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Dynamic Simulation of Gas-Lift Wells and Systems

API RECOMMENDED PRACTICE 19G11 (RP 19G11)DRAFT #7, Oct. 9, 2009

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API RP 19G11 Dynamic Simulation of Gas-Lift Wells and Systems Page 1

Foreword

This Recommended Practice (RP) is under the jurisdiction of the API Committee on Standardization of Production Equipment (Committee 19).

This document presents Recommended Practices for Dynamic Simulation of Gas-Lift Wells and Systems. Other API Specifications, API Recommended Practices, and Gas Processors Suppliers Association (GPSA) documents may be referenced and should be used for assistance in design and operation.

API Recommended Practices may be used by anyone desiring to do so, and diligent effort has been made by the Institute to assure the accuracy and reliability of the data contained therein. However, the Institute makes no representation, warranty, or guarantee in connection with the publication of any API Recommended Practice and hereby expressly disclaims any liability or responsibility for loss or damage resulting from their use, for any violation of any federal, state, or municipal regulation with which an API Standard may conflict, or for the infringement of any patent resulting from the use of an API Recommended Practice or Specification.

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This is the first edition of this recommended practice.

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This Recommended Practice shall become effective on the date printed on the cover but may be used voluntarily from the date of distribution.


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API RP 19G11

Document Outline

I. Summary of Recommended Practices for Dynamic Simulation of Gas-Lift Wells

and Systems

To be Drafted: Chapter I will be drafted after the other chapters are completed. An outline for this chapter is included in the document. Juan Carlos Mantecon and Cleon Dunham will have primary responsibility.

This chapter contains a brief summary and description of all of the recommended practices contained in this Recommended Practices document.

a. The recommended practices must be organized by the chapters in this document where they are presented.

b. This chapter must contain a cross reference to the detailed chapter(s) where each recommended practice is discussed and described.

c. After reading this chapter, the reader should have a full grasp of all of the recommended practices that must be followed to successfully use and deploy dynamic simulation of gas-lift wells and systems.

d. The reader should have an immediate reference of where to turn in the document for more detailed information on any specific recommended practice.

II. Introduction to Dynamic Simulation of Gas-Lift Wells and Systems

Already Drafted: Chapter II has been drafted by Juan Carlos Mantecon.

a. Document Objectives

b. Dynamic Simulation - Definition and Basic Concepts

c. Difference Between Nodal® (steady-state analysis) and Dynamic Simulation

III. Typical Gas-Lift Well and System Operations

Already Drafted: Greg Stephenson and Cleon Dunham have drafted Chapter III. They are reviewing it with Stan Groff and Luis Gonzalez.


This chapter describes all of the typical gas-lift well and system operations. It must clearly identify both the steady-state and the dynamic aspects of each operation. It must identify how dynamic simulation is or can be used to address each of the dynamic aspects of these operations.

a. Continuous gas-liftb. Intermittent gas-liftc. Gas-assisted plunger liftd. Dual gas-lifte. Single-point gas-liftf. “Auto” gas-lift where gas from one zone is used in the same well to lift other zonesg. Riser gas-lifth. Gas-lift for gas well deliquificationi. Gas-lift unloadingj. Kick-off of gas-lift wellsk. Use of gas-lift for wellbore clean-upl. Gas-lift system distribution with various types of system configurationsm. Use of un-dehydrated gasn. Use of non-hydrocarbon gases such as CO2 and N2.

IV. Recognize When Dynamic Simulation is Needed

Already Drafted:Chapter IV, Sections a, b, c, d, and h have been drafted by Arun Kallal.

To Be Drafted:Octavio Reyes will draft Chapter IV, sections e, f, and g. He will review them with Yula Tang.

a. Use dynamic simulation to determine and respond when a well or system may be unstable.

b. Use dynamic simulation to determine when to start gas-lift when wells can’t be re-started if they need to be stopped.

c. Use dynamic simulation to determine when to start gas-lift if the economic benefit of injecting gas is positive, even if the well is still flowing.

d. Describe how to use dynamic simulation to determine the need to start gas-lift when production is limited due to liquid loading in gas wells. Describe a method to predict when the gas well will load and die. Describe how to deal with liquid loading in gas wells.

e. Describe how dynamic simulation may provide understanding of when cross flow and/or commingling occur; it may occur downhole when the well is shut-in at the surface.

f. Describe how to deal with applications in intelligent and complex well completions.

g. Describe how gas-lift well and system shut-in and start-up works, when it may unstable, and how dynamic simulation is (can be) used to address associated problems.

h. Describe how dynamic simulation may provide understanding of when cross flow and/or commingling occur; it may occur downhole when the well is shut-in at the surface.


V. Information Required for Dynamic Simulation

Already Drafted:Murat Kerem has drafted Chapter V, Sections a, b, and c.

This chapter describes the information required to use dynamic simulation for gas-lift wells and systems. This includes:

a. Fluid properties:- When are “black oil” correlations sufficient?- When must fluid compositional data (e.g. PVT analysis) be used.- How does one pick the best PVT model?- How does one tune the model to fit the measured data for the well and/or system?

b. Wellbore profile:- True Vertical Depth, Measured Depth, Well Bore Profile- RT location

c. Well schematic:- Casing size(s), liner size(s), tubing(s), flow path- Flow path restrictions

VI. Application of Dynamic Simulation

Already Drafted:Cleon Dunham has drafted Chapter VI, Section c.Juan Carlos Mantecon has drafted Chapter VI, Section d.

To be Drafted:Octavio Reyes will draft Chapter VI, Section a. He will review it with Yula Tang.Dan Dees will draft Chapter VI, Section b. He will review it with Shanhong Song.

This chapter describes when and how dynamic simulation modelling may be applied:

a. Integrated modelling:- When is it sufficient to only model the wellbore?- When must an integrated model of reservoir, near wellbore reservoir area, inflow,

outflow, flowline, and surface systems be used?

b. Real-time modelling:- What is real-time modelling and what is its role?- When must it be used?

c. Use of dynamic simulation modelling in typical aspects of gas-lift system management:- Design - Design confirmation - Problem identification - Problem diagnosis- Troubleshooting - Operational optimization

d. Appropriate dynamic simulation techniques:- What are the different dynamic simulation techniques?


- What techniques are appropriate for any given situation?- What part of the gas-lift well/system needs to be simulated?

VII. Information Provided by Dynamic Simulation Already Drafted:

Juan Carlos Mantecon has drafted Chapter VII, Section b.Juan Carlos Mantecon has drafted Chapter VII, Section c.Ken Decker has drafted Chapter VII, Section d.Arun Kallal has drafted Chapter VII, Sections e.Arun Kallal has drafted Chapter VII, Sections f.

To Be Drafted:Juan Carlos Mantecon will draft Chapter VII, Section a. He will review it with Murat Kerem

This chapter describes the information that can be provided by dynamic simulation of gas-lift wells and systems.

a. Slugging flow:- Understand the effects of slugging flow and the impact of this on well performance.- Understand when, where, and under which conditions slug flow is originated- Understand how slug flow conditions can be minimized or eliminated

b. Water and/or hydrates:- Understand the effects of accumulated water and hydrates in lines, gas-lift valves, etc.- Understand the effects of water-induced corrosion and how this can affect stability.- Understand when, where, and under which conditions hydrates may be formed.

c. Production chemistry:- Understand the effects scale, wax, and/or paraffin formation and the impact of this on

well performance and stability.

d. Gas-lift valve performance:- Understand information and models for dynamic gas-lift valve and orifice performance,

and how and when to use them.

e. Well equipment:- Understand the effects of downhole pressure restrictions such as safety valves,

corrosion deposits, scale deposits, wax deposits.- Understand the effects of tight spots or holes in the tubing.- Understand the effects of risers.- Understand the effects of surface equipment such as flowlines, manifolds, separators,

separator back-pressure, etc.

f. Well design:- Understand the effects of well design and the associated dynamic effects on well

operations. This includes:o Vertical wells.o Horizontal wells.o Multi-lateral wells.

- Understand when and where to inject gas into a well: in the vertical, in the knee, in a rat hole, in the horizontal section


VIII. References and Case Histories

Already Drafted:Chapter VIII, Part 1, References, has been drafted by Juan Carlos Mantecon.

To Be Drafted: Yula Tang, Octavio Reyes, Adam Ballard, and Murat Kerem will draft Chapter VIII, Part 2. Also, Yaser Salman will provide a Case History on the Penguin Project in the North Sea, and Fernando Ascencio Candejas and Dr. Fernando Samaniego will help with this part of the document.

This chapter contains a comprehensive set of references for dynamic modelling of gas-lift operations, and a set of case histories where this modelling has been applied.

References

Case Histories



API RP 19G11

Introduction

This API Recommended Practice covers application of dynamic simulation for gas-lift wells and systems. Dynamic simulation is an important tool to assist in the design, operation, surveillance, troubleshooting, and diagnosis of gas-lift processes. Here-to-fore, most gas-lift design and diagnosis programs have used steady-state models. This can lead to incorrect and costly designs and operations because gas-lift wells and systems are seldom, if ever, steady. They usually exhibit dynamic pressure and flow rate fluctuations. It is important to understand the true behaviour of wells and systems so they can be properly designed, operated, and diagnosed.

Until recently, dynamic simulation systems were not readily available for use in industry, so Service and Operating Companies were required to rely on steady-state models. However, within the last few years, dynamic simulation systems have become more readily available, and their use has proven valuable in many instances. This is especially important as gas-lift operations have become much more challenging with more deep wells, deviated wells, multi-lateral wells, deep water and sub-sea completions, long sub-sea flow lines and risers, etc. These operations are very expensive and it is essential that they be designed and operated in an optimum fashion.

This document contains eight chapters to assist the gas-lift industry in understanding and applying dynamic simulation for gas-lift:

1. Summary of Recommended Practices for Dynamic Simulation of Gas-Lift Wells and Systems. This contains a brief summary of and cross reference to the recommended practices found in the other seven chapters.

2. Introduction to Dynamic Simulation of Gas-Lift Wells and Systems. This describes the objectives of the document, defines dynamic simulation and its basic concepts, and describes the differences between steady-state analysis (sometimes called Nodal® Analysis) and dynamic simulation.

3. Typical Gas-Lift Well and System Operations. This describes fourteen different typical gas-lift well and system configurations and how dynamic simulation may be used to enhance the design and operation of each method.

4. Recognize When Dynamic Simulation is Needed. This describes various typical gas-lift well and system operating conditions or problems where dynamic simulation can be used to better understand and design solutions.

5. Information Required for Dynamic Simulation. This describes the information that must be available to successfully use dynamic simulation models.

6. Application of Dynamic Simulation. This describes specific applications where dynamic simulation can be used to assist with and enhance gas-lift well and system performance.

7. Information Provided by Dynamic Simulation. This presents some of the specific information and results that can be provided by dynamic simulation models.

8. References and Case Histories. This provides a bibliography of pertinent references and case histories where dynamic simulation has been successfully employed for gas-lift wells and systems.


I. Summary of Recommended Practices for Dynamic Simulation of Gas-Lift Wells and Systems

To be Drafted: Chapter I will be drafted after the other chapters are completed. An outline for this chapter is shown below. Juan Carlos Mantecon and Cleon Dunham will have primary responsibility.

This chapter contains a brief summary and description of the recommended practices contained in this Recommended Practices document.

The recommended practices are organized by the chapters in this document where they are presented. This chapter contains a cross reference to the detailed chapter(s) where each recommended practice is discussed and described.

After reading this chapter, the reader should have a good grasp of the recommended practices that must be followed to successfully use and deploy dynamic simulation of gas-lift wells and systems. The reader should have an immediate reference of where to turn in the document for more detailed information on any specific recommended practice.

Ch. Sect. Topic Practice Number

Summary of Recommended Practices

II Introduction to Dynamic Simulation of Gas-Lift Wells and Systemsa Document Objectives

b Dynamic Simulation - Definition and Basic Concepts

c Difference between nodal analysis (steady-state analysis) and Dynamic Simulation

III Typical Gas-Lift Well and System Operationsa Continuous gas-lift

1 Design continuous gas-lift wells to lift: as deep as possible, as stable as possible, and to obtain optimum gas-lift performance.

2 Use dynamic simulation to validate the gas-lift design. Will it allow deep and stable operation?

3 Use dynamic simulation to diagnose the causes of instability in continuous gas-lift wells.

4 Use it to check for multi-pointing where more than one gas-lift valve is open, all or part of the time.

b Intermittent gas-lift1 Design intermittent gas-lift wells to: inject the optimum

amount of gas per cycle, and inject at the optimum frequency.

2 Consider using “choke” control to minimize upsets to the surface gas-lift distribution system.

3 Use dynamic simulation to diagnose inefficiencies in the intermittent gas-lift operation.




c Gas-assisted plunger lift1 Design the plunger so it can pass through the gas-lift

mandrels.2 Carefully evaluate the economics of using a plunger. It

may not be needed.3 Use dynamic simulation to diagnose inefficiencies in the

plunger-assisted gas-lift operation, similar to the way it is used for intermittent gas-lift.

d Dual gas-lift1 Design a dual gas-lift well to unload to the bottom valve.2 Design it so gas can be injected at the desired rate into

both sides of the dual.3 Use dynamic simulation to evaluate the design, in much

the same way it is used to evaluate the design of continuous single-string gas-lift.

4 Use it to evaluate and troubleshoot the operation of a dual gas-lift well. Is the well lifting deep on both stings? If not, why not?

e Single-point gas-lift1 Single point gas-lift is a special form of continuous gas-lift,

with only one point of gas entry.2 Use dynamic simulation to troubleshoot and diagnose

causes of instability, such as over or under injection.f “Auto” gas-lift where gas from one zone is used in the same well to lift

other zones1 Use dynamic simulation to troubleshoot and diagnose

causes of instability, such as over or under injection that may result from a too large or too small orifice in the gas-lift injection valve..

g Riser gas-lift1 Treat riser gas-lift like continuous gas-lift and use dynamic

simulation to evaluate design of the system.2 Use dynamic simulation to evaluate and diagnose

problems in the system.3 Use it to predict pressure fluctuations and flow rate slugs

thay may upset facilities on the platform.h Gas-lift for gas well deliquification

1 Use dynamic simulation to assist in designing the unloading system, which must essentially the same as for unloading an oil well.

2 Use dynamic simulation to evaluate the gas-lift injection rate and keep it at the optimum level to maintain critical gas flow rate in the tubing

i Gas-lift unloading1 Use dynamic simulation to help design the unloading gas-

lift mandrel spacing and the setting of the unloading valves.2 Use it to evaluate the liquid and gas flow rate though the

gas-lift valves during the unloading process, to assure that no valve port or seat damage occurs.

3 Use it to evaluate the unloading process to determine if the well was unloaded to the desired depth.

j Kick-off of gas-lift wells1 Use dynamic simulation to determine if a gas-lift well can




be started in “normal” operation, or if a special kick-off process must be followed.

2 Use it to determine if the well will flow naturally after it has been kicked off, or if continued gas-lift will be needed.

k Use of gas-lift for wellbore clean-up1 Use a dynamic simulator to help determine the gas-lift

injection pressure and rates needed to remove the debris, sand, and completion fluids from the wellbore.

l Gas-lift system distribution with various types of system configurations1 Use a dynamic simulator to help distribute gas to the wells

served by the system when the gas supply into the system has changed due to an operational or operator-initiated reason.

m Use of un-dehydrated gas1 Use a dynamic simulator to understand the potential for

hydrate formation when a pressure drop exists in the system.

n Use of non-hydrocarbon gases such as CO2 and N2

1 Use a dynamic simulator to help understand the particular problems that may be caused due to the differing properties of non-hydrocarbon gases.

IV Recognize When Dynamic Simulation is Neededa Use dynamic simulation to determine and respond when a well or system

may be unstable

b Use dynamic simulation to determine when to start gas-lift when wells can’t be re-started if they need to be stopped

c Use dynamic simulation to determine when to start gas-lift if the economic benefit of injecting gas is positive, even if the well is still flowing

d Use dynamic simulation to determine the need to start gas-lift when production is limited due to liquid loading in gas wells

e Describe how dynamic simulation may provide understanding of when cross flow and/or commingling occur; it may occur downhole when the well is shut-in at the surface

f Describe how to deal with applications in intelligent and complex well completions

g Describe how gas-lift well and system shut-in and start-up works, when it may unstable, and how dynamic simulation is (can be) used to address associated problems




h Describe how dynamic simulation may provide understanding of when cross flow and/or commingling occur; it may occur downhole when the well is shut-in at the surface

V Information Required for Dynamic Simulationa Fluid properties

b Wellbore profile

c Well schematic

VI Application of Dynamic Simulationa Integrated modelling

b Real-time modelling

c Use of dynamic simulation modelling in typical aspects of gas-lift system management

1 Use dynamic simulation models to assist in designing mandrel spacing and unloading valve designs.

2 Use them to evaluate designs and confirm that the design will work to allow the well to be unloaded to the desired depth.

3 Use dynamic simulation models to evaluate the causes of unstable operation; is it caused by over or under injection, or by a too large or too small injection port or choke size, or by multi-point injection into more than one valve at a time.

4 Use a simulator to adjust the operating parameters until the unstable performance of a well can be matched (simulated). This can help to determine the cause(s) of the problem(s).

5 Once the cause(s) of instability have been determined, the simulator can be used to help determine the “best” solution to the problem(s).

6 A simulator can be used to determine the best (optimum) operation of a gas-lift well, in cases where the true optimum gas injection rate is not available, and the “best situation must be found for a non-optimum case.

7 Use a simulator to determine the optimum gas injection rate and pressure for riser gas-lift, to minimize slugging and pressure surges in the riser.

8 If slugging can not be eliminated, use a simulator to predict the size and arrival time of slugs so mitigating




circumstances can be used on the platform to avoid upset to the production; facilities.

d Appropriate dynamic simulation techniques and ways of implementing them

e

VII Information Provided by Dynamic Simulationa Slugging flow

b Water effects on corrosion and hydrates

c Production chemistry

d Gas-lift valve performance

e Well equipment

f Well design

VIII References and Case Historiesa References

b Case Histories


II. Introduction to Dynamic Simulation of Gas-Lift Wells and Systems

Already Drafted: Chapter II has been drafted by Juan Carlos Mantecon.

a. Document Objectives

The application of multiphase flow transient numerical simulation (dynamic simulation) techniques in wells and production systems has become a standard methodology to ensure: Wells/field life cycle sound engineering design, Optimal operation guidelines, Optimisation of investment and operating costs, Production optimisation, and Minimization of risk, safety hazards and environmental impact.

The development of oil and gas fields continues to progress towards increasingly hostile environments requiring sub-sea and deepwater facilities and more complex well completions. The use of long horizontal, multi-layer, multi-lateral, big-bore and intelligent wells became the norm and are no longer the exception. HP-HT reservoirs and deepwater cooler environments present more complex production chemistry and flow assurance problems. Therefore, the requirements to more accurate model these systems are critical.

The main objective of this document is to create best practice recommendation standards for the application of dynamic simulation in Gas-Lift wells and systems and present some guidelines to facilitate the application of this technique in order to optimise well/system integrity, well/system operations, well/system life cycle design and production. Although our primary focus is on gas-lift, we address a broad range of artificial lift and natural flowing systems and topics (i.e. gas wells liquid loading – by principle gas lift is an extension of natural flow systems).

This document is design for managers, production technologists, reservoir engineers, facilities engineers, production engineers, well testing engineers, well analysts, operators and researchers wanting to gain a general understanding of dynamic simulation, areas of application (best recommended practice), added value and benefits. This document also compares transient versus steady state techniques (NODAL® analysis) and provides readers with the required understanding of when and how dynamic simulation techniques should be applied.

b. Dynamic Simulation - Definition and Basic Concepts

The term “Dynamic simulation” has been used and misused in too many occasions. In this document “Dynamic Simulation” is defined and used as the common abbreviation for multiphase flow transient numerical simulation. Some problem-solving schemes inter-relating the solution given by steady state techniques at different snapshots in time are also called (wrongly) dynamic simulation techniques but these are not truly transient/dynamic considering that there is not continuous analysis of the multiphase phenomena - what is happening between the selected time snapshots points is unknown. It is also perhaps important to understand the differences between “dynamic behaviour” which describes changes in real time and “transient behaviour” which describes changes over time.

Dynamic simulation is a proven technique applied for years (+25) by facilities engineers for pipeline and slug catcher design. The application of multiphase flow transient numerical simulation in wells is a new practice which requires different understanding and


expertise. Multi-discipline teams or cross-discipline experience is required to properly build and integrate the well model (with the corresponding reservoir inflow performance boundary conditions) into the total production system model.

The development of offshore, subsea, and deepwater fields and the use of more sophisticated drilling techniques and well completions require more robust pressure, temperatures, flow regime and liquid hold-up predictions. The unique features and flow assurance requirements, along with the high associated capital costs, clearly merit detailed dynamic analysis in wells and integrated production systems. The multiphase flow transient conditions must be fully evaluated and understood for a sound engineering design and safe operations.

Dynamic simulation provides the possibility of building a virtual well model that can be used to analyse any "what if" case scenario and predict specific results. It is an excellent tool to understand transient well/system behaviour and determine the optimum process to eliminate/minimise any transient problem that cannot be predicted by using NODAL®

analysis techniques[1]. Furthermore, once the dynamic well model is validated it can also be used as an implicit gauge and/or a virtual DTS during production/injection operations.

Well Dynamic Simulation should be used during feasibility and conceptual studies, FEED and at any stage of the well life cycle to "virtually" run through a complete case scenario and predict the well multi-phase flow behaviour (including liquid hold-up, pressure and temperature trends and profiles). The new field development complex operational situations demand this technique to optimise technical, operational and HSE integrity during design and operation of production systems[2}.

Dynamic simulation is capable of modelling the well/system multi-phase flow behaviour from the static initial conditions (zero rates) to the steady state flow conditions inclusive, confirming more accurately whether these conditions can be reached. Therefore, the area of applicability is dramatically increased over steady-state techniques – Table 1.

Table 1 – Main areas of application of Dynamic Simulation

Dynamic Simulation does not replace steady-state techniques but completes the areas not covered or poorly covered by these methods. The use of steady state techniques to describe transient events it is not recommended. The earlier dynamic simulation is adopted in the life of a project, the better design and economic decisions can be made, speeding-up and enhancing the whole study process [3-4] – Fig. 1. Furthermore, the original dynamic model can be updated and upgraded when more data is available [5] and can be used in real-time after commissioning[6].


Figure 1 – Dynamic simulation wheel

Dynamic simulation techniques use a one dimension (1D Grid), transient multi-phase flow numerical simulator program capable of rigorously modelling the hydraulic and heat transfer effects at any point and time in wells, pipelines, risers and networks – from the reservoir to the facilities[7-8]. Equipment such as valves, chokes, packers, compressors, separators, controllers, etc., can be included in the model. Annular flow and counter-current heat transfer effects can also be modelled when necessary.

Several dynamic simulation models exist (OLGA®, TACITE®, ProFes®,[formerly Plac®] etc.) and have been available over two decades. OLGA® and ProFes® are based on complex one-dimensional multi-fluid representations of the multiphase hydrodynamics, whereas TACITE® is based on a drift-flux formulation (gas is assumed to be drifting along with liquid and where the e.g. gas velocity can be described by a slip relation). Drift-flux models are simpler models (no rigorous physical description of multiphase flow) developed to be fast to compute and avoid convergence problems when the well models are coupled with reservoir simulators[9]. OLGA® has been verified over the widest range of applicability and it has been accepted by the industry as one of the market-leading simulator for transient multiphase flow of oil, water and gas in wells and pipelines. This simulator is able to quantitatively predict complex and varied physical phenomena such as severe slugging and compositional tracking in any well and pipeline configuration. Therefore, OLGA® is used as an example for the dynamic simulator descriptions presented below[10], but we recommended to evaluate all the available commercial software packages before making a selection:

The numerical solution scheme is a semi-implicit integration method which allows for relatively long time steps with efficient run times. A set of coupled first order non-linear, one dimensional partial differential equations, with rather complex coefficients are used:

• 5 Mass conservation– Gas– Hydrocarbon bulk– Hydrocarbon droplets– Water bulk– Water droplets

• 2 Momentum conservation– Gas + droplets– Liquid bulk

• 1 Energy conservation– Mixture (only one temperature)

• Constitutive equations

The closure laws (mass, momentum and energy transfer) are semi-mechanistic and required experimental verification. The SINTEF and IFE flow loops were used to verify OLGA®. No flow loop can represent all possible multiphase flow situations but the SINTEF loop is the only one combining: a vertical elevation higher than 50 meters,


working pressures as high as 90 bar, internal diameters from 4 to 12 inch, and a maximum total length of 1000 meters with the possibility of changing the slope in defined sections[11]. The OVIP (OLGA verification and Improvement Project) JIP and three-phase flow research projects by SINTEF and IFE have improved the three-phase flow models used in OLGA®. They are been continuously improved through research programs and benchmarking against industry data obtained from on-line, real-time OLGA®

implementation (from OVIP JIP industry participants).

The 1D pipe geometry is divided in sections – Fig. 2.

100m - 2 pipes - 8.861" 160 m MODU ID WallP13 P12

P11 8.861" Wall Riser-air130 m Sea Level

P10 8.861" Wall Riser-seaOlga 0 m SS Tree 7.0625"

Wellhead 6.25"Riser P9 6.184" Wall 1

70 SCSSV 6.25"Wellbore

P8 8.861" Wall 70345 m 20" Csg shoe

P7 8.861" Wall 3451100 m TOC

P6 8.861" Wall 11001950 m Mandrel 6.18"

P5 6.765" Wall 19502000 m Nipple 5.75"

P4 8.681" Wall 20002100 m

P3 8.681" Wall 21002850 m

P2 6.184" Wall 28503000 m

P1 6.184" Wall Reservoir3050 m

Well XX14 - OLGA Wellbore Model

Figure 2 – Wellbore Model Pipe Sectioning

Each section has to be bigger than half or smaller than twice the size of the adjacent section. Section length is selected based on a compromise between accuracy and simulation run time depending on the case under study. The longer the section length, the smaller the simulation time but the smaller is the accuracy. Mass transients travel generally much slower than pressure transients, therefore, in order to obtain more stable simulation runs, an staggered grid is used: volume variables such as pressure, temperature and liquid hold-up are calculated at the centre of the section, and boundary variables such as flow rates, flow patterns and velocities are calculated at the section boundaries – Fig. 3.

1 2 543

1 2 543

1,2,3,…,5 (inside) : section volumes

1,2,3,…,6 (outside): section boundaries

6

P, T and liquid Hold-up are calculated at the volume centre Qg, Qo, Qw, flow regime and velocities are calculated at the section boundaries

Figure 3 – Section Volume and Section Boundaries


In order to rigorously calculate the radial temperature for each pipe section a Wall is associated describing the number of radial concentric layers (casings and annular space fluid properties) based on the well completion schematic – Fig. 4.

Steel

Cement

Formation

MD 4935.9 m

MD 3153.8 m

BRANCH: WELL-LOWWALL: Tubing-3

MD 2766.1 m BRANCH: WELL-LOWWALL: Tubing-2

MD 1432.2 m

BRANCH: WELL-UPPWALL: Tubing-1

Figure 4 – Walls: Well Completion Schematic

Properties include thickness, density, thermal capacity and conductivity. In addition, the surroundings temperature and outer convective heat transfer coefficient are required.

Two basic flow regime classes are modelled – Fig. 5:• Distributed (bubble and slug flow)• Separated (stratified and annular mist flow)

Figure 5 – Dynamic simulator flow regime groups

Transitions between the regime classes are determined on the basis of a minimum slip concept (in combination with additional criteria). Flow is divided into gas, vapour, oil and water droplets and oil and water film. Changes in fluid composition in the direction of flow are determined base on the pressure and temperature changes.

Initial conditions should be defined:• Start with the well full of drilling fluids (mud, brine and/or diesel), or• Start with the well filled with production fluids – water and oil to a certain fluid level

and gas

Stratified flow

(Annular flow)

Dispersed bubble flow

Slug flow

SEPARATED

DISTRIBUTED


If the well is connected to a flowline, the flowline initial conditions should be defined:• Start with the flowline filled with gas (empty), or• Start with the flowline filled with water or any liquids

Boundaries conditions should also be defined:• Reservoir• Topsides (i.e. ambient temperature and wind velocities), and• Surroundings (i.e. sea floor and sea current velocities)

The reservoir boundary input is explicit. The reservoir parameters are given at steady state reservoir conditions, which is adequate when the “predictive” approach is used. The available IPR models in the dynamic simulator are:• Constant productivity index• Forchheimer model (gas wells)• Single Forchheimer model (high pressure gas wells)• Vogel equation (oil wells)• Backpressure (gas wells)• Normalised (saturated oil wells)• Tabulated IPR curved (any preferred IPR input)

New software developments allow for a quasi-dynamic time-series boundary input (quasi-transient IPR input) for the key reservoir properties such as pressure, temperature, mechanical skin, non-Darcy skin, permeability and net pay [1]. This option is relevant when the “matching” approach is used – validating the model by matching actual data. Nevertheless, the best option to gather all the well-reservoir dynamic interaction is to couple the dynamic well model to a near-wellbore [12-13-14] or full reservoir dynamic simulator[15] but obviously this adds complexity to the simulations.

The simulator offers different visual outputs (and numerous variables can be selected) to analyse the results: • Trend plots – show the change in a parameter versus time at a specific location• Profile plots – show the change in a parameter along the flow path at a specific time

for different times• The “Viewer” – shows a video-like representation of a parameter continuous change

with time within a simplistic pipe model – Fig. 6. The option of dynamic (automatically changing with time) 3D and 2D standard plots are also available.


Plot 1 = Initial ConditionsPlot 2 = Brine arrival at surface (Gas in grey)Plot 3 = LCM (Mud) being producedPlot 4 = Clean-up completed (residual liquid in rat hole)

Figure 6: Well Clean-up with LCM “Viewer Snap-Shots Sequence

c. Difference between nodal analysis (steady-state analysis) and Dynamic Simulation

Due to its historical development and the type of operational conditions, the analysis of multiphase flow phenomena can be divided into two different techniques:• Steady State Techniques• Dynamic Simulation (Transient) Techniques

Steady State techniques can be divided in two different methodologies:• Empirical Methods• Mechanistic Methods

The old Empirical Methodology (with origin in the early 50’s) is based in the production of correlations from data collected by the corresponding researcher. These correlations are simple equations that can be used for prediction purposes for a defined range of operating conditions. Some correlations perform better than others and a number of different correlations are required to predict the hydraulic conditions of the resulting flow regime. Selecting the best set of correlations becomes the key process step affecting the accuracy


of the predictions. For a good selection process, the specific conditions for which the correlations were develop needs to be known. Extrapolation beyond these specific conditions makes the process unreliable. Empirical Methods do not address the complex physical phenomena that occur in multiphase flow.

Since the mid 1970’s significant progress has been made to understand the physics of multiphase flow in wells and flowlines, and several multiphase flow mechanistic models (a mechanistic transport equation for each of the phases within the system) were commercially available to simulate wells and pipelines under steady state and transient conditions. Mechanistic models try to mathematically describe the multiphase flow mechanisms (including related fluid properties and physical relationships).

Mechanistic models tend to be more accurate and applicable to a wider range of fluids and operating conditions than Empirical models.

Nevertheless, some mechanistic models have been formulated separately for wells, pipelines, for vertical and horizontal flow, for one, two or three-phase, and for the prediction of specific steady state or transient conditions such as the onset of slug flow, slug flow, annular flow, etc. Furthermore, as the empirical correlations, most of the mechanistic models have been develop using small diameter pipes/tubing (< 4”), so this has to be also taken into account in big diameter wells.

Therefore, an evaluation of the existing empirical and mechanistic models needs to be performed and he specific conditions for which the models were develop needs to be known. The most popular empirical and mechanistic correlations are listed in Table 2 below:

Name Published Recommended Applications

Ansari Develop by the University of Tulsa (TUFFP). A mechanistic model design for well flows

Aziz, Govier & Fogarassi 1972 A semi-empirical method designed and tested for gas-condensate flows in wells. Overpredicts minimum stable flow rate for gas-water and gas-condendate wells.

Beggs & Brills 1973-77Built for all angles of inclination. Mainly for horizontal flow. Very poor results in verical flow. Uses exponents based on flow pattern. In general gives poor results specially for gas-condensate.

Duns & Ros 1963Developed for vertical flow of gas and liquid mixtures in wells and on experimental work using air and oil simulants

Eaton 1967Horizontal flow empirical model developed for hold-up. Does not include angle of inclination and caan underpredict hold-up in hilly terrain

Eaton & Oliemans 1976Improved for frictional pressure losses. Good for gas-condensate or gas-water systems

Gray 1974-78Develop by Shell for modelling vertical flows of gas-condensate mixtures in tubes up to 3.5". Only for gas wells. Does not use flow pattern.

Gregory 1989 A mechanistic model for vertical flow. Based on AGF with better transition to AMF

Hagedorn & Brown 1965Developed using data gathered from 1500 ft experimental well but restricted to tubing diameter of less than 1.5". No flow pattern thus can't predict minimum stable flow rate.

Hughmark & Dukler 1962-64Horizontal empirical model. Overpredicts hold-up for gas-condensate but is OK for gas-oil

Flanigan 1958Horizontal flow empirical model. Drastically underpredicts pressure losses for liquid gas ratios > 50 bbl/MMscf

Lockhart & Martinelli 1949Horizontal empirical model. Good correlation for laminar flow. Overpredicts in turbulent flow.

Oliemans 1987 Includes angle of inclination. Only for horizontal stratified or wave flow

OLGAS 1991-2000

Mechanistic model developed using data collected in the 4-8-12" SINTEF flow loop with a 50m riser. The only correlation that can claim to have been developed for flows in risers and larger diameters. All well and flowline configurations. Excellent P-T and liquid hold-up predictions.

Orkiszewski 1967 Developed for flows in vertical and deviated wells

Empirical and Mechanistic Correlations

Table 2 – Most Popular Multiphase Flow Correlations


These empirical and mechanistic models can be used as correlations in a NODAL®

analysis steady state software package, if listed in the selection options. For instance, OLGAS® (listed in the above Table) can be part of a NODAL® analysis steady state software package and can be selected as the preferred correlation for a particular case. OLGAS® is the steady state version of the numerical multiphase flow simulator OLGA®

(described in Chapter 1, section b) and should not be confused with it. OLGA® (transient simulator) with compositional fluid should provide more accurate results than OLGAS®

in a NODAL® analysis steady state software package.

Even though they are case dependant, in general we can state that the weaknesses of traditional steady state NODAL® analysis techniques, when analysing steady state flow cases, are:• Unable to properly predict slugging flow induced by risers, flowlines and horizontal or

deviated wells (terrain ups and downs) – terrain induced slugging• Unable to perform sound stability analysis – a transient event• Unable to look at gas/condensate gas well liquid load-up issues – a transient event• Selection of the best flow correlations: even though correlations (empirical and

mechanistic) and selection options have been improved the predictions can still be incorrect when using steady state tools. For instance, the P-T and hold-up profiles may be less accurate in a steady state tool using only OLGAS® than in the OLGA® dynamic simulator.

The main weak point of traditional steady state NODAL® analysis techniques is that they do not offer robust sound engineering solutions for cases transient in nature, such as:• Prediction of flow behaviour during well start-up and shut-down• Prediction of stable flow• Prediction of flow behaviour during rate changes• Prediction of flow behaviour during well clean-up• Prediction of flow behaviour during well testing• Prediction of flow behaviour during liquid loading• Prediction of flow behaviour during Gas-Lift unloading• Prediction of dynamic behaviour of Plunger Lift• Perform Flow Assurance studies

• Corrosion• Chemical injection

A good comparison example of Steady State versus Dynamic Simulation techniques is flow stability predictions. As highlighted above Steady State techniques are unable to properly predict stable flow if the production system contains a riser and/or the wells are horizontal or deviated (terrain induced slugging generators). In these cases, one of the main applications of dynamic simulation is to define if the flow is going to be stable in each of the probable well/system design options. The multiphase flow slugging conditions must be fully evaluated and understood for a sound engineering design and safe operation. A wrong prediction of stable flow can lead from the start to the wrong tubing size selection. The right tubing size, in some cases, may eliminate the slugging conditions. To eliminate or minimise slugging conditions it is necessary to know what is creating the slugging flow conditions and where they originate. Dynamic simulation cannot only define but can also establish the size and frequency of the gas bubble (slugging severity) – Fig. 7.


Figure 7 – Slugging flow: front and tail of the gas bubble

Tracking of the development of the individual slugs along the well and flowline is necessary to estimate the volume of the liquid surges out of the system. The main potential problems for stable multiphase flow are:• Terrain – snake-type wells• Inclination / elevation• Rate changes• Condensate – Liquid content in gas• Shut-in / Start-up• Risers

For subsea and deepwater, the fluid behavior in the well, flowline and riser may actually dictate the artificial lift method, not the wellbore environment itself. Ensuring stable flow or minimising unstable flow is one of the best practice recommendations for well production optimisation. Dynamic simulation offers a more robust and sound engineering technique to predict slugging, select the best method to eliminate or minimise slugging, and optimise production.

Dynamic Simulation should be applied when the following (individual or combined) O&G productions systems and conditions are encountered:• Horizontal-Inclined Wells[16] – Terrain induced slugging• Production-Injection Systems with risers[17] – Terrain induced slugging• Tubing and Annular flow with relevant counter-current heat transfer effects[18]

• Fluid composition that will significantly change in the flow path upstream of system output point – Flashing or Condensation[19]

• Commingling Fluids – Multi-layer, Multi-Lateral Wells[20]

• Smart Well Completions – Well control and Production optimisation[21]

• Gas-Lift Wells – Prediction of stable flow and optimisation[22-23-24-25-26-27-28-29-30]

• Gas Wells with Liquid Loading[31-32-33]

• ESP Wells – Prediction of stable flow and optimisation[34-35-36]

• Transient conditions – Flow behaviour during well start-up and shut-down[27]

• Flow Assurance – Production Chemistry and Corrosion[37-38-39-40]

• Well Testing – Wellbore storage and segregation effects[41-42-43]

• Well Clean-up – removal of drilling and/or completion fluids from the wellbore[41-43-44]

• Well Control – Blowouts. Killing Procedures, etc.[45-46]

• Workover Evaluation – Fluid displacement (Annulus-Tubing, Tubing-Annulus)[47]

In Gas-Lift systems, the maximum benefits of Dynamic Simulation are obtained when the model is also applied on real-time[48-49]


III. Typical Gas-Lift Well and System Operations

Already Drafted: Greg Stephenson and Cleon Dunham have drafted Chapter III. They will review it with Stan Groff and Luis Gonzalez.

This chapter describes all of the typical gas-lift well and system operations. It must clearly identify both the steady-state and the dynamic aspects of each operation. It must identify how dynamic simulation is or can be used to address each of the dynamic aspects of these operations.

A variety of gas lift system configurations and operational practices are in use throughout the industry. Each of these has unique characteristics and requires special considerations when using dynamic simulation techniques to model well performance. This chapter provides an overview of each operation, discusses both the steady state and dynamic aspects of each of these operations and provides recommendations for how dynamic simulation can be used to address the dynamic aspects of each of these operations.

a. Continuous gas-lift

Continuous flow gas-lift is one of two major classes of gas-lift systems. In the continuous flow gas-lift process, relatively high pressure gas is injected downhole into the fluid column. This injected gas joins the formation gas to lift the fluid to the surface by one or more of the following processes: Reduction of the fluid density and the column weight so that the pressure differential

between the reservoir and wellbore will be increased. (Fig. 3-1A) Expansion of the injection gas so that it pushes liquid ahead of it, which further reduces

the column weight, thereby increasing the differential between the reservoir and the wellbore. (Fig. 3-1B)

Displacement of liquid slugs by large bubbles of gas acting as pistons. (Fig. 3-1C)


Fig. 3-1

3.1.1 Steady-state aspects

Continuous gas-lift is intended to be a continuous, steady process. The gas-lift injection pressure and rate are intended to be steady. The gas-lift injection rate from the annulus into the tubing is supposed to be steady. The pressure profile in the tubing is supposed to be steady. The pressure drawdown on the formation is supposed to be steady. However, some or all of these processes are usually not steady, and they can be very unstable, leading to significant well problems and loss of production.

3.1.2 Dynamic aspects

Often, due to operational issues, the gas-lift injection rate and pressure are not stable. In some cases, the injection rate can be controlled and made to be stable, but unless it is controlled, it will often vary with time and production processes.

Even if the injection rate is controlled, the injection pressure may fluctuate due to conditions in the well. This is most often caused by an imbalance between the injection rate at the surface and the gas flow rate from the annulus to the tubing through the gas-lift valve or orifice.

This may be exacerbated if a well is “multi-pointing,” that is if it is injecting through more than one valve, or through a valve and an orifice at the same time. This can be caused by inappropriate gas-lift valve design relative to the current operating conditions of the well.

Any unstable (dynamic) situation in a continuous gas-lift well is less efficient than continuous, stable gas-lift. Therefore, steps are needed to reduce or eliminate instability.


3.1.3 Dynamic simulation

Dynamic simulation can be used in two primary ways on continuous gas-lift wells.

a. Design

Design of continuous gas-lift wells is discussed in API RP 11V6. For continuous gas-lift design, a design program (or manual process) is used to determine the spacing of the gas-lift mandrels, the setting of the gas-lift valves, the sizing of the gas-lift valve or orifice flow path, and the desired gas-lift injection rate and pressure. Typically, this design is performed in stages, with the mandrels being spaced when the well is first drilled and completed, or recompleted after a workover. The valves are run when it is necessary to place the well on gas-lift, or when the valves need to be changed to improve operation. The injection pressure is normally essentially fixed for a given field. But the injection rate can be, and often is, changed frequently by the Operator, or by circumstances in the field.

A dynamic simulator can be used to evaluate a design by running the simulator with the intended mandrel spacing, valve configuration, and injection pressure and rate. The dynamic simulator can give an indication if the planned design will unload properly and produce continuous, stable operation or if the well will be unstable. If it will not unload as intended, or if it will be unstable, the design can be modified until the predicted performance of the well is stable. If the dynamic simulator can be included in an “optimization” program, the program can automatically modify various parameters such as the valve or orifice port/choke size, and the gas-lift injection rate, to determine the “best” design for continuous, stable operations.

b. Problem diagnosis

When a continuous gas-lift well is on production, it may not unload as desired, or it may be unstable as indicated above. A dynamic simulator can be used to help diagnose the cause(s) of the problem, and to indicate how the design or operation may be changed to stabilize the well.

In an automation system, a dynamic simulator can be automatically run on each gas-lift well whenever a problem is detected by the surveillance system. Problem diagnoses and recommended solutions can be presented to the operating staff on a routine basis.

b. Intermittent gas-lift

If a well has a low reservoir pressure or a very low producing rate, it can be produced by a form of gas-lift called intermittent lift. This is the second major classification of gas-lift systems. As its name implies, this system produces intermittently or irregularly and is designed to produce at the rate at which fluid enters the wellbore from the formation.

In the intermittent flow system, fluid is allowed to accumulate and build up in the tubing at the bottom of the well. Periodically, a large quantity of high pressure gas is injected into the tubing very quickly underneath the column of liquid and the liquid column is pushed rapidly up the tubing to the surface. This action is similar to firing a bullet from a rifle by the expansion of gas behind the rifle slug. The frequency of gas injection in intermittent lift is determined by the amount of time required for a liquid slug to enter the tubing. The length of the gas injection period will depend upon the time required to push one slug of liquid to


the surface. This method of lift is a cyclic operation and the cycle can be divided into four periods. Normally, a standing valve is installed beneath the gas-lift valve to prevent pressure and gas flow back into the low pressure formation.

Inflow period - During this period, the liquid flows from the formation into the well bore and collects in the tubing above the standing valve and the gas-lift valve. The gas-lift valve is closed during this period and the tubing pressure is reduced to a minimum to allow the maximum inflow rate. (Fig. 3-2A)

Lift period - When sufficient liquid has collected in the tubing, the gas-lift valve opens and injects high pressure gas to lift the slug to the surface. Fallback occurs due to liquid coalescing in a film on the wall of the tubing and liquid droplets in the gas slug which lack sufficient velocity to travel to the surface. (Fig. 3-2B)

Production period – Fluid is produced at the surface. A rapid drop in tubing pressure pulls in gas from the casing. No inflow occurs during this period. (Fig. 3-2C)

Pressure reduction period - After the gas-lift valve closes and the slug flows through the separator, the lift gas pressure is dissipated and the inflow period begins again. The intermitting cycle is controlled by regulating the frequency of injection, the gas flow rate during injection, and the total quantity of gas injected during each lift period. (Fig. 3-2D)

There are two primary means of intermittent gas-lift control – choke control and timer control. In choke control, gas is slowly injected into the tubing from the surface, with the injection rate controlled by a surface choke or control valve. In timer control, a surface control valve is periodically opened and closed on a time cycle.

The advantage of choke control is that it causes a minimum impact or upset to the overall pressure of the surface injection system. The disadvantage is that the downhole injection process can only be modified by changing the operating gas-lift valve. The advantage of timer control is that the frequency and volume of each intermittent gas-lift “slug” can be controlled at the surface. The disadvantage is that the sudden rate and pressure changes on the surface can upset other wells in the system.


Fig. 3-2: Intermittent Lift Cycle


Intermittent gas-lift is, by its very nature, a dynamic process. However, for it to be successful, each intermittent cycle must be consistent in its frequency, and gas injection volume per cycle.

And intermittent gas-lift wells must be unloaded using the same process as is used for continuous gas-lift wells.


For an intermittent gas-lift well to be successful, its dynamic aspects of injection frequency and volume per cycle must be correctly operated.

The gas injection frequency must be designed and operated to permit an optimum amount of liquid inflow from the formation to the tubing during each cycle. If the frequency is too high, not enough liquid will have accumulated in the tubing, and production will be minimum and gas will be wasted. If the frequency is too low, an excessively large slug of liquid will be produced into the tubing. This can place an excessive amount of back pressure on the formation, thus inhibiting inflow from the formation to the wellbore; and it can be difficult for the gas to lift the excessively large slug of liquid to the surface.

The gas injection volume per cycle must be correct to lift the volume of liquid to the surface. If it is too small, the liquid may not reach the surface and it may fall back to the bottom of the well. If it is too large, gas will be wasted.


Dynamic simulators can be used in a manner similar to that described for continuous gas-lift.


a. Design

Design of continuous gas-lift wells is discussed in API RP 11V10. A dynamic simulator can be used to evaluate the design of an intermittent gas-lift well, determine if it will unload properly and determine if it will operate properly from the operating gas-lift valve.


A dynamic simulator can evaluate the current operation of an intermittent gas-lift well. The simulator can determine if the injection frequency and gas injection volume per cycle are correct, or if changes should be made to optimize production per cycle, and the volume of gas injection per cycle.

c. Gas-assisted plunger lift

One special application of intermittent flow gas-lift is termed gas-assisted plunger lift, or plunger assisted intermittent lift. In these applications, the intermittent gas-lift installation is equipped with a plunger and related accessory equipment. The piston traverses the length of the tubing string in a cyclic manner, providing an interface between the lifting gas and the produced liquid. The plunger sweeps more of the liquid film from the tubing wall, minimizing the liquid fallback. Although sand or solids in the tubing could prevent the plunger from operating successfully, plungers are commonly used to control paraffin deposits.

Fig. 3-3 shows a down hole plunger installation with the gas-lift valve located below the plunger. The surface wellhead equipment shows the lubricator/catcher to hold the plunger for its short time at the top.

There are at least two issues with plunger lift that need attention. First, the plunger must be designed so it can successfully pass through the upper gas-lift mandrels as it rises and falls in the tubing. Second, adding the plunger increases the cost and complicates the operation of the well, and many operators find that the added production that can be achieved by the lifting of the plunger is not sufficient to justify the added cost and complexity.


Fig. 3-3: Gas-assisted Plunger Lift


Gas assisted plunger lift is an enhanced form of intermittent gas-lift. The desired steady aspects of intermittent gas-lift pertain here as well. The goal is to time the injection cycles to optimize liquid inflow from the formation and control the injection volume per cycle to optimally lift the plunger and the liquid above.

However, this is more difficult than normal intermittent gas-lift because the release and fall of the plunger must be coordinated with the gas-lift injection frequency and volume per cycle.


The dynamic aspects are similar to normal intermittent gas-lift, except now the dynamic aspects of catching, releasing, and timing the fall of the plunger must be taken into consideration.


As with normal intermittent gas-lift, a dynamic simulator can be used to assist with design and problem diagnosis. Frankly, this could be difficult and may not be justified.

d. Dual gas-lift

In certain cases, wells are completed as dual gas-lift producers. This is generally driven by the desire to reduce drilling and completion costs where multiple formations are located in close vertical proximity to one another. Such installations are discouraged in conjunction


with intermittent gas-lift applications. For this reason, dual gas-lift installations can be considered a special application of continuous flow gas-lift.

Dual gas-lift is defined as the producing of two zones from the same wellbore by gas-lift without commingling the well fluids in the wellbore. A variety of wellbore configurations exist for achieving dual gas-lift. The most common involves the use of a single bore packer to isolate the upper zone from the lower zone and a dual bore packer to isolate the upper zone from a common gas-filled annulus. Such a completion is depicted in Fig. 3-4 below.

Fig. 3-4: Schematic of Dual Gas Lift Installation

In an attempt to reduce the interference issues which arise from injecting gas through a common casing-tubing annulus, dual gas-lift installations are often equipped with production pressure operated gas lift valves. However, many other approaches are also used. This is discussed in detail in API RP 19G9.


Operation of a dual gas-lift well is intended to be continuous and stable, just like a single-string gas-lift well. However, due to the interference that can occur between the two sides of the dual, it is even more difficult to obtain stability.

3.4.2 Dynamic Aspects

Dual gas-lift can experience all of the dynamic aspects of single-string continuous gas-lift. But these are often compounded by the interference between the two zones. One typical problem is gas being “over injected” in one side of the dual while the other side is starved for gas.


3.4.3 Dynamic Simulation

As with single-string continuous gas-lift, dynamic simulation can be used to assist with design and problem diagnosis.

a. Design

Design of dual gas-lift wells is discussed in API RP 19G9. In principle, the design issues are similar to those of single-string wells. The differences are:

Usually one side of the dual is used for unloading. From this stand-point, it is similar to a single-string continuous gas-lift well.

The amount of gas injection must be shared between the two sides of the dual. To achieve this, the gas flow passage through the operating gas-lift valves or orifices must be carefully designed and controlled.

A dynamic simulator can be used to evaluate a dual gas-lift design, in a manner similar to that of a single-sting gas-lift well. The primary issues are:

Will the well unload to the bottom valve, which is normally installed slightly above the dual packer?

Will the desired amount of gas be injected into each side of the dual?

Will this injection be at the desired depth in both sides of the dual?

Will the injection pressure and rate be continuous and stable?


A dynamic simulator can be used to diagnose problems in dual gas-lift wells.

Is gas being injected into both sides of the dual?

Is it being injected at the desired depth?

Is the well stable?

If the well is not performing correctly, what needs to be done to modify the design or the operation to correct the problem(s)?

e. Single-point gas-lift

Single-point gas-lift is a special application of continuous gas-lift systems in which a single point of injection is installed in the well. Usually this is some form of orifice, with no unloading valves installed above that depth.

Such installations are often preferred in applications where reliability is of prime importance, as with subsea producers where interventions can be costly or impractical. By limiting injection to a single depth, these installations eliminate the possibility of re-opening upper valves during normal operation. Also, the need to re-enter the well to replace failed unloading valves is eliminated.


In most applications, a significantly higher operating pressure is required to unload the well to this single depth. Also, the lack of unloading valves may result in a shallower operating point and reduced draw-down versus conventional applications.

The steady-state aspects, dynamic aspects, and dynamic simulation of a single-point gas-lift well are similar to the same items for a continuous gas-lift well, with the following exceptions.

There are no unloading valves, so these don’t need to be considered.

There is only one point of injection, so multi-point injection doesn’t need to be considered.

Often this technique is used for sub sea wells so it can be more difficult to diagnose problems, since it can be difficult to obtain information on well-head injection pressure, etc.

f. “Auto” gas-lift where gas from one zone is used in the same well to lift other zones

Auto gas lift is a term that refers to continuous gas-lift systems that use gas from a gas-bearing formation to lift fluids from another zone. The lift gas is produced downhole and allowed to enter the tubing through some form of gas-lift valve or flow control device, as depicted in Fig. 3-5. Because of the dependency between gas passage and the changing inflow performance of the gas bearing zone over time, it is often desirable to use intelligent flow control devices as the injection point in such wells. This allows operators to continually adjust the size of the orifice to provide appropriate gas passage as the gas bearing zone is depleted.

Fig. 3-5: Schematic of Auto Gas Lift Well


The steady-state aspects, dynamic aspects, and dynamic simulation of an “auto” gas-lift well are similar to the same items for a continuous gas-lift well, with the following exceptions.

There are no unloading valves, so these don’t need to be considered.

There is no surface injection, so this doesn’t need to be considered.

There is only one point of injection, so multi-point injection doesn’t need to be considered.

If a gas-lift valve or orifice is used to control the rate of gas injection into the producing well, there is little adjustment that can be performed, other than possibly controlling the back pressure on the producing well.

If an “intelligent” flow control device is use to control the rate of gas injection into the producing well, it may be possible to control this valve to optimize the well’s production, minimize pressure fluctuations, etc.

Dynamic simulation could potentially be used to help determine the optimum injection rate and/or back pressure to hold on the well.

g. Riser gas-lift

In some sub-sea installations, gas is injected at the base of the riser to assist with artificially lifting the well. This may be done in addition to injecting gas in the sub-sea well, and/or injecting it at the wellhead of the sub-sea well. Or, if it is too difficult or expensive to inject gas in the well or at the wellhead, this may be the sole form of gas-lift used on the well.

Use of riser gas-lift can reduce the complexity of artificially lifting such wells while reducing the cost of completing such wells. In addition, the injection of gas at the base of the riser may help to mitigate instability problems which are common in subsea wells containing long flowlines and/or long risers.

Ideally riser gas-lift is like continuous gas-lift. It may use unloading valves in the riser, or it may use single-point injection at the base of the riser. A significant issue is that the liquid and gas that is entering the base of the rise, from the sub-sea flowline from the wellhead to the riser, may be very unstable. There may be long slugs of liquid followed by long slugs of gas in the flowline. This can complicate control of gas injection into the riser base.

An important use of dynamic simulation is to understand the pressure and liquid/gas rate fluctuations in the flowline as they arrive at the riser base. Then, the simulator can be used to determine how to control the gas injection into the riser to assist in producing the liquid and gas up the riser and to assist in mitigating the large pressure and rate fluctuations or surges that can occur in the riser.

h. Gas-lift for gas well deliquification

Continuous flow gas-lift may be used for deliquifying gas wells that experience liquid loading. Liquid loading occurs when liquids (normally water but sometimes condensate as well) accumulate in the wellbore below the end of tubing in the casing, or in the tubing itself. The liquid, being much heaver than the gas, holds back pressure on the formation and inhibits gas flow into the wellbore and up the tubing. Liquid will accumulate if the gas flow velocity is lower than the “critical” velocity that is needed to carry the liquid out of the well.


Deliquification is a process used to remove liquids from the wellbore so gas can freely flow. Many methods are used, including plungers, chemical systems, pumping systems, gas-lift, and others.

In gas-lift applications, the volume of gas injected is designed such that the combination of formation gas and injected gas will exceed the critical rate needed to prevent liquid loading. While gas-lift may not lower the flowing bottom hole pressure as much as an optimized pumping system, there are a number of advantages to using gas-lift for deliquification including the ability to produce solids-laden fluids, the ability to operate at high GLR’s, and insensitivity to well trajectory.

The goal of such systems is to inject gas at a continuous, stable rate that is just high enough to exceed the critical velocity. Ideally, the gas can be injected below or near the bottom of the perforated interval so that all or most of the liquid is removed and kept from holding a back pressure on the formation. While enough gas is needed to reach the critical velocity, too much gas will be wasteful and may actually inhibit production due to excessive pressure losses in the production tubing.

A gas-lift system in a gas well may (probably will) require unloading valves. It may use an operating gas-lift valve or an orifice for the actual gas injection into the tubing.

A dynamic simulator can be used to help design the unloading process, select the depth of gas injection, and choose the rate of gas injection to just reach critical velocity without injecting too much. It can be used to help diagnose problems in wells where the well may be unstable or is beginning to experience liquid loading due to insufficient gas injection.

i. Gas-lift unloading

The initial unloading process of a gas-lift well is the period of the well’s life during which the health of the artificial lift system is at greatest risk. This is because the entire volume of fluids in the casing-tubing annulus must be displaced through the gas-lift valve ports or orifices, placing the valves at risk of flow cutting and subsequent failure. In addition, the unloading process is an inherently unstable one which can result in large variations in pressures and fluid rates/volumes into the production system. For these reasons, it is desirable to study this process in great detail prior to performing the operation in the field.

In most cases, the unloading process is “designed” using steady-state methods or programs. The gas-lift mandrels are spaced using these methods. The gas-lift valves are chosen, sized, and set using these programs. The unloading process is “designed” using these processes, or rules of thumb. This is not sufficient.

The unloading process is and must be dynamic. First, liquid in the tubing/casing annulus is displaced through the valves in the well by applying gas pressure to the annulus. When the top valve is “uncovered,” gas can flow through it and begin to lighten the weight of liquid in the tubing. This can allow the level of liquid in the annulus to be depressed to the second valve. The process of moving to the second (and subsequent) valves, which requires the closing of the upper valve(s), is dynamic.

An important role of dynamic simulation is to model the unloading process, both during its design and subsequently during its operation. During design, the simulator can help to determine the best depths for each gas-lift mandrel, the best liquid and gas flow characteristics of each gas-lift valve, and the best closing pressure of each gas-lift valve. A way to do this is to run the simulator with the design that has been calculated, and have it determine if the well will unload successfully with this design. If it will not, the design must be modified until successful unloading can occur. It is far better to run this process in


advance with the simulator, rather than trying the unloading process in the field and potentially damaging the well and not unloading as desired.

During operation, the simulator can help determine if the unloading process worked as intended to unload the well to the desired operating depth, without damaging any of the unloading valves in the process. If an unloading valve is damaged (caused to leak), the well will not be able to work below this depth and will forever be inefficient.

j. Kick-off of gas-lift wells

After a well has been unloaded and placed on production, it will be necessary to re-start the well from time to time, following shut-in periods. This process is commonly referred to as “kick-off”.

Unless the tubing, a mandrel, a valve, or an orifice are leaking fluid from the tubing back into the tubing/casing annulus, no fluids need to be displaced from the annulus through the valves, as in the unloading process. However, kick-off has some similarities to initial unloading. Like the unloading process, kick-off requires the well to step down to the operating point through a series of unloading valves. Also, like the unloading process, the kick-off process is inherently unstable. In such applications, dynamic simulation is useful for:

Determining if the well will flow naturally after it has been kicked-off. Some wells merely need to be started and then they will flow naturally without the need for gas injection.

Predicting the water cut limits for kick-off of naturally flowing wells after a shut-in period. In other words, which wells will need to be kicked off so they can return to natural flow.

Determining the required injection rate schedule to successfully return the well to production. Many operators merely begin normal operation (injection rate), when a well needs to be kicked off and returned to production. Is this the right approach?

k. Use of gas-lift for wellbore clean-up

Wellbore clean-up is defined as the period when drilling debris, frac sand, completion fluids, etc. need to be produced out of the well, along with produced hydrocarbons. The minimum rate and time required to clean-up the well are important.

It may not be necessary or desired to use “conventional” gas-lift for this process. For example, it may be desired to not install gas-lift valves in the mandrels during this process. If normal gas-lift can’t be used, it may be necessary to inject nitrogen at a high pressure through a special “clean up” orifice installed in the bottom mandrel.

A dynamic simulator can be used to help determine the gas injection pressure(s) and rate(s) needed to achieve the desired production rates to clean the debris, sand, completion fluids, etc. out of the wellbore.

l. Gas-lift system distribution with various types of system configurations

Various configurations of injection gas distribution systems exist in the field. Because of the interdependency between production system components, the availability of injection gas and supply pressure can be affected by the operating regimes of adjacent wells and production equipment in such systems. For this reason, it is useful to model the performance of gas lift wells in the context of the gas distribution system.


Often this modelling is performed with steady-state systems, but clearly there are dynamic effects that occur when wells are added to a distribution system, removed from the system, or their injection rates are changed. To understand this process, a field-wide simulator is needed that can model the performance of the entire distribution system, the source(s) of gas into the system, and the wells which are served by the system.

This dynamic simulator can help define how to control the distribution of gas to the wells served by a system when there is a change in the supply of gas into the system, or a change in the demand for gas from the wells served by the system.

m. Use of un-dehydrated gas

While it is recommended to always inject dehydrated gas, it is not always possible to do so. Injection of un-dehydrated gas can lead to a number of problems including the formation of hydrates in the injection system and the flow cutting of gas lift valves. Such issues should be studied in order to determine operating practices which may mitigate their occurrence.

Of particular importance is the formation of hydrates. These may form wherever a pressure drop occurs in the system, such as across a surface control valve or choke. A dynamic simulator can be used to understand the hydrate formation potential of the “wet” gas, help diagnose problems that can arise due to hydrate formation, and recommend procedures to mitigate these problems. Hydrate formation may be mitigated by dehydrating the gas, injecting methanol or another chemical in the gas, heating the gas injection line or control valve, etc.

n. Use of non-hydrocarbon gases such as CO2 and N2.

In certain situations, wells may be gas lifted using an inert gas such as CO2 and N2. For example, nitrogen is often used to kick-off wells for initial clean-up and testing when a high pressure hydrocarbon gas source is not available. Similarly, in a CO2 flood, the nearly pure injection CO2 or the mixture of hydrocarbon gas and CO2 returning to the production station can be used to gas-lift the producing wells. Due to the specific properties of these gasses, special considerations should be taken when simulating, analyzing, and designing such installations.


IV. Recognize When Dynamic Simulation is Needed

Already Drafted:Chapter IV, Sections a, b, c, d, and h have been drafted by Arun Kallal.

To Be Drafted:Octavio Reyes will draft Chapter IV, sections e, f, and g. He will review them with Yula Tang.

More and more wells are being placed on artificial lift due to reservoir depletion, when native water production increases, or when water flood support increases the water cut in the produced fluid. Generally more than one method of lift can be used; and individual methods may be classified from excellent to poor in performance. In a depletion type reservoir, high initial production rates may be expected and rates will decline quickly due to declined reservoir pressure and changes in inflow parameters. In this case one of the most preferred artificial lift methods is continuous gas-lift.

To make the gas-lift to work efficiently, a good modelling tool is required. Basically there are two different types of modelling tools available; one is static and the other is dynamic. The advantage of using a dynamic modelling tool, even though it is complex, is that it is helpful in understanding transient behaviour of fluid flow and flow instability inside the tubing.

a. Use dynamic simulation to determine and respond when a well or system may be unstable.

- How to recognize instability.

There are many reasons for instability of a naturally flowing well. To name a few: decreasing reservoir pressure, increased water cut, over sized tubing for the current fluid production, decreased total gas-liquid ratio, and increased back pressure in the surface gathering system. To recognize and rectify these problems, there must be a tool to model the well in its current flowing conditions. The model may either be a steady state or dynamic model but certain flowing conditions warrant dynamic modelling to understand liquid loading, phase separation, and gas velocity issues due to multiphase fluid flow up the production tubing. Usually most completions are not vertical and highly deviated where theoretical correlations are not capable of predicting the actual fluid flow results.

Even more in depth analysis may be required for gas lifted wells as there are few additional reasons for unstable flow due to:

inefficient lifting caused by leak through shallow unloading gas lift valves, insufficient injection gas rate for the fluid rate and the tubing size, and operating orifice not properly sized putting slugs of large amount of gas into the

tubing.


- How to recognize the situations where instability may arise.

Well production trend data, and surface tubing & annular flow pressure and temperature measurements, may indicate the probable onset of instability. Gradual decline in produced fluid with increased annulus pressure may indicate liquid loading.

If tubing pressure starts fluctuating and there is a gradual increase of pressure band may also be an indication of gas separation and liquid fall back in the tubing. Reducedtotal gas liquid ratio from the gas measurement may indicate that well need some gas lift assistance to maintain the stable flow.

- Determine how to stabilize unstable wells.

To stabilize the well, it is very important to identify the root cause for the instability.Use accurate field data to build the well model and analyse the causes for instability. There may be one or multiple causes that made the well unstable and it is critical to address all the probable causes for unstable flow and rectify them to make the well normal to perform efficiently.

b. Use dynamic simulation to determine when to start gas-lift when wells can’t be re-started if they need to be stopped.

This may be due to:- High water cut.- Low bottom-hole pressure.- Poor well inflow performance.- High back pressure in surface facility - Other issues.

Use a dynamic simulation model to match the well’s current flowing conditions with the measured field data and run sensitivity analysis for a range of different water cuts, different reservoir pressures, and other inflow condition changes that are expected, like change in PI, change in skin etc. Properly matched well performance characteristics will validate the model which should give a clue about the onset of instability due to changes in water cut, reservoir pressure and other inflow parameters. It may be necessary to couple the model with a reservoir model predicting the decline rate and water break through with time.

(attach Olga plots for stable and loading-up scenarios)


c. Use dynamic simulation to determine when to start gas-lift if the economic benefit of injecting gas is positive, even if the well is still flowing.

A dynamic simulator may be used to analyse natural flowing wells for any optimization opportunities and to help determine when to start gas lift. The purpose of gas lift is to maintain a reduced flowing bottom hole pressure so that the reservoir can deliver the desired production rates. A dynamic model can predict the required amount of gas lift injection for optimum well performance as well as when to start gas lift.

(attach Olga plots for stable and loading-up scenarios)

d. Use dynamic simulation to determine the need to start gas-lift when production is limited due to liquid loading in gas wells. Describe a method to predict when the gas well will load and die. Describe how to deal with liquid loading in gas wells.

One of the major issues with gas well production is liquid loading predominantly with water and sometime with condensate. Depending on the reservoir inflow characteristic well might see loading issues either soon or later part of the well’s life cycle either due to decrease in gas rate or due to increase in liquid production. In this illustration, it is assumed that the tubing end does not extend to the mid-perforations so that there is a section of casing from the tubing end to mid-perforations.

Fig - Life history of a gas well.

The well may initially have a high rate so that the flow regime is in mist flow in the tubing. However it may be in bubble, transition, or slug flow below the tubing end to the perforations. As time increases and production declines, the flow regimes from the perforations to the surface will change as the gas production declines. Flow at surface remains in mist flow until the conditions change sufficiently at the surface so that the flow exhibits transition flow. At this point the well production becomes somewhat erratic, progressing to slug flow as the gas rate continues to decline. This transition will often be accompanied by a marked increase in the decline rate. Eventually, the unstable slug flow at surface will transition to a stable, fairly steady lower production rate. This event occurs when the gas rate is too low to carry liquids to surface and simply bubbles


through a stagnant liquid column. If corrective action is not taken, the well will continue to decline and will eventually log off.

Dynamic simulation can be used to predict the onset of liquid loading with the field well performance data and reservoir decline rate. Well completion also plays a major role in liquid loading depending on the provisions made for type of artificial lift deployment, available resources like infrastructure, electric power, lift-gas, and wellbore accessibility.

- Dynamic simulation will be used to predict the following in a gas well:

Onset of liquid loading Simulation of various Deliquification methods Best suitable Deliquification method with the existing completion Better completion options for recompletion/work-over Prediction of hydrate inhibitor requirement and quantity

- Method to predict when the gas well will load and die.

To effectively plan and design for gas well liquid loading problems, it is essential to be able to predict accurately when a particular well might begin to experience excessive liquid loading. Generally Turner et al critical velocity concept and nodal analysis (TM of Macco-Schlumberger) are used to predict the onset of liquid loading and has been shown to be reasonably accurate for near vertical wells. It is generally believed that liquids are lifted in the gas flow velocity regimes as individual particles and transported as a liquid film along the tubing wall by the shear stress at the interface between the gas and the liquid before the onset of severe liquid loading. Turner et al developed simple correlation to predict the critical velocity in near vertical gas wells assuming the droplet model. In practice, the critical velocity is defined as the minimum gas velocity in the production tubing required moving droplets upward. Two variation of the correlation were developed; one for the transport of water and the other for condensate. Although critical velocity is the controlling factor those equations are easily converted into more useful form for computing a critical well gas flow rate as well as critical tubing diameter.

These equations can be used to compute the critical gas flow rate required to transport either water or condensate. Again, when both liquid phases are present, the water correlation is recommended.


Dynamic Simulation provides a better understanding of multiphase flow and the role of parameters such as P-T gradients, deviation, and tubing size, as well as the effect of inflow performance in liquid loading. Transient multiphase flow modelling, offers new insights into the mechanisms of liquid loading. The onset of liquid loading is triggered by film flow reversal rather than droplet flow reversal (when the droplet drag force no longer exceeds the droplet gravity force). Dynamic simulation should match actual liquid loading better than Turner – definitely, provides a more realistic description of liquid loading. Dynamic simulation is essential to obtain realistic values of reservoir abandonment pressures based on actual surface data measurements.

To predict when the gas well will load and die, it is required to accurately model and history match the well with current field data. Run the model with time series for expected reservoir pressure decline, gas/liquid fraction changes and surface boundary condition changes. (attach Olga plots for stable and loading-up scenarios)

- Deal with liquid loading in gas wells.

Based on the simulation results of onset of liquid loading and the amount of liquid production, apply source of artificial lift either gas-lift, HDI pump, plunger lift, rod pump to the model. Run simulations to select and optimize proper Deliquification method suitable for the gas well based on economics and available resource. Based on the uplift predictions and work-over/recompletion cost decide on suitable Deliquification method and apply to the existing completion or add to the new recompletion design.


Fig – Tubing performance curve in relation to well deliverability curve.

e. Describe how dynamic simulation may provide understanding of when cross flow and/or commingling occur; it may occur downhole when the well is shut-in at the surface.

f. Describe how to deal with applications in intelligent and complex well completions.

g. Describe how gas-lift well and system shut-in and start-up works, when it may unstable, and how dynamic simulation is (can be) used to address associated problems.

h. Describe how dynamic simulation may provide understanding of when cross flow and/or commingling occur; it may occur downhole when the well is shut-in at the surface.

Recent well completions are more complex as it is producing from multiple zones either vertical or multiple zones horizontal wells. In either case there are possibilities for cross flow between zones during well shut-in and even while well is flowing with very little drawdown or flowing BHP is higher than one of the producing zones. To understand transient flow conditions and cross flow occurrences, it is very important to model the well accurately as per well trajectory. Create enough pipe segments to precisely estimate and optimize commingled production from each zone of interest. Also model each zone as a separate source/well so that fluid production, cross flow and fluid movement from each zone can be evaluated for different flowing BHP.

Intelligent well completions enable multiple reservoirs to be accessed with a single well while avoiding the common problem of cross-flow caused by different reservoir pressures. In addition, intelligent completions of injection wells enable greater control of water injection and improve the recovery of hydrocarbons from offset production wells.

The following plot shows possible cross flow situations during well flowing.

There are situations one lateral may not even contribute when flowing BHP (Pwf) is slightly higher than the layer pressure of that lateral near wellbore.


The following plot shows possible cross flow situations during shut-in.

Generally the cross flow is aggressive soon after the well is shut-in and in most cases possibly it will die out slowly once the pressures are equalized near the well bore region depending up on the reservoir characteristics.

3860 ft

Lateral - A

Lateral - B

Zone - A Zone - B Zone - C

Pr=2690psia

Pr=2775psia Pr=3140psia Pr=2725psia

Pwf=2550psia

60 ft


i. Describe how to deal with applications in intelligent and complex well completions.

Intelligent well completion technology was developed as part of the global industry trend toward improving reservoir productivity. This technology enabled multiple reservoirs to be controlled remotely and contained within a single well. The ability to manage reservoirs remotely also reduces potential well intervention costs. Although most intelligent well completion systems have been installed in offshore wells, service providers have begun installing systems on land as well. An intelligent well is a well that is equipped with sensors/monitoring equipment and completion components that can be remotely adjusted to optimize production. This optimization of production typically involves flow control that takes place down hole via remote control from the surface, without physical intervention.

While there are exceptions, the key components of a typical intelligent completion are:

1. Flow control devices, which are usually hydraulically-operated internal control valves used to control flows into and out of the reservoir.

2. Feed-through isolation packers that enable hydraulic control lines to be fed through to subsurface control valves.

3. Down hole sensors, which report pressure, temperature and flow rates back to the surface.

4. Control systems, comprising hydraulic and/or electrical surface systems, used to monitor and control subsurface conditions.

3860 ft

Lateral - A

Lateral - B

Zone - A Zone - B Zone - C

Pr=2690psia

Pr=2775psia Pr=3140psia Pr=2725psia

Psi=2710psia

60 ft


Production from multiple zones historically required complicated completions with multiple packers and multiple tubing strings, assuming the wellbore could accommodate two or three tubing strings. If this was not feasible or cost-effective, then a sequencing of production was implemented, starting with the bottommost (highest-pressure), and then moving to the upper reservoirs as the lower ones deplete, to avoid cross-flow. Intelligent well completions enable the operator to alternately produce the lower and upper reservoirs, accelerating total production and increasing the Net Present Value of the well.

Finally, reduced well intervention costs can make a big difference, given the expense of rig time, especially in deepwater and sub sea wells, and lost production caused by schedule delays. The ability to reconfigure wells remotely reduces the need for physical intervention.

To deal with intelligent or smart well completions, the dynamic simulator should able to model those down hole jewellery such as ICD, SSD, hydraulic control valves/devices. At least model should capable of using simple control valves to represent and simulate the same effect of those devices.

V. Information Required for Dynamic Simulation

Already Drafted:Murat Kerem has drafted Chapter V, Sections a, b, and c.

This chapter describes the information required to use dynamic simulation for gas-lift wells and systems. This includes:

a. Fluid properties- When are “black oil” correlations sufficient?- When must fluid compositional data (e.g. PVT analysis) be used.


- How does one pick the best PVT model?- How does one tune the model to fit the measured data for the well and/or system?

SUBJECT: Data requirement for transient performance analysis in gas lifted systems

Similar to any other hydraulic analysis, studying transients because of simultaneous flow of liquid and gas hydrocarbons in gas lifted systems requires input in the following main categories;

Flow path, geometry and equipment.

Pressure – volume – temperature (PVT) characteristics of the fluids.

Well inflow performance relationship (IPR).

Reference pressures and temperatures.

The main difference between a steady state well performance calculation and a transient simulation is that the latter also provides changes in the hydraulic status of a well with respect to a reference time when the initial conditions are specified. The input set for a transient hydraulic analysis should therefore include the initial pressures, temperatures and rates in the system. This of course also means that any operational event to create a change in the system needs to be specified together with its associated time. The rest of this chapter aims to explain these inputs in detail without taking any application or a software package as a basis.

Flow Path, Geometry and Equipment

Calculating pressure drop, whether it is steady state or transient, requires estimation of losses due to friction, gravity and acceleration. The distance traveled by the fluid along the hole and in the vertical direction determines the friction and the gravity (hydrostatic) components, respectively. The multiphase flow pattern map, which is sued to determine flow regimes and accordingly the governing equations to calculate the pressure drop, is affected by the well deviation. An accurate representation of a well trajectory is therefore the first step in constructing a transient model for a gas lifted well. Some software packages accept only along hole and true vertical depth couples for specifying the well trajectory. Some others also work with along hole depth versus deviation data.

It is very important to represent the exact profile of horizontal and deviated wells, because these well profiles can generate terrain induced slugging. Some software limit the number of points required to describe the well profile which can lead to errors on slugging predictions.

Specifying the geometry where the flow takes place is the next step in constructing a transient flow model for a gas lifted well. In other words, diameters with associated depth intervals from the completion diagram need to be entered in the model. This is an important input because the velocities, Reynolds number and consequently the friction in the system are directly affected by the diameter. In cases where the flow is multiphase, such as in a gas lifted oil well, it even affects the hydrostatic pressure drop because the liquid holdup is determined from the flowing phases actual velocities. The reader should however keep in mind that there is no need to reflect a minor diameter change which occurs over a short section such as selective or no go nipples, down hole pressure gauge mandrels etc.

Any well equipment, such as valves and chokes, should be entered together with their pressure drop characteristics, openings and depths. For example, in order to model unloading of an annulus, all the unloading valves need to be specified together with their valve pressure drop characteristics, opening and closing pressures. Some software packages, which were


originally designed only for gas lift design and analysis may already have a valve database to capture this kind of information.

In order to obtain accurate P-T calculation along the well (profiles and wellhead values), the well completion diagram including all casings and annular fluids, cement and formation (thickness and heat transfer coefficient for each constitutive material) should be entered in detail. This data will be used to estimate the radial heat losses in the wellbore which will be a function of the number of concentric walls. The use of overall heat transfer coefficients to estimate these heat losses is a simpler option but it not recommended. In the cases of risers, a detailed T profile from sea surface to mudline including variations in water current velocities is recommended when there is potential for hydrates or production chemistry problems. This information is used in the model as a boundary condition and may influence riser T profiles and flowhead T calculations at the platform or MODU.

Pressure – volume – temperature (PVT) characteristics of the fluids

A successful design an operation of a gas lifted well, like any other well, requires predicting flowing fluid characteristics. Most of the transient multi-phase flow simulators give users an option to choose the amount of detail in calculating the fluid properties. The possibilities are:

Black oil correlations. PVT tables. Fully compositional modeling.

Using a black oil correlation is a rather simplistic approach to describing phase behavior of hydrocarbon mixtures. The word simplistic should not be mistaken as inaccurate. In situ fluid properties are calculated by making the use of user specified oil density (API gravity), gas specific gravity (density) and gas to oil ratio at standard conditions. Most of the software packages also provide a module to tune the selected black oil correlation for better matching the fluid properties. The engineers should however not forget that tuning a correlation more than 10 % would kill the predicting power of the correlation, and an unsuccessful tuning is always worse than no tuning. Most of the black oil correlations are based on the properties of hydrocarbon mixtures from one specific region, and they are valid in a range of pressures and temperatures. It is important to know the applicability of a black oil model as in pressure and temperature ranges and the hydrocarbon type.

Using a PVT table is another way of specifying fluid properties for transient hydraulic analysis of gas lifted systems. In this case the transient multi-phase flow simulator does not do any fluid property calculations, but it picks up the in situ fluid properties such as phase fractions, densities etc from a previously constructed PVT file. In order to create a PVT file, a fluid model needs to be prepared in a PVT package. This model includes the hydrocarbon composition, and its measured properties at different pressures and temperatures. The measured properties are used to tune the composition so that the selected equation of state will best reflect the flowing fluid characteristics. A PVT report normally contains different types of laboratory data sets. These are the saturation point, the separator tests, constant composition expansion (CCE), constant volume depletion (CVD) and viscosity measurements. The sets which best describe what the fluid goes through in a gas lifted production system are the first three. The engineer should therefore use these sets to tune the fluid, and then apply viscosity tuning before creating the PVT tables. The CVD data is very much related to the reservoir engineering applications, and is not for the short term performance analysis of a production system. The question of “would it harm to involve the CVD data” may rise here. It is impossible to obtain tuning which 100% matches all the different data sets. In other words, while trying to get close to one type of measurement, the accuracy of the model in another type of measurement needs to be sacrificed. The aim of the analysis should therefore determine the sort of data to be used in tuning the fluid model. After these preparations, a


PVT table with sufficiently large pressure and temperature ranges should be generated for using in the transient hydraulic analysis of the gas lifted well, riser or the pipeline. The same PVT file can be used for the lift gas, as the produced gas is normally recycled in the gas lift systems.

Fully compositional modeling is not widely preferred because it is computationally very demanding. In this case the hydrocarbon composition is flashed at every in situ pressure and temperature. There are however a few occasions that require this capability. One of the best examples, where fully compositional PVT modeling within the transient simulation is required, is the commingled production of significantly different reservoir fluids. In this situation the produced fluid composition will differ from one flowing bottomhole pressure to the other. Hydrocarbon compositions for different reservoirs need to be known and tuned with the appropriate data sets as it was explained in the previous paragraph. Then, these compositions or the fluid models can be used in the transient calculations. Some software packages make the use of compositions themselves, and some are used together with a separate PVT package in an integrated fashion.

As a rule of thumb, if phase changes are expected in the system (i.e gas/condensate wells from reservoir to wellhead), a fully compositional model will give more accurate P-T and liquid hold-up calculation results than a black-oil correlation.

Well inflow performance relationship (IPR)

The IPR determines how much reservoir fluid can be produced at different flowing bottomhole pressures and temperatures. In other words it quantifies the flow resistance in the formation and in the well reservoir interface. Keep in mind the IPR as well as PI (productivity index) is a steady state definition. For studying pressures and temperatures in a steady state system where there is an expected, or a known production rate, this information may not be required. Studying any transient phenomena however, needs to be coupled with a well’s inflow performance because the production will vary depending on the changes in flowing bottomhole pressure.

For an existing well, the IPR can be obtained from a multi-rate well test data. The curve, which connects measured flowing bottomhole pressures at different rates, is the IPR. If the well is not on production yet, analytical methods need to be utilized for predicting the IPR by taking the formation permeability and its thickness, expected drainage area, well radius, well/reservoir interface and the PVT of the reservoir fluids into account. In any case the IPR has to be entered in the transient simulator in a required format, which can either be a table, the linear (PI) type or Vogel. Depending on the purpose of the analysis, e.g. for studying the initial startup of a gas lifted well and its cleaning up performance, it would be better to use the distributed IPR approach. This requires dividing the productive interval into segments, and calculating individual IPRs depending on the segment characteristics as in permeability etc. These inflow sources then can be inputted to the transient simulator at their corresponding depths to accurately visualize the inflow distribution and mud cleaning up performance. The number of the inflow sources depends on the required level of accuracy. A similar approach can also be used in commingled gas lifted producers. In other words, if the well is producing from a several different reservoirs, every inflow zone can be mimicked as a separate IPR for a more detailed analysis. Changes in reservoirs’ pressures and temperatures due to elevation differences also need to be reflected.

As explained in Chapter 1 section b, in dynamic simulation techniques there are two IPR options, the standard IPR definition for steady state conditions or the quasi-dynamic IPR definition where the key reservoir properties can change with time. When the dynamic model is use for forecasting purposes (i.e. “what if” scenarios), the use of the standard IPR definition is good enough considering that can give reliable indication of worst case scenarios. When


the dynamic model is used for matching measurement (history matching) then the use of the quasi-dynamic IPR is required. Reference Pressures and Temperatures

For every inflow source, a reservoir pressure has to be specified. Some applications also require a separate reservoir temperature, and some others extract this information from a user defined geothermal gradient. A wellhead pressure (assuming the model stop in the wellhead) is also required for the calculations. If the transient model has a wellhead bean in it, the wellhead pressure needs to be specified at the downstream of the wellhead bean. A surface injection pressure together with the amount of injected gas needs to be inputted as well (if the injection model start at the wellhead). In this case, if the surface control valve on the casing side is included in the model, the surface injection pressure and temperature has to be defined at the upstream of the surface control valve. Apart from these nodal pressures and temperatures, the fully transient simulators require the well’s initial content and the associated pressures and temperatures. These are used as the start up conditions for the calculations at time equal to zero.

Any planned changes in the wellhead and surface injection pressures, or in gas lift rate has to be entered as time series so that its impact on the transient behavior can be reflected.


VI. Application of Dynamic Simulation

Already Drafted:Cleon Dunham has drafted Chapter VI, Section c.Juan Carlos Mantecon has drafted Chapter VI, Sections d and e.

To be Drafted:Octavio Reyes will draft Chapter VI, Section a. He will review it with Yula Tang.Dan Dees will draft Chapter VI, Section b. He will review it with Shanhong Song.

This chapter describes when and how dynamic simulation modelling may be applied:

a. Integrated modelling- When is it sufficient to only model the wellbore?- When must an integrated model of reservoir, near wellbore reservoir area, inflow,

outflow, flowline, and surface systems be used?

b. Real-time modelling- What is real-time modelling and what is its role?- When must it be used?

c. Use of dynamic simulation modelling in typical aspects of gas-lift system management

Dynamic system models may have several practical uses in gas-lift. These include: (1) assisting with designing gas-lift installations, (2) confirming or validating that a given design will work properly, (3) helping to identify problems with a gas-lift system operation, (4) helping to diagnose the causes of gas-lift operating problems, (5) helping to troubleshoot or find solutions to specific problems, and (6) helping to optimize a gas-lift system operation.

- Design

Gas-lift design actually consists of at least two components: (1) determining the spacing of the gas-lift mandrels, and (2) determining the size and settings of the gas-lift valves and/or orifice. Normally, the mandrels are spaced when the well is first completed or if it worked over and/or recompleted.

Most conventional gas-lift design programs use steady-state pressure traverses for the injection pressure profile and the production pressure profile. The mandrels are spaced based on these profiles. The mandrels must be spaced so the well can unload to the deepest operating point, and be such that they well can continuously operate at this depth, without multi-pointing or working back up the hole. This design process is discussed in API RP 11V6.

But for many gas-lift wells, especially wells with horizontal completion intervals, the actual pressure profiles may not be steady; they may fluctuate dynamically as the well unloads and operates.

By using dynamic simulation, the mandrels can be spaced such that unloading and continued operation from the deep operative valve/orifice can be assured, even if the well operates in an unstable, dynamic fashion.


When it is time to actually place a well on gas-lift, the gas-lift valves and orifice must be sized and set. The valves must be designed to permit the correct amount of gas-lift injection for unloading, but not so much as to waste gas or prevent working down to lower unloading mandrels/valves. Steady state design of injection pressure operated (IPO) gas-lift valves is covered in API RP 11V6. Design of production pressure operated (PPO) valves is covered in API RP 11V9. Again, use of dynamic simulation can help assure that the valves are selected and set properly to permit effective downloading and operation from the desired deepest point.

- Design confirmation

Normally, the mandrels are installed when a well is first completed or recompleted. Normally, this was done using a conventional design program with steady-state pressure profiles.

In this case, it isn’t possible to use dynamic simulation to space the mandrels, but the design can be checked with a dynamic simulator to confirm or validate that it will work properly for unloading and consistent operation from bottom. If the simular indicates that the design will not work as desired, it may be possible to make adjustments in the gas-lift design pressure or rate, or in the selection of different types of gas-lift valves.

In this case, it is much preferable to know in advance if a design is going to work properly or not, rather than to install an incorrect design and find out later that it didn’t unload or operate as desired.

It is not normally recommended to do this, due to the potential for leaks and other problems, but if the dynamic simulator indicates that the well can not work down due to improper mandrel spacing, it may be possible to insert and additional unloading valve using a wireline-set insert valve.

- Problem identification and diagnosis

Many gas-lift wells operate in an unstable manner at some time(s) in their life cycle. This may be due to a change in the injection pressure or rate, a change in the fluid being produced (e.g. more water), a change in the inflow performance from the reservoir to the wellbore, or a leak or change in the tubing, one or more mandrels, or one or more valves.

Typical problems include unstable operation due to a too large port size in an operating valve or orifice, or multi pointing on two or more valves due to periodical re-opening of an upper unloading valve, leaks in the tubing, mandrels, or valves, or changes in the inflow performance of the well.

When an operating gas-lift well is observed to be unstable, dynamic simulation can be used to confirm or diagnose the cause of the instability. This is done by adjusting the parameters used by the simulator until the predicted well performance (e.g. tubing and casing pressure fluctuations, production and injection pressure profiles, etc.) match the actual observed or measured pressures and profiles. When a match is achieved, the cause of the instability can be determined with confidences.

- Troubleshooting

When the causes of ineffective operation have been determined, dynamic simulation can assist in troubleshooting to determine the best solutions to the problem. For example, it may be necessary to reset one or more of the unloading valves; it may be


necessary to change the port size in one or more of the valves or in the orifice; it may be necessary to change the gas-lift injection rate or pressure.

Dynamic simulation can be used, much as it is used in design confirmation, to check any proposed change in valve setting, injection rate or pressure, etc. to verify if the change will accomplish the desired result in gas-lift operation. If not, the proposed change(s) can be adjusted until they produce the desired results.

- Operational optimization

Gas-lift optimization is discussed in API RP 11V5 and 11V8. The objective is to determine the amount (rate) of gas-lift injection that will optimize the value of oil and gas production with consideration for the cost of gas injection, water treating, etc.

The problem is that in an operating gas-lift field, there is (almost) never the right amount of gas to optimize all of the gas-lift wells: there is either too much or too little gas available.

Dynamic simulation can be used to determine the range of gas-lift injection over which a well can be operated and still continue to lift from bottom and remain at least relatively stable. Too much gas will be wasteful and my cause upper gas-lift valves to open due to an increase in the injection pressure at depth. Too little gas may not provide enough lifting effect to continue to operate from the desired depth. This may lead to an unstable, multi pointing operation.

As gas availability changes in a field, due to changes in compression capacity or changed in demand from wells in the field, the field control system (wether it be manual or automatic) must adjust the injection rates into the wells so the total injection is equal to the total supply. Otherwise, the pressure in the distribution system will go too high or too low. Dynamic simulation can provide the range of injection rates that can safely be used in each well. If the overall rate is too high, some gas should be sold or re-cycled to avoid over injection. If the overall rate is too low, it may be necessary to temporarily shut in some of the wells to avoid under-injecting all of them.

- Special considerations

The above discussion focuses on gas-lift wells with mandrels, unloading valves, and an operating valve or orifice. There are at least two other forms of gas-lift which are becoming in common use: injection through a single point, and riser gas-lift.

Injection through a single point is possible when the injection pressure is high enough to inject deep in the well without the need for unloading. This can be stable if there is good correspondence between the injection rate at the surface and the ability of the downhole injection port (valve) to transmit gas from the annulus to the tubing. However, if there is not a good match, these wells can also be unstable and need dynamic simulation to assist with analysis and correction of the problem.

Riser gas-lift is sometimes used when long risers are required to bring production from the sea floor to the surface of a production platform. In some cases, the height of lift in the riser can be as great as or even greater than the height of lift in the wellbore itself. Gas-lift is needed to overcome the significant pressure drops between the sea floor and the surface. There are often problems with instability in the riser and dynamic simulation can assist with analysis and correction of the problem here as well.


d. Appropriate dynamic simulation techniques and ways of implementing them

- What are the different dynamic simulation techniques?

Due to its historical development and the type of operational conditions, the analysis of multiphase flow phenomena can be divided into two different techniques:• Steady State Techniques• Dynamic Simulation (Transient) Techniques

A detailed comparison of these techniques, highlighting pros and cons as well as areas of application, is presented in Chapter 1, Section c.

The traditional steady state techniques using empirical and mechanistic correlations are being progressively displaced by the more advanced multiphase flow transient numerical simulation techniques.

Optimal design and operation of multiphase injection and production systems relay on the understanding of flow behaviour. To ensure technical, operational and HSE integrity during the field life cycle, dynamic simulation is required.

- What techniques are appropriate for any given situation?

Dynamic simulation techniques can be use to design and/or optimise multiphase production/injection systems for any given situation (steady state and transient conditions). Steady state techniques should be used only for steady state conditions. The application of steady state and/or dynamic techniques is based on the level of complexity of the production/injection system and fluids to be modelled.

The major advantage of steady state techniques is that the model can be constructed and sensitivities run quickly, therefore they are low cost, and perceived as low expertise requirement tools. They are reasonable accurate over a well defined range of operating conditions. They are very easy to use as design and optimization tools. The common error is to use these techniques to describe transient conditions. The other common source of errors is the use of this technique by inexperienced engineers – trust of software results with no proper scrutiny.

On the other hand, dynamic simulation techniques required more experienced personnel and more time for building models and analysing results, resulting in a more costly technique and more difficult to justify to inexperience management.

Both techniques are normally applied and the economic challenge is to optimise the combined use of them without compromising the quality of the design and the operational integrity of the system. In most cases, risk reduction and potential catastrophic failures are more difficult to quantify if dynamic simulation is not used.

For instance, when evaluating a number of gas-oil field development options using parametric studies to identify the steady state operating scenarios with the most limiting capacity constraints, the use of steady state techniques can be very useful to provide a 1st order approximation. But dynamic techniques are required to more accurate calculate capacity requirements, as well as, to take into account time dependant operating practices – i.e. the effect on system design (diameters, slug catcher size, etc.) if we quickly open a well to the desired production versus slowly increasing choke size in steps during a control ramp-up. Furthermore, transient analysis is needed is we also want to evaluate the effect on hydrate and or wax formation and establish operational guideless to avoid production chemistry problems during shut-in or start up operations.


Another application that efficiently combines the best of both techniques is to build initially a transient model to analyse all the start-up and shut-in operations and more accurately define the steady state or the slugging unstable resulting conditions, as well as maximum wellhead pressures and temperatures (and P-T profiles). Once the steady state conditions have been confirmed and defined by transient simulation, and therefore, the wellhead P-T and P-T profiles validated, these profiles can be input as correlations in steady state software models and make good use of the quickness of the steady state technique but obtaining more reliable results.

- What part of the gas-lift well/system needs to be simulated?

Typically, reservoir, well, surface and process facilities are optimised as individual components. The part of the total gas-lift system to be modelled depends on the objectives of study objectives. To understand the multiphase flow behaviour of the total production/injection system and evaluate the interaction between well, gas injection annulus, flowline and riser (i.e. interaction between the slugging originated in the well and the slugging originated in the riser), sooner or later the total system needs to be integrated. The advantage of integrated modelling is the avility to dynamically link these components into one interactive full-field production system. Integrated systems can give more realistic results. There are different levels of model integration and they should be applied following an increasing order of complexity:

1 st Integration Level (Typically one software package) : Typically, E&P companies divide the design and optimisation of the system in different areas of expertise – production technologists look at well design and optimisation, facilities engineers at pipeline-facilities design and optimisation, and operation engineers at production optimisation and operating costs. Therefore, it is not unusual to find a dynamic model of the well (from reservoir to wellhead-flowhead) built by production technologists and a separate flowline-riser model (from wellhead to separator) built by facilities engineers. These models are built to analyse different design and operational issues and they are optimised separately, but at some point in time when the number of sensitivities have been reduced to a minimum in both models, they need to be connected to analyse the interaction between the system components. To be able to do that, it is necessary to use the same fluid file in both models constructed using with the P-T extremes of the entire system (i.e. reservoir P-T and separator P-T).

In the case of Gas lift wells and systems, it is recommended to start the modelling from the simpler case (well only) and upgrade the model as required (well+flowlinw+riser) based on the obtained results. The order of complexity of the gas lift injection system is as follow:

1. Model gas-lift injection gas as a Source (composition, P-T and rate) at the desired injection point. In this type of model, sensitivities can be run for different gas rates and different injection points to obtain the optimum rate and position.

2. Model gas-lift gas injection as a Source (composition, P-T and rate) at the wellhead and include the annulus in the model. This will account for the cross heat transfer effects of injecting a cold fluid (gas) downhole in the annulus and producing a hot fluid inside the tubing. This type of model will improve the calculation of the temperature profile and the temperature at each GLV location. It will also take into account other annular flow effects like condensation of liquids from the gas composition – some liquids may be being injected with the gas at desired depth). This type of model will also take into account the interaction between the production and the gas injection system comprising the total system..


3. Model gas lift injection gas as a Source (composition, P-T and rate) at the compressor output, including the well annulus and the gas injection flowline from the compressors to the annular wellhead in the model. This type of model will also take into account the behaviour of the gas-lift gas in the injection line. The compressor can also be included in the model, if necessary.

2 nd Integration Level (Typically two software packages) : When the interaction between the reservoir (near-wellbore) and the well plays a dominant role in the description of the dynamic behaviour of the complete system, to properly model the multiphase flow behaviour, the well+flowline+riser model may need to be integrated to the near-wellbore reservoir model. Some of the cases where the dynamic wellbore/reservoir interactions may be strong are:

– Liquid loading– Dynamic water and gas coning– Formation heading and wellbore slugging– Complex bullheading and injection cases– Complex crossflow cases– Complex Well kick-off and cleanup cases– Complex Well testing cases– Complex Shut-in/start-up cases

As explained in chapter 1 section b, a quasi-dynamic IPR option where the user can specify Pres, Tres, WC, K-h, Skin and n-D Skin, as time series, can be used when matching interactive dynamic measurements, but it is clear than a near-wellbore reservoir model can more accurately and realistically model the reservoir-well interaction than the quasi-dynamic IPR.

Nevertheless, the benefits of the quasi-dynamic reservoir input are:– No software connectivity requirements (all done in one software package)– faster runs– key variables value range quicker defined– less sensitivity runs– The analyst will be in better position to:

• define the value of using near-wellbore • define key variables and the value range

Depending on the objectives set for the study, certain models will be more appropriate than others.

3 rd Integration Level (Multi software packages) : Multiphase flow transient models can be integrated with geo-science software, and risk simulation and decision analysis software packages to obtain a technical-operative information management system and improve field development decisions. Uncertainties across surface-subsurface, well location, producing scenarios, fluid-rock dynamic properties, probabilistic analysis, can be introduced and NPV and recovery estimates can be obtained. Integrated Multiphase-flow/Geo-science/Risk/Decision-analysis modelling has the potential to:

– Optimise Field Productivity (through the production life cycle)– Improve Reserves Recovery– Enhance Surveillance– Enhance Troubleshooting– Improve Decision Making– Improve Risk Management– Improve Work Processes


Integrated Technical Operative Information Management System

PRODUCTION

CONTROLMANAGEME

NTCENTER

ON-LINEMONITORING

ANDPREDICTION

OF CORROSION

ON-LINE OPERATION

& MAINTENANC

EOPTIMIZATIO

N

ADVANCEDPROCESSCONTROL

SAFETY STUDY

SIMULATOR

WELL AND ARTIFICIAL

LIFT SYSTEMS

DESIGN

PROCESSDESIGN

OFF-LINEOPERATORTRAINING

SIMULATOR

DECISIONMAKING

DIAGNOSIS-PROGNOSIS

PVT-FLUIDTRANPORT

ANDPROCESS

PROPERTIES

Integrated simulators under a unified software environment


The major drawback of integrated systems is the convergence problems experienced during simulation runs – different software packages and explicit connective software.

VII. Information Provided by Dynamic Simulation

Already Drafted:Juan Carlos Mantecon has drafted Chapter VII, Section b.Juan Carlos Mantecon has drafted Chapter VII, Section c.Ken Decker has drafted Chapter VII, Section d.Arun Kallal has drafted Chapter VII, Sections e.Arun Kallal has drafted Chapter VII, Sections f.

To Be Drafted:Juan Carlos Mantecon will draft Chapter VII, Section a. He will review it with Murat Kerem

This chapter describes the information that can be provided by dynamic simulation of gas-lift wells and systems.

a. Slugging flow:

- Understand the effects of slugging flow and the impact of this on well performance.- Understand when, where, and under which conditions slug flow is originated- Understand how slug flow conditions can be minimized or eliminated

b. Water effects on corrosion and hydrates:

- Understand the effects of accumulated water in lines, gas-lift valves, etc.

Hilly terrain, deviation and changes in flow direction, can induced water hold-up in wells and flowlines. In deviated and horizontal wells and flowlines, local water cuts can exceed 20% or 50% despite of low inflow rates from production zones and/or low production rates being measured at surface – even with water cut measurements of 1% or less. The amount of water going in and out the system does not represent the amount accumulated in the system.

Figure X : Water accumulation in horizontal wells and flowlines The water accumulation in the dips can be for hours or days (be a transient effect) or can be permanent (steady state conditions). Established local water cut values will change at different production rates scenarios (i.e. WHP, gas velocities, Gas-Lift injection rates, etc.).


Well and flowline liquid content as function of gas production rate

Dynamic simulation describes the multiphase flow behaviour and provides all the key flow characteristics to define water accumulation at any point in the system, as well as the water accumulation effects on the slugging conditions (hydrodynamic and terrain induced), liquid loading conditions and related internal corrosion susceptibility, as a function of time and for any changing operating scenarios.

The dynamic simulator provides the resulting trends and profiles for all the key variables:

Pressure Temperature Water, oil, gas, water vapour, water droplets, oil droplets and gas bubbles

velocities, superficial velocities and fractions Water film fraction and velocity (sweet corrosion exist only if a water film is

wetting the pipeline) Flow regime and separated/dispersed flow Water wetting (is the water dispersed in oil?) – normally, it is assumed that

water is the continuous phase when the water cut is larger than 30 % .The inversion point is usually somewhere between 30 % and 50 %.

Water condensation rate pH Partial pressure of CO2

Shear stress between water film and pipe wall, and Critical velocities (for water loading and erosion) – erosional areas are more

susceptible to corrosion

Figure X is an example of a profile plot showing the pressure, temperature, and water condensation rate changes along the system at a point in time.

Gas Production Rate

Liqu

id C

onte

nt

Initial amount

Final amount


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e (b

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Figure X: P, T and Water Condensation Rate values along the flowline (Profiles)

In the case of Gas Lift systems, the amount of Gas-Lift injected gas can be simply simulated as a source (rate, pressure, density, viscosity, corrosive content, CO2, etc.). The location of the Source can be selected depending on the objectives of the study at:

1. the injection point2. the wellhead3. the compressor output

Multiphase flow resulting trends and profiles (as listed above, including water accumulation and corrosion ambient conditions) can also be obtained and analysed for the Gas-Lift injection system, in any of these three cases. Cases 2 and 3 will improve the temperature calculation in the well due to the consideration of the counter current heat transfer effects generated by the cooler Gas-Lift annular flow. Cases 2 and 3 will also take into account any interaction between annulus and production tubing (i.e. slugging generated in the annulus and being transfer to the tubing). Case 3 will further improve the Gas-Lift optimisation analysis by providing the amount of condensate (oil and/or water) generated in the annulus-injection flow line Gas-Lift system as well as the location and amounts accumulated and/or being injected into the well. The virtual model in the dynamic simulator can be as complex as necessary.

- Understand the effects of water-induced corrosion.

Most downhole tubular corrosion is associated with the exposure of downhole steel to low-pH environments, encouraged by the combination of groundwater with a variety of acid-forming elements. CO2 dissolves in water to form a weak acid and therefore the solution has a low pH value. A low solution pH accelerates corrosion. The corrosion will take the form of uniform surface or weight loss and localised pitting corrosion. The primary factors that affect CO2 corrosion are the partial pressure of CO2, temperature and chloride content. An important aspect of including corrosion models in the dynamic simulator is the possibility of identifying not only the areas of the well and flowline with the highest risk for corrosion problems but also the corrosion rates. The location with the


highest corrosion rate can be determined by the temperature and pressure variation along the pipeline or by flow effects like liquid accumulation, flow velocity variations and changes in flow regime.

It is important to select a dynamic simulator that can break the multiphase flow (at any time and system location) into the following components: gas, vapour, oil/condensate droplets, water droplets, oil/condensate film, and water film. It is important to know if water or oil wets the steel surface since corrosion takes place only when water is present at the surface. If water is present as vapour or droplets then the contact with steel surface it is not as relevant as with water as a film. The duration of water as film contact is also important because this contact can be transitory and no long enough to be relevant.

The selection of the best corrosion prediction model would generate a debate that is beyond the content of this chapter. Different oil companies and research institutions have developed a large number of prediction models for CO2 corrosion of carbon steel in oil and gas wells and pipelines. Many of these models take flow-related parameters like liquid velocity or water, oil and gas production rates into account. However, most of the models are point models, i.e. they can only be used to predict the corrosion rate at a given location in a well or pipeline where the temperature, pressure, water chemistry and flow conditions are specified. The models either take liquid velocity as input or assess the flow effect on corrosion by a simplified fluid flow calculation in a point. In order to perform a corrosion evaluation for a specific well or flowline it is therefore often necessary to first perform a fluid flow simulation with a dynamic simulator and then use the results from this simulation as input for running a corrosion model at different points in the well or flowline. It is obviously advantageous to combine fluid flow models and corrosion models into a single package. This has been done using the basic corrosion models:

de Waard Model – 1993 and 1995 versions NORSOK M-506 model IFE Top of Line Corrosion Model

The de Waard model was developed by Shell and it is probably the most widely used CO2 corrosion model. The NORSOK model was developed by Statoil-Hydro and Saga Petroleum in collaboration with IFE and it is one of the most recent models. The IFE Top of Line corrosion model is based on laboratory studies performed in 1990 with emphasis on the effects of variation of water condensation rate, temperature and CO 2 partial pressure on corrosion at the top of wet gas pipelines (other models describe corrosion at the bottom of the line). Figure X shows a case comparing de Waard and Norsok corrosion rate estimates – The Norsok model gave smaller corrosion rates than the de Waard model for formation water at high temperature with bicarbonate present. This is because the Norsok model takes more account for protective corrosion films. With no bicarbonate present the two models predicted similar corrosion rates.


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Cor

rosi

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te /

(mm

/y)

de Waard model

Norsok model

Figure X – de Waard and Norsok Corrosion Rates – Shows peaks in corrosion rate in downhill slopes with higher flow velocities

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)Fe2+ sat.

TOL corrosion rate supersaturation

Figure X: Top of Line Corrosion Rate (TOL)

The usual dynamic simulator outputs are:– Pressure and temperature profile– Liquid velocity or wall shear stress– Flow regime and separated/dispersed flow– Water wetting – Water condensation rate

The usual corrosion specific inputs are:– CO2 mole fraction in the gas– CO2 partial pressure (total gas pressure x CO2 mole fraction)


– Water chemistry: bicarbonate content and ionic strength – Glycol concentration and inhibitor efficiency

Calculation of the pH value in the water is based on:– CO2 partial pressure, temperature and water chemistry

The Dynamic Simulator offers three options for pH calculation:– condensed water without corrosion products - low pH (around 4)– condensed water saturated with iron carbonate - higher pH– formation water with specified bicarbonate content - buffered

Water chemistry in condensing water may be very different from the bulk water phase:– Salts from formation water not present in top of line– Condensing water will have low pH and high corrosivity– Corrosion products accumulate rapidly in the condensing water– pH increases until the water is saturated with iron carbonate– Corrosion reduced by formation of protective iron carbonate films

Typical formation water values as a reference are:– 60 - 600 ppm bicarbonate (1 - 10 mM)– 0.5 - 2 M ionic strength– pH often in the range 5 - 5.5

Studies in the literature show that for small amounts of H2S, CO2 is the dominant corrosive species. However, for a ratio of pCO2/pH2S > 200-500 (small amounts of H2S in a CO2 dominant system) H2S can affect the corrosion rate mainly by formation of more or less protective films (FeS). For pCO2/pH2S < 200-500, H2S usually dominates the corrosion rate and we have sour corrosion (or cracking corrosion). Sour corrosion or cracking corrosion is a very different phenomenon (H+ penetrate the steel and makes it more brittle so it finally crack). The corrosion models are not made for H2S corrosion

– Not valid for CO2 to H2S ratios below 20– Should not be used when the H2S partial pressure is above 0.1 bar

In summary:

The modelling and understanding of multiphase fluid flow and CO2 corrosion is of decisive importance for economic and safe design and operation of wells and flowline systems as well as corrosion mitigation plans. Depending on operating conditions the corrosion mitigation plan will need to change.

Operating and related multiphase flow conditions may affect corrosion of well and flowline steel in different ways. It is important to know if water or oil wets the steel surface since corrosion takes place only when water is present at the surface. Water wetting depend on the fluids, the flow conditions, the water cut, etc.

The multiphase flow characteristics may also affect corrosion if corrosive species are involved. Higher flow velocities give more turbulence, better mixing and thus larger transport. The flow also affects the structure and strength of protective corrosion product layers, which reduce the transport of corrosive species towards the steel surface.

Within each flow regime water and oil may be separated or dispersed. If they are separated both water and oil wet the wall, but at different parts of the wall. Water is heavier and wet the bottom of the pipe but this is not valid in vertical flow.

Dynamic simulators are unique tools which can provide most of the information required to develop risk-based corrosion susceptibility profiles.


The whole production system can be model including the well annulus and Gas-Lift injection lines. Trend and profile results of the key variables are obtained at any location and time of the model.

An important aspect of including corrosion models in a dynamic simulator is the possibility of identifying the areas of the well and flowline with the highest risk for corrosion problems. The location with the highest corrosion rate can be determined by the temperature and pressure variation along the pipeline or by flow effects like liquid accumulation, flow velocity variations and changes in flow regime.

The dynamic simulator provides pressure, temperature, shear stress and water wetting predictions to calculate CO2 partial pressure, pH and corrosion rate profiles along the pipeline. The basic corrosion models are included in the simulators, but the implemented models can be extended with other CO2

corrosion models if necessary.

The dynamic simulator can be used to estimate the amount of inhibitor requirements to eliminate or minimise corrosive conditions as well as to predict inhibitor distribution and estimate the right type and amount to be used during transient, steady state and changing operating conditions.

- Understand the effects of hydrates in lines, gas-lift valves, etc.

Gas hydrates are crystalline compounds formed by water and natural gas molecules at high pressures and low temperatures (below approximately 35°C). They are solid ice-like crystals consisting of geometric lattices of water molecules containing cavities occupied by light hydrocarbons (methane, ethane, propane) or other light gaseous compounds (nitrogen, carbon dioxide, hydrogen sulfide) – unlike ice, they can form at

temperatures higher than 0°C. They can take many forms from slushy, sticky lumps to a fine powder.

Hydrates can form in gas, gas-condensate and black-oil systems and can block any type of flowline. Hydrate blockages can form very rapidly when suitable P-T conditions and composition are present. Severe P-T changes in chokes and/or gas lift injection valves can originate hydrates. Transient operations as start-up and shutdown are very susceptible to hydrate blockages because this is when the production system is likely to fall into the hydrate region. It is very important to model transient operations,

For deep wet and dry subsea wellheads and subsea tiebacks in deep waters, shut-in and restarting from shut-in conditions can create significant flow assurance problems. Hot fluids from the wellbore will come in contact with a cold flowline and can form hydrates during restart. Subsea wellheads conditions at the mudline are often within the hydrate formation region. As the water depth increase, boundary temperatures decrease and the potential for higher shut-in pressures increase (additional liquid head), as well as the probability of hydrate formation.

Clearing hydrates blockages in subsea equipment or flowlines poses safety concerns and can be time consuming and costly.

Hydrates are the most prevalent flow assurance problem in offshore oil and gas operations (an order of magnitude worse than waxes).


There are different hydrate control design and remediation options (controlling P-T, removing water, shifting thermodynamic equilibrium with inhibitors):

• Insulation (passive thermal control)– Used for tie-ins and short to medium pipelines– Not usual for long gas-condensate lines

• Bundles (active thermal control)– Complex bundles used for deep off-shore. – Generally used for risers, tie-ins and short to medium pipelines

• Active Heating– Electrical.– Hot fluid circulation.

• Depressurization: provide capacity for depressurization and displacement• Inhibition

– Typical for gas-condensate systems– Used for oil systems at critical points

(e.g. well-heads, wellbore) and during critical operational phases shut-in-cool-down-start-up).

In the case of inhibitors the most common are:

• “Thermodynamic” i.e. they move the melting curve of the hydrates towards lower T:– Alcohols (MeOH)– Glycols (Mono Ethylene Glycol – MEG)

• LDHI (Low Dosage Hydrate Inhibitors) modify crystal growth or crystal structure to avoid blockage.

Water salinity tends to reduce hydrate temperature. To be conservative, the hydrate inhibitor requirement estimates do not account for the inhibitor effect of produced water salinity.

A significant effort is required in the design phase to develop a production system with an acceptable level of risk. At the beginning of the flow assurance design process, basic design and operating philosophies (covering hydraulics, deliverability, hydrates and waxes) should be clearly set – prior to FEED.

To determine the P-T conditions under which hydrates can form is common to use thermodynamic models to predict hydrate behaviour by calculating the hydrate equilibrium curve (hydrate dissociation curve) – a prediction of T at a given P above which hydrates will not form.

The RP78 PenelouxSRK-P EOS was used to obtain the hydrates curve for the fluid shown in Error: Reference source not found below


0

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300

400

500

600

-50 -40 -30 -20 -10 0 10 20 30 40

Temperature - deg C

Pres

sure

- ba

ra

Fig: Hydrates PT curve – EOS: PR78 Peneloux

The hydrates curve represents the thermodynamic boundary between hydrate stability and dissociation. A hydrate formation curve represents the pressure temperature relationship at which hydrates may form whereas a hydrate dissociation curve represents the points when a hydrate crystal once formed, will dissociate. The dissociation curve is typically 2 to 3 ºC above the formation curve and the region between these two curves is the zone where the hydrates are unstable. The hydrate dissociation curve, also termed ‘hydrate curve’ therefore presents a conservative scenario and is used in studies for all hydrate assessment calculations.

- Understand when, where, and under which conditions hydrates may be formed.

The cost of thermodynamically inhibiting production systems under steady state and/or transient operations can be prohibitive. It may not be possible to avoid the hydrate formation region in all probable operating scenarios. It is therefore critical to accurately estimate the risk of forming a hydrate plug during a restart operation or in a new field design.

Modelling is a very effective way to reduce uncertainty by rigorous screening of various options. Dynamic simulation offers an accurate methodology to estimate when, where, and under which conditions hydrates may be formed in a production system (during transient and steady state conditions) based on the difference between a hydrate temperature (Thyd) and fluid temperature (Tf) at section pressure. It calculates the output variable: DTHYD = Thyd – Tfif DTHYD>0 then section is within hydrate region.

Plots of the difference between the hydrate formation temperature and the fluid temperature (DTHYD) at any time are termed subcooling profiles. Positive temperature numbers in this profile curves indicate the overlying in the hydrates region and the potential for hydrates formation in these locations. The figure below shows an example of hydrates margins in a riser during well kick off.


Profile dataGB5-A-caseA_60MM-15wc_rst: DIFFERENCE BETWEEN HYDRATE AND SECTION TEM.,RISER-BRANCH [C], Time: 0GB5-A-caseA_60MM-15wc_rst: DIFFERENCE BETWEEN HYDRATE AND SECTION TEM.,RISER-BRANCH [C], Time: 1GB5-A-caseA_60MM-15wc_rst: DIFFERENCE BETWEEN HYDRATE AND SECTION TEM.,RISER-BRANCH [C], Time: 2GB5-A-caseA_60MM-15wc_rst: DIFFERENCE BETWEEN HYDRATE AND SECTION TEM.,RISER-BRANCH [C], Time: 3GB5-A-caseA_60MM-15wc_rst: DIFFERENCE BETWEEN HYDRATE AND SECTION TEM.,RISER-BRANCH [C], Time: 5GB5-A-caseA_60MM-15wc_rst: DIFFERENCE BETWEEN HYDRATE AND SECTION TEM.,RISER-BRANCH [C], Time: 10

C

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Length [m]300250200150100500

Fig: - Hydrate Margin in Riser profile (black) vs time during well kick-off – 15 WGR (red = 0 sec)

Dynamic simulation offers the possibility of applying different hydrate control methodologies in the virtual model (insulation, active heating, inhibition, etc), and selecting the most effective one for steady state and transient operating conditions.

Figure below shows the influence on the amount of MeOH used in the hydrate dissociation curves and Figure shows the overlap of the worst P-T trends in the riser profile, during well clean-up (blue), shut-in (purple), end of shut-in (doted purple) and kick-off (green), indicating the amount of MeOH required to avoid falling in the hydrates region.

Hydrate Curves with MeOH Inhibition(kg MeOH / kg Aqueous)

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Hydrate Dissociation Curves with MeOH inhibition (0% to 60%)


Hydrate Curves with MeOH Inhibition - (kg MeOH / kg Aqueous)

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Hydrate FormationRegion

Hydrate Inhibition Curves with the overlap of the worst cases P-T trends from Riser Profile Plots

The well should be included in any hydrate formation study. Inhibitors injection only protect components in the production system downstream of the injection point, therefore the location of the injection point is a very important decision. The most common locations are downhole in the well immediately above the SCSSV, at the tree (between master and wing valves), and on the manifold.

Inhibitors can provide complete protection against hydrate formation if sufficient quantities are injected, but under injecting may accelerate the kinetics of hydrate formation. Therefore, is typical to plan to overdose to be safe. Significant saving can be obtained using dynamic simulation to estimate the right amount of inhibitor and the times when inhibitor injection is no longer required. The water production rate needs to be known. The main uncertainty is the dissolved salt in the produced water and their effect on hydrate formation. Salinity in produce water tends to reduce hydrate temperature but to be conservative, the calculated hydrate inhibitor requirements do not account for the inhibitor effect of produced water salinity. Dynamic simulation allows the tracking of the amount of inhibitor in the well/pipeline to ensure enough is available for inhibition purposes. Inhibitor can be tracked in both water and gas phase. Dynamic simulation gives:

– Inhibitor (MeOH, MEG, etc.) concentration along well / flowline – Time to reach a desired inhibitor concentration

Dynamic simulation allows to develop operating guidelines that ensure proper injection (right amounts) and distribution of inhibitors for all operating modes.

An example of MeOH injection tracking is given in the figure below. Upon restart cold gas enters the flowline due to Joule Thomson cooling on expansion of gas. This is inhibited by injection of MeOH into the jumper prior to start up and continuously for the first 3 hours. Additionally, as the cold liquid residing in the flowline is pressured, it heads towards the


hydrate region. This takes some time as restart is slow, and is worse at high water cuts (later in life).

Dynamic simulation can answer the following questions:• What’s the predicted hydrate dissociation temperature profile? How far the conditions are

from hydrate formation? Where? When?• When the temperature falls into the hydrates region?• How deep from the wellhead the well will experience hydrates problems? When?• What’s the best solution for the well flow assurance problems?

– Pressure Control, Temperature Control– Remove supply of water – Hot-cold re-start– Preheating the flowline– Flowline depressurization– Insulation, inhibitor injection? Where? When?

Hydrate remediation schemes can be divided mainly in two schemes:(1) reducing pressure in the system to the point where ambient temperatures allow melting(2) active heating

These schemes should be dynamically model prior to execution. Dynamic simulation is an excellent tool to evaluate and design successful hydrate remediation operations. For instance, the wellbore thermal-hydraulic transient simulation can be very useful to assess the feasibility of injecting hot oil in the tubing-casing annulus for melting hydrate plugs formed inside the tubing in dry-tree gas lift wells. Dynamic simulation can provide the required:

Bleed-off tuning pressure Amount of inhibitor Time for inhibitor distribution Heating temperature and oil injection rate


Operational guidelines to restart well production

Due to the potentially severe economic impact of forming hydrates plugs, and in order to better estimate the time required to hydrate plug formation as well as better understand hydrate kinetics, new hydrate growth, deposition, sloughing and jamming models are currently being developed. The Colorado School of Mines has been developing the CSMHyK model with extensive testing against industrial flowloop data from ExxonMobil and The University of Tulsa, as well as field data. This still considered experimental but some field applications results are very encouraging, Calculates rate of formation of hydrates and mass of hydrate in pipe.

c. Production chemistry:

- Understand the effects scale, wax, and/or paraffin formation and the impact of this on well performance and stability.

Production chemistry issues associated with wells and production/injection systems are highly relevant to cost-effective field developments as well as operating integrity. Plugging due to wax, paraffin, asphaltene, scale, and hydrates (as explained above), reduced the ability of the well-flowline production/injection system to deliver the fluids. In addition, deep water operations amplify the environmental and safety concerns. The increased risk associated to long subsea tiebacks, dry tree risers and extended export pipelines in cold ambient water temperatures should be consider by operators when planning their development scenarios. Under these severe adverse conditions, it is important to understand multiphase fluid properties and the design options to prevent or mitigate flow assurance challenges. Dynamic simulation is a realistic and powerful tool to provide understanding of flow behaviour as well as the resulting internal ambient conditions at any point of the production system.

Focussing the analysis on waxes, wax deposition, and wax gelation are two potentially catastrophic issues in crude oil and gas condensate systems.

Waxes are high molecular weight straight long-chain hydrocarbons (C17 to C75) that precipitate from the produced fluid (n-paraffin). They are crystalline and usually characterised by the wax appearance temperature (WAT) and pour point (the point at which the first wax crystals start to precipitate out of solution).

The deposition of n-paraffin will commonly occur along the well/flowline walls when the temperature of produced fluids falls below the WAT or cloud point, Deposition rates can be attributed to many factors including paraffin content, fluid viscosity, flowrates, gas/ oil ratio and the heat transfer coefficient (U-value).

The problems caused by waxes are twofold: Wax formation producing choking (reduced production) or total blockage:

increased apparent inner wall roughness and decreased diameter effects) Increased apparent fluid viscosity: viscosity can reach the point where it forms a

gel and excessive pressure may be required to generate flow.

Wax gelation is less common in steady-state than wax deposition, but it can have even greater impact if during transient operations (like shutdowns and start ups) fluid temperatures cool below WAT and pour point, allowing the formation of a solid wax column. This condition can completely block the flowline – during restart operations, there might not be sufficient pressure available to "break" the gel and allow the well to flow.


When dealing with high wax content crudes, strategies for wax prevention must be developed at the conceptual engineering phase.

For waxy crude production systems, the criterion used for thermo-hydraulic studies is that the prediction of the fluid temperature along the system (perforations to facilities) is above the WAT. Dynamic simulation can accurately calculate (if input data and fluid characterization from lab studies has been properly done) where and when fluid temperatures fall below WAT and the wax deposition rate as a function of time and space (Fig).

Dynamic simulation can also model realistic pig runs as well as the results of the run. The pig-plug model utilizes, tracking of the masses on each side of the pig, calculates the leakage through it, and modify the forces acting on the control volumes surrounding it. The pig velocity is set based on the local volumetric flux, taking into account any pig leakage rate. The influence of the pig on the flow conditions are through pig and related friction forces, the gravity forces due to the mass of the pig, and any leakage of the pig.

Dynamic simulation predicts the need of: Pigging

– Efficiency of Wax Removal After Pigging– Pressure Requirements for Wax Scrapping– Frequency required for wax removal operations

Thermal insulation required to minimise or eliminate wax formation Non-Newtonian behavior of viscosity due to wax precipitation into oil phase Self-regulation of wax deposition due to release of latent heat Active heating required to minimise or eliminate wax formation Chemical injection – Diluents can reduce viscosity and cause a depression in

the WAT resulting in a reduction of frictional losses and a decrease in thermal insulation requirements)

Gas Lift injection (as diluent) to reduce cloud point and cause WAT suppression Investigate increase in 1st separation pressure results

Dynamic simulation can: Perform a Material Balance of Wax Components

– Wax in dissolved oil– Precipitated wax suspended in oil– Precipitated wax deposited on walls

Describe the Dynamics of Wax Formation/Dissipation– Wax precipitation– Molecular diffusion and shear stripping


– Wax melting

Wax deposition rate analysis is done using one or running sensitivities to the following multiphase flow wax deposition models:

1. RRR (Rygg, Rydahl & Rønningsen)The RRR model considers a laminar velocity sub-layer in turbulent flow. The wax deposition is estimated from the diffusion of wax from the bulk towards the wall as a result of temperature gradients as well as shear dispersion effects. Varying inner pipe wall friction due to wax deposition is also included. It seems to under-predict wax deposition rate for single phase oil cases.

2. HEAT ANOLOGY - TULSA The heat analogy model was introduced to extend the wax deposition model to handle laminar flow. Deposition rate reduction due to shear stripping and rate enhancement due to entrapment of oil and other mechanisms not accounted for by the classical Fick's mass diffusion theory were incorporated through the use of dimensionless variables and empirical constants derived from the wax deposition data. The kinetic model, although semi-empirical, predicts wax thickness with an acceptable accuracy, especially at high oil superficial velocity, and provides an insight for future model development..

3. MATZAIN The Matzain model, considers a concentration boundary layer as for laminar flow. It has a diffusion enhancement effect which is not directly related to the shear stripping part of the Matzain/Tulsa model (shear stripping effect itself may be tuned)

A typical wax analysis should include:• Fluids and wax characterisation – laboratory

– Compositional Analyses (HTGC)– WAT– Pour Point– Wax Content– Viscosity– Deposition Rate or Diffusion Coefficient– Deposit Analysis – Yield Strength, Trapped Oil– Gel Strength– Impact of Inhibitors– Impact of Diluents

• Thermal hydraulics analysis (e.g. insulation, cooldown)• Wax deposition rate analysis• Pig-ability analysis• Gel restart-ability analysis

A good wax thermo-hydraulic study strategy should include:• Define/understand the characteristics of fluids, wax and gel• Keep the fluid hot, insulate or direct heating the well-pipeline• Alter wax characteristics – blend with less waxy fluids or use wax or gel inhibitors• Quantify the extent of wax buildup to establish frequency of wax removal operation• Remove wax frequently by pigging, melting or removal by chemicals• Quantify cooldown to gel formation – displace out the fluids to avoid plugging

Dynamic modelling can also provide key indicators for profiling well-pipeline temperatures and wax buildup. These tools can greatly assist the operator in making economic decisions and exploring multiple design options. Current modelling technology includes real-time, online well-pipeline monitoring and advisory systems that help


manage a series of flow assurance issues including pigging operations and any “what-if” scenario.

d. Gas-lift valve performance:

- Understand information and models for dynamic gas-lift valve and orifice performance,and how and when to use them.

Gas-lift valve performance relates to the valve’s flow performance. Many other criteria, such as reliability, robustness, corrosion resistance, and ease of maintenance are also performance factors but in this section, the subject will be focused on flow performance.The purpose of a gas-lift valve is to open at a predefined pressure and allow gas to flow from the annulus to the production string. The valve should then close at a predefined pressure shutting off flow from the annulus. The pressures at which the valve opens and closes are determined by performing a gas-lift design. Each well is different and therefore each valve will have a different opening and closing pressure.

The flow performance of a gas-lift valve will be a function of the valve design and application conditions. There are two major categories of gas-lift valve designs; Injection Pressure Operated (IPO) and Production Pressure Operated (PPO). An IPO valve is designed to have opening and closing pressures that are most sensitive to annulus pressure. PPO valves are designed to have opening and closing pressures that are most sensitive to production pressure. The flow performance of IPO and PPO valves are markedly different.

The single most important factor effecting flow performance of a gas-lift valve is the port size. In most cases, the larger the port, the greater the flow rate. This is true for both IPO and PPO valves. The next most important factor is the ratio of injection pressure (Piod) to opening pressure (Pvot). The higher this ratio, the greater the flow rate. Finally, load rate and stem travel of the valve have a significant effect on performance. The port size and ratio of Piod to Pvot are application dependent. They are not a function of the valve design. The load rate and stem travel are a function of valve design.

For many years the flow performance of a gas-lift valve was determined using the Thornhill-Craver equation. This equation was developed to predict the flow performance of wellhead bean chokes. These chokes were used to control the flow of gas from the gas injection line into the annulus. The beans were cylindrical, about 6-7 inches long, and had a hole drilled through the center. The size of the hole determined the choke size. Many tests were performed to determine the flow rate at different pressures and the equation is quite accurate for this type of choke.

The Thornhill-Craver equation existed long before the industry had the ability to test gas-lift valves and as a result, was used as the best approximation of the flow performance of gas-lift valves. Most gas-lift design programs continue to use Thornhill-Craver to compute flow through gas-lift valves even though it was never intended for use with gas-lift valves. Recent testing of IPO and PPO gas-lift valves has shown that the Thornhill-Craver equation will over estimate gas-lift valve flow rate by a factor of two to three. When used to estimate the flow through orifice valves, Thornhill-Craver will over estimate by about 20-30%.

The graph below shows the difference in performance of a 1” IPO valve using both the Thornhill-Craver equation and a tested performance model.


1 Inch IPO with 12/64thsVPCPvoT= 917 Pcf= 900Temp=125

1 Inch IPO with 12/64thsThornhillPvoT= 917 Pcf= 900Temp=125

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As noted, the flow performance using the Thornhill-Craver equation shows typical orifice flow characteristics. The tested performance model shows the valve flow rate increasing as differential pressure increases and then decreasing and finally closing. The Thornhill-Craver equation predicts flow rates much higher than is actually possible.The next graph shows the same valve when the ratio of Piod to Pvot has been increased sufficiently to ensure the IPO valve will operate as an orifice.



1 Inch IPO with 12/64thsThornhillPvoT= 917 Pcf= 950Temp=125

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In this case, the 1 Inch IPO valve has flow performance similar to Thornhill-Craver. These two examples show the importance of the ratio of Piod/Pvot to valve performance. With a Pvot of 917, when Piod is 900 (Piod/Pvot = 900/917 = 0.981), the valve has a peak flow rate of 225 Mscfd and throttles closed as production pressure decreases. When the ratio is 1.036, the valve performs as an orifice with a peak flow rate of 500 Mscfd.

This difference in flow behavior is caused by a valve property referred to as load rate. Load rate is a measure of the gas-lift valve’s ability to expose a full open port. Historical models of valve behavior and those employed in gas-lift design programs postulate that


when the injection pressure (Piod) reached the opening pressure (Pvot), the valve would be fully open and function as an orifice. Load rate prevents this from happening.The nitrogen charge in the dome and the bellows act as a spring holding the valve in a closed position. As with all springs, increased force is required to compress the spring. In the case of gas-lift valves, increased pressure is required to compress the bellows and allow the valve to expose a full open port. The higher the load rate, the higher the ratio of Piod/Pvot required to cause the valve to fully open.

The graph below shows the difference in performance for a 1 inch IPO valve with different load rates. The performance curve with the lower flow rate corresponds with the valve with a higher load rate.



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The amount the bellows can be compressed is referred to as stem travel. The bellows compresses in a nearly linear manner until the bellows convolutions begin stacking. At this time the load rate increases dramatically. The distance the bellows compresses in the linear portion is referred to as the stem travel. Stem travel must be sufficient to fully expose the port. This amount differs depending on the port size.The graph below shows a valve with a 16/64ths port and varying amounts of stem travel.


1 Inch IPO with 16/64ths VPC Pcf= 875psig PvoT= 917psig Temp 125F

Trvl .04

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One last variable effecting flow performance is the size of the valve. 1-1/2” valves have much higher flow rates than do 1” valves with the same port size. The graph below compares a 1” and 1-1/2” IPO valve with the same port size and configured to have the same opening and closing pressures.


Camco R-20 with 12/64thsVPCPvoT= 920 Pcf= 913Temp=125

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The flow performance of a gas-lift valve is markedly different from the Thornhill-Craver equation. The amount of gas flowing through a valve has a significant effect on how a gas-lift well performs. A simulator must have good gas-lift valve performance models in order to give accurate result. Several valve performance models are available.

TUALP ModelThe Tulsa University Artificial Lift Projects (TUALP) performed many tests on gas-lift valves during the ‘80’s and ‘90’s. The results of these tests and the models were


published as thesis and are available from the Tulsa University Library. These models were statistical and attempted to predict performance by adjusting the coefficients in the parabola equation. The models did not account for valve properties such as load rate and stem travel. The models have an accuracy of about 30-40% within the range of pressures used for the tests. Beyond this range the accuracy drops off significantly.

Winkler-Eads ModelThis model uses the Thornhill-Craver equation with a modification. Normally the Thornhill-Craver equation uses the full area of the port but this model uses only that amount of the port open to flow. This is calculated using the force balance equation for the valve and the load rate and assuming the area open to flow is equal to the surface area of the frustum of a right cone. This model is entirely theoretical and does not require any testing of the gas-lift valve. As such, it will not account for specific valve design characteristics. The model is accurate to within 15-25% for valves with ports less than 12/64ths. The description of the model with equations is available in a published SPE paper.

API Simplified ModelThis model is a combination of theoretical and tested valve performance factors. The model uses the ISA method of testing valves to determine the flow coefficient (Cv) and combines this with the force balance equation for the valve. The model has an accuracy of 15-25% for all port sizes and pressure ranges. This model was first published in API 11V2 RP. It is also available in ISO 17078.2.

Valve Performance Clearinghouse (VPC™)This model is a combination of theoretical and tested valve performance factors. The model uses the ISA method of testing valves to determine the flow coefficient (Cv) and actual flow performance tests of the valve. This is combined with the force balance equation using a tested load rate of the valve to determine the amount of stem travel. A correlation is then developed using actual test data to predict flow performance. The model has an accuracy of 10-15% for all valves, all port sizes, and all pressure ranges.This model is the proprietary property of the VPC™ but is available by license from Decker Technology, Inc. The correlations have been released to anyone who wants them but the database which enables the correlations is held by the VPC™. Most of the current gas-lift design programs now have the ability to enable the VPC™ models.

Shell Valve/Choke ModelShell Oil Company had experimented with placing chokes downstream of the port in IPO valves and found that the flow performance of the valve was superior to a valve without chokes. This seems counter-intuitive but anecdotal evidence proved it to be true. The VPC™ conducted tests and verified that in some cases, a downstream choke in an IPO valve actually increased the flow rate. This model is available from the VPC™ and has an accuracy of about 10-15%.

SummaryThe unloading sequence of a gas-lift well is critical. If the gas-lift design is unable to unload to the orifice valve, the well will under perform with injection through upper valves and possibly multipointing.

Unloading gas-lift valves do not perform as well as Thornhill-Craver predicts. This is particularly true for 1” valves. The ability to design a gas-lift well that performs as expected is directly connected to the ability to predict flow performance of the unloading gas-lift valves. With respect to Gas-lift Simulators, the accuracy of the results is directly connected to the use of a good valve performance model.

How to use the models


All of the models use equations or correlations to compute flow rate. The degree of complexity of the equations or correlations is a function of the model used. In all cases, it is best to use a computer to solve the equations.

The models will predict a single flow rate for a specific set of conditions. In order to develop a full performance curve as shown in the graphs above, the annulus pressure (Piod) is held constant while the production pressure (Ptf) is iterated from a value equal to Piod and decreasing to atmospheric or until the valve closes. This type of curve shows typical performance but is not realistic for an actual unloading scenario. During unloading, and sometimes during lifting, the annulus pressure and production pressure are constantly changing.

To give accurate results, a simulator must invoke the model at each point in time for the specific conditions existing at that time. Use of a pre-calculated performance curve as shown in the graphs above to predict valve flow rate during the entire unloading sequence will lead to simulator errors and could give false results.

When to use the modelsValve performance models are used during two distinct phases. Once during the design phase, and a second time during the simulation phase. The design phase is a static condition and the simulation phase is dynamic. In the design phase, valve performance models are used to determine a port size for the valve. In the simulation phase, the valve characteristics are given and the model is used to predict performance.

e. Well equipment:

- The effects of down-hole pressure restrictions such as safety valves, corrosion, scale and wax deposits, and the effects of tight spots or holes in the tubing.

Fig. 1 - Subsurface Safety Valve Fig. 2a – Scale deposits

Fig 2b – Gaslift Mandrel Cut a way

Generally there will be a slight reduction in internal diameter and less roughness in the down-hole safety valve, SSD, PBR and nipple profiles that affects the fluid flow pattern and frictional pressure drop. Give careful consideration to include all such down-hole jewellery while using dynamic modelling tool to predict the effect of those equipment on the fluid flow. Use wax modelling as appropriate to see the impact due to wax deposition and remedial wax prevention and cleaning schedules. SSSV should be located in a zone were no hydrate formation or wax deposition is expected, this could compromise its closure. Typically, the major pressure drop will be in the choke valve used to control flow, P-T conditions downstream may fall in the hydrate formation or wax deposition zones.


- The effects of risers and the effects of surface equipment such as flow lines, manifolds, separators, separator back-pressure, etc.

The gases and liquids may exist as a homogeneous mixture or the liquid may be in slugs with the gas pushing behind it. The liquid and gas may also flow parallel to each other or other combinations of flow patterns may be present. Figure 3 illustrates some common vertical and horizontal multiphase flow patterns. Each of these flow patterns will produce a different pressure drop over a given distance. In addition to flow pattern, factors affecting the pressure loss in multiphase flow include:

1. Inside diameter of flowing conduit2. Wall roughness3. Inclination4. Liquid density5. Gas density6. Liquid viscosity7. Gas viscosity8. Superficial Liquid viscosity9. Superficial Gas viscosity10. Liquid surface tension11. Wall contact angle12. Gravity acceleration13. Pressure gradient


Effect of critical velocity in horizontal/inclined flow.

Since the pressure drop is caused by a complex interaction of many factors, one of the major problems is analyzing flowing wells and designing gas lift installations has been the prediction of flowing pressure at depth. It is also important to understand the pressure drop in the horizontal flow line in order to determine the back pressure at the well head. This problem has been the subject of numerous studies.

Extra pressure is required in order to lift fluids through a riser. Furthermore, risers are terrain induced slugging generators. Dynamic simulation is required to proper model terrain slugging


f. Well design:

- The effects of well design and the associated dynamic effects on well operations. This includes:

o Vertical wells o Horizontal wells o Multi-lateral wells

Fig. 5 - Vertical well profile Fig. 6 - Horizontal Well profile Fig. 7 - Multilateral well profiles

Type of well completion and well profile plays a major role in designing gas-lift components and system. Dynamic modelling with the current well fluid properties and data should be used to simulate the effect of gas lift at different locations.

Slug flow and water accumulation are typical problems associated to horizontal wells:• Horizontal wells allow a reduced drawdown to obtain a desired rate, thereby

maintaining the reservoir pressure above the bubble point for longer periods of time, thus reducing GORs and improving recovery but gas velocity may be too slow leading to slug flow – the addition of gas lift gas increases the superficial gas velocity and changes the multiphase flow to a more stable flow regime

• Horizontal wells producing below bubble point pressure can act as downhole separators generating slug flow – the terrain can also be originating slug flow

Dynamic simulation is the optimum method for predicting slugging flow and estimating water accumulation (as explained above). Terrain induced slugging is typical in horizontal wells and cannot be properly predicted by steady state techniques. To properly model the production from horizontal wells several inflow points should be included in the dynamic model. Each inflow point can have different reservoir properties and IPRs. Free gas and/or water sources can also be included to model gas and water coning, respectively.

In the case of intelligent wells with multiple production zones (multilayer, multilateral, etc.) allowing remote selective production, dynamic simulation brings the additional benefit of virtually testing the zone individually or in combination to establish maximum


total production potential prior to any actual operation. Furthermore, well clean-up operations can be improved by simulating how layers and legs can be produced separately to generate the maximum drawdown. During production, gas and/or water coning can be avoided and slugging minimized by optimizing the opening of each ICV.

Dynamic simulation can justify the use of smart completions by demonstrating the added value – the well may not be producing from half of the reservoir section without smart completions.

In multi-lateral or multi-layer wells producing from different reservoirs with different fluid compositions, dynamic simulation offers the possibility of tracking the different fluids and estimating the resulting mixture composition along the production/injection path. Dynamic simulation can also predict any cross-flow between formations during static (well shut-in) or producing conditions.

- When and where to inject gas into a well: in the vertical, in the knee, in a rat hole, in the horizontal section.

Figure 8 shows where to inject lift gas for more effective fluid flow and to avoid liquid hold up and loading up in a riser section.

Fig. 8 - Vertical well profile

More in-depth analysis is required for when to start gas lift, where to inject gas and how much lift gas is required for optimum well performance etc. Dynamic modelling should be used and various scenarios run injecting gas lift at different locations in the well tubular or riser sections with the expected fluid rate, in order to understand the impact of gas lift and optimise total well production as well as lift gas utilization.

In offshore wells producing to a platform, gas lift optimisation may be obtained initially by injecting gas in the base of the riser rather that going all the way (subsea flowline length) to the wellhead with the pressurised gas lift gas. The operating cost savings can be enormous. Later in the field life, when reservoir depletion occurs and water cut increases, then gas lift injection through the tubing-casing annulus may be the


optimal scenario. Dynamic simulation can compare the different scenarios and define the optimum during a certain period of time. It can also forecast the optimum time to switch producing scenarios and methods.


VIII. References and Case Histories

References

Already Drafted:Chapter VIII, Part 1, References, has been drafted by Juan Carlos Mantecon.

To Be Drafted: Yula Tang, Octavio Reyes, Adam Ballard, and Murat Kerem will draft Chapter VIII, Part 2. Also, Yaser Salman will provide a Case History on the Penguin Project in the North Sea, and Fernando Ascencio Candejas and Dr. Fernando Samaniego will help with this part of the document.

This is a comprehensive set of references for text books, papers, articles, etc. that describe use of dynamic simulation technology for gas-lift wells and systems, and for similar or related oil and gas production applications.

1. Mantecon, J.C.: ”The Virtual Well: Guidelines for the Application of Dynamic Simulation to Optimise Well Operations, Life Cycle design and Production", SPE paper 109829, presented at the 2007 SPE Annual Technology Conference and Exhibition held in Anaheim, California, USA, 30-3 November 2007.(including gas-lift wells)

2. Lancy, M.F.: ”Dynamic Simulation of the Europa and Mars Expansion Projects: A New Approach to Coupled Subsea and Topsides Modelling”, SPE paper 56704, presented at the 1999 SPE Annual Technology Conference and Exhibition held in Houston, Texas, 3-6 October 1999.

3. Gayton, P.W., Miller, S.D., and Napalowski, R.: “Innovative Development Engineering Techniques”, SPE paper 65202, presented at the SPE European Petroleum Conference held in Paris, France, 24-25 October 2000. (gas-lift field life cycle design)

4. Schoppa, W., Jayawardena, S., Agbaje, T., Ebere, D., and Iyer, S. “Bonga Flow Assurance Benchmarking via Field Surveillance”, OTC paper 18949, presented at the 2007 Offshore Technology Conference held in Houston, Texas, U.S.A., 30 April - 3 May 2007. (Riser gas-lift and Gas- gas-lift systems)

5. Zakarian, E, Larrey, D.: “A systematic Investigation of Girassol Deep Water Field Operational Data to Increase Confidence in Multiphase Simulation”, IPTC paper 11379, presented at the Internationl Petroleum Technology Conference held in Dubai, 4-6 Dec 2007. (Riser gas-lift)

6. Gudimetla, R., Carrol, A., Havre, K. and Canon, J.: ”Gulf of Mexico Field of The Future Subsea Flow Assurance”, OTC paper 18388, presented at Offshore Technology Conference held in Houston, Texas, U.S.A., 1-4 May 2006. (Nakika field real-time flow assurance and gas-lift optimisation – required gas-lift automatically set along with minimum rate to maintain stable flow)

7. Costa, D., Vu, V-K, Barnay, G.C., Larrey, D., McClimans, O.T. and Sand, E.B.: “Investigation of a Subsea Separation Station Operating Envelope using Sub-surface to Topsides Integrated Dynamic Simulations”, OTC paper 18709, presented at Offshore Technology Conference held in Houston, Texas, 30 April-3 May 2007.


8. Bell, G.M., Chin, Y.D., and Hanrahan, S.: “State of Art of Ultra Deepwater Production Technologies”, OTC paper 17615, presented at Offshore Technology Conference held in Houston, Texas, USA, 2-5 May 2005.

9. Tagore, A., Utgard, M., Ramachandran, K., Alwazzan, A. and McDermott, J.R..: “Fluid Characterization: Impact on Deepwater Field Development”, SPE paper 115777, presented at the 2008 SPE Annual Technology Conference and Exhibition held in Denver, Colorado, USA, 21-24 September 2008.

10. Shi, H., Holmes, J., Aziz, K., Durlofsky, L., K., Diaz, L., Alkeya, B., and Oddie, G.: “Drift-Flux Modelling of Two Phase Flow in Wellbores”, SPE paper 84228, SPE Journal Vol 10 #1, March 2005.

11. Bendiksen, K. H. et al., “The Dynamic Two-Fluid Model OLGA: Theory and Application, SPE Production Engineering, May 1991.

12. Falcone, G., Teodoriu, C., Reinicke, K.M., Bello, O.O., and Clausthal, T.U.: “Multiphase Flow Modelling Based on Experimental testing: A comprehensive Overview of Research Facilities Worlwide and the need for Future Developments”, SPE paper 110116, presented at the 2007 SPE Annual Technology Conference and Exhibition held in Anaheim, California, USA, 30-3 November 2007.

13. Sturm, W.L., Belfroid, S.P.C., van Wolfswinkel, D., Peters, M., Verhelst, F.: “Dynamic Reservoir Well Interaction”, SPE paper 90108, presented at SPE Annual Technical Conference and Exhibition held in Houston, Texas, U.S.A., 26-29 September 2004. (near-wellbore)

14. Sagen, J., Sira, T., Ek, A., Selberg, S., Chaib, M. and Eidsmoen, H.: “A Coupled Dynamic Reservoir and Pipeline Model – Development and Initial Experience”, 13th International Multiphase Conference on Multiphase Production Technology 07’, Edinburg, UK, 13-15 June, 2007. (well and near-wellbore)

15. Hu, B., Sagen. G., Chupin, G., Haugset, T., Arild, E., Sommersel, T., Xu, Z., and Mantecon, J.: “Integrated Wellbore-Reservoir Dynamic Simulation: SPE paper 109162, presented at the 2007 SPE Asia Pacific Oil & Gas Conference and Exhibition held in Jakarta, Indonesia, 30 Oct -1 Nov 2007. (it includes gas-lift casing heading)

16. Ballard, A.L., Adeyeye, D., Litvak, M., Wang, C.H., and Stein, M.H., Cecil, D. and Dotson, B.D.: “Predicting Highly Unstable Tight Gas Well Performance”, SPE paper 96256, presented at the 2005 SPE annual Technology Conference and Exhibition held in Dallas, Texas, U.S.A., 9-12 October 2005. (unstable low rate gas wells – liquid loading – well-reservoir coupled model)

17. Kerem, M., Proot, M. and Oudeman, P.: “Analyzing Underperformance of Tortuous Horizontal Wells: Validation with Field Data”, SPE paper 102678, presented at the 2006 SPE Annual Technical Conference and Exhibition held in San Antonio, Texas, 24-27 September 2006. (Snake type horizontal smart gas-lift wells – production log matching using dynamic simulator)

18. Meng, W., Zhang, J.J., and Brown, R.J.: “Modelling and Mitigation of Severe Riser Slugging: A Case Study”, SPE paper 71564, presented at the 2001 SPE Annual Technology Conference and Exhibition held in Louisiana, New Orleans, 30 Sep – 3 October 2001. (Riser gas-lift to mitigate slugging)

19. Ascencio-Cendejas, F., Reyes-Venegas, O. and Nass, M.A.: “Thermal Design of Wells Producing Highly Viscous Oils in Offshore Fields in the Gulf of Mexico”, SPE


paper 103903, presented at the First International Oil Conference and Exhibition in Mexico held in Cancun, 31 August – 2 September 2006. (Traditional gas-lift plus the effect of heating the gas on heavy oil wells - different completion configurations)

20. Tang Y., and Huang, W.: “A Combined Well Completion and Flow Dynamic Modeling for a Dual-Lateral Well Load-up Investigation”, paper IPTC 11332, at the International Petroleum Technology Conference held in Dubai, U.A.E., 4–6 December 2007. (Coiled tubing gas-lift feasibility study)

21. Leemhuis, A., Nennie, E., Belfroid, S., Alberts, G., Peters E., and Joosten, G.: “Gas Conning Control for Smart Wells Using a Dymanic Coupled Well-Resevoir Simulator”, SPE paper 112234, presented at the 2008 Intelligent Energy Conference and Exhibition held in Amsterdam, 25-27 Feb 2008. (gas coning as auto-Gas-Lift evaluation)

22. Duncan, G.J. and Beldring, B.: “A Novel Approach to Gas-Lift Design for 40,000 BPD Subsea Producers”, SPE paper 77727, presented at the SPE Annual Technology Conference and Exhibition held in San Antonio, Texas, 29 September – 2 October 2002.

23. Eikrem, G.O., Foss, B., Imsland, L., Hu, B. And Golan, M.: “Stabilization of Gas-Lifted Wells”, Proceedings of the 15th IFAC World Congress on Automatic Control, Barcelona, Spain, 2002

24. Gaspari, E.F., Oliveira, G.P., Monteiro, M.R., and Dourado, R.J.: “Evaluating Transient Multiphase Model Performance for the Brazilian Offshore Environment”, OTC paper 17956, presented 2006 Offshore Technology Conference held in Houston, Texas, U.S.A., 1-4 May 2006. (Design and troubleshooting operations in gas-in-solution and undersaturated reservoir types with water injection and Gas-Lift as the artificial lift method)

25. Hu, B. and Golan, M.: “Gas-lift Instability Resulted Production Loss and Its Remedy by Feedback Control: Dynamic Simulation Results”, SPE paper 84917, presented at the SPE International Improved Oil Recovery Conference in Asia Pacific held in Kuala Lumpur, Malaysia, 20-21 October 2003.

26. Hu, B. and Golan, M.: “Occurrence of Density Wave Instability in Gas-Lifted Wells”, 4th North American Conference on Multiphase Technology, Banff, Canada, 3-4 June 2004.

27. Mantecon, J.C., Andersen, I., Freeman, D. and Adams, M.: “Impact of Dynamic Simulation on Establishing Watercut Limits for Well Kick-off”, SPE paper 88543, presented at the SPE Asia Pacific Oil and Gas Conference and Exhibition held in Perth, Australia, 18-20 October 2004. (When to start gas-lift injection)

28. Øverland, A.M. and Ramstad, H.J.: “Yme Marginal Field, 12 Km Subsea Gas-Lift Experience”, SPE paper 71539, presented at the SPE Annual Technology Conference and Exhibition held in New Orleans, U.S.A., 30 September – 3 October 2001.

29. Song, S., and Peoples, K.: “Impacts Of Transient Analysis on Kuito Production Operations”, OTC paper 15186, presented 2003 Offshore Technology Conference held in Houston, Texas, U.S.A., 5-8 May 2003. (Required gas-lift for stable flow)

30. Tang, Y., Schmidt, Z., Blais, R.N., Doty, D.R.: “Transient Dynamic Characteristics of the Gas-Lift Unloading Process”, SPE Journal, (Sep. 1999), 268-278.

http://www.spe.org/elibrary/servlet/spepreview?id=00057659&speCommonAppContext=ELIBRARY

http://www.spe.org/elibrary/servlet/spepreview?id=00057659&speCommonAppContext=ELIBRARY


31. Tang, Y.: “A New Method of Plunger Lift Dynamic Analysis and Optimal Design for Gas Well Deliquification”, paper SPE 116764, presented at the 2008 SPE Annual Technical Conference and Exhibition held in Denver, Colorado, U.S.A., 21-24 September 2008

32. Veeken, K., Hu, B., and Schiferli, W.: “Multiphase Flow Modelling of Liquid Loading”, presented at the Gas Well Deliquification Workshop, Denver, Colorado, 23-26 February 2009.

33. Veeken, K., Hu, B., and Schiferli, W.: “Transient Multiphase Flow Modeling of Gas Well Liquid Loading”, paper SPE 123357, presented at the 2009 SPE Annual Offshore Europe Oil & Gas Conference and Exhibition held in Aberdeen, U.K., 8–11 September 2009.

34. Tang Y., Wolff, M., Condon, P., and Ogden, K.: “A Dynamic Wellbore Modeling for Sinusoidal Horizontal Well Performance With High Water Cut”, paper SPE 109262, presented at the 2007 SPE Annual Technical Conference and Exhibition held in Anaheim, California, U.S.A., 11–14 November 2007. (ESP application that shows the importance of well profile on slugging)

35. Noonan, S.G., Kendrick, M.A., Matthews, P.N., Sebastiao, N., Ayling, I. and Wilson, B.L.: “Impact of Transient Flow Conditions on Electric Submersible Pumps in Sinusoidal Well Profiles: A Case Study”, SPE paper 84234, presented at the SPE Annual Technical Conference and Exhibition held in Denver, Colorado, U.S.A., 5-8 October 2003. (ESP application that shows the importance of well profile on slugging)

36. Barrett, N. and King, D.: “Oil/Water Slugging of Horizontal Wells – Symptom, Cause and Design”, SPE paper 49160, presented at the 1998 SPE Annual Technical Conference and Exhibition held in New Orleans, Louisiana, 27-30 September 1998. (ESP wells – example of slugging generated by liner-casing ID change)

37. Sisco, R., Kirby, M.: “Chemical Distribution During Normal and Transient Conditions”, IPTC paper 10706, presented at the International Petroleum Technology Conference held in Doha, Qatar, 21-23 Nov 2005.

38. Harun, A.F., Krawietz, T.E. and Erdogmus, M.: “Hydrate Remediation in Deepwater Gulf of Mexico Dry-Tree Wells: Lessons Learned”, OTC paper 17814, presented at the 2006 Offshore Technology Conference held in Houston, Texas, U.S.A., 1-4 May 2006. (Dry-Tree gas-lifted oil well – hydrate plug remediation)

39. Harun, A.F., Krawietz, T.E. and Erdogmus, M.: “Transient Simulation Assist Hydrate Remediation Efforts in Deepwater Gulf of Mexico Dry-Tree Wells”, SPE paper 100750, presented at the 2006 SPE Asia Pacific Oil & Gas Conference and Exhibition held in Adelaide, Australia, 11-13 September 2006. (Dry-Tree gas-lifted oil well – hydrate plug remediation)

40. Lunde, G.G., Vannes, K., McClimans, O.T., Burns, C., and Wittmeyer, K.: “Advanced Flow Assurance System for The Ormen Lange Subsea Gas Development”, OTC paper 20084, presented at the 2009 Offshore Technology Conference held in Houston, Texas, U.S.A., 4-7 May 2009.

41. Teng, D., Maloney, B. and Mantecon, J.C.: “Well Testing by Design: Transient Modelling for Predicting Behaviour in Extreme Wells”, SPE paper 101872, presented at the 2006 SPE Asia Pacific Oil & Gas Conference and Exhibition held in Adelaide, Australia, 11-13 September 2006.

http://www.spe.org/elibrary/servlet/spepreview?id=SPE-109262-MS&soc=SPE&speAppNameCookie=ELIBRARY







42. Harun, A.F.:”Planning and Executing Lost Distance Subsea Tie-back Oil Well Testing”, IPTC paper 1193, presented at 2007 International Petroleum Technology Conference, Dubai, 4-6 December, 2007.

43. Mantecon, J.C., and Hollams, R.R.F.:”Use of Dynamic Simulation to Refine Well Testing Procedures and Optimise The Data Required for Deconvolution Techniques”. OTC paper 19767, presented at the 2009 Offshore Technology Conference held in Houston, Texas, U.S.A., 4-7 May 2009.

44. Hu, B., Uv, E.H., and Xu, Z.G.:”Modelling and Simulation of Co-flow of Reservoir Fluids and Drilling/Completion Mud in The Ultra-Long Multilateral Horizontal Wellbores”, presented at the 14th International Conference Multiphase Production Technology, Cannes, France, 17-19 June 2009.

45. Rygg, O.R., Friedemann, J.D. and Nossen, Jan: “Advanced Well Flow Model Used for Production, Drilling and Well Control Applications”, 1996 IADC Well Control Conference for Europe, Aberdeen, 22-24 May, 1996.

46. Rygg, O.R.: “The Necessity of Modelling in Contingency Planning and Emergency Well Control Response”, 2005 IADC International Well Control Conference & Exhibition, Singapore, 8-9 November, 2005.

47. Harun, A.F., Fung, G. and Erdogmus, M.: “Experience in AA-LDHI Usage for a Deepwater Gulf of Mexico Dry-Tree Oil Well: Pushing the Technology Limit”, SPE paper 100796, presented at the 2006 SPE Asia Pacific Oil & Gas Conference and Exhibition held in Adelaide, Australia, 11-13 September 2006. (Continuous Gas-Lift thorough coiled tubing and use of lift gas to displaced fluids to operate SSSCV and avoid hydrates during shut-down and cold re-start)

48. Dalsom, M., Halvorsen, E. And Slupphaug, O.: ”Active Feedback Control of Unstable Wells at the Brage Field”, SPE paper 77650, presented at the SPE Annual Technology Conference and Exhibition held in San Antonio, Texas, 29 September – 2 October 2002. (gas-lift wells with gas injection and production chokes controlled by automatic feedback)

49. Jansen, B., Dalsmo, M., Nøkleberg, L., Havre, K., Kristiansen, V. And Lemetayer, P.: ”Automatic Control of Unstable Gas-Lifted Wells”, SPE paper 56832, presented at the 1999 SPE Annual Technology Conference and Exhibition held in Houston, Texas, 3-6 October 1999.

Case Histories

This is a brief summary of pertinent case histories where dynamic simulations have been used to address and solve real-world gas-lift well and system situations.

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Documents

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