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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Evolution of the analysis

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A simple solution

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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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