PIPESIM 2009.1 Artificial Lift Design and Optimization H
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PIPESIM Artificial Lift Design and Optimization
Version 2009.1
Schlumberger Information Solutions July 24, 2009
Schlumberger Public
Workflow/Solutions Training
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Table of Contents
About this Manual Learning Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What You Will Need . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What to Expect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Course Conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Icons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1 1 1 2 4 5
Module 1: Artificial Lift Design
PIPESIM Artificial Lift Design and Optimization
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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 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
Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extended Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Review Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
64 64 64 64
Module 2: Artificial Lift Optimization
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Prerequisites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lesson 1: Gas Lift Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Basic Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Gas-Lift Allocation Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Offline-Online Optimization Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Optimization Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exercise 1: Constructing a Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exercise 2: Optimizing Gas Lift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Review Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
67 67 67 68 71 71 72 73 79 83 83 83
Appendix A: Gas Lift Design 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
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PIPESIM Artificial Lift Design and Optimization
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About this Manual
About this Manual This course provides training for PIPESIM, a steady-state, multiphase 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 conceptual 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
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evaluate design scenarios by performing production forecasts
•
determine the optimal allocation of gas lift among a network of wells.
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What You Will Need In this training, you will need the following hardware and software: •
PIPESIM 2008.1 or later
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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
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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
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About this Manual
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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 commands 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."
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Identifies the name of Schlumberger software applications, such as ECLIPSE, GeoFrame or Petrel. Characters inside triangle brackets
Indicate values that the user must supply, such as and , or .
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 fixedwidth
Represent code, data, and other literal text the user sees or types. Examples include / or .
NOTE:
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Some of the conventions used in this manual indicate the information to enter, but are not part of the information 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.
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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.
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About this Manual
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Icons
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Throughout this manual, you will find icons in the margin representing 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.
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Title of SIS Training Manual
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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.
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About this Manual
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NOTES
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Title of SIS Training Manual
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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.
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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.
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Prerequisites To successfully complete this training, you must: •
Have a working knowledge of PIPESIM
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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.
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Lesson 1
Flowline and Riser Design
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: •
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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.
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•
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: = actual mixture velocity of fluid
VE
= API 14E Erosional velocity limit
ρm
= mixture density of fluid (lbm/ft3)
C
= empirical constant representing pipe material
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.
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Dual Flowlines: While more expensive, dual flowlines allow for several benefits including: •
Redundancy
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Round trip pigging
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Ability to test wells independently
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Ability to circulate fluids for remedial pipeline operations
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Ability to reroute wells to optimize production rates
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Better thermal management control
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Vactual
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Exercise 1
Sizing the Flowline-Riser Pair
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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.
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:
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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:
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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 – User’s data
10 cP @ 200 degF 70 cP @ 60 degF
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Select the following fluid property correlations: •
Solution Gas: Petrosky-Farshad
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Live Oil: Petrosky-Farshad
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Undersaturated Oil: Bergman & Sutton
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Emulsion viscosity method: Brinkman
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Watercut cutoff method: User specified – 65%
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.
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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)
(%)
12000
0
5
11000
0
10
10200
0
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
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0
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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.
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:
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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.
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Single flowline Line size inch
Manifold Pressure psi
Max EVR
Min Arrival Temp
6 7 8 9 10 11 12 Dual flowline Line size inch
Manifold Pressure psi
Max EVR
Min Arrival Temp
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7 8 9 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
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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
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•
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:
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where: 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.
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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
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perforated and frac-pack wells are illustrated in Figure 4 and Figure 5 on page 19.
Figure 4
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Perforated completion
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Figure 5
Frac-Pack completion
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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.
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3. Replace the Source representing the manifold with a well as shown below:
4. Enter the reservoir properties based on the data provided below. Reservoir Data
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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
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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
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Perforated Completion Damaged Zone Diameter
9 in
Deviation
39 degrees
Damage Zone Perm.
80 mD
Skin Method
McLeod
Compacted Zone Diameter
1 in
Perforation Diameter
0.5 in
Compacted Zone Perm.
40 mD
Shot Density
4 SPF
Vertical Permeability
200 mD
Depth of Penetration
36 in
Perforated Interval
60 ft
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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
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6. Enter a choke bean size of 4.67 in (equivalent to the tubing size). 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)
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11. Repeat the Nodal Analysis operation for the Frac-Pack option based on the design parameters given below. Frac-Pack Completion Gravel permeability
90000 mD
Fracture half length
20 ft
Screen Diameter
5.25 in
Fracture width
0.6 in
Casing ID
7.625 in
Proppant Perm
90000 mD
Perforated Diameter
0.5 in
Frac face depth of damage
8 in
Shot Density
4 SPF
Frac face damage perm.
250 mD
Vertical Perm.
200 mD
Choke length
3 ft
Perforated Interval
60 ft
Frac face choke perm.
90000 mD
Deviation
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12. Perform parametric studies with +/- 50% sensitivity on the 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?
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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 Branch
System analysis change in step
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Manual sensitivities
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Network
Look-up tables
Avocet IAM + PIPESIM
Network
Coupling to material balance tank
Avocet IAM + PIPESIM
Network
Dynamic coupling to reservoir simulation
Avocet IAM + PIPESIM
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Because the initial design may involve consideration of a number of design parameters, resulting in numerous simulation runs, the 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.
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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 P* (psi)
wcut (%)
0
12000
0
5
11000
0
10
10200
0
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
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Cum Liq (MMSTB)
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Reservoir Performance 120
100
watercut % Reservoir Pressure (psia/100)
80
60
40
20
0 0
20
40
60
80
100
Cumulative Oil (MMSTBD)
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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.
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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.
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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.
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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.
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Your plot should appear similar to the one shown below:
9. Save the model as base_forecast.bps.
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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: _______________
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Exercise 2
Determining Choke Location
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.
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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 flowline/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?
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Lesson 4
ESP Design
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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.
Figure 7
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ESP Lifted well and related downhole equipment
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Figure 8
ESP lifted offshore well
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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 expressed as pumping head, the height of the produced fluid that the ΔP created by the pump can support:
Which can be expressed in field units as:
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Where γL is the specific gravity of the liquid relative to water. 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:
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The pumping head of a centrifugal pump will decrease as the volumetric throughput increases. However, the efficiency of the pump, defined as the ratio of the hydraulic power transferred to the fluid (qΔp) to the power of the pump, has a maximum at some 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.
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Figure 9
Typical ESP performance curve
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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 curve. To achieve this rate, the pump must be designed to provide a pressure boost equivalent to ΔPpump, which is the 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.
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.
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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)
•
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
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•
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.
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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.
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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):
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?
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Plot the performance curve:
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7. Click the install pump button to install the pump.
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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.
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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.
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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: ________________________
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•
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.
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Lesson 5
Multiphase Booster Design
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 (2502000 m3/hr) of suction flowrate. Additional pumps can be modeled with data supplied by the vendor or acquired precommissioning tests. The Helico-axial pump is a type of rotodynamic pump manufactured by Framo. The fluid flows horizontally through a
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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. Schlumberger Public
This interstage mixing prevents the separation of the gas-oil mixture, enabling stable pressure-flow characteristics and 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
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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.
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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.
Figure 12
Typical Helico-axial multiphase booster performance curve
Relative advantages of multiphase boosters include the following:
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•
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)
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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%.
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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.
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Run the case and record the liquid rate in the table below: ESP Speed (Hz) MPB dP (psi)
0
60
0 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) Schlumberger Public
7. Rerun the performance forecast by adding an additional sensitivity variable to account for the contribution from the MFB, as shown below.
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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 Plot ESP & MFB power vs. inlet pressure and copy data tab into excel. Sum the MFB power and the ESP power times four for each pressure to determine total power requirement.
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8. Save the model as MFB_design.bps.
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Lesson 6
Gas Lift Design
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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.
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.
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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
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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.
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.
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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.
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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
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Power source can be remotely located
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Easy to obtain downhole pressures and gradients
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Serviceable with wireline unit
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Suitable for deviated wells
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Corrosion usually not as adverse
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Relative disadvantages of gas lift include the following: •
Lift gas not always available
•
Not efficient for lifting small fields with small no. wells
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Difficult to lift viscous crudes
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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.
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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. 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.
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9. Run the model.
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NOTE:
The liquid rate on the y-axis represents the liquid rate at the topsides and therefore incorporates production from all 4 wells.
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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
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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 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.
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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.
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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? ___________
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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. To determine the gas lift injection depth, click a point on the line and observe the coordinates at the bottom of the plot window.
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NOTE:
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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 of the gas lifting process?
Exercise 4
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•
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)
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(QL final = 20,000/4 wells or 5,000/well)
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.
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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:
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NOTE:
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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).
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5. Click Perform Design. You will see a result like the following.
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6. Click Graph to view the gas lift design plot.
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.
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2.
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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.
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4. Record your results in the table below. Reservoir Pressure at start of gas lift
psi
Plateau Production Time
days
Cumulative Recovery with Gas lift
MMSTB
Cumulative Recovery for base case (from prev. exercise)
MMSTB
Cumulative recovery with ESP (from prev. exercise)
MMSTB
Cumulative Recovery with ESP + MFB (from prev. exercise)
MMSTB
5. Save the model as GL_design.bps.
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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.
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Re-activate the multiphase booster and repeat the performance forecast. 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:
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•
select a completion design
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size a subsea tieback
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perform a multiphase booster design
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perform an ESP design
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perform a gas lift design
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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.
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NOTES
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Module 2
Artificial Lift Optimization
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
•
Network modeling with PIPESIM
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Gas lift concepts
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Production operations
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To successfully complete this training, you must have familiarity with:
Learning Objectives After completing this training, you will know how to: •
construct a network model for a gas lifted production system
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specify the operating parameters and constraints that dictate system performance
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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.
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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.
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.
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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
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Maximum drawdown pressure drop
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Bubble-point drawdown
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Maximum water rate
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Maximum wellhead temperature
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Maximum injection pressure
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Min/Max gas lift injection rate
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Maximum erosional velocity ratio
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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
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Maximum or fixed total produced gas
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Maximum produced oil, gas, or water
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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
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Water handling capacity
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Compression limits
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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.
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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 Schlumberger Public
Determining the optimal gas lift allocation over the set of gas lifted wells is a non-linear optimization problem. In addition, as 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.
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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)
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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 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.
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Exercise 1
Artificial Lift Optimization
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 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. Schlumberger Public
6. Using the icons on the toolbar at the left, construct the 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:
c.
•
Topsides flowline upstream of choke
•
choke
•
Topsides flowline downstream of choke
Right-click and select Cut.
d. Close the single branch window, double-click the chokeA branch and select Paste. e. Select the default flowline surrounded by the red box and press Del.
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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
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d. Right-click and select Cut. e. Close the single branch window, double-click the tiebackA 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 addermultiplier 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: •
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Connector downstream of choke
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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
a.
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12. For optimization purposes, assume that all gas lift is injected in the lowermost orifice valve and will therefore replace the gas lift valve system with a single injection point. 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.
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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:
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15. The choke-BC branch is identical to the choke-A branch. 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 addermultiplier. 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.
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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. PI (psi/STBD)
Perf MD (ft)
Perf TVD (ft)
Gas Inj MD (ft)
Res. Temp (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
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20. Each well group will be defined by a separate fluid model as shown in the following table. Bubble PointCalibration Group
API (deg)
GOR (scf/STB)
W cut (%)
Pressure (psia)
Temp. (degF)
Viscosity Calibration Viscosity @200 degF
Viscosity @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
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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 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).
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h. The Fluid Models table should appear as shown in the table below:
21. Select Setup > Flow Correlations. Ensure that the Vertical Flow Correlation is set to Hagedorn & Brown and the
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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.
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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).
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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.
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•
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 . 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.
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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: Total Oil
Archive Options
After each run
Generate Curves for
All wells
Max. Lift per Well
10 mmscfd
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Optimization Type
9. Click Advanced and select the advanced options as shown below:
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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.
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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.
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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.
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Appendix A
Gas Lift Design
Gas Lift Design
PIPESIM User Interface Dialogs This appendix gives a detailed description of the User Interface dialogs in PIPESIM.
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Appendix B
Recommendations
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.
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Shippen, M.E., Scott, S.L. Multiphase Pumping as an Alternative to Conventional Separation, Pumping and Compression. Pipeline Simulation Interest Group (PSIG) 34th Annual Meeting, Portland, OR, 23-25 Oct. 2002.
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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 inch
Manifold Pressure psi
Max EVR
Min Arrival Temp
7490
2.6
186
7
4610
1.87
162
8
3400
1.42
148
9
2820
1.1
139
10
2480
0.9
130
11
2290
0.73
123
12
2190
0.61
117
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Dual flowline:
Line Size inch
Manifold Pressure psi
Max EVR
Min Arrival 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
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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
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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
Exercise 2: Determining Choke Location
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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 (not topsides pipe)
.86
.42
Min. Arrival Temp.ºF
124
128
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Answers for Exercises
Lesson 4: ESP Design Pump model selected
Reda HN15500
Required no. stages
177
HP required
999.75
Design rate (STB/d)
13600
GVF at inlet
0
Gas separator required?
no
•
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
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Lesson 5: Multiphase Booster Design 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
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Total maximum horsepower requirement for the ESPs and booster: 8833 HP @ 7600 psia reservoir pressure
Lesson 6: Gas Lift Design
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Exercise 6: Forecasting Gas Lift Performance
Reservoir Pressure at start of gas lift
7600
psia
Plateau Production Time
267
days
Cumulative Recovery with Gas lift
100
MMSTB
Cumulative Recovery for base case (from prev. exercise)
40
MMSTB
Cumulative recovery with ESP (from prev. exercise)
75
MMSTB
Cumulative Recovery with ESP + MFB (from prev. exercise)
100
MMSTB
Module 2 Artificial Lift Optimization Lesson 1: Gas Lift Optimization Exercise 2: Optimizing Gas Lift
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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
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