Improving Modelling and understanding of Complex production Systems

Transcription

1 Improving Modelling and understanding of Complex production Systems Daniel Pacho 1, Amitosh Tiwari 2, John Tracey 1, Tim Halpin 1, Ravi Madray 1, Shiva Salunkhe 2, Ali Hamza 1, Neel Mani Sharma 2 1 Integrated Asset Modelling, Production Engineering, BG Advance Technical, Reading,UK 2 Production Optimisation, BG Exploration and Production India Ltd. Mumbai, India Asset Optimization Workshop 2015 London, October 29th 2015

2 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 By their nature, forwardlooking 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 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. 2

3 Background 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 3

4 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 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 4

5 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 5

6 Evolution of the analysis 6

7 A simple solution 7

8 Case-2 Steady State and Transient modelling integration for a network t=t i+1 Select time step t=t i Run Forecast in SS Network model SS Run gradient calculations Identify risk condition and raise flag Network model transient Own tool Write wells/network data to *.opi file Call OLGA and run in OPC mode Read relevant parameters (P, T, HOL) Generate sub network from general Input file Characteristics of the solution Non lineal Time dependent Path dependent Steps captured and organised in an Excel based interface to simplify software integration. This results in a simple to use yet robust tool 8

9 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 9

10 Step 2-3: Data Extraction & Gradient Calculations 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 10

11 Step 4-5: Initial & Boundary Conditions 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 11

12 Step 6: Transient Calculations & Approach to Steady State Data is imported from SS into Transient formulation Subnetwork initialised and key parameters monitored in configurable plots Evolution of the calculation is monitored until Steady state can be determined SS can be defined as a +/- oscillation around an average value Once SS is being determined results can be exported back to SS IAM 12

13 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 13