lo
ogic case histo
ory: broken pu
ump shaft
O
On 27 April, th
he intake pressu
ure began increeasing rapidly. The motor tem
mperature begaan increasing w
while the motorr amps and
ddischarge presssure began to drop.
d
In this exaample, one of the
t fuzzy logicc rules for the ““pump shaft brroken” was trigggered.
L
Later, field testts confirmed th
he broken shaftt.
F
For fuzzy logicc to be effectivee, it is extremeely important to
o set correct infference valuess for low/normaal/high. If these values
aare not set correectly, fuzzy log
gic will perform
m reasoning ag
gainst the wronng “base line”. In other words, if normal isnn’t known,
thhen it will be difficult
d
to deteect what is not normal.
U
Unfortunately, most of the weells included in
n ESP applicatiion have dynam
mic behaviors. For example, water cut for a well
ccould change, and
a therefore, it
i will affect pu
ump intake pressure. If an infference engine still uses old ppump intake prressure
ddata, it will incorrectly triggerr the rule base.. For this reaso
on, updating thee low/normal/hhigh values bassed on the currrent
ooperating condiition becomes critical to utilizing fuzzy logic in a meaninggful way. Thee engineer needds effective andd userffriendly tools to
o assist in keep
ping the ESP model
m
valid. Th
his challenge, aand the methodds for overcomiing it, are presented later
inn this paper.
V
Virtual Meterin
ng
A
Another advanttage of an integ
grated ESP mo
odel is virtual meters.
m
By com
mbining existinng signals from
m surface and doownhole
ssensors, in conjjunction with a properly tuneed model, vario
ous other param
meters can be m
metered virtually (Denney & Crossley,
22011). This allo
ows measurem
ments where eith
her physical (ee.g. casing size limitations) orr economic connstraints exist. One of
thhe primary, an
nd most useful, measurementss that can be acchieved is flow
w rate.
F
Flow rate in an ESP applicatio
on is typically received by a periodic
p
well ttest. In many E
ESP applicationns, this well tesst is
cconducted oncee a month; how
wever, it can bee much more in
nfrequent depennding on the loocal circumstannces. A real-tim
me flow
rrate offers the following
f
advaantages (Denneey & Crossley, 2011):
1.
2.
3.
4.
Allow
ws for optimizattion of ESP equ
uipment perforrmance; particuularly operatinng point-on-thee-pump curve
Gives information to
o diagnose non-catastrophic equipment
e
and well issues
Verification of meassurements taken
n by test separaator
Evaluaation of reservo
oir response
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F
Flow monitorin
ng typically utiilizes commonlly available reaal-time informaation from an E
ESP, includingg tubing pressuure (Ptbg),
ppump dischargee pressure (Pdp
p), pump intakee pressure (Pip
p), and controlller frequency. T
The model is uused to make a
rrepresentation of
o the pump beehavior (the pu
ump curve – mo
odified to actuaal operating coonditions) and tthe reservoir
((productivity in
ndex, reservoir pressures, fluiid characteristiics, etc.). Fig. 5 illustrates onne virtual flow m
meter:
1.
2.
Utiliziing the real tim
me measuremen
nts from Pdp an
nd Pip, the heaad generated froom the pump ccan be calculatted (Pdp –
Pip = head)
h
By plo
otting head on the operational pump curve (at
( the current ooperating frequuency), the estimated flow raate of the
pump can be achieveed
F
Fig. 5: Virtuall flow meter
A
As with the fuzzzy logic engin
ne, the accuracy
y of the virtual flow meter is directly depenndent on the quuality of the moodel used.
F
Fig. 6 illustratees this point (Denney & Crosssley, 2011). Th
he Neuraflow rreading trackedd the flow readding from a muulti-phase
fflow meter with
h better than 95
5% accuracy frrom the period
d between Septeember 2007 too May 2008 witthout the modeel needing
too be recalibrated. In June 200
08, there was a water breakth
hrough event w
which caused thhe density of thhe fluid to channge, and
thherefore, the behavior
b
of the pump change as well. If therre had been a m
method to detecct the change inn water cut (test
sseparator, surfaace flow meter,, etc.), a recalib
bration would have
h
been issuued to keep the model matcheed. However, inn this
eexample, the reecalibration waas not issued to illustrate the importance
i
of a valid well moodel for virtuall flow meter reeading.
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SPE 163704
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9
F
Fig. 6: Virtuall flow meter ca
ase history
D
Design Improveements
T
The primary methodology useed for sizing an
n ESP tends to be selecting seeveral cases too reflect the dynnamic nature oof the
ppump operation
n or to reflect unknowns
u
(for example, the PI
P in a newly ddrilled well). Inn most cases, thhese are just ‘bbest
gguesses’ at a reepresentation of a static scenaario, and tend to
o ignore the dyynamic nature of pump operaations. Some deesign
ssoftware have simulators,
s
whiich allow the ESP
E engineer to
o simulate certtain dynamic evvents (such as startup, gas sluugging,
eetc.), but simulation of all eveents would be impractical.
i
Du
uring actual puump operationss, design softw
ware can be flaggged in
thhese dynamic situations.
A
An example off when flagging
g is used would
d be if during pump
p
operationn, the formationn of emulsionss caused a shafft to be
ooverloaded. Wh
hile installing the
t subsequentt ESP, or an ES
SP in an offset well, the ESP engineer may decide to utilizze a high
sstrength shaft in
n their design. The formation
n of emulsion may
m not have bbeen one of thee cases that the engineer tested when
m
making his orig
ginal design.
P
Production and
d System Optim
mization
T
The initial sizin
ng of ESP equiipment is based
d on the best av
vailable data, bbut often the innformation is only partially acccurate.
F
For this reason,, it is paramoun
nt that the “theeoretical modell” is continuouusly updated wiith real data. B
By matching thhe real data
w
with the theorettical model, the system and production
p
can be improved.
T
To boost produ
uction, a steady
y state of produ
uction must firsst be reached too have an accuurate match. Once the well haas reached
a steady state, the
t pump intak
ke pressure (Pip
p), motor temperature (Tm), aand pump disccharge pressuree (Pdp) from thhe down
hhole gauge and
d motor amps, tubing
t
pressuree, and flow ratees from the surrface will be ussed to start the matching proccess. This
innformation willl be used with
hin the fuzzy lo
ogic engine to determine
d
wherre the pump is operating on tthe pump curvee. The
ffirst match thatt is performed will
w become th
he baseline from
m which all datta will be comppared to in the future (on the current
E
ESP install).
C
Continuously updating
u
the mo
odel within thee fuzzy logic en
ngine will enabble the engineeer to determinee when it is neccessary to
m
make changes to
t the operating
g parameters of
o the ESP to op
ptimize producction and reducce lifting costs ($/bbl/1000’ oof lift).
U
Using nodal an
nalysis, the engineer will be ab
ble to ascertain
n where in the system the botttlenecks are occcurring, and w
with the
aaid of a matcheed sizing file, be
b able to deterrmine where the bottlenecks aare within the E
ESP system. T
The changes made could
bbe as simple as adjusting chok
ke settings or more
m
complicatted such as whhere multiple isssues are causinng production
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deficiencies. Some of these issues could be related to scale build-up within the pump, or tubing or emulsion issues causing
decreased ESP efficiency. With an accurately matched sizing, the engineer will save time by skipping the trial and error
approach, and reach the correct conclusion with a more scientific approach.
Additionally, the software can be used to highlight opportunities for increased production. Because the software has
knowledge of its’ operating constraints, it can virtually step through new operating frequencies and their resulting production
increases until a new operating constraint is reached. At this point, the engineer is presented a classic ‘cost versus benefit’
analysis - do I want to increase my production by X% at the cost of exceeding Y constraint and perhaps cause a premature
failure? While this could be done manually, automating the process allows for quicker decision making and more time
vetting the opportunity than uncovering that the opportunity exists.
Improving the system is also important to decrease lifting costs because of the expense associated with running ESP with
electricity. An average system’s electrical cost can be as much as 5% of the original cost of the equipment every month. For
this reason, it would be beneficial to evaluate the ESP’s electricity use. The matched sizing file helps engineers determine
the correct ESP input voltage from field data. Because the data used to size ESP is only partially accurate, when the ESP is
being installed, the field setup reflects the theoretical model and not actual field data.
Once the well reaches a steady state, the optimal operation point can be determined. Depending on the outcome of the
matched file, changes could be made to the drive setup, transformer taps, or re-rate or de-rate a motor. These tasks ensure the
system operates the most efficiently. Also, as the well production follows the decline curve of the well, it will be important
to make changes as production changes. For example, two years after the ESP is installed, production could have declined by
10%-meaning it is no longer operating the most efficiently. At that point, the engineer would decide where the new best
efficiency point of the system is located and change the setup to match those conditions.
Challenges with Model Management
To realize the benefits listed above, an accurate ESP model must be maintained. The traditional method for maintaining such
a model required ESP engineers to manually run the ESP sizing software, and use a trial-and-error approach to adjust the ESP
design until the performance data (such as intake pressure, discharge pressure and flow rate) match the measurements from
the SCADA system. This process is tedious and error prone-especially when scaled to the thousands of ESP application
models which may need to be updated. A new method that includes soft-computing techniques is being investigated to allow
a computerized system to perform the recalibration automatically with limited human intervention. Preliminary testing
suggests this method can be used to perform ESP application model tune-ups for multiple ESP/wells.
One can think of automatching as “reverse sizing.” In sizing, the user enters the well condition parameters outlined in Table 1
to size an ESP system. In automatching, we try to find optimized data sets of unknowns (or dynamic variables) such as water
cut, gas to oil ratio, and pump modifiers to match SCADA measured values such as Pip, Pdp, flow, and motor amps.
Automatching presently uses these five values as inputs. The following are values which are currently captured by most
SCADA systems:
1.
2.
3.
4.
5.
Pip (Intake Pressure)
Pdp (Discharge Pressure)
Flow
Motor Amps
Frequency
Variables which will be modified to create a match for automatching include static pressure (Pr), productivity index (PI),
water cut (WC), and gas oil ratio (GOR). An experienced ESP application engineer may already notice that to match a certain
pump intake pressure (Pip) value acquired from a field, one can change Pr (static pressure) or PI (productivity index), or a
combination of Pr and PI to match a Pip value. Without more information, the engineer and software are just making a guess
as to what has changed.
SPE 163704
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To solve the problem, a customer’s production testing data should be analyzed. Periodically, a well test should be available to
verify production data such as water cut and GOR. This information can be used as a constraint to help to narrow the
solutions to more closely reflect reality.
The algorithms also allow a progressive automatch of an ESP well. Automatching constantly uses the latest SCADA data to
recalibrate the ESP model, in an attempt to keep it functional. When automatching finds a good solution, it sends the
information to the ESP engineer, as shown in Table 2 below.
Table 2: Automatching output
APC Inputs Old APC Input New APC Input
PI
0.14
0.17
Pr
2255
2255
WC
71.6
70.63
GOR
2431.47
1029.71
For this particular well, automatching suggests changing productivity index (PI) from 0.14 to 0.17, and GOR from 2431 to
1029 to match to the most recent SCADA data set.
From this automatching result, the ESP engineer can validate whether the automatching system has made valid assumptions.
If the automatch is correct, the ESP engineer can accept the changes with one click, and the model will be valid until the
SCADA data no longer matches the model. Additionally, the ESP engineer might go to the field and get more data on one of
the unknowns (for example, GOR). The ESP engineer could then constrain GOR to a known value and the automatching
system will only pick from the other variables to generate a match.
The automatching feature gives the ESP engineer a suggested match and can constrain the unknowns appropriately to reduce
nonsensical matches from being displayed. This will improve the quality of matching and reduce the amount of time that the
ESP engineer spends maintaining models.
Another benefit is allowing the ESP engineer insight into how changing well parameters will impact the equipment. In the
original sizing, the engineer may not have seen any equipment issues (based on his assumptions about water cut or GOR, for
example). As the well is produced, however, and water cut and GOR changes, the load on the shaft or the internal motor
temperature may change to a point that an alarm could be raised from the ESP sizing software.
Integrated models have proven beneficial in many ESP applications. There are, however, some ESP applications that are not
good candidates for utilizing an integrated model. As fields mature, additional measures will be taken to increase production
by implementing enhanced oil recovery/ incremental oil recovery (EOR/IOR). By the very nature of these projects, most
wells will experience very dynamic conditions downhole that will create additional challenges to accurately modeling well
performance.
One EOR technique that has been extremely challenging to model is water-alternating-gas (WAG) floods. A typical WAG
flood consists of alternating water and carbon dioxide (CO2). To accurately model an ESP in these conditions, the engineer
would have to match the actual field data every week because of the changing bottomhole pressures and fluid density. There
are two issues with having to make changes every week to accurately match the well. One is the time required to rematch
every well, and the other is making changes to the model every week will mask the downhole issues, making it impossible to
tell if operational changes need to be made. To accommodate these dynamic wells, additional research will be needed to
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determine if there is a way to accurately model these wells, and will still provide practical information to make operational
changes to the ESP system.
Furthermore, there are some ‘real world’ challenges which make implementation at scale difficult. Some of these challenges
include sensor reliability, availability of peripheral information (well test data, flow meter, field work, etc.), competitor
information, and system noise. All of these factors contribute to the challenge of keeping a model functional. There needs to
be further research into data processing and validation techniques to ensure that the automatching systems are provided
untainted data.
Conclusion
There are many benefits of having a functional ESP model. The challenges associated with keeping a valuable model are
substantial, and can be tedious. After all, a model is only a representation of the situation – we cannot expect the models to
perfectly capture the true complexity of the reservoirs and mechanical equipment at play. While there is significant
operational value in keeping a model valid, much effort is spent in the model management process. Further evaluation and
research needs to be performed on different methods, including case-based reasoning and other artificial intelligence
methods, which may allow for further scalability and reduce the overhead associated with model maintenance.
SPE 163704
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Works Cited
Baker Hughes Centrilift. (2008). Submersible Pump Handbook Eight Edition. Claremore: Baker Hughes.
Denney, T., & Crossley, A. (2011). Model-Derived Flow Monitoring in an Electrical Submersible Pump (ESP) Application.
ESP Workshop. The Woodlands: SPE.
Thornhill, D., & Zhu, D. (n.d.). Fuzzy Analysis of ESP Performance. 2009 Annual Technical Conference and Exhibition .
SPE.