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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
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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: [email protected]
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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
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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
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Original Project Schedule for 2005-2007
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End of current JIP phase
Project Schedule for 2007-2008(Projects approved by current sponsors)
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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)
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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
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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
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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
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IWFsT
JIP
Plans for 2008 –
2010
• To form basis of formal proposal
Joint Industry Project
“Added value from Intelligent Well & Fields systems Technology”
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# 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
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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
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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
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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.
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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
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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
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OptimisationHistory
Matching (update)
Reservoir Simulation
(Eclipse) & Real time data
System Model
Regulator
Error Quantification
Regulation Time
Task 4: Online Optimisation Flow Loop
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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
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Task 4: ICV management with regulatory flow loop
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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
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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?
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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
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•
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
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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
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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
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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
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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
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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
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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
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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?
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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
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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: [email protected]