Geothermal potential and estimation
methods

 Geothermal potential – definition

According to the recommendation of International Geothermal Association (IGA): geothermal potential = the exploitable amount of geothermal energy during a year → also depends on technical and economic parameters. 
Several (and no uniform) approaches worldwide:

• Prediction from production data: extrapolated from the annual production rates
• Static resource estimation: based on the total volume method
• Dynamic resource estimation: water and heat recharges also considered

Resource estimation methods

Static: Heat in Place calculation (volumetric method)
[Muffler és Cataldi (1978), Mufler (1979)]
H₀ = c x V x ΔT – huge numbers, not exploitable

Dynamic: water and heat recharge (porosity/permeability, conductive/convective heat flow)

Recovery factor (R): economically exploitable part of HIP

H₁ = R x H₀

Empiric estimations (Williams et al. 2008)

• Hydrogeothermal systems: R=0,1-0,25
• Fractured reservoirs: R=0,08-0,2

Production-reinjection doublet (Lavigne 1978)
R= 0,33 x T_reservoir - T_inj / T_reservoir - T_surface

Without reinjection (Gringarten 1978)
R=0,1

Elements of geothermal potential estimation

1. Local porosity model (porosity components)
2. Porosity versus depth (compaction trends in basin-fill sediments )
3. Thermal parameters (specific heat conductivity and heat capacity)
4. Estimation of permeability using porosity
5. Temperature, as function of depth (geothermal gradient)
6. Clay content of porous sediments
7. Carbonates in the basement (maps, and estimated thickness)
8. Thickness of altered zones and basic conglomerates on top of basement rocks
9. Geothermal heat flow (measured and estimated from basin depth)
10. Estimation of technical parameters

Ranking of the Pannonian basin filling formations according to porosity components

Ranking of the Pannonian basin fill formations according to heat content of effective  porosity taking into account the estimated technical parameters and temperature

Monte Carlo Simulation

• The elements of the physical model (reservoir parameters) are handled as probability variables.
• The expected value, standard deviation stems from measurement data, or from apriori knowledge.
• Sorting ascending order the results of calculations using variables with noise originated from random number generator, performed at least thousand times, which gives a probability distribution.
• From the distribution, we get the size of resource according to the probability levels P90, P50, P10.

Heat in Place calculations using Monte Carlo simulation

G1: Quantities associated with a high level of confidence (low estimate – P90)
G2: Quantities associated with a moderate level of confidence (best estimate – P50)
G3: Quantities associated with a low level of confidence (high estimate – P10)