PIPESIM Artificial Lift Design

and Optimization

Schlumberger Public
Workflow/Solutions Training
Version 2009.1

Schlumberger Information Solutions

July 24, 2009

Schlumberger Public
Copyright Notice
© 2008-2009 Schlumberger. All rights reserved.
No part of this manual may be reproduced, stored in a retrieval system, or translated in any
form or by any means, electronic or mechanical, including photocopying and recording,
without the prior written permission of Schlumberger Information Solutions, 5599 San
Felipe, Suite100, Houston, TX 77056-2722.

Disclaimer
Use of this product is governed by the License Agreement. Schlumberger makes no
warranties, express, implied, or statutory, with respect to the product described herein and
disclaims without limitation any warranties of merchantability or fitness for a particular
purpose. Schlumberger reserves the right to revise the information in this manual at any
time without notice.

Trademark Information
Software application marks, unless otherwise indicated, used in this publication are
trademarks of Schlumberger. Certain other products and product names are trademarks or
registered trademarks of their respective companies or organizations.

Schlumberger Public
Schlumberger Public
Table of Contents

About this Manual

Learning Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
What You Will Need . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
What to Expect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Course Conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Icons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Module 1: Artificial Lift Design

Prerequisites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Learning Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Lesson 1: Flowline and Riser Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Exercise 1: Sizing the Flowline-Riser Pair . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Lesson 2: Completion Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Exercise 1: Working with Perforated and Frac-Pack Completions . . . . . . . . . . . . . 20

Schlumberger Public
Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Lesson 3: Performance Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Exercise 1: Forecasting Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Exercise 2: Determining Choke Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Question . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Lesson 4: ESP Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Electric Submersible Pump Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Exercise 1: Placing an ESP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Lesson 5: Multiphase Booster Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Exercise 1: Placing a Multiphase Booster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Lesson 6: Gas Lift Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Overview of Gas Lift Injection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Evolution of Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Exercise 1: Evaluating Gas Lift Feasibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
Exercise 2: Determining the Deepest Injection Point . . . . . . . . . . . . . . . . . . . . . . . 54
Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Exercise 3: Determining the Future Gas Lift Response . . . . . . . . . . . . . . . . . . . . . 56
Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Exercise 4: Bracketing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Exercise 5: Designing for Gas Lift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
Exercise 6: Forecasting Gas Lift Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

PIPESIM Artificial Lift Design and Optimization i

 Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
Extended Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
Review Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

Module 2: Artificial Lift Optimization

Prerequisites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
Learning Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
Lesson 1: Gas Lift Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
Basic Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
The Gas-Lift Allocation Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Offline-Online Optimization Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Optimization Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
Exercise 1: Constructing a Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Exercise 2: Optimizing Gas Lift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
Review Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

Appendix A: Gas Lift Design

Schlumberger Public

PIPESIM User Interface Dialogs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Appendix B: Recommendations
Related Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Appendix C: Answers for Exercises

Module 1: Artificial Lift Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Lesson 1: Flowline and Riser Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Lesson 2: Completion Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
Lesson 3: Performance Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
Lesson 4: ESP Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Lesson 5: Multiphase Booster Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Lesson 6: Gas Lift Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
Module 2 Artificial Lift Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
Lesson 1: Gas Lift Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

ii PIPESIM Artificial Lift Design and Optimization

Schlumberger About this Manual

About this Manual

This course provides training for PIPESIM, a steady-state, multi-

phase flow simulator used for the design and diagnostic analysis
of oil and gas production systems. In Module 1, you will learn to
use PIPESIM to evaluate various artificial lift options for the con-
ceptual design of a deepwater field development. In Module 2,
you will learn how to optimize gas lift allocation for a field based
on current operating conditions and constraints.

Learning Objectives
After completing this training, you will know how to:
• select a completion design
• size a subsea tieback
• perform a multiphase booster design
• perform an ESP design
• perform a gas lift design
• evaluate design scenarios by performing production fore-

Schlumberger Public
casts
• determine the optimal allocation of gas lift among a network
of wells.

What You Will Need

In this training, you will need the following hardware and soft-
ware:
• PIPESIM 2008.1 or later
• Microsoft Excel

What to Expect
In each module within this training material, you will encounter the
following:
• Overview of the module
• Prerequisites to the module (if necessary)
• Learning objectives
• A workflow component
• Lesson(s), which explain about a subject or an activity in the
workflow
• Procedure(s), which show the sequence of steps needed to
perform a task

Title of SIS Training Manualt 1

 About this Manual Schlumberger

• Exercises, which allow you to practice a task by using the

steps in the procedure with a data set
• Scenario-based exercises
• Questions about the module
• Summary of the module
You will also encounter notes, tips and best practices.

Course Conventions
Characters typed in Bold Represents references to dialog box
names and application areas or com-
mands to be performed.
For example, "Open the Open
Asset Model dialog." or “Choose
Components.”
Used to denote keyboard commands.
For example, "Type a name and press
Enter."
Identifies the name of Schlumberger
software applications, such as
Schlumberger Public

ECLIPSE, GeoFrame or Petrel.

Characters inside <> triangle Indicate values that the user must
brackets supply, such as <username> and
<password>, or <C7+>.

Characters typed in italics Represent file names or directories.

"... edit the file sample.dat and..."
Represent lists and option areas in a
window, such as Attributes list or
Experiments area.
Identifies the first use of important
terms or concepts. For example,
"compositional simulation…" or “safe
mode operation.”

Characters typed in fixed- Represent code, data, and other lit-

width eral text the user sees or types.
Examples include <username>/
<password> or <0.7323>.

NOTE: Some of the conventions used in this manual indicate

the information to enter, but are not part of the informa-
tion For example: Quotation marks and information
between brackets indicate the information you should
enter. Do not include the quotation marks or brackets
when you type your information.

2 Title of SIS Training Manual

Schlumberger About this Manual

Instructions to make menu selections are also written using bold

text and an arrow indicating the selection sequence, as shown
below:
1. Click File menu > Save (the Save Asset Model File dialog
box opens.)
OR

Click the Save Model toolbar button.

An “OR” is used to identify an alternate procedure.

Schlumberger Public

Title of SIS Training Manual 3

 About this Manual Schlumberger

Icons
Throughout this manual, you will find icons in the margin represent-
ing various kinds of information. These icons serve as at-a-glance
reminders of their associated text. See below for descriptions of what
each icon means.
Schlumberger Public

4 Title of SIS Training Manual

Schlumberger About this Manual

Summary
In this introduction, we have:
• defined the learning objectives
• outlined what tools you will need for this training
• discussed course conventions that you will encounter within
this material.
In the following module, you will learn how to use PIPESIM to
create a conceptual design and forecast performance.

Schlumberger Public

Title of SIS Training Manual 5

 About this Manual Schlumberger

NOTES
Schlumberger Public

6 Title of SIS Training Manual

Schlumberger Artificial Lift Design

Module 1 Artificial Lift Design

In this module, you will learn how PIPESIM is used to perform a
conceptual design of a deepwater subsea production system and
forecast performance over time using several artificial lift options.
As Figure 1 shows, the system consists of four deviated wells
that will be manifolded at the drill center and produced through a
horizontal subsea tieback to a host platform located in 7000 feet
of water. The ambient temperature along the flowline is 38 degF
and the water current is 2 ft per second, typical values for
deepwater environments. The minimum arrival pressure of fluids
at the host platform is 200 psia to ensure adequate separation
and the oil is then pumped to shore through export pipelines.

Schlumberger Public

Figure 1 Deepwater subsea production system

For simplicity, during the conceptual design phase, the tubing

geometry, reservoir and properties are considered to be identical
for all wells.
The main objective of this study is to design a production system
that will sustain the maximum flow rate for the longest period of
time.
In Module 2 on page 67, you will skip ahead to when the system
is in operation and explore how to optimize production.

PIPESIM Artificial Lift Design and Optimization 7

 Artificial Lift Design Schlumberger

Prerequisites
To successfully complete this training, you must:
• Have a working knowledge of PIPESIM
• Be familiar with production engineering concepts including
artificial lift methods

Learning Objectives
In this module, you will use PIPESIM to analyze the following
production engineering objectives:
• completion design – perforated vs. Frac-Pack
• field performance forecasting
• subsea flowline/riser sizing (EVR)
• arrival temperature limits
• evaluation of gas lift feasibility
• gas lift design.

Lesson 1 Flowline and Riser Design

Schlumberger Public

In deepwater systems in particular, the influence of the

backpressure from the flowline and riser needs to be carefully
considered when designing the overall system. This requires
careful analysis of a number of factors.
Reducing the pressure loss in the flowline riser delays the need
for artificial lift and maximizes the reservoir energy, but at higher
capital cost. The following criteria should be considered during
the sizing process:
• Manifold Pressure: The manifold pressure needs to be as
low as possible to minimize backpressure on the system.
This is only critical later in the field life when artificial lift is
required to move fluids to the platform. The lower the
manifold pressure, the less boosting is required. The
abandonment pressure is lower, and hence ultimate recovery
is higher.

8 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

• Erosional velocity: The flowing fluid velocity must be kept

below the erosional velocity limit to maintain the integrity of
the pipe. A conservative first pass is to assume that the
maximum liquid rate can be achieved throughout the life of
the field through artificial lift methods. The erosional velocity
limit is most conveniently expressed as the erosional velocity
ratio, as shown below:

where:

Vactual = actual mixture velocity of fluid

VE = API 14E Erosional velocity limit

ρm = mixture density of fluid (lbm/ft3)

C = empirical constant representing pipe material

Schlumberger Public
EVR = erosional velocity ratio

• Arrival Temperature: If the arrival temperature is less than

the wax appearance temperature, wax deposition may occur.
Wax appearance temperature may vary significantly
depending on the nature of the crude oil. Determination of the
wax appearance temperature requires laboratory analysis.
• Insulation: To maximize arrival temperature, the pipe and
riser may be insulated with possibly a pipe-in-pipe (PIP)
configuration. Typical values for PIP insulation are 2.0 BTU/
hr/ft2/F. Adding syntactic foam insulation may lower the HTC
to 0.25 BTU/hr/ft2/F. The larger the pipe ID, the slower the
fluids move and the more heat transfer occurs with the
ambient seawater.
• Cost of Pipe: Though the cost is not considered here, for an
8 mile long subsea pipeline and a 7000 ft riser, the cost of
pipe is very high. Increasing the pipe diameter by one inch
may result in an increased cost of several million dollars.
• Dual Flowlines: While more expensive, dual flowlines allow
for several benefits including:
• Redundancy
• Round trip pigging
• Ability to test wells independently
• Ability to circulate fluids for remedial pipeline operations
• Ability to reroute wells to optimize production rates
• Better thermal management control

PIPESIM Artificial Lift Design and Optimization 9

 Artificial Lift Design Schlumberger

Exercise 1 Sizing the Flowline-Riser Pair

The fluids will need to be transported from the wellhead manifold,

through an 8-mile long horizontal flowline, and up a riser to the
host platform situated in a water depth of 7000 ft (Figure 2). The
ambient temperature along the flowline is 38 degF and the water
current is 2 ft per second, typical values for deepwater
environments.
Schlumberger Public

Figure 2 Schematic of a production system

Assume that the fluid is arriving at the manifold at a temperature

of 250 degF, and the heat transfer coefficient for both the flowline
and riser is 2.0 BTU/hr/ft2/F.
In this exercise, you will determine the optimal flowline-riser size
and configuration (single or dual) given the constraints discussed
above.
To size the flowline-riser:
1. Construct a flowline-riser as shown below:

10 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

Source Data

Pressure (initial assumption) 2400 psia

Temperature (initial assumption) 250 degF

Flowline Data

ID (initial assumption) 8 in

Undulations 0/1000’

Elevation change 0’

Length 8 miles

Ambient temperature 38 degF

HTC 2 BTU/hr/ft2/F

Riser data:

Schlumberger Public

PIPESIM Artificial Lift Design and Optimization 11

 Artificial Lift Design Schlumberger

2. Enter the black oil fluid properties based on initial producing

conditions as shown in the data below:

GOR @ bp 400 scf/STB

Pb 4100 psi @ 350 degF

Water cut 0%

Oil API 25º

Gas SG 0.71

Dead Oil Viscosity – 10 cP @ 200 degF

User’s data
70 cP @ 60 degF

Select the following fluid property correlations:

• Solution Gas: Petrosky-Farshad
• Live Oil: Petrosky-Farshad
• Undersaturated Oil: Bergman & Sutton
• Emulsion viscosity method: Brinkman
• Watercut cutoff method: User specified – 65%
Schlumberger Public

3. Click Setup > Flow Correlations to select the following

multiphase flow correlations:
• Vertical: Duns & Ros
• Horizontal: Beggs & Brill Revised, Taitel Dukler map
4. Click Setup > Erosion & Corrosion properties to change
the Erosional velocity constant to 150.
5. Select Setup > Define Output and ensure that Primary
Output Page and Auxiliary Output Page are the only options
selected.
6. Save the model as tieback.bps.
Constraints:
a. Rate Constraints
As this is a field expansion project, the maximum
production rates are constrained by the capacity of the
existing platform, such that:
• Total liquid is 60,000 BPD
• Total water treating capacity is 40,000 BPD.
b. Arrival Pressure
The minimum arrival pressure of fluids at the host
platform is 200 psia to ensure adequate separation. The
oil and gas are then transported to shore through single
phase export pipelines.

12 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

c. Arrival Temperature
The crude has a high wax content with a cloud point of
78 degF. Deepstar research, however, has shown that
wax deposition can occur above the dead oil cloud point
in some systems.
Therefore, to avoid wax deposition altogether, the system
temperature should remain at 20 degF above the cloud
point (at least 98 degF). If wax is allowed to deposit and
be removed though pigging operations, the minimum
system temperature needs to be above 78 degF to
maintain a manageable pigging schedule.
7. The performance forecast for the field, as obtained through
reservoir simulation is shown below:

Cum Liq P* wcut

(MMSTB) (psi) (%)

0 12000 0

5 11000 0

10 10200 0

Schlumberger Public
15 9500 0

20 8800 0

25 8200 0

30 7600 0

35 7100 0

40 6600 5

45 6200 10

50 5800 15

55 5450 25

60 5100 35

65 4800 45

70 4500 60

75 4250 70

80 4000 80

85 3800 84

90 3620 87

95 3480 89

100 3340 90

PIPESIM Artificial Lift Design and Optimization 13

 Artificial Lift Design Schlumberger

8. Perform a system analysis to ensure flowline integrity

throughout the life of the system using the maximum
allowable production rate at the platform. Sensitivity analysis
should include water cut (X-axis) as reported on the
performance tables (0 to 90%), subsea tieback ID, and riser
ID ranging from 6-12 inches in increments of 1 inch. Use the
Change in step option to ensure that each simulation case
corresponds to each row of sensitivity data.
Schlumberger Public

9. Record the initial manifold pressure, maximum erosional

velocity (EVR, i.e., actual velocity to API 14E Erosional
Velocity limit) and the minimum arrival temperature for a
single and a dual flowline. The quickest way to determine the
results is to configure the plot to display the variable of
interest on the y-axis and click on the Data tab to observe the
value.
NOTE: For a dual flowline, insert an adder/multiplier
between the source and flowline and multiply
the flow rate by 0.5. Insert a second adder/
multiplier at the top of the riser and multiply the
flow rate by 2.0.

14 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

Single flowline

Line size Manifold Pressure Max EVR Min Arrival Temp

inch psi

10

11

12

Dual flowline

Line size Manifold Pressure Max EVR Min Arrival Temp

inch psi

Schlumberger Public
7

10

11

12

10. Save the model as tieback.bps.

Questions
These questions are for discussion and review.
• For a given line size, how does the water cut affect:
• The arrival temperature
• Liquid holdup
• Liquid viscosity
• Pressure gradient
• Explain how and why the following quantities vary as a
function of line size:
• Manifold pressure
• Maximum erosional velocity ratio
• System outlet temperature

PIPESIM Artificial Lift Design and Optimization 15

 Artificial Lift Design Schlumberger

• What is the predominant flow regime in the pipe and riser?

• Which line size would you select? Would you opt for the
single or dual flowline?

Lesson 2 Completion Design

The Inflow Performance Relationship (IPR) relates the pressure
drop occurring between the reservoir boundary and the wellbore
entry point to the fluid flow rate produced by the reservoir. For
single phase liquid or gas flow, the flowrate may be predicted
using Darcy’s Law. The pseudo-steady state form of the Darcy
Law for radial liquid flow, expressed in terms of flow rate, is given
as follows:
Darcy’s Law - Pseudo-steady state, radial liquid flow:

where:
Schlumberger Public

QL = Liquid flowrate (BPD)

k = Permeability (md)
h = Thickness of reservoir (ft)
Pws = Static reservoir pressure (psia)
Pwf = Flowing bottomhole pressure (psia)
μL = Liquid Viscosity (cp)

BL = Liquid formation volume factor (STB/resBBL)

re = Drainage radius (ft)

rw = Wellbore radius (ft)
S = Mechanical skin factor
D = Rate dependent skin factor (1/BPD)

The mechanical skin factor S accounts for near wellbore

pressure losses specific to the completion design. Factors such
as perforation properties, near wellbore damage, fracture
properties, partial penetration, and wellbore deviation affect the
mechanical skin factor.
The rate dependent skin factor D accounts for non-Darcy flow
effects. This term becomes particularly significant for low
permeability reservoirs.

16 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

While the Darcy model is valid for single phase liquid flow (a
single phase gas form exists as well), for cases where reservoir
pressure falls below the bubblepoint pressure, two-phase flow
exists. The Vogel correlation (based on empirical data) predicts
the pressure loss below the bubblepoint and is expressed in
terms of flowrate as follows:
Vogel Equation:

where:

Qmax = the Absolute Open Flow Potential (AOFP) at P = 0

psia (STBD)

For liquid systems it is useful to formulate a composite IPR by

applying Darcy’s law above the bubble point and Vogel’s
equation below the bubble point as illustrated in Figure 3.
NOTE: If the non-Darcy skin term D is considered, the IPR will
exhibit a slight deviation from straight line behavior.

Schlumberger Public

Figure 3 Inflow performance relationship

The mechanical skin term S varies by completion type.

Completion parameters that influence the skin factor for

PIPESIM Artificial Lift Design and Optimization 17

 Artificial Lift Design Schlumberger

perforated and frac-pack wells are illustrated in Figure 4 and

Figure 5 on page 19.
Schlumberger Public

Figure 4 Perforated completion

18 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

Schlumberger Public
Figure 5 Frac-Pack completion

PIPESIM Artificial Lift Design and Optimization 19

 Artificial Lift Design Schlumberger

Exercise 1 Working with Perforated and

Frac-Pack Completions

To work with Perforated and Frac-Pack completions:

1. Save the model as well-tieback.bps.
2. Ensure that the diameter for the flowline-riser pair is 7.0” and
that the dual flowline-riser configuration is active.
3. Replace the Source representing the manifold with a well as
shown below:
Schlumberger Public

4. Enter the reservoir properties based on the data provided

below.

Reservoir Data

Static Pressure (initial) 12000 psia

Temperature 350 degF

Model type Pseudo steady state

Use Vogel? yes

Thickness 120 ft

Wellbore ID 6 in

Shape factor 4.513

Reservoir area 250 acres

Abs. Perm 300 mD

Mech skin calc

Rate Dep. skin calc

20 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

Relative Permeability Table

Sw (0-1) Kro (1-0) Krw(0-1)

0 0.9 0

0.1 0.9 0

0.2 0.9 0

0.3 0.6 0.02

0.4 0.45 0.06

0.5 0.36 0.13

0.6 0.22 0.2

0.7 0.15 0.3

0.8 0.08 0.45

0.9 0 0.5

1 0 0.5

Completion Options:

Schlumberger Public
Perforated Completion

Damaged Zone 9 in Deviation 39 degrees

Diameter

Damage Zone 80 mD Skin Method McLeod

Perm.

Compacted Zone 1 in Perforation 0.5 in

Diameter Diameter

Compacted Zone 40 mD Shot Density 4 SPF

Perm.

Vertical 200 mD Depth of 36 in

Permeability Penetration

Perforated Interval 60 ft

PIPESIM Artificial Lift Design and Optimization 21

 Artificial Lift Design Schlumberger

5. Enter the tubing data based on the table below using the
Simple Tubing Model.

Tubing Data

Tubing ID 4.67 in

Wall thickness 0.415 in

Casing ID 7.625 in

SSSV 4 in @ 500 ft

KOP 5000 ft

TVD 12000 ft

MD 14000 ft

HTC 2 BTU/hr/ft2/F

Tamb @ wh 38 degF

Tamb @ bh 350 degF

6. Enter a choke bean size of 4.67 in (equivalent to the tubing

size).
Schlumberger Public

7. To account for the contribution of the four wells, enter a value

of 4.0 for the Adder/Multiplier immediately downstream of the
choke.
8. Open the Nodal Analysis Operation, select Limits, enter 20
for the number of outflow points to plot and select the option
to limit the outflow curves to lie within the pressure limits of
the inflow curve.
9. Perform a Nodal Analysis operation at a wellhead pressure of
200 psia to determine the well deliverability on the basis of
reservoir parameters and tubing configuration.
10. Calculate the mechanical skin factor using the Completion
Options dialog.

Completion Type Perforated Frac-Pack

Mechanical skin factor

Flowing Pressure, psia

Flowing Liquid Rate, stb/d

AOFP (BPD)

22 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

11. Repeat the Nodal Analysis operation for the Frac-Pack option
based on the design parameters given below.

Frac-Pack Completion

Gravel Fracture half 20 ft

90000 mD
permeability length

Screen Diameter 5.25 in Fracture width 0.6 in

Casing ID 7.625 in Proppant Perm 90000 mD

Perforated Frac face depth of 8 in

0.5 in
Diameter damage

Frac face 250 mD

Shot Density 4 SPF
damage perm.

Vertical Perm. 200 mD Choke length 3 ft

Perforated Frac face choke 90000 mD

60 ft
Interval perm.

Deviation 39 degrees

12. Perform parametric studies with +/- 50% sensitivity on the

Schlumberger Public
completion parameters. Determine which completion design
parameters most influence the well performance.
Perforated completion:
____________________________________

Frac-Pack completion:
____________________________________

13. Save the model as well-tieback.bps.

Questions
These questions are for discussion and review.
• Which completion option should be used?
• Is it valid to characterize the IPR with a liquid PI rather than
the pseudo-steady-state model?
• Which parameters in the pseudo-steady-state model change
over time? How will this affect the PI over time?
• How does water cut relate to water saturation used in the
relative permeability table?

PIPESIM Artificial Lift Design and Optimization 23

 Artificial Lift Design Schlumberger

Lesson 3 Performance Forecasting

Once an initial design has been made, it is important to evaluate
how the system will respond to changing operating conditions.
There are several ways to perform a performance forecast as
shown in the table below:

Table 1: Performance Forecast

Model Forecasting operation Application(s)

Single System analysis change in PIPESIM

Branch step

Network Manual sensitivities PIPESIM

Network Look-up tables Avocet IAM + PIPESIM

Network Coupling to material balance Avocet IAM + PIPESIM

tank

Network Dynamic coupling to reservoir Avocet IAM + PIPESIM

simulation

Because the initial design may involve consideration of a number

of design parameters, resulting in numerous simulation runs, the
Schlumberger Public

system analysis – change in step operation is useful in identifying

a preliminary design. This process is called a parametric study.
Once an initial design has been selected, it can be tested against
more rigorous reservoir models using Avocet IAM for further
analysis.

24 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

Exercise 1 Forecasting Performance

A total system analysis can be performed to forecast

performance over time after evaluating the well performance and
sizing the flowline/riser.
To aid in forecasting future performance, reservoir simulation
was applied to generate a table to describe reservoir conditions
as a function of cumulative production. The reported reservoir
performance table is as follows:

Table 2: Reservoir Performance

Cum Liq P* wcut

(MMSTB) (psi) (%)

0 12000 0

5 11000 0

10 10200 0

15 9500 0

20 8800 0

25 8200 0

Schlumberger Public
30 7600 0

35 7100 0

40 6600 5

45 6200 10

50 5800 15

55 5450 25

60 5100 35

65 4800 45

70 4500 60

75 4250 70

80 4000 80

85 3800 84

90 3620 87

95 3480 89

100 3340 90

PIPESIM Artificial Lift Design and Optimization 25

 Artificial Lift Design Schlumberger

Reservoir Performance

120

100
watercut %
Reservoir Pressure (psia/100)

80

60

40

20

0
0 20 40 60 80 100
Cumulative Oil (MMSTBD)
Schlumberger Public

Figure 6 Reservoir performance forecast

Assume that the economic watercut limit for the wells is 90%,
which will allow for a total recovery of 100 MMSTB of liquid from
the reservoir, corresponding to approximately 70 MMSTB oil or
$3.5 billion at a price of ($50/bbl) (Figure 6).
To forecast a performance:
1. Save the model as base_forecast.bps.
2. Setup the physical model as shown below.

26 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

3. The two topsides flowlines are 12” ID horizontal (no

undulations) and 50 ft in length with an ambient temperature
of 60 degF. Set the choke bean size equal to pipe ID. The
heat transfer coefficient is 2 btu/hr/ft2/F.
4. From the previous exercise, ensure that there are two adder/
multipliers immediately downstream of the wellhead choke.
The first adder/multiplier multiplies the flowrate by 4 to
account for the four wells producing to the subsea manifold.
The second multiplier is used to reduce the rate in half if a
dual flowline-riser pair is selected. A third adder/multiplier at
the topsides combines the production from the parallel
flowline/risers into a common header by multiplying the rate
by 2.
5. Add report tools at the manifold, the riser base and the end of
the flowline at the topsides. Name them as such by selecting
the General tab. You will designate specific reports later.
6. Add a nodal analysis point between the first Adder/Multiplier
and the Report Tool. Right-click and inactivate this nodal
analysis point for now.

Schlumberger Public

PIPESIM Artificial Lift Design and Optimization 27

 Artificial Lift Design Schlumberger

7. Use a system analysis to forecast the production capacity of

the wells by calculating the liquid rate as a function of
reservoir conditions. Set up the System Analysis operation
as shown below, based on the reservoir performance
forecast table, and run the model.
Schlumberger Public

28 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

8. From the Series menu in the plotting utility, configure the

x-axis to display the inlet pressure (x-axis). Select Edit >
Advanced Plot Setup, and click the Axis tab. Configure the
inlet pressure axis to be inverted as shown below.

Schlumberger Public
Your plot should appear similar to the one shown below:

9. Save the model as base_forecast.bps.

PIPESIM Artificial Lift Design and Optimization 29

 Artificial Lift Design Schlumberger

Questions
These questions are for discussion and review.
• How long will the wells be able to produce at the 60,000 BPD
target if no artificial lift is employed? (Use Excel to calculate
based on the cumulative recovery at the inlet pressure at
which the rate falls off plateau.)
Time on plateau: _________________.

• At what inlet pressure will the wells no longer be able to

sustain the target rate?
Minimum Pinlet to produce 60,000 BPD: ______________.

• At what inlet pressure do the wells die?

Minimum Pinlet to produce at any rate: ______________.

• What is the cumulative recovery of liquids from the reservoir?

Cumulative recovery: _______________

Exercise 2 Determining Choke Location

Schlumberger Public

During the initial production phase, when the overall liquid

production rate is constrained by the handling capacity of 60,000
BPD, chokes are used to regulate production. The choke may be
located at the individual wellheads or on the topsides. This
exercise compares these two options to determine the optimal
location.
To determine choke location:
1. Select any point from the performance forecast table when
the wells are capable of producing more than the target
production rate of 60,000 BPD.
2. Enter the fluid and reservoir properties corresponding to the
selected point in the completion and black oil properties
dialogs.
3. Perform a Pressure/Temperature profile with the Liquid Rate
set at 15,000 BPD (individual well rate limit), the outlet
pressure set at 200 psia and other variable set as the
calculated variable. Select the wellhead choke as the other
variable and enter reasonable upper and lower limits for
choke bean size (for example, 0.1” > flowline ID). This
operation will determine the choke size required to match the
target production rate.

30 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

4. Record the results in the table below based on the summary

and/or output reports.

Choke Location Wellhead Topsides

Choke Size, ins

Critical?

Choke dP, psi

Flowline dP, psi

Predominant flow
regime in tieback

Maximum EVR in flow-

line/riser (not topsides
pipe)

Min. Arrival Temp. degF

5. Change the y-axis to liquid holdup and observe the results

(leave the plot window open).
6. Repeat for the topsides choke location.

Schlumberger Public
7. Compare liquid holdup plots.
8. Save the model as choke_lcoation.bps.

Question
During the initial production time, is it better to choke at the
wellhead or topsides?

PIPESIM Artificial Lift Design and Optimization 31

 Artificial Lift Design Schlumberger

Lesson 4 ESP Design

An Electrical Submersible Pump (ESP) is a multistage centrifugal
pump that is capable of handling very high volumes of fluid and
providing a significant boost in pressure resulting in a lower
bottomhole pressure and thus an increased reservoir drawdown.
Figure 7 and Figure 8 on page 33 illustrate ESP lift systems.
Schlumberger Public

Figure 7 ESP Lifted well and related downhole equipment

32 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

Schlumberger Public

Figure 8 ESP lifted offshore well

PIPESIM Artificial Lift Design and Optimization 33

 Artificial Lift Design Schlumberger

Electric Submersible Pump Overview

Dynamic pumps such as ESPs operate on the principle that
kinetic energy is transferred to fluid, which is then converted into
pressure. This occurs when angular momentum is created as the
fluid is subjected to centrifugal forces arising from radial flow
though an impeller. This momentum is then converted into
pressure when the fluid is slowed down and redirected through a
stationary diffuser.
The pressure increase provided by a centrifugal pump is usually

the ΔP created by the pump can support:

expressed as pumping head, the height of the produced fluid that

Which can be expressed in field units as:

Where γL is the specific gravity of the liquid relative to water.

Schlumberger Public

The pumping head is independent of the density of the fluid. For

a multistage pump, the head developed is the sum of the
pumping head from each stage, or:

34 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

The pumping head of a centrifugal pump will decrease as the

volumetric throughput increases. However, the efficiency of the

the fluid (qΔp) to the power of the pump, has a maximum at some
pump, defined as the ratio of the hydraulic power transferred to

flow rate for a given pump. The developed head and efficiency
for a centrifugal pump depend on the particular design of the
pump and must be measured. These characteristics are provided
by the pump manufacturer as a pump curve, such as that shown
in Figure 9.

Schlumberger Public
Figure 9 Typical ESP performance curve

PIPESIM Artificial Lift Design and Optimization 35

 Artificial Lift Design Schlumberger

Figure 10 illustrates a pressure profile (shown in blue) for an

ESP lifted well. Without the pump, this well is dead, with the fluid
column in the tubing represented by the static gradient (dP/dz)b.
A designed rate, QL, and the corresponding bottomhole flowing
pressure, Pwf, are identified from the (sideways projected) IPR

provide a pressure boost equivalent to ΔPpump, which is the

curve. To achieve this rate, the pump must be designed to

pressure difference between the discharge and the intake of the

pump. When the pump discharges pressure at the depth shown,
the fluids flow to the surface at the specified wellhead pressure,
Pth.
Schlumberger Public

Figure 10 Pressure profile of an ESP lifted well

The ESP design process involves the following procedure:

1. Selection of pump based on design flowrate.

36 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

2. Selection of pump depth based to minimize gas volume

fraction at suction.
3. Selection of the number of pump stages to achieve the target
pressure boost.
4. Selection of a motor to provide power to the pump.
5. Selection of a cable to transmit electricity to the motor.
Relative advantages of ESPs include the following:
• Can lift extremely high volumes
• Unobtrusive in urban locations
• Simple to operate
• Suitable for deviated wells
• Applicable offshore
• Corrosion and scale treatment easy to perform
• Availability in different sizes
• Lifting costs for high volumes generally very low
Relative Disadvantages of ESPs include the following:
• Cost of cabling (deep wells and offshore tiebacks)

Schlumberger Public
• Gas and solids are problematic
• Lack of production rate flexibility unless equipped with
variable speed drives
• Casing size limitation
• Requires electric power source and high voltages
• Impractical for low volume wells
• High intervention costs (requires rig to remove ESP)

Exercise 1 Placing an ESP

You will now install an ESP to boost the production as the wells
come off plateau. Prior to 2003, ESPs were not rated to operate
in temperatures of more than 350 degF. However, recent
advances have pushed this limit to approximately 435 degF
(http://www.slb.com/media/services/artificial/submersible/
hotline_br.pdf). Therefore, the ESP will be placed as deep as
possible to reduce the likelihood of vapor presence at the pump
intake later in the life of the well.

PIPESIM Artificial Lift Design and Optimization 37

 Artificial Lift Design Schlumberger

To place an ESP:
1. Starting with the model from the previous exercise, clear any
choke settings for the wellhead and topsides chokes by
setting these values to be equal to that of the upstream pipe
diameter.
2. Save the model as ESP_design.bps.
3. Perform an ESP design using the reservoir conditions
corresponding to the first point that the system was unable to
produce any fluid (Pr = 6600; wc% =5; GOR = 400 scf/STB):
Schlumberger Public

4. From the resulting list of pumps, filter the pumps

manufactured by Reda and select a pump that meets the
design range but provides adequate clearance for cabling.
5. Click Calculate to determine the pump parameters, ensuring
that stage by stage calculation is selected.
6. Check that the horsepower requirement does not exceed the
limit. If it does, repeat steps 3-5 by incrementally lowering the
lower rate specification to the nearest 100 BPD without
violating the power limit.

Pump model selected

Required no. stages

HP required

Design rate (STB/d)

GVF at inlet

gas separator required?

38 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

Plot the performance curve:

Schlumberger Public
7. Click the install pump button to install the pump.

PIPESIM Artificial Lift Design and Optimization 39

 Artificial Lift Design Schlumberger

8. Rerun the performance forecast with a third sensitivity

variable that sets the ESP speed to zero during the
production phase where the system is choked (reservoir
pressures that can meet the production target) and to 60 hz
thereafter.
Schlumberger Public

40 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

9. Configure the x-axis to display inlet pressure and invert the

axis from the Edit > Advanced Plot Settings menu. Your
plot should appear similar to the one below but do not worry if
your answers are slightly different.

Schlumberger Public
Questions
These questions are for discussion and review.
• Change the x-axis to inlet pressure and invert the axis. Based
on the cumulative production table, how long will the wells be
able to produce at the 60,000 BPD target if an ESP is
employed?
Time on plateau: ______

• At what inlet pressure are the wells no longer able to sustain

the target rate? At what inlet pressure do the wells die?
Minimum Pinlet to produce 60,000 BPD:_____________

Minimum Pinlet to produce any rate: ______________

• What is the cumulative recovery of liquids from the reservoir?

Cumulative recovery: ________________________

PIPESIM Artificial Lift Design and Optimization 41

 Artificial Lift Design Schlumberger

• Configure the y-axis of the system analysis plot to display the

gas volume fraction at the ESP suction. At any point in time,
does the inlet gas volume fraction exceed 5%? If so, what
can be done about this?
• Configure the y-axis of the system analysis plot to display
the maximum erosional velocity ratio. Is the erosional
velocity limit ever violated (after plateau production)?
• Configure the y-axis of the system analysis plot to display
the system outlet temperature. Does the arrival
temperature drop below 98 (to prevent wax deposition) or
below 78 F (minimum system temperature to allow
pigging control of wax deposition)? If so, what can be
done?
TIP: Rerun system analysis and change the multiplier
parameters for the cases in question such that all
production is fed through a single line (i.e.,
change the values to 1 for the multipliers before
the flowline and after the riser)
Save the model as ESP_design.bps.

Lesson 5 Multiphase Booster Design

Schlumberger Public

There are three types of multiphase pump models in PIPESIM: a

generic model, a twin-screw model, and a helico-axial model.
The simplest approach is to use the generic model that treats the
multiphase pump as a single-phase liquid pump and gas
compressor operating in parallel.
Conventional pump and compressor theory is used to calculate
the shaft horsepower required. Efficiencies of the pump and
compressor can be adjusted based on typical values taken from
field conditions. Due to the limiting assumptions in this approach,
use of the generic multiphase pump model is recommended only
as a preliminary analysis.
The twin-screw pump performance model is derived from
empirical data covering a wide range of volume fractions, suction
pressures and pump speeds. Pump performance at specific inlet
conditions is calculated by a rigorous interpolation routine that
determines differential pressure, flow rate, pump and power
requirement.
Seven pump sizes are available and are characterized in terms
of nominal capacity – that is, the theoretical rate at 100% speed,
0% GVF, zero differential pressure and with internal leakage.
Available nominal rates range from 37,500 to 300,000 BPD (250-
2000 m3/hr) of suction flowrate. Additional pumps can be
modeled with data supplied by the vendor or acquired pre-
commissioning tests.
The Helico-axial pump is a type of rotodynamic pump
manufactured by Framo. The fluid flows horizontally through a

42 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

series of pump stages, each consisting of a rotating helical

shaped impeller and a stationary diffuser (Figure 11).

Figure 11 Helico-axial pump stage

This configuration is like a hybrid between a centrifugal pump

and an axial compressor. Each impeller delivers a pressure boost
with the interstage diffuser acting to homogenize and redirect
flow into the next set of impellers.
This interstage mixing prevents the separation of the gas-oil
mixture, enabling stable pressure-flow characteristics and

Schlumberger Public
increased overall efficiency. As the gas is compressed though
successive stages, the geometry of the impeller/diffuser changes
to accommodate the decreased volumetric rate.
The impeller clearances are sufficient to allow production of small
amounts of sand particles. While helico-axial pumps are more
prone to stresses associated with slugging, installation of a buffer
tank upstream of the pump is generally sufficient to dampen
slugging effects so that they are not a problem.
The helico-axial pump model characterizes pump performance
using three correlating parameters. The flow parameter (FQ) and
the head parameter (FZ) characterize the size of the impellers
and the number of impellers respectively, thus defining a specific
pump.
A speed parameter representing the percentage of maximum
speed is then adjusted based on the desired differential pressure
for a given rate (or vice-versa). The requirement is calculated
based on a combination of pump performance and drive
mechanism. Drive options include a hydraulic turbine drive,
electric air-cooled drive and an electric oil-cooled drive.
Unlike single-phase pumps and compressors, no generalized
model exists that is able to accurately characterize the
performance of multiphase pumps. This is due in part to complex
and highly proprietary internal pump geometries.
Additionally, the variety of fluid properties and in-situ phase
distributions makes it extremely difficult to rigorously describe the
thermo-hydraulics occurring within the pump. For these reasons

PIPESIM Artificial Lift Design and Optimization 43

 Artificial Lift Design Schlumberger

it is common practice to characterize multiphase pumps with

performance curves of the type depicted in Figure 12.
Such curves are constructed on the basis of specified gas
volume fractions, suction pressures and liquid density and
viscosity. As inlet conditions change, the curve becomes invalid
and other curves must be applied.
The Framo Multiphase Booster Module in PIPESIM dynamically
generates the performance curve based on the user specification
for the head and rate parameters as well as the suction
conditions.
Schlumberger Public

Figure 12 Typical Helico-axial multiphase booster

performance curve

Relative advantages of multiphase boosters include the

following:
• Can lift extremely high volumes
• Can handle a wide range of gas volume fractions
• Can effectively lower tubing head pressure for multiple wells
• Can be serviced with an ROV for subsea applications (lower
intervention costs)

44 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

Relative disadvantages of multiphase boosters include the

following:
• Cost of cabling (deep wells and offshore tiebacks)
• Initial cost of booster is high relative to other lift methods
• Impractical for low volume wells

Exercise 1 Placing a Multiphase Booster

1. Starting with the ESP_design.bps model completed in the

previous exercise, save the model as MPB_design.bps.
2. As a preliminary analysis, place a generic multiphase booster
just downstream of the subsea manifold specified with a
maximum pressure differential of 1000 psi. Specify a pump
efficiency of 30% and a compressor efficiency of 60%.

Schlumberger Public
3. Set the completion properties and black oil properties to
reflect the first point that the non-lifted base case system was
unable to produce any fluid (Pr = 6600; wc% =5).
4. Ensure that the nodal analysis point at the bottomhole is
inactive and the nodal analysis point at the manifold is active.
5. Perform a Nodal Analysis Operation to determine the liquid
rate can be produced by the multiphase booster, multiphase
booster in combination with the ESP, and the ESP by itself.
a. Define ESP speed as the inflow sensitivity variable and
set it to 0 Hz (to effectively ignore the ESP) and 60 Hz for
full speed.
b. Define MFB pressure differential as the outflow sensitivity
variable and set to 0 psi (to effectively ignore the MFB)
and to 1000 psi to model maximum boosting pressure
differential.
c. Ensure that the outlet pressure is set to 200 psia.

PIPESIM Artificial Lift Design and Optimization 45

 Artificial Lift Design Schlumberger

Run the case and record the liquid rate in the table below:

ESP Speed (Hz)

MPB dP (psi) 0 60

1000

6. Run a Pressure/Temperature Profile for the case where a

MFB is used in combination with an ESP and inspect the
summary file to determine the multiphase booster
performance characteristics.

Result

GVF @ suction (%)

MFP Power (Hp)

ESP Power x4 (Hp)

Total Power (Hp)

7. Rerun the performance forecast by adding an additional

Schlumberger Public

sensitivity variable to account for the contribution from the

MFB, as shown below.

46 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

Change the x-axis to inlet pressure and invert the axis as

before.
• Based on the cumulative production table, how long will the
wells be able to produce at the 60,000 BPD target if the
MFP in addition to the ESP is employed?
Time on plateau: ______ ___________
• At what inlet pressure are the wells no longer able to
sustain the target rate? At what inlet pressure do the wells
die?
Minimum Pinlet to produce 60,000 BPD: _________
Minimum Pinlet to produce any rate: ___________
• What is the cumulative recovery of liquids from the
reservoir?
Cumulative recovery: _________________ __________
• What is the total maximum horsepower requirement for the
ESPs and booster?
__________________ HP @ ______ reservoir pressure

TIP: Plot ESP & MFB power vs. inlet pressure and

Schlumberger Public
copy data tab into excel. Sum the MFB power
and the ESP power times four for each pressure
to determine total power requirement.
8. Save the model as MFB_design.bps.

PIPESIM Artificial Lift Design and Optimization 47

 Artificial Lift Design Schlumberger

Lesson 6 Gas Lift Design

The operation of a continuously lifted gas lift well is very similar to
that of a naturally flowing well (Figure 13). Gas is continuously
injected into the tubing through a gas lift valve at a fixed depth.
The only difference between this type of operation and a naturally
flowing well is that the gas-liquid ratio changes at some point in
the tubing for the gas lift well.
Schlumberger Public

Figure 13 Gas lifted well and related downhole equipment

Overview of Gas Lift Injection

The basic principle behind gas lift injection in oil wells is to lower
the density of the produced fluid in the tubing. This results in a
reduction of the elevational component of the pressure gradient
above the point of injection and a lower bottomhole pressure.
Lowering the bottomhole pressure increases reservoir drawdown
and thus production rate.

48 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

The depth at which the operating gas lift valve can be located
depends on the gas injection pressure available. The more
pressure available, the deeper the injection point can be. Also, as
the depth of the injection gas is increased, less injection gas is
required to achieve the same bottomhole pressure.
Figure 14 on page 50 illustrates the concept of a continuous gas
lift well in terms of the pressure values, pressure gradients, well
depth and depth of injection. With the available flowing
bottomhole pressure and the natural flowing gradient (dp/xz)b,
the reservoir fluids would only ascend to the point indicated by
the projection of the pressure profile in the well. This would leave
a partially filled wellbore.
Addition of gas at the injection point would reduce the pressure
gradient (dp/dz)a, thereby allowing the fluids to be lifted the
surface. The intersection of the casing pressure gradient with the
lower tubing pressure gradient (dP/dz)b is shown as the “balance
point”. However, to the pressure loss across the lift valve, the
valve must be located at an injection depth higher than the
balance point.
As shown by the intersection of the bottomhole flowing pressure
and the (sideways projected) IPR curve, the flowrate is given.

Schlumberger Public

PIPESIM Artificial Lift Design and Optimization 49

 Artificial Lift Design Schlumberger

Other valves are required above the working valve in order to

unload the well. The methods used in the gas lift design
procedure for locating these valves along with detailed
descriptions of the gas lift design operations are described in
Appendix A: Gas Lift Design on page 85.
Schlumberger Public

Figure 14 Pressure profile of a gas lifted well

In terms of the overall pressure gradient, the trade-off to the

increased presence of gas is an increased frictional pressure
gradient. As shown in Figure 15 on page 51, as the rate of
injection gas increases, a point is reached where the benefits of
reducing the elevational gradient equals the drawback of
increasing the frictional gradient.

50 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

Further increase of injection gas has a detrimental effect on the

overall production rate. This point is called the optimal
unconstrained gas-lift injection rate and for individual wells is
relatively easy to calculate. If a long horizontal flowline is used to
connect the wellhead to the delivery point, the frictional effects of
the gas will be more pronounced, resulting in a lower optimal gas
lift value.

Schlumberger Public
Figure 15 Gas lift vs. Liquid production

Evolution of Technology
New deepwater subsea high-pressure gas-lift technology has
recently been developed by Schlumberger to minimize the risks
associated with traditional, bellows-operated gas-lift valves.
Subsea high-pressure gas lift valves can improve project
economics through increased production and enhanced reliability
at higher pressures. Utilizing unique bellows technology, these
valves can be set deeper in the well to provide additional
drawdown and increased production, depending on the
application.
The new high-pressure gas-lift technology rates reliable bellows
operation for 5,000 psi at the valve depth compared to the
previous 2,500 psi limit typically present with traditional gas lift
valves.
Relative advantages of gas lift include the following:
• Can handle large volumes of solids
• Handles large flowrates
• Power source can be remotely located
• Easy to obtain downhole pressures and gradients
• Serviceable with wireline unit
• Suitable for deviated wells
• Corrosion usually not as adverse

PIPESIM Artificial Lift Design and Optimization 51

 Artificial Lift Design Schlumberger

Relative disadvantages of gas lift include the following:

• Lift gas not always available
• Not efficient for lifting small fields with small no. wells
• Difficult to lift viscous crudes
• Difficult to retrieve valves in highly deviates wells

Exercise 1 Evaluating Gas Lift Feasibility

Determine how deep gas can be injected in the tubing using the
reservoir conditions corresponding to the first point that the
system was unable to produce any fluid (Pr=6600; wc% =5).
To evaluate gas lift feasibility:
1. Starting with the MFP_design.bps model completed in the
previous exercise, save the model as GL_design.bps.
2. Deactivate the Multiphase Booster by right-clicking on the
icon and selecting Active.
3. In the Tubing menu, remove the depth entry associated with
the ESP. This will effectively ignore the ESP, though it can
easily be reinstated later.
Schlumberger Public

4. Ensure that the static reservoir pressure in the completion is

set to 6600 psia, and the watercut is set to 5%.
5. Select Artificial Lift Design > Gas Lift Design > Gas Lift
Response. Vary the gas lift injection rate up to 12 mmscfd
and the surface injection pressure to determine the deepest
possible injection point such that the injection pressure at the
valve does not exceed 5000 psia.
6. Specify a maximum allowable injection depth of 11940 ft. (60
feet above the perforations).
7. Ensure that the Annular Lift Gas Pressure Gradient Method is
set to Use Rigorous Friction & Elevation DP. This option
will consider the frictional pressure losses of the injection gas
as it flows down the annulus.
8. Leave all other parameters to their defaulted values.

52 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

9. Run the model.

Schlumberger Public
NOTE: The liquid rate on the y-axis represents the
liquid rate at the topsides and therefore
incorporates production from all 4 wells.

PIPESIM Artificial Lift Design and Optimization 53

 Artificial Lift Design Schlumberger

10. Inspect the resulting plot to determine the approximate

amount of gas lift to inject into the tubing and the depth at
which the gas may be injected. The plot above suggests a
gas lift rate of about 8 mmscfd and a corresponding depth of
about 9,500 ft. This corresponds to a Liquid production rate of
approximately 38,650 BPD.
11. Record your answers below. (Your answers may differ
slightly).
Gas lift rate: __________ mmscfd
Gas injection depth: ___________ft.
Liquid Production Rate: ________ BPD

Questions
These questions are for discussion and review.
• There is no data point corresponding to a gas lift injection rate
of 0. What does this suggest?
• Explain the shape of the liquid flowrate curve. Why not inject
12 mmscfd?
• Explain the shape of the gas injection depth curve. Why does
Schlumberger Public

a higher gas injection rate allow for a deeper injection point?

Exercise 2 Determining the Deepest

Injection Point

You now need to ensure that the gas lift injection pressure
corresponding to the gas lift rate determined above does not
exceed the 5000 psia limit.
To determine the deepest injection point:
1. Select Artificial Lift > Gas Lift > Deepest Injection Point.
2. Enter an Outlet Pressure of 200 psia, Injection Gas Rate
corresponding to the rate determined in the previous
exercise.

54 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

3. Assume an available wellhead injection pressure of 4000

psia. Note that this value does not take into account any
pressure loss in the gas injection network.
NOTE: Your answers may differ slightly.

Schlumberger Public
From the plot above, it is observed that the casing pressure
at the gas lift injection point is approximately 4700 psia, which
is within the operating limit for the valve. The predicted
Deepest Injection Point (DIP) True Vertical Depth (TVD) is
9500 ft. which reaffirms the results produced by the Gas Lift
Response Curve.

Questions
These questions are for discussion and review.
• Why does the production pressure (blue) curve change slope
above the injection point?
• Based on the results of your analysis, is gas lift feasible in
this case? ___________

PIPESIM Artificial Lift Design and Optimization 55

 Artificial Lift Design Schlumberger

Exercise 3 Determining the Future Gas Lift

Response

Over the life of the well, reservoir performance is probably the

most difficult item to predict with any certainty. It is therefore
essential that any well analysis covers the likely range in
pressures and PIs to be encountered in the producing life of the
well. These have a big impact on the production and thus the
flowing gradient of the well. All of these factors influence the
mandrel spacing and maximum depth of injection.
You will now determine the gas lift conditions late in the life of the
well.
1. Change the static reservoir pressure in the completion to
3340 psia, and the watercut in the Black Oil Property menu to
90%.
2. Generate a new set of gas lift response curves with gas lift
injection rates.
NOTE: To determine the gas lift injection depth, click a
point on the line and observe the coordinates
at the bottom of the plot window.
Schlumberger Public

56 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

As illustrated by the curves, at these conditions, gas lift can

be injected at the deepest depth possible (11940 ft TVD)
which is 60’ above the perforations. This implies that the
casing pressure required for gas lift injection will be less than
the 4000 psia limit.
The plot above suggests a gas lift rate of about 10 mmscfd
and a corresponding liquid production rate of approximately
20,000 BPD.
3. Record your results :
Optimal Gas lift rate: __________ mmscfd
Gas injection depth: ___________ft.
Liquid Production Rate: ________ BPD

Questions
These questions are for discussion and review.
• Why is the gas lift injection depth line flat?
• How will increasing watercut affect the efficiency of the gas
lifting process?
• How will the declining reservoir pressure affect the efficiency

Schlumberger Public
of the gas lifting process?

Exercise 4 Bracketing

The Bracketing operation can be used to confirm the range of

gas lift injection depths for the range of reservoir conditions and
also sensitize on the injection pressures if necessary.
Enter the initial and final conditions as shown in the dialog that
follows:
NOTE: The total liquid rate results above are for the four wells
in the system. The Bracketing operations require that
the liquid rate for only one well be entered. Therefore,
divide the liquid rate obtained in the Gas Lift Response
Curve operations above by 4.

Example:
(QL initial = 38,650/4 wells or 9,660/well)

PIPESIM Artificial Lift Design and Optimization 57

 Artificial Lift Design Schlumberger

(QL final = 20,000/4 wells or 5,000/well)

Schlumberger Public

The range of depths shown in the results above is consistent with

the results from the gas lift response curves. Furthermore, the
Bracketing plot shows that even at the deepest injection depth,
you do not exceed the casing pressure limit of 5000 psia.

Exercise 5 Designing for Gas Lift

With the above considerations in mind for the particular well the
next step is to initially design the well at the worst-case
conditions for gas lift. Positioning the mandrels for the worst case
design allows for well unloading and for the well to be gas lifted,
though not necessarily optimized as well conditions change.
The worst-case conditions for gas lift design are:
• High reservoir pressure
• High productivity index (PI)
• High water cut percentage
In this case, while the high watercut does not occur until late in
the life of the wells, the worst case conditions correspond to the
time when the reservoir pressure is high as well as the
productivity index. Recall from the earlier analysis that the well PI
is the highest initially and declines over time.

58 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

To design for gas lift:

1. Change the static reservoir pressure in the completion back
to 6600 psia, and the watercut to 5%.
2. Open the Tubing dialog and select Convert to detailed
tubing model. Change the view to Detailed model
3. Select Artificial Lift > Gas Lift > Gas Lift Design.
4. Enter the following information into the Gas Lift Design
dialogs:

Schlumberger Public

PIPESIM Artificial Lift Design and Optimization 59

 Artificial Lift Design Schlumberger
Schlumberger Public

NOTE: The Unloading Production Pressure is based

on the static pressure gradient in the riser
(0.465 psi/ft X 7000 ft. + 200 psia = 3455 psia).

60 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

Schlumberger Public
5. Click Perform Design. You will see a result like the following.

PIPESIM Artificial Lift Design and Optimization 61

 Artificial Lift Design Schlumberger

6. Click Graph to view the gas lift design plot.

Schlumberger Public

7. Click Report to observe the formatted gas lift design report.

8. Click Install Design.
9. Open the Tubing dialog and select Downhole equipment to
observe that the valves are installed in the tubing.

Exercise 6 Forecasting Gas Lift

Performance

The model can now be simulated over time to determine the

behavior of the gas lift system as conditions change. The basis
for the performance forecast is described in Lesson 3 and the
related exercise.
To forecast gas lift performance:
1. Select Setup > Gas Lift System Properties. Observe the
gas lift properties that will be used for each simulation case.

62 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

2. Select Operations > System Analysis and rerun the case

with the gas lift system installed
3. Configure the axis to display System Inlet Pressure on the
lower axis and Injection Depth on the 2nd y-axis. Invert the
bottom axis using the Edit > Advanced Plot Setup menu.

Schlumberger Public
4. Record your results in the table below.

Reservoir Pressure at psi

start of gas lift

Plateau Production Time days

Cumulative Recovery with MMSTB

Gas lift

Cumulative Recovery for MMSTB

base case (from prev.
exercise)

Cumulative recovery with MMSTB

ESP (from prev. exercise)

Cumulative Recovery with MMSTB

ESP + MFB (from prev.
exercise)

5. Save the model as GL_design.bps.

PIPESIM Artificial Lift Design and Optimization 63

 Artificial Lift Design Schlumberger

Questions
These questions are for discussion and review.
• Why is the gas lift injection depth zero even as production
falls off the plateau?
• Why does the production rate increase as the reservoir
pressure declines from 4500 to 4250 psia?
• Would it be advantageous to supplement gas lift with
multiphase boosting? Explain.

Extended Exercises
Currently, the system is set up to inject a constant value of 8
mmscfd throughout the life of the system. From earlier analysis,
you know that the ideal gas injection rate will increase to
approximately 10 mmscfd late in life. To specify the gas injection
rates for each set of conditions, a new sensitivity column can be
added to the system analysis. Rerun the forecast with gas lift
injection rates that vary over time.
Re-activate the multiphase booster and repeat the performance
forecast.
Schlumberger Public

Use the system plot to evaluate the arrival temperature over

time. At what point does wax deposition become a concern?
What can be done to mitigate wax problems?
Use the system plot to evaluate erosional velocity ratio over time.
Is erosional velocity in the flowline-riser a problem? If so, what
actions can be taken to mitigate erosion? Select Reports >
Profile Plot to determine the location of maximum erosion.

Review Questions
• Based on the results of the various artificial lift options
analyzed, what are the advantages and disadvantages of gas
lift compared to ESPs for this system?
• What are the advantages and disadvantages of
supplementing wellbore artificial lift with a multiphase
booster?

Summary
In this module, you learned how to:
• select a completion design
• size a subsea tieback
• perform a multiphase booster design
• perform an ESP design
• perform a gas lift design

64 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Design

• evaluate design scenarios by performing production

forecasts.
In the following module, you will learn how to determine the
optimal amount of gas lift and injection pressure at a given time.
To do this, the system developed in Module 1 must be converted
into a network model for detailed gas lift optimization.

Schlumberger Public

PIPESIM Artificial Lift Design and Optimization 65

 Artificial Lift Design Schlumberger

NOTES
Schlumberger Public

66 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Optimization

Module 2 Artificial Lift

Optimization
In Module 1, you investigated several design scenarios based on
modeling performed during the conceptual design phase of the
development. In Module 2, you will investigate artificial lift
optimization applied to a system that is in the operations phase of
development.
You will update and expand the model built in Module 1 to reflect
operating conditions during production. Based on this data, the
optimal artificial lift conditions will be determined.
NOTE: While it is recommended that Module 1 be completed
prior to beginning Module 2, Module 2 may be studied
independently starting with the gl_design.bps model
provided by your instructor.

Prerequisites
To successfully complete this training, you must have familiarity
with:

Schlumberger Public
• Network modeling with PIPESIM
• Gas lift concepts
• Production operations

Learning Objectives
After completing this training, you will know how to:
• construct a network model for a gas lifted production system
• specify the operating parameters and constraints that dictate
system performance
• determine the optimal allocation of gas lift among a network
of gas lifted wells.

Lesson 1 Gas Lift Optimization

Optimization, by definition, is a mathematical procedure that
aims to determine the optimal configuration of a set of control
variables for a prescribed objective function that is to be
optimized, possibly including constraints. In production
operations, the objective function may be to maximize oil or gas
production rates, minimize gas-oil or water-oil ratios, or maximize
economic KPIs.

PIPESIM Artificial Lift Design and Optimization 67

 Artificial Lift Optimization Schlumberger

Gas lift optimization at the field level is far more complex than
that for individual wells (Figure 16). The production system must
be modeled as an interconnected network to account for the
interaction among the wells. Additionally, field equipment must be
incorporated into the model and operating constraints properly
accounted for.
Schlumberger Public

Figure 16 Gas lift production network

Basic Principle
The basic principle behind gas lift injection in oil wells is to lower
the density of the produced fluid in the tubing. This results in a
reduction of the elevational component of the pressure gradient
above the point of injection and a lower bottomhole pressure.
Lowering the bottomhole pressure increases reservoir drawdown
and thus production rate.
In terms of the overall pressure gradient, the trade-off to the
increased presence of gas is an increased frictional pressure
gradient. As shown in Figure 17 on page 69, as the rate of
injection gas increases, a point is reached where the benefits of
reducing the elevational gradient equals the drawback of
increasing the frictional gradient.

68 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Optimization

Further increase of injection gas has a detrimental effect on the

overall production rate. This point is called the optimal
unconstrained gas-lift injection rate and for individual wells is
relatively easy to calculate.

Figure 17 Gas lift vs. Liquid production

Schlumberger Public
In practice, when dealing with a network of many gas lifted wells,
the optimal injection rate is largely dependent on the flowline
hydraulics where a reduced elevational pressure gradient may
provide little benefit.
Additionally, the complex interaction of wells producing into a
common gathering network determines the backpressure against
which the individual wells must produce. Furthermore, operating
constraints may restrict the amount of gas that can be injected
into the well.
Thus, optimization of the complete system necessitates an
optimal allocation of the available lift gas amongst all the gas
lifted wells. For networks with hundreds of wells this becomes a
mathematically complex problem.

Constraints

Careful consideration must be given to operating constraints

including handling capacities, compression requirements and the
availability of lift gas. In addition, local, global or mid-level
constraints may be specified. Local constraints are those that
pertain to the local behavior of individual wells or branches and
include for example:
• Maximum coning GOR
• Maximum drawdown pressure drop
• Bubble-point drawdown
• Maximum water rate

PIPESIM Artificial Lift Design and Optimization 69

 Artificial Lift Optimization Schlumberger

• Maximum wellhead temperature

• Maximum injection pressure
• Min/Max gas lift injection rate
• Maximum erosional velocity ratio
• Min/Max liquid rate
Mid-level constraints are those that act at the group level, for
example, maximum liquid rate at a manifold. Finally, global
constraints are those that apply to the entire network.
For instance, if you want to optimize oil production from a field,
you may be limited by the following global constraints:
• Maximum or fixed available lift gas
• Maximum or fixed total produced gas
• Maximum produced oil, gas, or water
A thorough understanding of how the limitations affect the
performance of the field can be modeled, and help the operator
to optimize the system while still maintaining controls over:
• Erosional velocity
• Water handling capacity
Schlumberger Public

• Compression limits
• Gas limits due to fuel gas and gas sales

Solution Approach

Once the model is defined, a system of performance curves is

generated to describe the relationship of liquid flow rate with
respect to the gas lift injection rate for varying wellhead pressure,
as shown in Figure 18 on page 71. Noticeably, as the wellhead
pressure increases the potential liquid flowrate decreases for a
given level of lift gas injection.
These lift profiles are generated for each well in a pre-processing
step and are employed in an offline optimization procedure in
which the well performance is accounted for without directly
having to run the entire network.

70 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Optimization

Once an optimal allocation is obtained, an online call to the real

network simulator obtains the real wellhead pressures. This
decoupling significantly aids the speed up of the optimization
procedure, which will be elaborated below.

Figure 18 Lift performance curves

The Gas-Lift Allocation Problem

Determining the optimal gas lift allocation over the set of gas
lifted wells is a non-linear optimization problem. In addition, as

Schlumberger Public
discussed above, there may be many constraints imposed on the
system.
Several well established techniques exist for the treatment on
non-linear constrained problems, including, for example,
Sequential Quadratic Programming (SQP) or Augment
Lagrangian Methods (ALM). Alternately, stochastic based
solvers, such as the Genetic Algorithm (GA), can be employed.
However, one shortcoming of simply applying these solvers for
direct optimization (optimizing the system as given) is the cost
associated with running the network simulation for each objective
function call. If numerical derivatives are required the problem is
further compounded. To overcome this computational and time
burden, a new solution approach is presented that uses an
iterative offline-online procedure to provide greater solution
flexibility and performance.

Offline-Online Optimization Procedure

The optimization scheme calculates the optimal injection rates
for all of the wells based initially on given wellhead pressures
using the extracted lift profiles. Subsequently, an online call to the
real network model provides updated well pressures. The
procedure repeats iteratively until convergence of wellhead
pressures is reached. The actual optimal allocation can be run to
maximize on either total liquid produced or total oil produced
based on specified available injection gas or the constrained total
permissible produced gas. This approach achieves a significant
solution speed up while maintaining the rigor of the full network
model.

PIPESIM Artificial Lift Design and Optimization 71

 Artificial Lift Optimization Schlumberger

Optimization Methods
Two methods are available to determine the optimal allocation of
lift gas. These are the Newton Reduction Method (NRM) and the
Genetic Algorithm (GA). The choice of which method is used
depends on the constraints applied to the network model.
Selection can be made automatically.
In general terms, the NRM technique does not handle mid-level
constraints, such as those imposed on a manifold. The GA on the
other hand, penalizes those solution candidates that exceed the
constraint. Following is a description of each of these methods.

Newton Reduction Method (NRM)

NRM is a deterministic solver specifically designed to allocate all

the available lift gas. That is, the sum of the gas lift injection rates
will always equal the amount of gas made available. Treating the
available lift gas as an equality constraint enables NRM to
convert the original multi-dimensional problem to a solution of a
composite residual function of one variable. It must be called
several times to ensure true solution optimality with respect to
the original inequality constraint. It is fast, but limited with respect
to manifold (branch) level constraints and used for networks that
Schlumberger Public

have only primary well-level and global constraints.

Genetic Algorithm (GA)

The GA is a probabilistic solver that belongs to the class of

evolutionary algorithms that use the principles of evolution to
(stochastically) evolve a population of candidate seeds to
progressively better states. The best candidate after a given
number of generations is accepted to be the optimal. The GA
performs a multi-dimensional parallel search and does not
require derivative information. Because of the higher number of
function evaluations required and potentially a greater number of
network solves, it can be more costly in calculation time, but is
generally robust.
The GA is most useful when mid level constraints (for example,
maximum flow at a manifold) are imposed in the network model.
The GA solver is useful in these situations, as its use of implicit
global search through the use of a population of search points
allows it to overcome poorer local solutions.

SDR Lexico

The SDR Lexico optimizer implements a variation of a

constrained downhill simplex method proposed by Takahama
and Sakai. As with the Genetic Algorithm, the SDR Lexico solver
is designed to handle complex non-linear constraints.

72 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Optimization

Exercise 1 Constructing a Network Model

To construct a network model:

1. Open PIPESIM and select File > New > Network.
2. Select a production well icon from the left-side toolbar and
insert it into the flow diagram.
3. Right-click the well icon and select Import single branch
model. Browse to the gl_design.bps file completed in Module
1 or provided by your instructor.
4. From within the single-branch well model, select Setup >
Black Oil, change the fluid name to <group A> and click
Export.
5. During the design phase, when limited information was
available, the four wells were assumed identical and modeled
in the single branch environment using the adder/multiplier
utility. To perform an optimization study, the wells need to be
treated independently in a network model. Close the single
branch window for the well in order to enter the network
modeling environment.
6. Using the icons on the toolbar at the left, construct the

Schlumberger Public
following network diagram and name the branches and
junctions by right-clicking and selecting General:

7. Move the topsides choke and pipe to the correct branch in the
network model.
a. Double-click Well 1.
b. Hold down Shift and select the following objects:
• Topsides flowline upstream of choke
• choke
• Topsides flowline downstream of choke
c. Right-click and select Cut.
d. Close the single branch window, double-click the choke-
A branch and select Paste.
e. Select the default flowline surrounded by the red box and
press Del.

PIPESIM Artificial Lift Design and Optimization 73

 Artificial Lift Optimization Schlumberger

f. Reconnect the topsides-A junction to the flowline inlet

and, using the connector tool, connect the topsides-A
and Header junctions to the flowlines upstream and
downstream of the choke respectively.
g. Close the single branch window.
8. Move the flowline-riser pair and multipliers to the respective
network branch.
a. Double-click Well 1.
b. Highlight the connector leaving the multiphase booster
and click Delete.
c. Hold down Shift and select the following objects:
• Adder-multiplier immediately upstream of the tieback
flowline
• Tieback flowline
• Riserbase report tool
• Riser
• Topsides adder-multiplier
d. Right-click and select Cut.
Schlumberger Public

e. Close the single branch window, double-click the tieback-

A branch, and select Paste.
f. Select the default flowline surrounded by the red box and
press Del.
g. Using the connector tool, connect the upstream adder-
multiplier to the DC-A junction and the downstream
adder-multiplier to the topsides-A junction.
h. Close the single branch window.
9. Double-click the Separator-line branch and modify the
default flowline object with the following properties:

Rate of undulations 0 /1000

Horizontal Distance 25 ft

Elevation Difference 0 ft

Inner Diameter 12 in.

Ambient Temperature 60 degF

Heat Transfer U-value bare in air

Close the single branch window.

10. Remove remaining objects from the imported well model.
a. Double-click well_1.
b. Hold down Shift and select the following objects:
• Connector downstream of choke

74 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Optimization

• Remaining objects downstream of connector

c. Click Delete.
d. The well should appear as shown below:

11. Analysis of flowing pressure surveys suggest that the

Hagedorn & Brown Correlation is a better match to measured
data. Select Setup > Flow Correlations and specify
Hagedorn & Brown as the vertical flow correlation
12. For optimization purposes, assume that all gas lift is injected

Schlumberger Public
in the lowermost orifice valve and will therefore replace the
gas lift valve system with a single injection point.
a. Double-click the tubing and select the Downhole
Equipment tab.
b. Click the G/L Valve System button.
c. Select the Edit valve details checkbox.
d. Select Remove all valves and click OK.
e. In the Downhole Equipment tab, specify a gas lift
injection point at a MD of 8600 ft.
f. Click the Properties button next to the gas lift injection
point and specify the following:

Injection rate 0 mmscfd

Surface injection Temp. 38 degF

Gas Gravity 0.62

13. For completion design purposes, the wells were modeled

using the pseudo-steady state flow model. However, during
the operational phase, the availability of downhole pressure
measurements coupled with knowledge of the average
reservoir pressure allows for an accurate characterization of
the inflow performance using a simple productivity index
method.
a. Double-click the completion.

PIPESIM Artificial Lift Design and Optimization 75

 Artificial Lift Optimization Schlumberger

b. Change the completion model from pseudo-steady state

to Well PI.
c. Specify a PI value of 10 psi/STBD and select the Use
Vogel below bubblepoint option.
d. Return to the main network diagram.
At this phase in the development, wells from 2 additional drill
centers (groups B & C) produce to the FPSO. These wells
are piggy-backed along a separate tieback-riser pair and
produce to a common header.
14. Add the following branches and junctions to the network and
name them as shown below:

15. The choke-BC branch is identical to the choke-A branch.

Schlumberger Public

Copy the flowline-choke-flowline in the choke-A branch

into the choke-BC branch.
16. The tieback-BC branch is nearly identical to the
tieback-riser-A branch. Copy the objects in the tieback-A
branch into the tieback-riser-BC branch. Ensure that the
DC-B junction is connected to the adder-multiplier attached
to the flowline. Modify the length of the tieback flowline to
1000 ft. (Be careful with the units.)
17. The tieback-C branch contains a dual flowline configuration.
a. Copy the contents of the tieback-BC branch into the
tieback-C branch.
b. Delete the riser object and reconnect the flowline outlet
from the riser-base report tool to the outlet adder-
multiplier.
c. Connect the outlet adder-multiplier to the DC-B junction
with the connector tool.
d. Connect the inlet adder-multiplier to the DC-C junction.
e. Delete the riser-base report tool.
f. Modify the flowline object so that the length is 5 miles and
the elevation difference is 400 feet.
18. Update the well models with current production data.
a. Select Well_1 in the network diagram.
b. Right-click and select Copy.

76 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Optimization

c. Right-click and select Paste 9 times.

d. Hold down Shift, select the branch icon, and connect the
wells to the drill centers as shown below, noting the well
names:

19. Modify the individual well models based on the properties

given in the following table.

Well PI Perf Perf Gas Inj Res.

No. (psi/STBD) MD TVD MD Temp

Schlumberger Public
(ft) (ft) (ft) (degF)

1 10 14000 12000 8600 350

2 11 14000 12000 8600 350

3 8 14000 12000 9000 350

4 9 14000 12000 8600 350

5 6 10000 8020 8900 300

6 5 10000 8000 8900 300

7 11 11000 9500 8700 320

8 13 10800 9520 8400 320

9 15 11200 9560 8600 320

10 12 11500 9600 8800 320

20. Each well group will be defined by a separate fluid model as

shown in the following table.

Bubble PointCalibration Viscosity Calibration

Group API GOR W cut Pressure Temp. Viscosity Viscosity

(deg) (scf/STB) (%) (psia) (degF) @200 degF @60 degF

A 25 400 25 4100 350 10 70

B 21 200 15 3800 300 18 105

C 22 300 0 3950 320 15 130

PIPESIM Artificial Lift Design and Optimization 77

 Artificial Lift Optimization Schlumberger

a. Select Setup > Fluid Models.

b. Highlight the row corresponding to Well_1 and click Edit.
c. Click Import and select Group A from the dropdown list.
Click Apply.
d. By default, all wells will use the Group A fluid model.
e. To assign a fluid model to the Group B wells, highlight the
row corresponding to Well_5, select Local fluid model
and click Edit. Change the fluid properties according to
the table above. Rename the fluid <Group B> and click
Export.
f. From the Fluid Models list, ensure that Wells 5 and 6 are
using local fluid models based on the Group B template.
NOTE: Local fluid models must have different names.
As shown by the table below, the fluids for
wells 5&6 are named “GroupB_W5” and
“GroupB_W6” accordingly.
g. Repeat the previous two steps for Group C wells (7-10).
h. The Fluid Models table should appear as shown in the
table below:
Schlumberger Public

21. Select Setup > Flow Correlations. Ensure that the Vertical
Flow Correlation is set to Hagedorn & Brown and the

78 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Optimization

Horizontal Flow Correlation is set to Beggs & Brill Revised

(Taitel-Dukler map).
22. To ensure that the model runs without gas lift and to validate
the data entry, assume for the moment that all reservoir
pressures are 8,000 psia and the separator pressure is 200
psia.
a. Select Setup > Boundary conditions.
b. Specify reservoir pressures for the wells and the
separator pressure.
c. Run the model.
d. Select Reports > Report Tool to open the report tool,
then click Clear. Click on the DC-A, DC-B, and DC-C
junctions and the Separator (Sink). Check the results
against the table below. If your results are within 10%,
you may proceed to the next exercise.

Schlumberger Public
23. Save the model as GL_Network.bpn.

Exercise 2 Optimizing Gas Lift

To optimize gas lift:

1. Save the model as GL_optimization.bpn in a new directory.
2. Select Setup > Boundary Conditions and enter reservoir
pressures for the wells associated with the groups as defined
in the table below:

Group Reservoir Pressure

(psia)

A 5400

B 6100

C 6300

3. Perform the gas lift optimization. Select Operations > Gas

Lift Optimizer to open the Gas Lift Optimizer interface.
The Gas Lift Optimizer contains several tabs described as
follows:
• Local Constraints: Define constraints to individual gas-lift
wells, branches, or sinks (or to groups of wells, branches,
or sinks that you create).

PIPESIM Artificial Lift Design and Optimization 79

 Artificial Lift Optimization Schlumberger

• Global Constraints: Define overall constraints for the

entire production system including whether you have a
fixed or maximum supply of lift gas available.
• Validate: Allows comparison of various pressure and rate
values in your network models against field measurements
based on specified gas lift injection rates,
• Optimize: Create any required well curves, set up and run
your optimization study, and specify how to archive the run
results.
• Results: Graphical and tabular displays of the optimization
results. Also allows reporting and comparison of key
performance indicators (KPIs) and comparison of multiple
archived runs.
• Network Viewer: View a graphical representation of any of
the networks in your optimization including bubble plots of
results, such as total oil production rate and gas lift injection
rate.
Schlumberger Public

4. In the Local Constraints tab, select Gas Lift Wells.

5. Define groups corresponding to the drill centers in our
network model.
a. Holding the Ctrl key, select wells 1, 2, 3 and 4.
b. Right-click and select Create Group.
c. Name the group <Group A>.
d. Repeat for Group B (wells 5&6) and Group C (Wells
7,8,9&10)
6. Define local constraints for all wells.
a. Ensure that the top-level node Gas Lift Wells is selected
so that the settings may be easily applied to all wells.

80 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Optimization

b. Select the Dynamic Minimum Gas Lift Rate option. This

ensures that the gas lift injection rate is sufficient to
ensure well stability and avoid issues such as heading.
c. Select the checkbox in the Select column for the above
constraints and click Apply Selected Constraints. This
applies the specified constraints for all gas lift wells.
Alternatively, individual well or group constraints can be
specified individually.
7. Select the Global Constraints tab to define global
constraints and specify the following:

Maximum Gas Lift Injection Rate 40 mmscfd

Maximum Liquid Production Rate 60000 STBD

Maximum Water Production Rate 25000 STBD

8. Select the Optimize Tab and specify the following:

Optimization Type Total Oil

Archive Options After each run

Generate Curves for All wells

Schlumberger Public
Max. Lift per Well 10 mmscfd

9. Click Advanced and select the advanced options as shown

below:

PIPESIM Artificial Lift Design and Optimization 81

 Artificial Lift Optimization Schlumberger
Schlumberger Public

NOTE: When specifying the total maximum lift gas as

a global constraint, the shape of the system
performance curve (i.e., total lift gas vs. total oil
production) will often be rather flat such that
incremental increases in the gas rate yield little
gain in the objective. The marginal gradient
specifies the minimum amount of oil that is
acceptable to produce per unit of gas injected.
Therefore, use the Marginal Gradient field to
specify a positive gradient that will force a
solution point to the left of the flat region of the
performance curve.
10. Click Run to start the optimization and observe messages in
the message log.
NOTE: In the verbose messages, the gas lift injection
lower and upper limits associated with the
stability and drawdown constraints vary by
iteration. Depending on the complexity of the
model and constraints, the maximum number
of iterations in the Advanced tab may need to
be adjusted.
NOTE: When specifying the total maximum lift gas, a
series of runs are performed at various fixed
total gas lift injection rates. To monitor the
results from each of these runs, select the
Iteration View button from the Global
Constraints tab.

82 PIPESIM Artificial Lift Design and Optimization

Schlumberger Artificial Lift Optimization

11. Once the optimization is complete, select the Results tab

and the Lift Curves and Rates sub-tab.
a. Select Gas Lift Wells and then select the Curves tab to
display the gas lift performance curves for each well and
the gas lift injection rates.
b. From the navigation panel, select individual wells and
groups to observe individual curves.
c. Select the Gas Lift Wells group and select the Tables
tab. Note the following:

Total Gas Lift injection rate mmscfd

Total Liquid Rate STBD

Total Oil Rate STBD

Total Water Rate STBD

12. Select the Network Viewer tab. Select the results overlay
to configure the display various results on the network
diagram.

Schlumberger Public
Questions
These questions are for discussion and review.
• Which global constraint limited the total gas lift consumed?
• Which well group consumed the most/least lift gas?

Review Questions
• In what situations would you not inject the optimal total
amount of gas lift?
• Which optimization algorithm is better at handling complex
constraints?

Summary
In this module, you learned how to:
• construct a network model to account for the interactions
amongst the wells
• define the constraints and operating parameters for a gas lift
optimization study
• execute a gas lift optimization study and analyze the results.

PIPESIM Artificial Lift Design and Optimization 83

 Artificial Lift Optimization Schlumberger

NOTES
Schlumberger Public

84 PIPESIM Artificial Lift Design and Optimization

Schlumberger Gas Lift Design

Appendix A Gas Lift Design

PIPESIM User Interface Dialogs

This appendix gives a detailed description of the User Interface
dialogs in PIPESIM.

Schlumberger Public

PIPESIM Artificial Lift Design and Optimization 85

 Gas Lift Design Schlumberger
Schlumberger Public

86 PIPESIM Artificial Lift Design and Optimization

Schlumberger Gas Lift Design

Schlumberger Public

PIPESIM Artificial Lift Design and Optimization 87

 Gas Lift Design Schlumberger
Schlumberger Public

88 PIPESIM Artificial Lift Design and Optimization

Schlumberger Gas Lift Design

Schlumberger Public

PIPESIM Artificial Lift Design and Optimization 89

 Gas Lift Design Schlumberger
Schlumberger Public

90 PIPESIM Artificial Lift Design and Optimization

Schlumberger Gas Lift Design

Schlumberger Public

PIPESIM Artificial Lift Design and Optimization 91

 Gas Lift Design Schlumberger
Schlumberger Public

92 PIPESIM Artificial Lift Design and Optimization

Schlumberger Gas Lift Design

Schlumberger Public

PIPESIM Artificial Lift Design and Optimization 93

 Gas Lift Design Schlumberger
Schlumberger Public

94 PIPESIM Artificial Lift Design and Optimization

Schlumberger Gas Lift Design

NOTES

Schlumberger Public

PIPESIM Artificial Lift Design and Optimization 95

 Gas Lift Design Schlumberger

NOTES
Schlumberger Public

96 PIPESIM Artificial Lift Design and Optimization

Schlumberger Recommendations

Appendix B Recommendations

Related Publications
The concepts presented in this training course are based on
example applications described in the following published
papers.
Gutierrez, F., Hallquist, A., Shippen, M., Rashid, K. A New
Approach to Gas Lift Optimization Using an Integrated Asset
Model. Paper presented at the 2007 International Petroleum
Technology Conference held in Dubai, U.A.E., 4-6 December,
2007.
REDA Hotline High-Temperature ESP Systems, http://
www.slb.com/media/services/artificial/submersible/hotline_br.pdf
(accessed October 2008).
Shepler, R., White, T., Amin, A., Shippen, M. Lifting, Seabed
Boosting Pay Off. Harts E&P, April 2005 (63-66). Paper originally
presented at the Deepwater Offshore Technology Conference,
New Orleans, LA, USA, Nov. 30 to Dec, 2, 2004.
Shippen, M.E., Scott, S.L. Multiphase Pumping as an Alternative

Schlumberger Public
to Conventional Separation, Pumping and Compression. Pipeline
Simulation Interest Group (PSIG) 34th Annual Meeting, Portland,
OR, 23-25 Oct. 2002.

PIPESIM Artificial Lift Design and Optimization 97

 Recommendations Schlumberger

NOTES
Schlumberger Public

98 PIPESIM Artificial Lift Design and Optimization

Schlumberger Answers for Exercises

Appendix C Answers for

Exercises

Module 1: Artificial Lift Design

Lesson 1: Flowline and Riser Design

Exercise 1: Sizing the Flowline-Riser Pair

Line Size Manifold Max EVR Min Arrival

inch Pressure psi Temp

6 7490 2.6 186

7 4610 1.87 162

8 3400 1.42 148

9 2820 1.1 139

10 2480 0.9 130

Schlumberger Public
11 2290 0.73 123

12 2190 0.61 117

Dual flowline:

Line Size Manifold Max EVR Min Arrival

inch Pressure psi Temp

6 3660 1.22 116

7 2890 .88 103

8 2540 .67 93

9 2380 .52 84

10 2330 .42 77

11 2320 .35 71

12 2340 .29 66

PIPESIM Artificial Lift Design and Optimization 99

 Answers for Exercises Schlumberger

Lesson 2: Completion Design

Exercise 1: Working with Perforated and Frac-Pack

Completions

Completion Type Perforated Frac-Pack

Mechanical skin factor 4.744 .669

Flowing Pressure, psia 8140 9030

Flowing Liquid Rate, stb/d 22300 26730

AOFP (BPD) 63800 108200

Lesson 3: Performance Forecasting

• Time on plateau: 267 days
• Minimum Pinlet to produce 60,000 BPD: 9400 psia
• Minimum Pinlet to produce at any rate: 6600 psia
• Cumulative recovery: 40 MMSTBD
Schlumberger Public

Exercise 2: Determining Choke Location

Choke Location Wellhead Topsides

Choke Size, ins .76 1.89

Critical? no yes

Choke dP, psi 2205 1794

Flowline dP, psi 711 552

Predominant flow regime in tieback intermittent liquid

Maximum EVR in flowline/riser .86 .42

(not topsides pipe)

Min. Arrival Temp.ºF 124 128

100 PIPESIM Artificial Lift Design and Optimization

Schlumberger Answers for Exercises

Lesson 4: ESP Design

Pump model Reda HN15500

selected

Required no. stages 177

HP required 999.75

Design rate (STB/d) 13600

GVF at inlet 0

Gas separator no
required?

• Time on plateau: 500 days

• Minimum Pinlet to produce 60,000 BPD: 7600 psia
• Minimum Pinlet to produce any rate: 4250 psia
• Cumulative recovery: 75 MMSTBD

Lesson 5: Multiphase Booster Design

Schlumberger Public
Exercise 1: Placing a Multiphase Booster

ESP Speed (Hz)

MPB dP (psi) 0 60

0 0 54100

1000 35000 60200

Result

GVF @ suction (%) 24

MFP Power (Hp) 4783

ESP Power x4 (Hp) 3776

Total Power (Hp) 8559

• Time on plateau: 680 days

• Minimum Pinlet to produce 60,000 BPD: 6500 psia
• Minimum Pinlet to produce any rate: 3340 psia
• Cumulative recovery: 100 MMSTBD

PIPESIM Artificial Lift Design and Optimization 101

 Answers for Exercises Schlumberger

• Total maximum horsepower requirement for the ESPs and

booster: 8833 HP @ 7600 psia reservoir pressure

Lesson 6: Gas Lift Design

Exercise 6: Forecasting Gas Lift Performance

Reservoir Pressure at start of gas 7600 psia

lift

Plateau Production Time 267 days

Cumulative Recovery with Gas lift 100 MMSTB

Cumulative Recovery for base 40 MMSTB

case (from prev. exercise)

Cumulative recovery with ESP 75 MMSTB

(from prev. exercise)

Cumulative Recovery with ESP + 100 MMSTB

MFB (from prev. exercise)
Schlumberger Public

Module 2 Artificial Lift Optimization

Lesson 1: Gas Lift Optimization

Exercise 2: Optimizing Gas Lift

Total Gas Lift injection rate 33.2 mmscfd

Total Liquid Rate 60,515 STBD

Total Oil Rate 54,147 STBD

Total Water Rate 6368 STBD

102 PIPESIM Artificial Lift Design and Optimization

Schlumberger Answers for Exercises

NOTES

Schlumberger Public

PIPESIM Artificial Lift Design and Optimization 103

 Answers for Exercises Schlumberger

NOTES
Schlumberger Public

104 PIPESIM Artificial Lift Design and Optimization