The Den Haag Geothermal District Heating Project
3-D Models for Temperature Prediction and Reservoir Characterization
Renate Pechnig Darius MottaghyGeophysica Beratungsgesellschaft, Aachen
Guus WillemsenIF Technology, Arnheim
Erik SimmelinkTNO, Utrecht
GEOTHERM 2009Geothermal Energy
• Heating for 6000 houses in the “Den Haag Zuidwest” district.
• Investors: Eneco Energy, E.ON Benelux, City of Den Haag and three housing companies Vestia, Staedion, Haagwonen.
Deep Geothermal Installation - Doublet System
Aardwarmte Den Haag Zuid-West
2
• Thermal power geothermal doublet: ~ 5MWth
• Well depth: ~ 2200 m, deep sandstone reservoirs (Rijswijk and Delft)
• Production temperature: ~ 75°C
• Well flow: ~ 150 m³/h
Background
Well knownSubsurface geology and structures from oil and gas exploration activities - Seismic data and wells.
Not so well knownTemperature field – only sparse BHT data.
The planning of a the geothermal doublet requires a detailed knowledge of the subsurface geology and temperature conditions.
Targets: Prediction of the steady-state temperature at reservoir depth considering the geometrical heterogeneity of the subsurface Prediction of temperature evolution at producer well
Motivation
Geological Setting
Exploration wells in the surrounding of the target area
Well Locations
1. Acquisition and compilation of basic data sets
2. Laboratory measurements on cuttings samples
3. Integration of log and laboratory data for prediction of thermophysical properties for the stratigraphic units
4. Set up and test of a 3-D numerical models and simulation runs
Built up of a 3-D Temperature Model (25 km x 25 km)
Working Stages
Listing of the wells and and theavailability of log data and corematerial.
Yr Analogue Digital CoredataBoring SP res gr dt rhob nphi Res
BRK-03 1955 DEL-08 1994 gr dt rhob nphi ResHAG-01 1954 SP Res KNNSRHAG-02 1955 SP Res KNNSRKDZ-02 1986 gr dt rhob nphi ResLED-01 1956 KNNSRLIR-45 1982 gr dt rhob nphi Res KNNSRMED-01 1958 KNNSRMON-01 1956 KNNSRMON-02 1982 gr dt rhob nphi Res KNNSRMON-03 1990 gr dt rhob nphi ResPNA-02 1955 KNNSR, SLDNDPNA-03 1955 KNNSRPNA-04-S2 1981 gr rhob nphiPNA-07 1957 KNNSRPNA-10 1957 KNNSRPNA-14 1985 gr dt rhob nphiPNA-15 1994 gr rhob nphiRTD-01 1984 SLDNDRWK-01 1953 KNNSR, SLDNDRWK-02 1953 KNNSRRWK-03 1953 KNNSRRWK-04 1954 KNNSRRWK-05 1954 KNNSRRWK-06 1954 KNNSRRWK-07 1954 KNNSRRWK-08 1955 KNNSRRWK-09 1955 KNNSRRWK-11 1956 KNNSRRWK-14 1956 KNNSRRWK-18 1954 gr dtQ13-07-S2 1990 gr dt rhob nphi ResQ14-01 1984 KNNSRQ16-01 1970 gr dt Res KNNSRQ16-02 1978 gr dt rhob nphi ResWAS-01 1956 KNNSRWAS-02 1957 KNNSRWAS-05 1957 KNNSRWAS-23 1960 gr dt rhob nphi Res KNNSR
Cuttings available at repository in Zeist:
Q16-01Q16-02KDZ-02WAS-23MON-02
Cutting material per bagvery limited at the Monster well (< 50 g per sample)
Key Well Selection
Zone Gamma
GR (GAPI)0. 200.
Depth
DEPTH(M)
Resistivity
ILD (OHMM)0.2 200.
ILM (OHMM)0.2 200.
SP
SP (MV)-100. 100.
Caliper
CALI (IN)5. 30.
BS (IN)5. 30.
Density
RHOB (G/C3)1. 3.
DRHO (G/C3)0. 0.75
Porosity
NPHI (V/V)0.5 0.
Velocity
DT (US/M)550. 150.
NU
NLM
CK
KNSL
GK
ATR
NR
BZE
RO
DC
500
1000
1500
2000
2500
3000
3500
Logs KDZ-02
Thermal Conductivity Measurements / TK04
Measurement of 50 cuttings samples.
Measuring a rock-water mixture with a half-space device.
Results: Rock matrixconductivtity
Laboratory Work
Determination of thermophysical properties for the stratigraphic unitsRock matrix components, rock porosityThermal property prediction by logging data Calibration of log data with laboratory measurements
Log Analysis
KIJKDUIN ZEE-02Scale : 1 : 15000DEPTH (400.M - 3774.8M) 03.03.2008 22:16DB : DenHaag (1)
1
DEPTH(M)
2 3
DT (US/M)500. 100.
4
GR (GAPI)0. 200.
6
V_SHALE (DEC)0. 1.
PHI (DEC)1. 0.
Shale
Matrix
Water
7
TCm_log (W/M/K)0. 10.
TCm_cut (W/M/K)0. 10.
TCm_cut2 (W/M/K)0. 10.
500
1000
1500
2000
2500
3000
3500
NN
LMC
KK
NS
LGK
AT
RN
RB
ZER
OD
C
Comparison of measuredthermal conductivities on cuttings and matrixconductivities calculatedfrom logging data.
TC Rock Matrix
Calculation of effective thermal conductivitiesby considering rock porosity.
Effective thermal conductivity logs allowcalculate statistical values for the model units.
03.03.2008 17:42
6
V_SHALE (DEC)0. 1.
PHI (DEC)1. 0.
Shale
Matrix
Water
7
TCm_log (W/M/K)0. 10.
TCsat_log (W/M/K)0. 10.
TCm_cut (W/M/K)0. 10.
TC Formation
2.302.3Basement (DC)
0.754.0Rotliegend (RO)
1.33
1.61
1.44
0.75
0.92
0.46
1.05
Heat production(μW m-3)
1.90 – 3.14 – 4.35Permian Zechstein Group (ZE)
1.93 – 2.80 – 3.67Lower Germanic Trias Group (RB)
1.81 – 2.17 – 2.53Altena Group (AT)
2.75 - 3.75 – 4.57Jurassic Supergroup (S)
1.94 – 2.51 – 3.08Lower CretaceousSupergroup (KN)
1.80 – 2.20 – 2.60Upper CretaceousSupergroup (CK)
2.3North Sea Supergroup (N)
Bulk Thermal Conductivity (W m-1 K-1)Mean–Stdv. Mean Mean+Stdv.
Unit
3-D Model: Thermal Properties
Heat transport steady-state 3D-Simulation
9 Geological units: Base Layers from seismic survey (TNO)
Basal heat flow: 65 mW/m2 , Surface temperature: 11 °C
Dimensions: 22.5 km x 24.3 km x 5 km, about 2.4 Mio nodes
Properties are functions of temperature
Motivation:
Temperature prediction at location
Sensitivity analysis
3-D Model set up
Location of the 3-D Model
N
3-D Model: Layers an Temperature Distribution
Comparison with BHT in the Area
Cross section: z-plane / 1900 m Depth
Iso slice: Top Unit 4, Reservoir
1. The combination of laboratory and logging data allows to properly assign thermal parameters for the geological units.
2. Estimates of thermal property values for subsurface models can yield unreliable temperature predictions with large overestimates or underestimates, respectively.
3. 3-D Temperature models can support the process of drilling design (optimizing well path, deviation, drilling length, target depth).
Summary Temperature Model
Build up and test of a 3-D reservoir model
and simulation runs for
a) Transient simulation of the running system
b) Predicting temperature evolution at the producer
c) Determining radius of cooling around injector
d) Identifying possible thermal breakthrough
Reservoir Model
InjectorProducer
Grid Location
Reservoir
Comparison with Regional Model
Reservoir Model and Boundary Conditions
55 mReservoir thickness
2.75*10-4 m2 s-1Transmissivity
Function of Temperature Thermal conductivity 5.6 W m-1 K-1TC matrix Delft40 °CTemperature of injected water150 m3 h-1Injection and production rate
500 mDPermeability17 %Porosity5.5 x 3.5 x 1.105 kmExtension170 560Number of nodesValueParameter
Variation
5.5*10-5 m2 s-1Transmissivity100 mDPermeability
5.5*10-4 m2 s-1Transmissivity1000 mDPermeability
Model Properties
Producer
Injector
Temperature (°C) after 100 years (500 mD)
Depth: 2320 m
Initial temperatures from conductive model, calibrated to regional model
Temperature Evolution at Producer
1. The reservoir models don’t show a thermal breakthrough
until 100 years
2. The radius of cooling around the injector extents to
roughly 1 km
3. The temperature drop at the producer is about 1.5 K after
50 years.
4. Constraints: thickness of reservoir layer only estimated ,
permeability and porosity is assumed to be homogeneous
within the reservoir.
CONCLUSION
Statistic study regarding thermal and hydraulic parameters to reflect their natural variation.
Regional Model: One possible realization for the thermal conductivity λ in the reservoir (Vogt et al., 2009)
Outlook – Cooperation with RWTH Aachen
Thank you for Attention!
Geotherm 2009
