production petroleum software

PROSPER WELL MODELLING FUNDAMENTALS

PREPARED BY Ahmed mohamed Abdullah Refaat Galal Abol Fotoh Nader Ali Fahim Hesham Ahmed Abo-zaid Yahia Ali Shawky

CONTENTS

Introduction Well Modelling Fundamentals Setting up a well model PVT Modelling IPR modelling VLP modelling VLP / IPR matching and model validation Conclusions

PETEX (PETROLEUM EXPERTS)

Started business @ 1990 in the UK

Developing a set of petroleum engineering

software tools.

model oil reservoirs, production and injection

wells and surface pipeline networks as an

integrated production system.

SOFTWARE PACKAGES

IPM PACKAGE

The engineer is able to design complex field models.

The Reservoir, Wells and Complete Surface Systems model, having been matched for production history, will accurately optimize the entire network and run predictions.

IPM PACKAGE

IPM PACKAGE GAP enables the engineer to build

representative field models, that include the reservoirs, wells and surface pipeline production and injection system.

MBAL package contains the classical reservoir engineering tool, using analytical techniques to analyze the fluid dynamics in the reservoir.

IPM PACKAGE PVTP allows tuning of Equations of State

(EoS) to match laboratory data. The tuned EoS can then be used to simulate a range of reservoir and production processes, which impact equipment sizing and reservoir recovery.

REVEAL is a specialized reservoir simulator modeling near well bore effects including mobility and infectivity issues. Thermal and chemical effects are modeled rigorously.

PROSPER

PROSPER is designed to allow the building of reliable and consistent well models

Design and optimize well completion Tubing size Artificial lift method IPR model

CONTENTS

Introduction Well Modelling Fundamentals Setting up a well model IPR modelling VLP modelling VLP / IPR matching and model validation Conclusions

WELL MODELLING FUNDAMENTALS

Well modelling defines the pressure/rate relationship to facilitate: Well design Predicting well performance Identify well performance sensitivity to changes in operating

parameters or design Involves:

PVT Wellbore IPR Nodal Analysis

Well Modelling Fundamentals

Nodal Analysis It is the methodology used in well modelling to analyse

the performance of a multi-component system Objectives are to:

Quantify total pressure loss as a function of rate Quantify components within total pressure loss Identify bottlenecks to flow Optimise system design and operation given constraint Address specific well issues such as Artificial lift, well load

up, completion design optimisation and productivity improvement opportunities.

Important: Nodal analysis assumes a steady state and does not allow transient flow behaviour.


Common Nodes used in Nodal Analysis


Fundamental Concept

P P

P ? OUTFLOWINFLOW

Solution node

• Pressure defined at start and end nodes

• Solution node can be any intermediate position where pressure must be calculated

• Components upstream of solution node determine INFLOW performance

• Components downstream of solution node determine OUTFLOW performance

• For system continuity Qin = Qout and pressures must be equal

• From above, system can be solved to determine solution node pressure at a given rate

Qin Qout


Top Node Bottom Node Solution Node Comments

Wellhead Reservoir Mid-perf Separates IPR from VLP

Wellhead Reservoir ESP, GL, etc To establish artifical lift reqirements

WH Choke Gauge Depth Wellhead To match given test data

Separator Reservoir Wellhead Separates well-reservoir from surface

Separator Reservoir Choke Combines choke effect with well-reservoir

Separator Wellhead ManifoldConcentrating on Network modelling with known contribution from well(s)

CONTENTS Introduction Well Modelling Fundamentals Setting up a well model PVT modelling IPR modelling VLP modelling VLP / IPR matching and model validation Conclusions

SETTING UP A WELL MODEL

What information do you need? Completion diagram / tally and directional survey

data, together with any recent work-over info/data Fluid data (PVT reports or existing PVT model) Complete production test data (recent as well

historical sets) comprising of rates, phase ratios, end pressures, etc.

Reservoir and available near-wellbore data (reservoir pressure & temperature, FBHP/downhole gage pressure, PI, skin, permeability and rel perm, etc).


Surface choke

Sales LineGas

SeparatorLiquid

StockTank

To Sales

Bottom holerestriction

Psep

P5 = (Pwh - PDSC)

PDSC

P6 = (PDSC - Psep)

P8 = (Pwh - Psep)

Pwh

PDSV

PUSV

PDR

PUR

Pwf Pwfs Pr Pe

P3 =(PUR - PDR)

P7 = (Pwf - Pwh)

P4 = (PUSV - PDSV)

P2 = (Pwfs - Pwf) P1 = (Pr - Pwfs)

P9 = (Pr - Pwf)

P8 = Pwh - Psep

P6 = PDSC - Psep

P5 = Pwh - PDSV

P4 = PUSV - PDSV

P7 = Pwf - Pwh

P3 = PUR - PDR

P2 = Pwfs - Pwf

P9 = Pr - Pwf

P1 = Pr - Pwfs = Loss in porous medium= Loss across completion= Loss across restriction= Loss across safety valve= Loss across surface choke= Loss in flowline= Total loss in tubing= Total loss in flowline= Total loss in reservoir / completion

Surface choke

Sales LineGas

SeparatorLiquid

StockTank

To Sales

Bottom holerestriction

Psep

P5 = (Pwh - PDSC)

PDSC

P6 = (PDSC - Psep)

P8 = (Pwh - Psep)

Pwh

PDSV

PUSV

PDR

PUR

Pwf Pwfs Pr Pe

P3 =(PUR - PDR)

P7 = (Pwf - Pwh)

P4 = (PUSV - PDSV)

P2 = (Pwfs - Pwf) P1 = (Pr - Pwfs)

P9 = (Pr - Pwf)

P8 = Pwh - Psep

P6 = PDSC - Psep

P5 = Pwh - PDSV

P4 = PUSV - PDSV

P7 = Pwf - Pwh

P3 = PUR - PDR

P2 = Pwfs - Pwf

P9 = Pr - Pwf


P8 = Pwh - Psep

P6 = PDSC - Psep

P5 = Pwh - PDSV

P4 = PUSV - PDSV

P7 = Pwf - Pwh

P3 = PUR - PDR

P2 = Pwfs - Pwf

P9 = Pr - Pwf P9 = Pr - Pwf


Sources of pressure loss in a production system


Pre-processing data Completion data consistent with directional

survey and other work-over info. Fluid data/PVT model consistent with other wells

and formation info. Production test data complete and consistent

with current well performance. Reservoir data dates consistent with the

production test dates.

SETTING UP A WELL MODELSystem Summary Screen

Can model up to5 stages for compmodelling

Select 1. tubing or 2. annular or 3. tubing AND annular

Information only

Useful repository for well test and model information

Reservoir connection options –

influence later inflow options

Specify whether a single well or

multilateral

Specify type of temperature modelling

Define fluid type and PVT method (i.e. black oil or equation of state model)

PVT Property

(Pb) Bubble-point

Pressure (psia)

(Bo) Bubble-Point

Oil FVF (rb/stb)

(GOR or Rs) Gas/Oil

Ratio (scf/stb)

Reservoir

Temperature (ºF)

Stock Tank Oil

Gravity (ºAPI)

Gas Specific Gravity

(air = 1)

Separator Pressure

(psia)

Separator

Temperature (ºF)

PVT Property

(Pb) Bubble-point

Pressure (psia)

(Bo) Bubble-Point

Oil FVF (rb/stb)

(GOR or Rs) Gas/Oil

Ratio (scf/stb)

Reservoir

Temperature (ºF)

Stock Tank Oil

Gravity (ºAPI)

Gas Specific Gravity

(air = 1)

Separator Pressure

(psia)

Separator

Temperature (ºF)

Standing

130 – 7000

1.024 – 2.15

20 – 1425

100 – 258

16.5 – 63.8

0.59 – 0.95

265 – 465

100

Standing

130 – 7000

1.024 – 2.15

20 – 1425

100 – 258

16.5 – 63.8

0.59 – 0.95

265 – 465

100

Lasater

48 – 5780

N/A

3 – 2905

82 – 272

17.9 – 51.1

0.574 – 1.22

15 – 605

36 - 106

Lasater

48 – 5780

N/A

3 – 2905

82 – 272

17.9 – 51.1

0.574 – 1.22

15 – 605

36 - 106

Vazquez-

Beggs

15 – 6055

1.028 – 2.226

0.0 – 2199

75 – 294

15.3 – 59.5

0.511 – 1.351

60 – 565

76 – 150

Vazquez-

Beggs

15 – 6055

1.028 – 2.226

0.0 – 2199

75 – 294

15.3 – 59.5

0.511 – 1.351

60 – 565

76 – 150

GlasØ

165 – 7142

1.087 – 2.588

90 – 2637

80 – 280

22.3 – 48.1

0.65 – 1.276

415

125

GlasØ

165 – 7142

1.087 – 2.588

90 – 2637

80 – 280

22.3 – 48.1

0.65 – 1.276

415

125

Petrosky-

Farshad

1574 – 6523

1.1178 – 1.622

217 – 1406

114 – 288

16.3 – 45.0

0.5781 – 0.85

N/A

N/A

Petrosky-

Farshad

1574 – 6523

1.1178 – 1.622

217 – 1406

114 – 288

16.3 – 45.0

0.5781 – 0.85

N/A

N/A

Macary

1200 – 4600

1.2 – 2.0

200 – 1200

180 – 290

25 – 40

0.7 – 1.0

N/A

N/A

Macary

1200 – 4600

1.2 – 2.0

200 – 1200

180 – 290

25 – 40

0.7 – 1.0

N/A

N/A

Black Oil Correlations can be selected based on the applicability of the test range of the data in question:

Setting up a well model

CONTENTS Introduction PVT Fundamentals Well Modelling Fundamentals Setting up a well model PVT Modelling IPR modelling VLP modelling VLP / IPR matching and model validation Conclusions


PVT Model Tuning

Select PVT correlations relevant to the given fluids.

If PVT matching data absent or sparse use correlation which has proved appropriate in offset wells / fields

Use corrected PVT data to tune the selected PVT correlations

SETTING UP A WELL MODELPVT Summary

• PVT modelling involves:

– Gathering quality test data

– Convert Diff Lib data to flash conditions if required (correction)

– Selecting appropriate correlation/EoS.

– Tuning selected correlation/EoS.

– Generating PVT properties at all pressure-temperature combinations encountered in flow stream.

• There is no substitute for quality test data.

• Incorrect PVT model has detrimental effect on IAM modelling, which is quite often incorrectly accounted for by adjusting flow correlations.

• Note that in gas condensate wells, inaccurate temperature modelling can have a profound effect on PVT – often neglected

STEP 1: BASIC PVT DATA INPUT

Basic Data Input from PVT report, DST testing (may sometimes be all that is available)



Match Data input from PVT report – use only flash corrected data. Normally enter as much data as possible to optimise correlation matching

Step 2: PVT Match Data Input

STEP 3: MATCHING PVT CORRELATIONS TO REAL PVT DATA

PVT correlations are empirically derived mathematical fits of real experimental data Correlations approximate real fluid behaviour – some more suitable than other for

certain fluid systems Matching is a regression process which reduces the error between correlation and PVT

data User can specify which gas properties it is critical to match (to reflect possible

uncertainty in input data accuracy Parameter 1 and 2 statistics provide match quality and correlation predictive reliability –

Parameter 1 is the “multiplier” which has to be applied to correlation (should be within 10% of unity)

Parameter 2 is the shift


SETTING UP A WELL MODELEntering a physical description of the well and its subsurface environment

Enter up to 18 depth pairs (measured & TVD)

Include effect of any pipework from wellhead to manifold (incl choke)

ID / OD and roughness of all tubing and casing, restrictions etc down to the reservoir. Mid-perf depth is bottom depth entered.

Input formation temperatures versus depth, and overall Heat Transfer coefficient (“U” value)

Enter specific heats for oil, water and gas – use default Values In this example


Only enter minimum number of points required to describe basic shape of wellpath

Tip: normally use survey points giving >5% change in inclination

Entering Deviation Survey Data


Manifold (or other constant pressure

node in system)

Surface equipment

NB:

• Enter UPSTREAM end TVDs for each section of pipe (i.e. nearest the tree for producers)

• Use “Plot” to visualise pipework layout and check for errors

• Can use an “X-Y” coordinate system if required to enter more detailed pipework desciption (applicable to subsea)

Entering the Surface Equipment Description


Notes:

• Typically use drilling depth references i.e. relative to rotary table - e.g. in a subsea well Xmas tree depth may be +400 ft• Enter bottom depth of each section of same diameter tubing, associated ID and roughness• Enter SSSV’s and restrictions• Casing depth where you wish pressure loss calculations to begin (typically mid perf).

• In a long perforated interval may be better to use more complex inflow model

Downhole Equipment DescriptionRoughness Guidelines

Plastic .0002 inCr Steel .0006 inSS .0006 inC Steel New .0018 in to Old .0060 in


Notes:

• Enter a temperatures survey obtained from STATIC logging, or best offset data• Ensure a survey point for the bottom node in the equipment data is included.

Geothermal Gradient calculations enable Prosper to predict flowing wellbore temperatures from reservoir to wellhead under various scenarios, based upon an Overall Heat Transfer Coefficient or U value.

Typical Values are: Oil wells 8 BTU/h/ft2/FGas wells 3 BTU/h/ft2/FGas Cond wells 3.7 BTU/h/ft2/F

Geothermal Gradient

CONTENTS Introduction PVT Fundamentals Well Modelling Fundamentals Setting up a well model IPR modelling VLP modelling VLP / IPR matching and model validation Conclusions

IPR MODELLING Fundamental Input information:

Reservoir Pressure & temperature At least one stable flowing BHP and rate (ensure

reservoir pressure consistent in time with FBHP if varying)

GOR (oil well) / CGR (gas well) Watercut (oil well) / WGR (gas well)

Theoretical vs empirical IPR models Reservoir / Completion parameters:

Rock permeability & anisotropy Producing interval, perforations, deviation &

drainage area Gravel Pack properties & dimensions

IPR MODELLING

The Inflow Performance Relationship (IPR) defines the pressure drawdown in a well as a function of production rate

Drawdown is a complex function of PVT, permeability (absolute & relative), effective overburder etc

Several IPR model available – optimum choice depends on data available and calculations required including:-

Gas Well PI Models

• Jones ~ includes a linear (Darcy) pressure drop and a rate-squared (non-Darcy) term. Uses pseudopressure, better for high reservoir pressures (>2000 psi)

• Backpressure,

• Forcheimer,

• C and N ~ use various “backpressure” equations to describe the Darcy and non-Darcy inflow behaviour

• Petroleum Experts ~ uses a multi-phase pseudo pressure function to allow for changing gas and condensate saturations with pressure – applicable to gas condensate modelling or dry gas

IPR Fundamentals

IPR MODELLING

Oil Well PI Models

• PI entry ~ simplest, useful where no where no reservoir perm or skin data available, and where the PI is already known

• Vogel ~ uses an empirical correlation to account for deviation from straight line PI below bubble point

• Composite ~ interpolates a Vogel IPR for oil and straight line IPR for oil as a function of watercut – useful for sensitivities on increasing watercut

• Darcy ~ classic radial flow equation useful for estimating productivity from petrophysical data

• Fetkovich ~ adapted from isochronal theory – gives similar results to Vogel

IPR MODELLING

Options will dependon fluid type selectedin System Summary

Skin model definition

Select the “Jones” model (modified form of Darcy Equation)

Defining IPR model to be used:

IPR MODELLING

Enter data in all sheets with highlighted tabs (working left to right)

Entering IPR data

IPR MODELLING

When data entry complete, click on “Calculate” button to generate IPR plot

Entering IPR data

IPR MODELLING

AOF: Absolute Open Hole Flow Potential(theoretical flow potential assuming zeroBackpressure)

Static reservoir pressure

Flowing bottom hole pressure (FBHP)

IPR curve – gas well

CONTENTS Introduction PVT Fundamentals Well Modelling Fundamentals Setting up a well model IPR modelling VLP modelling VLP / IPR matching and model validation Conclusions

VLP MODELLINGFlow Patterns/Regimes in Vertical Upward Flow

BUBBLYFLOW

SLUGFLOW

CHURNFLOW

ANNULARFLOW

BUBBLYFLOW

SLUGFLOW

CHURNFLOW

ANNULARFLOW

COMMON FLOW REGIME IN GAS / GAS CONDENSATE WELLS

ROLE OF MULTI-PHASE FLOW CORRELATIONS

• VLP correlations predict the pressure loss in pipe, allowing for the gravity, friction and acceleration effects

• Correlations handle Slip, holdup and multiphase flow pattern in different ways e.g. slip, flow regime accounted for / not accounted for

• Correlations using flow maps may give discontinuous results – modern mechanistic correlations overcome this.

• No single correlation is “best”, and comparison of the correlations is recommended to select the the optimum one for a given application

VLP Modelling

VLP MODELLING

Author Year Data Source Nominal ID Fluids & Rates CommentDuns & Ros Original 1961 185' high experimental loop+field

data1.26" to 5.6" with 2 annulus config.

Air, water & liquid hydrocarbon

Good over a wide range, more so for mist flows, tend to overpredict VLP in oil wells

Duns & Ros Modified

Francher & Brown 1963 Field data from plastic coated tubing 1.995 ID Gas and water at < 400stb/d & GOR

>5000

Being no-slip always predicts lowest pressure drops therefore good for data QC

Hagedorn & Brown 1965 475 test data sets from 1500' deep vertical experimental well

1" to 2.5" Air, water & crude oils of 10, 30 & 110cp

Most widely used VLP correlation - good over a wider range particularly for slug flows

Petroleum Experts ? Uses the Gould et al flow map, Hagedorn & Brown for slug, Duns

and Ros for mist

Generally obsolete

Petroleum Experts 2 ? Improved version of PE1, better for preditcing low rate VLP

Petroleum Experts 3 ? Include PE2 featues with additional features for viscous, volatile and

foamy oils

Preferred for gassy, foamy heavy oils

Petroleum Experts 4 ? Advanced mechanistic model suitable for any fluid (including

condensates)

Good all round correlation, avoids discontinuities which apply to empirical correlations, runs slower than empirical

Orkiszewiski 1967 Huge set of field data various! various! 'Hybrid' model of different 'best' correlations. Hence found discontinuous! Use not

encouraged!Beggs & Brill 1973 90' long acrylic pipe with ±90

inclination changes. 584 measure tests with flow pattern observations.

1" to 1.5" Air & water Better for all angles. Mukherjee & Brill attempted to improve it in 1985

GRE BP Mechanistic Correlation Developed to model slug flow in pipelines but also found to be applicable to tubing

Gray 1978 108 well test data with 88 producing free liquids

3.5" Condensate up to 50b/MM & water up to 5b/MM with velocities

up to 50ft/s

Excellent for gas and gas-condensate wells but should be used with caution for higher

WGR/CGR

Multiphase Flow Correlations available in Prosper

Correlations suitable for gas wells

CONTENTS

Introduction PVT Fundamentals Well Modelling Fundamentals Setting up a well model IPR modelling VLP modelling VLP / IPR matching and model validation Conclusions

THANK YOU

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