Using Decision support models for EGS performance optimisation - - PowerPoint PPT Presentation

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Using Decision support models for EGS performance optimisation - - PowerPoint PPT Presentation

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Using Decision support models for EGS performance optimisation Jan-Diederik van Wees, Damien Bonte Albert Genter WP9 Leiden Workshop, november 2007: D. Bruhn, E. Huenges, C. Karytsas, T. Kohl, P. Ledru, E. Simmelink and other participants

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SLIDE 1

Using Decision support models for EGS performance optimisation

WP9 Leiden Workshop, november 2007:

  • D. Bruhn, E. Huenges, C. Karytsas, T. Kohl, P. Ledru, E.

Simmelink and other participants

Jan-Diederik van Wees, Damien Bonte Albert Genter

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SLIDE 2

ENGINE final meeting 2

Leiden Workshop: three aspects to performance

  • Make the project fly economically
  • techno-economic assessment
  • EXCEL
  • Constraints for the project by government (e.g. induced

earthquakes within limits

  • HAZOP, contingency plans)
  • Legislation and PR bottlenecks (delaying tfirst electricity)

Leiden workshop, november 2007

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SLIDE 3

ENGINE final meeting 3

AIM of future research

  • bring UTC below 15cts/kWh

Unit Technical Cost 1 cts/kWh 30 cts/kWh 15 cts/kWh Risk ( Costs*P(UTC>15cts) ) Unit Technical Cost 1 cts/kWh 30 cts/kWh 15 cts/kWh Risk ( Costs*P(UTC>15cts) )

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SLIDE 4

ENGINE final meeting 4

Contents

  • Performance calculation -EXCEL
  • Taking into account uncertainties and

engineering options

  • Best practices Asset development decisions E&P
  • Decision support system
  • Applications to EGS
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SLIDE 5

ENGINE final meeting 5

CO- CashFlow

Opex + Capex Time (years) Cash flow (M ) 30 20 10

  • 10
  • 20
  • 30

1 2 3 4 5

Production

tfirst-electricty

Revenues UCF = Cash in - Cash out Pay out time

  • DCF

DCF (DISCOUNT_RATE) NPV

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SLIDE 6

ENGINE final meeting 6

Techno-ecomonic calculation

  • Fast computational models

driving philosophy is to trade-off accuracy for completeness

basin properties Underground development Surface development Economics Indicators

Technical Economic

NPV DPR, IRR

  • Max. Exposure

Payout Time

  • Econ. Lifetime

Unit Technical Cost Toutlet Well design

UD SD CF BAS

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SLIDE 7

ENGINE final meeting 7

FAST ANALYTICAL MODEL for EGS, EXCEL

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SLIDE 8

ENGINE final meeting 8

Brief explanation of rationale behind these models

Streamline approach for fracture flow (Pruess and Bodvarsson, 1983; Heidinger et al., 2006)

Area of fractures (A) and flow rate (Q) and Number

  • f fractures (N) primarily

relate to the sustainibility in time of the high temperatures.

  • tdel

t Q Narea c c erfc T T T T

seg Seg F G G G res in G G res

  • ut

) ( ) (

_ _

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SLIDE 9

ENGINE final meeting 9

Circulation test :

  • II: L/s MPa at res
  • PI: l/s MPa at res

Target flowrate

  • injection+production pumps
  • friction and thermal

expansion

Brief explanation of rationale behind these models

Model based on fracture flow (Pruess and Bodvarsson, 1980; Heidinger et al., 2006) PI II

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SLIDE 10

ENGINE final meeting 10 0.00E+00 1.00E-01 2.00E-01 3.00E-01 4.00E-01 5.00E-01 6.00E-01 7.00E-01 8.00E-01 9.00E-01 1.00E+00 0.00E+00 2.00E-01 4.00E-01 6.00E-01 8.00E-01 1.00E+00 1.20E+00 psi=0 (BORDER) 180-150 150-120 120-90 90-60 60-30 30-0

FAST ANALYTICAL MODEL for EGS, EXCEL

Temperatures in the production well

140 150 160 170 180 190 200 210 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Time Temperature Bottom of the production well Top of the production well

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SLIDE 11

ENGINE final meeting 11

Taking into account uncertainties and engineering options

TNOs experience from Oil and Gas E&P “Decision and risk management” Research consortia (1997-2003)

BHPBilliton

  • Scenarios vs. Continuous probability functions (MC)

AND

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ENGINE final meeting 12

DSS model : Mixing discrete and continuous uncertainties, decision trees

  • Design decision (discrete)
  • Normal plant (costs 1.5 mln/ MWe, eff=0.68)
  • Higheff plant (costs 2 mln/ MWe, eff=0.8)
  • Uncertainties (continuous)
  • Fracture area 2-4 km2
  • Inflow other than “connected” fracture 50-90%
  • Uncertainties (discrete)
  • Having 1 (80%) ,2 or 3 fractures (each 10%)
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ENGINE final meeting 13

NPV

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ENGINE final meeting 14

  • 1.8

1 2.32

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ENGINE final meeting 15

  • 0.25
  • 0.25

Introducing an information acquisition phase, which allows to rule out N1 Costs are 250 kEURO

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SLIDE 16

ENGINE final meeting 16

  • 0.25
  • 0.25

1.66 1 2.32 0.13

NPV

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ENGINE final meeting 17

  • 0.971
  • 0.155

Inflow_other [50 .. 90%] Fracture_area [2.. 4 km2]

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SLIDE 18

ENGINE final meeting 18

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SLIDE 19

ENGINE final meeting 19

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SLIDE 20

ENGINE final meeting 20

AIM of future research

  • bring UTC below 15cts/kWh

Unit Technical Cost 1 cts/kWh 30 cts/kWh 15 cts/kWh Risk ( Costs*P(UTC>15cts) ) Unit Technical Cost 1 cts/kWh 30 cts/kWh 15 cts/kWh Risk ( Costs*P(UTC>15cts) )

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SLIDE 21

ENGINE final meeting 21

Conclusions EGS

  • Fast models are available in EXCEL
  • Excel spread-sheet (and DSS) to be distributed as

Engine WP9 deliverable. Excel can be easily modified (no black box). DSS fully probabilistic, scenario trees etc.

  • Models allow to rationalize added value of new

research for exploration and production