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Improving Modelling and
understanding of Complex
production Systems
London, October 29th 2015
Asset Optimization Workshop 2015
Daniel Pacho1, Amitosh Tiwari2, John Tracey1, Tim Halpin1, Ravi Madray1,
Shiva Salunkhe2, Ali Hamza1, Neel Mani Sharma2
1 Integrated Asset Modelling, Production Engineering, BG Advance Technical, Reading ,UK
2 Production Optimisation, BG Exploration and Production India Ltd. Mumbai, India
Legal notice
The following presentation contains forward-looking statements concerning BG Group plc’s strategy, operations, financial performance or condition, outlook, growth opportunities or circumstances in the countries, sectors or markets in which BG Group plc operates, or the recommended cash and share offer by Royal Dutch Shell plc for BG Group plc announced on 8 April 2015. By their nature, forward-looking statements involve uncertainty because they depend on future circumstances, and relate to events, not all of which can be controlled or predicted. Although the Company believes that the expectations reflected in such forward-looking statements are reasonable, no assurance can be given that such expectations will prove to have been correct. Actual results could differ materially from the guidance given in this presentation for a number of reasons. For a detailed analysis of the factors that may affect our business, financial performance or results of operations, we urge you to look at the “Principal risks and uncertainties” included in the BG Group plc Annual Report & Accounts 2014. Nothing in this presentation should be construed as a profit forecast and no part of this presentation constitutes, or shall be taken to constitute, an invitation or inducement to invest in BG Group plc or any other entity, and must not be relied upon in any way in connection with any investment decision. BG Group plc undertakes no obligation to update any forward-looking statements.
No representation or warranty, express or implied, is or will be made in relation to the accuracy or completeness of the information in this presentation and no responsibility or liability is or will be accepted by BG Group plc or any of its respective subsidiaries, affiliates and associated companies (or by any of their respective officers, employees or agents) in relation to it.
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Background
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• BG strives for greater accuracy and
consistency in modelling of complex fields
• Production systems can be large networks
requiring understanding at both steady
state and transient conditions
• Principal challenge: maintaining numerical
consistency between the non-linear
equations (SS) and partial differential
equations (Transient) using available real
time data
• Non-trivial problem requiring
multidisciplinary solution
Collaboration is the key
Motivation
• IAM-Production Engineering – Technical group within BG head office
• Supporting Assets build and maintain fit for purpose models to
understand whole systems (Reservoir to Sales point)
• Models are used for forecasting, screening scenarios, production
optimisation and for risks and opportunities identification
• Integrated models are used for all project stages from initial conception
throughout operating life
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Models can be tuned to predict
better if operating data is available
Connecting IAM models to data is
a major task
Two cases are presented
a) -Process models Integration
b) -SS & Transient model
integration
Case 1: Process model integration in IFM
• Model maintenance and tuning of process units demands great effort
which deviates the focus from process analysis to data processing.
• Each stream requires C+3 defined variables to be fully defined, in
addition different units require other values/ specifications to be fully
defined
• A strategy is required to identify the points around each process unit that
would provide the best combination of field data required to verify
simulation results.
• This data gathering exercise needs to be taken on regular basis thus
demanding important resources
• IFM offers tools to make this task efficient however the development
cycle is not straightforward
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Case-2 Steady State and Transient
modelling integration for a network
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t=ti+1
Run Forecast in SS
Identify risk condition
and raise flag
Write wells/network data
to *.opi file
Generate sub network
from general Input file
Call OLGA and run in
OPC mode
Read relevant
parameters (P, T, HOL)
Select time
step t=ti
Run gradient
calculations
Steps captured and organised
in an Excel based interface to
simplify software integration.
This results in a simple to use
yet robust tool
Network
model SS
Network model
transient
Own tool
Characteristics of the solution
Non lineal
Time dependent
Path dependent
Step 1: Model Production System
• Complex network created in Steady State
• Operation modelling includes operating constraints at different times in the
life cycle of the field
• Reservoirs, wells and flowlines linked and their interaction can be optimised
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Step 2-3: Data Extraction & Gradient Calculations
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Wells & Flowlines data can be extracted for
specific time steps automatically via open link
commands
Flowlines requiring
attention due to
possible liquid loading
issues flagged
Flow regimes of
interest defined
Step 4-5: Initial & Boundary Conditions
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Gradient calculations (prosper online) generate initial conditions
while nodal points provide boundary conditions
Problem input file formulated in
OLGA based on extracted data.
Wells are modelled using standard
IPR correlations or reservoir
parameters thus keeping modelling
practices consistent
Wells performance can be included or
simplified depending on network
complexity
Step 6: Transient Calculations &
Approach to Steady State
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Data is imported from SS into
Transient formulation
Subnetwork initialised and key
parameters monitored in
configurable plots
Once SS is being determined results can be
exported back to SS IAM
Evolution of the calculation is monitored until
Steady state can be determined
SS can be defined as a +/- oscillation around
an average value
Main Features of the Solutions
• Model based operation offers a target against which performance can be compared
• Modelling effort moves from data analysis to process analysis
• Workflows can be adapted to model most production networks and process units
• Interfaces can be expanded to add more features as required
• Transient effects and multiphase flow conditions can be identified and included in order to
advise Operations
• Consistent platform based on BG standard software
• Deployment aligned to BG IT model creating sustainable solution
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