investigation of soft stimulation measures from a techno ... · business case - key performance...

Investigation of soft stimulation measures from a techno-economic point of view under consideration of risk factors »

GeoTHERM 2019Sören Welter, Dorothee Siefert, Elif Kaymakci, Thomas Kölbel15. February 2018

Key facts:

Demonstration of stimulation measures within the EU H2020-program

› 16 partners from the EU, Switzerland and South Korea

› 5 „demonstration sites“

› Mezöberény (HR), Soultz-sous-Forêts (FR), Middenmeer (NL), Geldinganes (IS), Bedretto (CH)

› 48 months – March 2016 – February 2020

› Modelling of stimulation activities is done and published

› Planning and tendering of stimulation activities are currently done

› Stimulations will be performed in 2019

DESTRESSStimulation of geothermal wells with a minimum environmental impact

2GeoTHERM 2019 – EnBW Sören Welter, Carl Bormann, Dorothee Siefert, Elif Kaymakci, Thomas Kölbel

GasPlus-Lab (DVGW-EBI)

Reservoir improvement Preservation of injectivityMinimizing environmental

impact

Project status

Research goals:

Business case - key performance indicators based analysis


1. Technical modelling

› Simulation/optimization of geothermal plant

1. Production reservoir

2. Wellbore

3. Thermal water circuit

4. Energy utilization

5. Wellbore

6. Injection reservoir

2. Economic modelling

› Cost engineering based on technical input data

Module costing approach

Development of functions for specific geothermal items/services

Adaption to different European markets

4. Uncertainty/risk factors

› Identification & evaluation of uncertainties/risk factors unique to stimulation activities

› Uncertainties within techno-economic models

3. Mapping of stimulation measures

› Mapping of stimulation activities

Costs of different stimulation measures

Effect of stimulation on reservoir properties

Probability of success of stimulation measures


› More effort

Collection of probability distribution functions

Expert elicitation

Historical data

Setting up a techno-economic model

› More complex to present and understand

Methodology of Monte-Carlo simulation

Probabilistic KPIs

› Sensitivity analysis

Impact factors can be identified and evaluated

Automatically available through Monte-Carlo-Sampling

› Robustness of results

Unbiased by scenario definitions

More/all uncertain parameters can be evaluated

Interaction of different parameters can be considered

Why make it so complicatedoradvantages of probabilistic evaluation

determ. planed valuee

ffe

ct

Positive deviation

chances

threats

Negative deviation

t futuretoday

Integrated geothermal model (IGM)

5

Power/heat plant

ORC

TW-Kreislauf

Pro

du

kti

on

Inje

kti

on

Ris

k f

act

ors

sub

mo

de

lsE

con

om

ic

Reservoir

Integrated geothermal model

Hydraulic effects of deviated wells


Ref.: Welter(2018) - Technisch-ökonomische Analyse der Energie-gewinnung aus Tiefengeothermie in Deutschland

Detailed evaluation of deviated wellsTechno-economic comparison of deviated wells to the base case

7

Present value Net power Levelized costs of energy

[M€] Δ-% [kW] Δ-% [€/kWh] Δ-%

Base case 42,13 1.076 0,21

Deviated wells 41,31 -1,93% 1.370 27,29% 0,16 -22,97%

-1,59 -1,51

10,17 9,79

1,22 1,104,09 3,64

2,34 2,38

23,74 23,80

41,57 40,70

-5

0

5

10

15

20

25

30

35

40

45

50

base case deviated well

Pre

sen

t v

alu

e [

M€]

residual value operating costs on-demand costs

replacement IDC Invest

163

144

140

271

278

42

42

460

326

1.074

1.366

2.154 2.152

0

250

500

750

1000

1250

1500

1750

2000

2250

base case deviated well

Gro

ss p

ow

er

[kW

el]

P Inj.-Pump P F.-Pump P air con.

P others P Prod.-Pump P (net)

GeoTHERM 2019 – EnBW Sören Welter, Carl Bormann, Dorothee Siefert, Elif Kaymakci, Thomas Kölbel

Ref.: Welter(2018) -Technisch-ökonomische Analyse der Energie-gewinnung aus Tiefengeothermie in Deutschland

investment

Stimulations with published PI

Relative improvement through stimulations

8

Stimulations with published II

Probability of successStatistical evaluation of geothermal stimulation measures worldwide

Key messages

› Statistical evaluation of 181 geothermal stimulation jobs since 1970 in 15 countries

Growing number of cases increases validity of distribution function

› Relative Improvement shows a wide range depending on technical and geological issues

› Probability of having no or negative improvement is 26%

› For wells with published injectivity 88% show an improvement

› When focusing on stimulations with PI-data, the probability of no-effect decreases to 7%

0%

20%

40%

60%

80%

100%

-100%400%900%1400%1900%2400%2900%

Pe

rce

nti

le [

%]

Relative improvement [%]

Expected value = 314%

In 75% of all cases the improvement is >23%

84% of all cases show an improvement

0,001

0,01

0,1

1

10

Hyd

rau

lic

Hyd

rau

lic

Hyd

rau

lic

Hyd

rau

lic

Hyd

rau

lic

Hyd

rau

lic

Hyd

rau

lic

Ch

em

ica

l

Hyd

rau

lic

Ch

em

ica

l

Hyd

rau

lic

Hyd

rau

lic

Ch

em

ica

l

Hyd

rau

lic

Ch

em

ica

l

pre PI [l/(s*bar)] post PI [l/(s*bar)]

93% of all cases show an improvement

GeoTHERM 2019 – EnBW Sören Welter, Carl Bormann, Dorothee Siefert, Elif Kaymakci, Thomas Kölbel

-100%200%500%800%

1100%1400%1700%2000%2300%2600%

Re

lati

v Im

pro

vem

en

t

[%]

n = 69

n (< 50%) = 24

n (< 1%) = 8


Line Shaft Pump (e.g. Soultz-sous-Forêts)

› Easy maintenance

Motor above ground

Line shaft drives turbine

› Longer service life

~ 4 years

› More expensive

𝐶𝐴𝑃𝐸𝑋(𝑧) = 775000 + 500 ∗ 𝑧

› Higher efficiency

~ 70%

› Limited in depth

~ 700 m

Electrical submersible pump (e.g. Bruchsal)

› Shorter service life

Motor below ground

Motor directly drives pump

~ 1 year

› Cheaper than LSP 𝐶𝐴𝑃𝐸𝑋(𝑃ℎ𝑦𝑑𝑟; 𝑧) = 𝑃ℎ𝑦𝑑𝑟*(14,2*

𝑃ℎ𝑦𝑑𝑟−0,319

+ 𝐶𝐶𝑎𝑏𝑒𝑙 𝑧 + 𝐶𝑃𝑖𝑝𝑒 𝑧

› No depth limits (< 1000 m)

› Lower efficiency

50% - 60% (Welter, 2018)

Modelling of pump technology


› Optimization of power plant

Monte-Carlo-Simulation instead of heuristic optimization

Pressure condenser outlet; Pressure feed pump outlet; Temperature superheater outlet; Recuperated energy

› Adaption of heat exchanger

Tube heat exchanger instead of plate heat exchanger

› Validation of power plant

Revision of superheating

Revision of condensator

Progress in power plant modelling

Input data

Working fluid properties

Thermal water properties

Process restrictions

Output data

Working fluid properties

Thermal water properties

Heat exchanger design

Preheater Evaporator Superheater

CHP - paralell

CHP - row

Brine Feed pumpAir

Air condenser

Air condenser

Working fluid

GeneratorEvaporator

16

7

18

74

17

61

11

2

15

0

18

10

17

25

12

9

1

10

100

1000

10000

Feed pump Turbine Generator Fan

Po

we

r [k

We

l]

Model Soultz-sous-Forêts

Identification and prioritization as part of risk analysis

11

› Expert interviews as data basis

Biased by subjectivity

Availability of data / Effort for data collection

› Structured approach for identification of risk factors

› Prioritization of risk factors

Fit-for-purpose modelling

Pre-selection before in-deep modelling

12

Prioritization - Continuous distributions in a risk mapR

isk

ma

pB

ino

min

al

pro

ba

bil

ity

Co

nd

itio

na

l va

lue

at

risk

Co

nst

ruct

ion

of

PD

F

Case study Soultz – Monetary influence of risk factors

13

Frame conditions

› Electricity production, no heat

› 150 iterations for risk factors

Technical frame conditions Soultz project

› R&D character

Deep wells

High temperature losses

Low flow rate

Economic LCOE were not a focus of project development

The risk associated costs show a linear influence on the LCOE

› Find different modelling approaches?

› Go rather for a technical implementation of risk factors?

Le

veli

zed

co

sts

of

en

erg

y [€

/MW

h]

Risk associated costs [M€]

Base case

Monte Carlo

Case study Soultz – Technical influence of risk factors


Production wellInjection well

Le

veli

zed

co

sts

of

en

erg

y [€

/MW

h]

Permeability [10-13 m²]

Base case

Monte Carlo

Base case

Monte Carlo

Le

veli

zed

co

sts

of

en

erg

y [€

/MW

h]

Le

veli

zed

co

sts

of

en

erg

y [€

/MW

h]

Permeability [10-13 m²]

Contact details


Research and development, Applied geology and geothermal energy

Sören Welter, Dorothee Siefert, Elif Kaymakci, Thomas Kölbel

Sören [email protected]+49 721 63-17890

Durlacher Allee 9376131 Karlsruhe

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