Joint Industry Project Management Issues

Added value from Intelligent Well systems Technology

Project Director Dr. David DaviesTeam Members: Dr. Yang Qing, Dr. George Aggrey,

Faisal Alkhelaiwi, Vasily Birchenko, Andrey Scherbakov, Khafiz Muradov

Introduction

This presentation contains a:Review of the achievements of the current (2005-7) project Proposal for the next (2008-10) project phase based on the current sponsor suggestionsExpressions of interest and all enquiries to:

Dr. David DaviesInstitute of Petroleum Engineering,

Heriot-Watt University, Edinburgh, EH14 4AS, U.K. Tel: +44 (0) 131 451 3569, Fax: +44 (0) 131 451 3127,

email: david.davies@pet.hw.ac.uk

The 2007 Sponsors

JIP Project Sponsors in Heriot-Watt University.(BP, BG, ENI, Statoil, Norsk

Hydro, ExxonMobil & WellDynamics)

Input from sponsors important for project success

1.

Weatherford

2.

Petroleum Experts

3.

ChevronTexaco

4.

Total

5.

Kuwait Oil Company

6.

Saudi Aramco

7.

Rosneft

8.

Repsol

Possible additional Sponsorswith whom recent contacts have been made

Original Project Schedule for 2005-2007

End of current JIP phase

Project Schedule for 2007-2008(Projects approved by current sponsors)

Task 2007 IWsT JIP Deliverables*

1H-field study Value criteria for selection between ICV & (A)ICD

1,2 & 7 Experience with new optimisation tools by working with realistic case histories. Lessons learned to be used as a tool for value identification.

4 Analysis of Real-Time, Down Hole data

5Update the manual with Addenda incorporating lessons learnt & industry developments

7 Optimisation routines and tools for ICV(s) and IWsT* From 2007 proposal

•

The 2005-7 project will be concluded with publication in December of the updated manual (the final project report)

1.

H-Field Case Study

2.

Introduction to ICDs

& AICDs

3.

Impact of Reservoir Uncertainty on Selection of Advanced Completion Type

4.

Choice between ICV and ICD

5.

Temperature Behaviour in Intelligent Wells

6.

Real time water influx detection

7.

Optimisation & Real-Time Production Optimisation

Model Predictive Control: Intelligent Well application

Deliverables: IWsT JIP 2007

Manual Addendum

1.

SPE105,374

"A Novel Approach of Detecting Water Influx

Time, Source and Relative Strength in Multi-Zone and Multilateral Completions with Downhole Pressure Gauges in Real-Time" jointly with Statoil

2.

SPE108,700

“Inflow Control Devices: Application & Value

Quantification of a Developing Technology”3.

SPE 107198

“Tracking the state & diagnosing Down Hole

Permanent Sensors in Intelligent Well Completions with Artificial Neural Network"

4.

SPE 107197

"A Rigorous Stochastic Coupling of Reliability

and Reservoir Performance to define the Value of Intelligent Wells”

5.

SPE 107171

“Successful Application of a Robust Link to

Automatically Optimise Reservoir Management of a Real Field”

jointly with Statoil

6.

SPE 110207

“Enhancing Production from Thin Oil Column

Reservoirs Using Intelligent Completions”

jointly with BG

Deliverables: IWsT JIP 2007

Publications

Look Back:

HWU’s

“Added Value of Intelligent Wells systems Technology”

(IWsT) JIP

Application & Value of Advanced Wells 1.

Integrated reservoir simulation & wellbore / surface network models & other studies

2.

Real & Synthetic Reservoirs studied

3.

Well Candidate Selection & Economic Evaluation tools developed

4.

Advanced Well manual written

Techniques developed over 9 yearsAverage of 4 PhD students working on the project during the last phase

5.

New Phase starts January 2008

Several projects ongoingDirection will be reoriented to (new & existing) sponsor requirements

IWFsT

JIP

Plans for 2008 –

2010

• To form basis of formal proposal

Joint Industry Project

“Added value from Intelligent Well & Fields systems Technology”

# Objective Background/Scope Deliverables date

1 Comparison of Inflow Control Systems

Develop guidelines for application of Interval Control Valves, Interval Control Devices (ICD) & Autonomous Devices (AICD)

Selection Criteria for (ICSs)

Mid-

2008

2 Advanced well optimisation

Modelling, optimisation & integration of advanced well control systems (ICVs, ICDs, AICDs

etc.) Modelling/optimis

ation case studies 2009

3 Automatic optimisation of ICV settings

Synthetic & Field studies used to quantify Advanced Completions’

value. Commercial & other tools used for optimisation efficiency.

Software Application

Early 2008

4 Online optimisation of Intelligent Well & Field Systems

Develop & use an online optimiser operating via a feedback decision loop using real-time data for model update and optimisation. The model predictive controller is a regulating tool that maintains a parameter’s set point so as to operate optimally. Real-time data generated by Eclipse

Model predictive controller and online optimiser for IWsT

2nd

quarter 2008

5 Reconciliation of field short-term optimisation & long-

term management

The problem of reconciling short-term production optimisation approach and long-term objectives, e.g. total recovery, NPV optimisation or meeting to-day’s production targets. To be addressed after further discussions with the sponsors

Guidelines & a toolbox

2010

6 Integrating downhole

temperature and pressure data into well modelling

Extensive experience interpreting downhole

P and T data separately has been built up. Use of combined P&T will provide a better understanding of downhole

processes. Analytical & numerical tools will be used to analyse both synthetic & sponsor supplied field data.

Integrated Temperature & Pressure datainterpretation tool

Late 2008

7 Field case study: H Field

Production challenges of this heavy oil field, produced by long horizontal & multilateral wells with gas injection for pressure maintenance, include interference between wells and laterals, gas channelling and coning, water production.

Short term optimisation strategy for advanced wells

Early 2008

8 Quantifyvalue

of information from Downhole

Sensor (DHS) data

Sensor Value Quantification tools have been built. Real time techniques for detecting water influx time & source have been established. Artificial Intelligence applications to create value from sensor data are being explored.

Artificial Intelligence techniques to utilise DHS data

2007

Task 1: Comparison of Inflow Control Systems (ICS)

1.

Compare their strong and weak points (e.g. cost,

flexibility etc.) to prepare guidelines for the

selection of Inflow Control Systems

Interval Control Valve (ICV)

Inflow Control Device (ICD)

Autonomous Inflow Control Device (AICD)

2.

(automated) Workflow for ICD/AICD/AFI

Completion Design

Task 2: Advanced Wells Modelling & Optimisation

Wellbore nodal flow configuration in reservoir simulator Wellbore nodal flow configuration in

network simulator

Advanced well completions incorporate ICVs, ICDs & AICDsIntegrate reservoir simulation & wellbore / surface network modelsNeed to overcome limitations of current (commercially) modeling techniques

e.g. split flow between annular and tubing and reversible opening action of AICD.

Develop & automate value optimisation techniques

Task 3: Automated Optimization with Commercially Available Products

Synthetic and Field studies used to:Automatically Quantify the value of Advanced Completions usingDevelop experience with commercially available optimization tool e.g. ease of operation, reliability, etc.

GAPReoECLIPSE 300COUGAREtc.

Task 3: Optimization with GAP

Uses Sequential Quadratic Programming

Connected to the reservoir simulator via Resolve

and S3connect

Applicable for optimisation of ICV control

Very efficient for controlling water production

Requires appropriate IPR data to control gas coning

Outside control of the simulator by appropriate

algorithms to be implemented shortly

Task 3: Optimization with Geoquest

tools

Eclipse 300Simple reservoir model produced under BHP & Rate control:

Demonstrated stability of answer with respect to change of initial guessSimilar results to quick manual optimization

ICV controlResult appears dependant on initial guessManual optimization can give better results

Cougar:Provides an efficient framework to implement

uncertainty modeling in the production prediction

OptimisationHistory

Matching (update)

Reservoir Simulation

(Eclipse) & Real time data

System Model

Regulator

Error Quantification

Regulation Time

Task 4: Online Optimisation Flow Loop

Segment 1 Segment 2

The Regulatory Flow Loop

Eclipse simulator

New calculateICV area

ICV 1 & 2 liquid flow rates

Set points

Normalmode

Time step: long,Constant ICV area

Regulating mode

Time step: short

Controller(GPC)

Error >upperlimit

NoRegulating mode

No

Error < lower limit

Yes

No

Yes

Task 4: Online Optimisation Flow Loop

Yes

Task 4: ICV management with regulatory flow loop

Task 4: ICV management with regulatory flow loop

Extend General Predictive Control Algorithm to 3 and even more ICV control zones

Apply to a more complex reservoir model

Add noise to mimic real measured input data

Task 5: Long and Short-term Optimisation

Project in the early phase of definition

Possible approach:Quantify Value of reservoir decision using near-real-time dataDefine value of updating the reservoir model at monthly rather than yearly intervalsComparison with yearly update may illustrate reconciliation of short / long term data

Do we need the full-field model or a proxy model?What constraints should be included?

Task 6: Integrating downhole P and T data into well modelling

Zonal Allocation and Real Time Data Analysis of commingled, multi-zone wells:

Pressure data across ICV allocates zonal flow ratesTemperature data refines the calculation of phase behavior and viscosity/density variations in the well

Temperature data across ICV and along the wellboreallocates zonal flow rates and detects (gas) influxesPressure data improves the calculation of fluid behavior in the well

Combining the results from both methods increases the accuracy of the flow rate prediction

•

Flow rate values calculated for each zone based on temperature measurements

•

I-well control system can then be optimized with this information

Task 6: Integrating downhole P and T data into well modelling

Zonal Allocation & Real Time Data Analysis, based on temperature data:

Flow rates

Value

Task 6: Integrating downhole P and T data into well modelling

Zonal Allocation and Real Time Data Analysis of commingled, multi-zone wells:

Pressure data across ICV allocates zonal flow ratesTemperature data refines the calculation of phase behavior and viscosity/density variations in the well

Temperature data across ICV and along the wellboreallocates zonal flow rates and detects (gas) influxesPressure data improves the calculation of fluid behavior in the well

Combining the results from both methods increases the accuracy of the flow rate prediction

Task 6: Integrating downhole P and T data into well modelling

ii wq ,

11, wq

nn wq ,nn WQ ,

uiP

diP

Zonal Allocation and Real Time Data Analysis, based on pressure data:

•

Flow rate

values

can be calculated in each producing zone without using downhole

multiphase flow meters

•

Reservoir parameters can be calculated or

updated

Task 6: Integrating downhole P and T data into well modelling

Zonal Allocation and Real Time Data Analysis of commingled, multi-zone wells:

Pressure data across ICV allocates zonal flow ratesTemperature data refines the calculation of phase behavior and viscosity/density variations in the well

Temperature data across ICV and along the wellboreallocates zonal flow rates and detects (gas) influxesPressure data improves the calculation of fluid behavior in the well

A Combination of these two methods will increase the accuracy of the flow rate calculation

Task 7:

H-Field A Heavy Oil Reservoir employing ICDs

& AICDs

A high permeability, heterogeneous reservoir19o API Oil gravity Evaluation of the in-house developed optimisation techniques for ICD, AICD & ICV application to enhance multilateral well performance

Task 7:

Other Field Case Studies

Study 1: Application of ICDs

to WAG Injector DesignSufficiently strong ICDs reduce water injectivity misbalance for a 30 - 700 mD permeability contrast for 30 M STB/d water injection & 35 MM SCF/d gas injection

Gas flow through recommended nozzles is sub-critical while pressure drop for water injection exceeds current field experience

Annular isolation (packers) is required near high permeability streaks & zones where fracturing is expected

Study 2: K field (StatoilHydro)Under discussion

Questions being Addressed in HWU IWsT JIP

Are we exploring the data sufficiently to realise

the maximum value from the information?

Are we quantifying their value properly?

Incorporation of reliability

Task 8: Value of Information

Summary:

IWsT “Look Ahead”

2008-10

Optimisation Techniques Real Time Operations

Data Analysis & Decision Making

Reconciliation of daily routine optimisation with longer term, field management objectives Advanced Well Modelling & Optimisation Field Case studiesOther Sponsor Suggestions / Requests for

studies?

IWFsT

2008 -

10 Contract

JIP name slightly changed

JIP price £ 25,000 / year

Late joiners purchase previous (6 years) deliverables for £ 12,500

3 year contract

Contract conditions unchanged from current phase

Project Meeting Schedule

Next JIP Steering Committee Meeting planned for 23-24 April 2008

All enquiries to:

Dr. David Davies

Institute of Petroleum Engineering,

Heriot-Watt University, Edinburgh, EH14 4AS, U.K. Tel: +44 (0) 131 451 3569 Fax: +44 (0) 131 451 3127

email: david.davies@pet.hw.ac.uk