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Novel Data Integration for Production Optimization: Two Case Studies

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Presentation on theme: "Novel Data Integration for Production Optimization: Two Case Studies"— Presentation transcript:

1 Novel Data Integration for Production Optimization: Two Case Studies
Øystein Tesaker October 23, 2002 Force Presentation

2 Outline What is reservoir management? Why? Components
Huldra field case North-sea field case

3 A Typical Field Reservoir Loop Production Loop manifold o Producer
choke separator g w o Completion Artificial Lift gas Lift gas manifold Production from other fields Producer Injector Reservoir Loop Time Scale: Month-Year Production Loop Time Scale: Day-Month

4 Reservoir Management Reservoir Loop (months-years) Production Loop
Reservoir Characterization Development Planning & Execution (includes economic) Production Loop (days-weeks) Identify KPI Update OP Production Monitoring Review PLP and RLP Reservoir Monitoring KPI: Key Performance Index PLP: Production Loop Performance RLP: Reservoir Loop Performance OP: Operating Plan

5 Economics 2-3% production rate improvement acheivement by use of production optimization is documented For Statoil 1% increase in production will give 300 MNOK extra

6 Production Allocation
Components Dynamic link between databases and applications Production optimization Production Follow-up system Allocation Well Relevant data storage Well history Expert System (offline/online) Production Allocation Data Acquisition Data Acquisition/distribution Data Integration Well Performance Indicators Automation Field Instrum entation Good instrumentation (downhole gauges, accurate wellhead pressures) Accurate well testing equipment (multiphase meters, test separators)

7 Field Examples Huldra Data integration system License X
Production Optimization system

8 Huldra Gas-condensate reservoir
Start of production: Nov (life ~11 years) Reservoir depth 3800m, initial pressure 676 bar and temperature 146oc 6 wells in separate fault blocks All wells have downhole P&T gauges and wellhead multiphase meters Objective of production system One place to maintain time series data (raw data, eclipse simulation forecasts, official allocated data, other calculated time series data) Should be able to link up to various systems to consolidate data Should allow easy import and export of data

9 Huldra Data Acquisition
Measurements from Instrument Bottom-hole P & T Meter FlowRates, P & T, diagnostics Measurements via Control System link WellHead Pressure & Temperature Sand Production Choke Positions Export line flowrates Automatic Import PROSTY allocated daily rates Manual Import Eclipse Simulation PVT Tables Vertical Lift Tables Total of about 100 tags per well Control System P&T Gauges Meter Dacqus

10 Sharing Data Between Partners
Client Firewall Log-Server Platform Conoco and TFE Offices Client (several) Client Replicator Office Pseudo Sensor Engine Client SQL Allocation DB Client Statoil Facilities Possible due to Client-Server System

11 Dacqus Client on Huldra

12

13 Statoil Dacqus Configuration

14 Production Information Management
Information management of time series data Works with/without offline/online data Client 1 Client 2 Client N . Data Visualization Calculation of online & historical data on the fly TimeSeries database Data Acquisition Import/Export To/From Files Atm/Manual Import From SQL Dbs Import of Simulation Forecasts Data Distribution Data Acquisition Data Calculation

15 Production Allocation
Field X Data Acqusition PROSTY Production Allocation Excel Interface Gap Network Modelling Prosper Well-Modellling

16 Gap Network Model

17 Well-testing/optimisation Basics
choke separator g w o Completion manifold Production from other fields Pressure Rate Sandface Wellhead Pres Choke

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20 Well-test Approval

21 Result 1000 m3/day production increase is indicated by GAP
In fall 2002 the system will go in active use to verify results Similar interfaces now being developed for other Statoil licenses

22 Conclusion Active reservoir management pays-off.
Reservoir description without proper data management is impossible. Data acquisition and distribution is key. Dynamic links between databases and expert systems are key to make full use of data.

23 Acknowledgements Raghu Kulkarni, Statoil Alf-Øverland, Statoil
Helge Ramstad, Statoil


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