Estimation of the deep geothermal potential within the Tertiary Limagne basin (French Massif...

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Geothermics 51 (2014) 496–508

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Geothermics

journa l homepage: www.e lsev ier .com/ locate /geothermics

stimation of the deep geothermal potential within the Tertiaryimagne basin (French Massif Central): An integrated 3D geologicalnd thermal approach

. Calcagnoa,∗, C. Baujardb,1, L. Guillou-Frottiera, A. Dagalliera,2, A. Genterc,1

BRGM, Orléans, FranceGEOWATT AG, Zurich, SwitzerlandGEIE EMC, Kutzenhausen, France

r t i c l e i n f o

rticle history:eceived 30 November 2012ccepted 6 February 2014vailable online 4 April 2014

eywords:eothermal potentialesource assessmentD modellingeological modellinghermal modellingimagne, France

a b s t r a c t

The geothermal potential of a deep sedimentary-rock reservoir, in a Tertiary graben, the Limagne d’Allierbasin (Massif Central, France), is assessed. The most interesting geothermal target is identified as a thickbasal Tertiary sandstone overlying crystalline Paleozoic basement. The total amount of recoverable energyin this clastic aquifer is estimated at over 500 PJ (500 × 1015 J) in the modelled area. The most promisingzones appear along the north-western edges of the basin, where sediment infill is thickest. The method-ology used for estimating geothermal potential starts from geological field data. The first step is to obtaina better understanding of the structure and geometry of the target zone, using various data such as fieldmeasurements, and borehole and geophysical data. These data are reinterpreted through the construc-tion of a 3D geological model. Inconsistencies are checked and turned into a coherent 3D interpretation.The second step consists in meshing the geological model into an unstructured 3D finite-element meshwhere realistic thermal boundary conditions are applied. The temperature field is computed in a thirdstep. The thermal calculation is achieved by assuming a purely conductive behaviour and through com-parison with existing borehole profiles. The computed temperatures fit the measurements in the deepest

. Introduction

The Limagne d’Allier basin is located in a promising zoneor geothermal energy (Hurtig et al., 1991). This Tertiary basinas never been exploited for its geothermal resource, but haseen investigated since the 1970s for this purpose (Gable, 1978;éoservices, 1979; Geotherma, 1981). In recent years, a revival ofeothermal exploration and permit deposits has taken place in this

∗ Corresponding author. Tel.: +33 2 38 64 30 54.E-mail address: p.calcagno@brgm.fr (P. Calcagno).

1 Now at ES-Géothermie, Haguenau, France.2 Now at Gaudfrin (SA), Saint Germain en Laye, France.

characterization of the Limagne basin and to give an estimate ofits geothermal potential, paying special attention to the role of thelocal hydrological regime in the creation of thermal anomalies.

Two major aspects must be considered for geothermal explo-ration. First, good understanding of the geological structure isindispensable for determining whether an area may host geother-mal resources (Teng and Koike, 2007; Calcagno et al., 2012).Suitable exploration methods were described in detail by Bruhnet al. (2010), emphasizing the major role of delineating suitablegeological settings for geothermal exploration. Second, inferringthe available energy is a guide for estimating the viability of thegeothermal resource (Lavigne, 1978; Muffler and Cataldi, 1978;Jung et al., 2002; Kohl et al., 2003; Dezayes et al., 2008). In

P. Calcagno et al. / Geothermics 51 (2014) 496–508 497

ting g

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Fig. 1. Proposed workflow for estima

nterpretation and estimation of the geothermal potential are oftenisconnected. We thus propose an integrated 3D methodology forstimating the geothermal potential, taking into account a coher-nt and well-constrained interpretation of the geological structure.his integrated methodology consists in the four steps shown inig. 1:

. A coherent 3D geological interpretation uses field-, borehole-,and seismic data.

. The geological model is turned into a mesh where thermalboundary conditions are applied to prepare the temperaturecomputation.

. The 3D temperature model considers geological structure andthermal properties of the rocks.

. Finally, the geothermal potential is calculated in 3 dimensionsby taking into account the volumes of the rocks, the temperaturefield, and a recovery factor applied to the heat in place.

In the following sections, the geological and thermal settings areresented before describing the data. Then, each step of our inte-rated methodology is detailed and the results are shown. As a finaloint, both methodology and results of our study are discussed.

. Geological and thermal settings

Located north of Clermont-Ferrand, France, the Tertiary Limagne’Allier basin is roughly north-south oriented and is bounded byegional faults (Fig. 2). From the Late Eocene to the Oligocene, aalf-graben was formed due to progressive subsidence resulting

rom extension within the West-European plate.The Limagne d’Allier basin is delimited by the Clermont-Ferrand

ault to the west and by its northern prolongation, called theigueperse fault (see Fig. 2 for locations). Both faults have a normal

hrow of several hundred metres; the Aigueperse fault has also atrike-slip component and is well-known for its recent seismogenicctivity. The structural configuration of both faults is at the originf the deepest part of the basin located around the town of Riom.eismic-line interpretation indicates that most of the faults withinhe basin had no clear vertical displacement after the Oligocene,ut historic seismicity data for the two main bordering faults showvidence of more recent movement. Overlying the Limagne d’Allierasement, four main sedimentary sequences, labelled S1 to S4, fillhe basin: Middle Eocene (S1), Upper Eocene (S2), Rupelian (S3) andhattian (S4). Each sequence is composed of a sedimentary cycleanging from detrital formations at the base (“Reservoir”), followedy alternating layers of detrital and carbonate sediments (“Inter-ediate”), and capped by marl and carbonate deposits at the top

“Top”).The area was selected and investigated for several large-scale

eological features. First, this area is a Tertiary graben belongingo the Western European Rift System with a rather shallow Moho

eothermal potential from field data.

(<30 km depth). Most of these grabens in Europe show promisinggeothermal gradients in the shallow sediments, as well as highlyfractured zones in the deep underlying basement (Genter et al.,2003a, 2010). Second, many thermal springs occur along the grabenborder faults, indicating potential hydrothermal circulation (Millotet al., 2007). Third, there was some recent volcanic activity (<4000 yB.C.) that might represent an additional heat source.

In view of these geological features, the area was explored foroil and gas until the 1980s. The Limagne d’Allier basin has been alsostudied for its geothermal potential for many years (Gable, 1978;Géoservices, 1979; Geotherma, 1981; Genter et al., 2003a,b, 2005;Bouchot et al., 2008). Fig. 3 shows that the temperature extrap-olated to 5 km depth in the modelled area is anomalously high,as confirmed by a detailed analysis in Genter et al. (2003a). Theoriginal map (i.e. Fig. 3 without coloured ellipses) was based on atemperature atlas of subsurface temperatures (Hurtig et al., 1991),where several uncertainties were not discussed. These and the sub-sequent subsurface temperature maps were built with downwardextrapolation of available temperature data, and correspond to acollage of individual national maps, thus involving several sourcesof uncertainties (Hurter and Schellschmidt, 2003). Genter et al.(2003a) used published data on equilibrium-temperature gradientsto check the suggested temperatures at 5 km depth. A linear extrap-olation from the deepest temperatures was assumed. It turned outthat the Limagne basin actually is probably hotter (see red ellipsein Fig. 3) than previously assumed, with temperatures possiblyexceeding 240 ◦C at 5 km depth.

3. Data

3.1. Structural data

Among the existing wells within the Limagne d’Allier basin,eighteen are located in the study area (Fig. 2). Most of them reachformations older than S4 and a few reach the basement, such asthe Croix-Neyrat and Beaumont boreholes (see Section 3.3). Thegeology of these 18 wells was reinterpreted in terms of lithofa-cies and sequence stratigraphy. A database with the thickness ofeach lithofacies was constructed and used for the geometrical mod-elling. In this area, several seismic campaigns were conducted aswell for oil and uranium exploration from 1958 to 1979. Fig. 2shows the location of the existing seismic profiles re-used for ourstudy. In particular, 26 seismic profiles were calibrated on bore-hole data, digitized and reprocessed before their reinterpretation;three main geological interfaces acting as seismic reflectors were

498 P. Calcagno et al. / Geothermics 51 (2014) 496–508

Fig. 2. Location of the 18 boreholes (triangles) in the study area; among the boreholes discussed in the text (white triangles), temperature measurements are available int s arei Aigue2

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he Croix-Neyrat and Beaumont boreholes (white closed triangles). Seismic profilencluding the two major bordering faults: the Clermont-Ferrand fault (CFF) and theare shown in Fig. 4. Coordinate system: Lambert II Extended in m.

.2. Petrophysical data

Nine rock samples from different outcrop locations were col-ected in the Limagne d’Allier basin for laboratory measurements.

ost outcrops are affected by heavy weathering and only thearder rocks can be identified in the field. In addition, nineteen coreamples from the St Beauzire, Cournon, and Aigueperse boreholesere used for permeability and thermal conductivity measure-ents (Table 1).Permeability and thermal conductivity were measured at room

emperature on these dry samples (Dagallier, 2004). Correc-ions were applied for the effect of temperature (Vosteen andchellschmidt, 2003) and for the effect of saturating fluid onhermal conductivity values. The obtained permeability valuesre generally lower than 1 mD, except for rock samples of the3 Reservoir formation. Depending on local physical conditions andn the thickness of this unit, fluid circulation could thus be hostedithin the S3 Reservoir formation (see Section 6). Thermal con-uctivity was measured using the divided-bar method. The divided

arl and limestone and from 1.40 to 3.51 W m−1 K−1 for sandstone.ermeability and thermal conductivity data are shown in Table 1.

located along the 26 yellow traces. The fault network crossing the basin is shown,perse fault (AF). The seismic profile RC2 (red dotted line) and the oil well Crouelle

3.3. Thermal data

In the considered domain, the two available temperatureprofiles were measured in the Croix-Neyrat and the Beaumontboreholes (Genter et al., 2003b; Martelet et al., 2003, see Fig. 5).These two data sets show very different values. The temperaturedata of the Beaumont well show an extremely small temperaturegradient at depth (around 6 ◦C km−1). The Beaumont borehole isclose to the regional border fault, and its trajectory follows thatof the sediment–basement interface. According to Gable (1982),heat-refraction effects due to the vicinity of Paleozoic crystallinebasement could explain the low temperature gradient. Lateral heatrefraction probably occurs since thermal conductivity values aretwo to three times lower than typical values for basement rocks.However, it seems difficult to explain why the temperature gra-dient is almost cancelled over a large depth range. The very lowtemperature gradient over several hundreds of metres can hardlybe explained by a simple lateral lithological contrast, which wouldonly involve a low thermal conductivity ratio (definitely lower than

Fig. 3. Temperature map extrapolated to 5 km depth. The Limagne d’Allier basin (star) is located in an area of anomalously high temperatures.Excerpt from Genter et al. (2003a); modified from Hurtig et al. (1991).

Table 1Permeability and thermal conductivity measured on outcrop (T) and core (S) rock samples (from Dagallier, 2004). In some cases, the permeability or thermal conductivityhas not been measured (“–”).

Sample code Depth (m) Plug orientation Formation Permeability (mD) Thermal conductivity (W/m K)

Outcrops T1 0.00 Random S3 Reservoir 2.14E−02 2.41T4 0.00 Random S4 Top 6.12E−01 0.91T5 0.00 Random S4 Top 4.62E−01 0.57T6 0.00 Random S3 Top 7.61E−02 1.06T7 0.00 Random S3 Top 3.77E−01 1.17T8 0.00 Random S3 Reservoir 1.50E+01 3.53T10 0.00 Random S3 Reservoir – 3.39T11V 0.00 Vertical S3 Reservoir 6.35E+02 2.55T11H 0.00 Horizontal S3 Reservoir 1.13E+03 2.57T12 0.00 Random S2 Top 3.11E−01 2.02

St Beauzire S0 488.00 Vertical S3 Top – 2.00S1 790.50 Vertical S2 Top – 1.41S2 1360.30 Horizontal S2 Intermediate 1.68E−02 1.82

Cournon S3 529.00 Random S3 Top 1.99E−01 0.93S5 545.00 Random S2 Top 6.60E−01 1.39S7 556.00 Random S2 Top 5.80E+00 –S8 738.60 Random S1 Reservoir 8.00E−02 2.24S9 771.60 Random S1 Reservoir – 1.51

Aigueperse S10 247.80 Vertical S3 Top 6.00E−02 0.76S11 346.00 Random S2 Top 4.30E−01 –S12 349.00 Horizontal S2 Top 8.86E−02 1.14S13 351.00 Random S2 Top 6.98E−01 0.93S14 386.00 Random S2 Top 2.33E−02 1.02S16 454.80 Vertical S2 Top 4.57E−02 0.93S17 579.50 Random S2 Intermediate 1.01E−01 1.44S20 587.50 Random S2 Intermediate 1.01E−01 1.69S21 670.00 Random S2 Reservoir 4.56E−01 2.44S26 802.20 Random S1 Intermediate 2.13E−02 1.81

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ig. 4. Interpretation of a seismic profile acquired in 1979, called RC2 oriented NE–ault and three main geological levels: Rupelian (S3 Top), top of Eocene (S2 Top) anrouelle 2 was drilled to 1232 m depth and was used for calculating the conversion

his vertical flow, if sufficiently fast, would homogenize temper-tures (e.g. Turcotte and Schubert, 2002). However, temperatureselow 1000 m depth seem colder than the expected values com-uted with an average temperature gradient of 30 ◦C km−1. A thirdossibility is to consider a neighbouring convective cell within aermeable layer (e.g. Simms and Garven, 2004). By thermal diffu-

ee Fig. 2 for location) in the Limagne d’Allier basin, showing the offset of a normala Eocene (S2 Intermediate). Length of the profile is about 4 km. The former oil welleen time and depth.

conclusion, thermal data from this borehole may be affected bylocal or regional disturbances and cannot be considered as repre-sentative of the equilibrium thermal regime.

Concerning the Croix-Neyrat well, two well loggings were car-ried out in 1981. The first, in July 1981, showed a constant deviationto a mean gradient, from the bottom to the top of the borehole. The

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ig. 5. Digitized temperature logs of the Croix-Neyrat (left) and Beaumont (right) beft hand-side of the profile.

he anomalously high temperatures at shallow depths (0–200 m)nd the constant temperature increase with depth yield a tempera-ure of 90 ◦C at 1600 m depth. The whole temperature profile is notlearly related with the lithological variations (cf. Fig. 5, left-handide). Between 1300 and 1400 m depth, this well intersected the2 Intermediate and S2 Reservoir detrital formations that can beonsidered as a reservoir.

. 3D modelling

.1. Geological model

The geological model was constructed with the scalar-field-nterpolation method (Lajaunie et al., 1997) in the 3D GeoModeller3

oftware developed by BRGM (Calcagno et al., 2008). Formationoundaries and dips measured in the field and interpreted from theeological and geophysical data were used in a coherent 3D inter-retation. Locations of the geological-boundary data (3D points)nd geological-dip data (3D vectors) were interpolated through aeostatistical method combining them (co-kriging). The result ofhe interpolation was a 3D scalar potential field that models theeology of the studied zone. In this model, geological layer thick-ess is controlled by data location. The model enables positioning ofhe geological formations at any point in the 3D space. Geologicalnterfaces are derived from isovalues of the interpolated poten-ial field. A geological pile describes the chronological relationsf the geological events (Fig. 6). This succession is composed ofeveral formations, chronologically ordered, and gathered in inter-cting series. Each series is interpolated using a single potential

3 3D GeoModeller is commercial software developed by BRGM and Intrepid Geo-hysics. For further information, please refer to Calcagno et al. (2008), and visit:ttp://www.geomodeller.com.

les; see Fig. 2 for location. The geological formations intersected are shown on the

In order to model the erosional surfaces that define the topsof each sedimentary cycle, the top of marl and carbonate of eachsequence was assigned an “Erode” relationship. In other words, thetop of each sedimentary cycle is bounded by a surface eroding ear-lier deposits. The thickness of each formation varies both spatiallyand temporally. “Reservoir” formations are thicker along the basin-margins than in the centre of the basin. “Intermediate” formationscorrespond to a transitional environment, and lacustrine-carbonateformations are thicker in the central, deeper, part of the basin. Sincethe thickness variability does not correlate from one formation tothe next within a sequence, their interfaces cannot be representedby isovalues of a single potential field. Consequently, each forma-tion must be modelled with an independent potential field, i.e. eachformation must belong to an independent series (Fig. 6).

The faults bordering the basin and interacting with the sedimen-tary formation were modelled through drift functions impactingthe potential-field interpolation (see Calcagno et al., 2008, fordetails). They are gathered into a fault network (Fig. 2) manag-ing the topology of the interaction between faults, and betweenfaults and series combining geological formations. Fig. 7 shows thegeometry of the faulted basement.

The complete resulting model is shown in Fig. 8 with a 3D viewof the “Reservoir” formations, some boreholes and two orthogonalsections (Fig. 8a), and an E–W section of the basin (Fig. 8b).

4.2. Mesh

The geological layers were discretized using hexahedra andprisms (3D elements). Faults were discretized as squares and tri-

4 Winfra, the Orion extension, and FRACTure are packages developed byGEOWATT AG. Please refer to Kohl and Hopkirk (1995) for more details.

Fig. 6. The geological pile as derived from the general geological knowledge of the basin. Four sedimentary sequences (S1 to S4) were deposited on top of the basement( , “Inter ng indb

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“Basement”). Each sequence is divided into three depositional phases (“Reservoir”elation for the top series of the four sequences). Each formation is modelled usietween formations.

etails). The result is an unstructured mesh composed of more than00,000 elements. The basement and a suite of four sedimentaryequences (defined by top of S1, S2 and S3, and topography) wereaken into account for the meshing. Each sedimentary sequencencludes the “Reservoir”–“Intermediate”–“Top” cycle defined in theeological setting chapter earlier and presented in Fig. 6.

The faults are incorporated in the sediment layers, but not in theasement. Most of them were assumed as vertical, except two thatere manually meshed in 3D in order to keep their non-verticalip. However, the faults do not play a major role in the diffusivehermal model, because of their insufficient thickness. They were

odelled in the mesh for limiting different geological regions andor modelling sharp discontinuities in the surface of each layer, asan be seen in the geological model. Fig. 9 shows the obtained meshn detail.

.3. Thermal model

No relevant information about convection was available forhe 3D studied zone. For that reason, we tested if a conductive

patial indications on the validity of such a purely conductive

rmediate” and “Top”). The sequences are separated by erosional surfaces (“Erode”ependent potential fields, because the thickness variability cannot be correlated

regime hypothesis. Two boundary conditions were used: aDirichlet boundary condition at the surface of the model (topog-raphy) where a constant 10 ◦C temperature was imposed, anda Neumann imposed-flux boundary condition at the bottom ofthe model. The other lateral boundaries are no-flow insulatingboundaries.

The simulation was run under steady-state, neglecting effects ofpast climate change or transitory effects in the sediments. Modelcalibration was through an iterative manual process. The tempera-ture was computed in 3D using the finite-element code FRACTure 4(see Kohl and Hopkirk, 1995, for details), and then extracted along1D profiles, corresponding to a known borehole where temperaturelogs were available (see Section 3.3). The computed temperaturewas then compared with the measured data. Thermal parameters(heat conductivity, heat-production rates, and basal heat flux) wereadjusted within the range of the data (see Table 1) in order tocompute the most realistic temperature profiles.

The best fit on the thermal data was obtained with realis-tic values of thermal conductivities (between 2 W m−1 K−1 and3 W m−1 K−1) and radiogenic heat production (3.0 × 10−6 W m−3

for the basement and 0.5 × 10−6 W m−3 for the sedimentary layers).The values agree with those derived from samples and core analysis(see Section 3.2). A basal heat flux of 105 mW m−2 was applied atthe base of the model, in order to retrieve realistic surface heat-flow

Fig. 7. View to the NW of the basement below the Limagne d’Allier basin, illustrating the fault network used for modelling the basement structure (model dimensions:30 km × 35 km × 5 km). Basement geometry was modelled by using field geology observations, seismic sections and boreholes, together with a basement surface derivedf

m(a(ntmtctwzfdkadid

rom gravimetric inversion. Coordinate system: Lambert II Extended in m.

alues in this area, around 120 mW m−2, as measured by Lucazeaund Vasseur (1989).

Fig. 10 presents the temperature values generated by the ther-al model on the 3D area. The temperature extracted at 3 km depth

Fig. 10b) varies between 143 ◦C and more than 158 ◦C. The positivenomaly inferred between the Riom and Clermont-Ferrand citiesFig. 10b, red spot in the middle-west) is due to an important thick-ess of sediments. Modelled temperatures can also be extracted athe top of the aquifers that could be promising targets for geother-

al exploitation, such as S1 Top on Fig. 11. The result of the 3Dhermal model in the Croix-Neyrat well is presented in Fig. 12. Theomputed temperature agrees with the data in the deepest part ofhe recorded temperature profile between 1400 and 1800 m depth,here calibration of the pure-conduction model was possible. This

one of the Croix-Neyrat borehole corresponds to the S1 Reservoirormation (Fig. 5). In other words, it is suggested that purely con-uctive heat transfer only occurs below 1400 m, and thus that someind of fluid circulation could explain the misfit between measurednd computed temperatures between the topography and 1400 mepth (see Section 6). Thus, estimation of the geothermal potential

n this area, presented in the next section, is conducted only in theeepest part of the basin.

. Geothermal potential

Based on the above results, we can assume that the conductiveodel is suitable for the deepest part of the study area and in partic-

lar in the S1 formations. We propose to go further by calculatingn estimate of the geothermal potential in this part of the reser-oir. The total amount of geothermal energy available is computedccording to the following parameters:

Thickness and extension of the aquifer derived from the geolog-ical model,Temperature distribution derived from the numerical model,

– Reinjection temperature (assumed at 30 ◦C) and heat capacity ofthe host rock.

The heat in place can be computed as follows (Muffler andCataldi, 1978):

EHIP = �cP · V · (Tprod − Treinj)

where EHIP is the heat in place, �cp is the rock’s heat capacity(2.2 × 106 J m−3 K−1), V is the resource volume, Tprod is the fluid’sproduction temperature (temperature of the rock), and Treinj isthe fluid’s reinjection temperature (reference temperature, hereassumed to be 30 ◦C).

The deepest aquifer, S1 Reservoir, which shows the highest tem-perature and the greatest thickness, is the most promising energysource, with a total heat in place of around 11,300 PJ.

However, this amount of energy cannot be fully exploited.Geothermal potential is defined by the amount of recoverableenergy of the medium. The recovery factor is defined by the ratiobetween the geothermal potential (or utilizable energy) and theavailable energy.

EUT = R · EHIP

where EUT is the recoverable heat, R is the recovery factor, and EHIP

is the heat in place.A crucial problem of geothermal resource quantification is the

evaluation of this recoverable heat EUT. Two different approachesare mentioned in the literature. The first consists in evaluatingdirectly a value of the recovery factor. Such an approach was intro-duced by Lavigne (1978) and applied by Jung et al. (2002) forquantifying the geothermal resources of Germany, and by Dezayeset al. (2008) for the geothermal resources of the Upper RhineGraben. The recovery factor in that case is the product of a tem-

Fig. 8. View of the Limagne d’Allier basin geology model (30 km × 35 km × 5 km). Locations of the four detrital formations (“Reservoir”) are shown as S1 R to S4 R. See Fig. 6for detailed list of formations and colour chart. (a) 3D view from the south-east of the four detrital formations (“Reservoir”) volumes in the central part of the model. Boreholesin the area are shown together with two cross-sections (N–S and E–W). Coordinate system: Lambert II Extended in m. (b) E–W section through the whole basin. See (a) forsection location.

Fig. 9. Views of the Limagne finite-element mesh adapted from the geology model. Coordinate system: Lambert II Extended in m. (a) 3D view of the basement and of thefaults. (b) 3D view of the basement and of the formations S1, S2, S3 and S4 (from bottom to top). (c) View of the mesh along an E–W cross-section, see trace on (b).

F nded( the va

ig. 10. Results of the temperature simulation. Coordinate system: Lambert II Exteb) Temperature map extracted at 3 km b.s.l. The heterogeneities are mainly due to

etween 100 ◦C and 130 ◦C, the recovery factor is estimated to varyetween 14% and 27% for a porous aquifer, and between 2.4% and% for a fractured aquifer.

The second solution – the one we used – was suggested byringarten (1978) and solves the problem indirectly. It considersnetwork of geothermal doublets (distance between the wells D,owrate Q). The optimal D and Q are resolved by putting into equa-ions the following assumptions:

The thermal breakthrough time should not occur before a period�t (30 years).The hydraulic drawdown in the well is limited (300 m).

in m. (a) 3D temperature distribution simulated in the Basement and in the faults.rying thickness of the sedimentary cover.

The aquifer’s transmissivity and porosity are directly taken intoaccount by this formulation of the geothermal potential estimation.

Then, as the energy produced by a single doublet is theintegral of the power of the doublet throughout its lifetime(30 years), it is possible to compute and map directly the recov-erable heat of the domain. The recovery factor is then deducedby the computations, rather than being used as an assumption.This method was applied for a quantification of the geothermalresources of Switzerland (Signorelli and Kohl, 2006; Baujard et al.,

These works showed that a recovery factor varying between5 and 10% could be considered as a representative value for aporous aquifer. Dagallier (2004) mentioned that the permeability

F of theI

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ig. 11. Simulated temperatures extracted at the top of the Reservoir S1. The depthI Extended in m.

alues measured on cores from the different reservoir rocks areuite heterogeneous: 0.08 mD for the S1 Reservoir, 0.5 mD for the2 Reservoir, and between 0.02 and 1000 mD for the S3 Reservoir.s we focus here on S1 formations, a relatively small recovery

actor was chosen for computation of the geothermal potential;n acceptable value of 5% was used. This value may be seens conservative, compared to what was suggested by Lavigne1978) or Jung et al. (2002), but is realistic for our study ashe permeability measured in the samples is relatively low; con-equently, the mapped geothermal potential shows a minimalalue of the recoverable heat in the aquifer. Fig. 13 shows thestimate of the recoverable heat in the most promising aquifer,1 Reservoir, with a total recoverable energy on the range of over00 PJ.

. Discussion and concluding remarks

Our work covers an integrated approach for quantifying theeothermal potential of a region, from classical field data (boreholesnd seismic-line interpretation), through geological modelling,eshing, and temperature simulation, to a geothermal-potential

stimation of the basin. As a result, the deepest reservoir, whichs the hottest and also the thickest, was identified as the mostnteresting target for geothermal exploitation.

However the thermal model run in a purely conductive regimeoes not fit with the temperature data from the Croix-Neyratorehole above 1400 m depth. The observations described in the

Thermal data’ section, above, may indicate that the measured tem-

top of S1 Reservoir (in m b.s.l.) is shown on the map. Coordinate system: Lambert

is recorded in the Croix-Neyrat borehole between 400 and 900 mdepth, where sedimentary S3 Top rock is located. Convection,implying convection cells in the aquifer, is not a good candidate toexplain this phenomenon, because the permeability reported forS3 Top varies between 0.07 and 0.3 mD (cf. Table 1), which is toosmall to allow convective circulation.

Both temperature profiles recorded at Beaumont and Croix-Neyrat strongly suggest that fluid circulation occurs and disturbsthe local thermal regime, either through water flow within theborehole or through larger-scale fluid circulation within the per-meable parts of the rock. It must be noted that a lateral temperaturedifference of 35 ◦C between the two boreholes is reached at1200 m depth; the distance between the holes is about 6 km. Interms of thermal convection, the Beaumont borehole would belocated around a cold down-welling current setting, while theCroix-Neyrat borehole would show the thermal signature of ahot upwelling. However, though a small temperature gradient(such as that measured at Beaumont) is a typical signature ofconvective flow, the excess heat recorded from the temperatureprofile at Croix-Neyrat – i.e. hot temperatures at shallow levels,90 ◦C at 1600 m depth – is more probably related to local upflowwithin the borehole or within neighbouring fractures, disconnectedfrom a larger-scale fluid-circulation pattern. At depth, the largetemperature gradient of 46 ◦C km−1 (involving basal heat-flowexceeding 100 mW m−2) could represent the necessary heat input,triggering buoyancy-driven fluid circulation (thermal convection)within the rock. Another mechanism, involving pressure-drivenfluid circulation may be another possibility, especially if the neigh-

The quality of the geological model would be improved byacquiring new geological data for constraining the geometry. In

P. Calcagno et al. / Geothermi

Fig. 12. The temperature profile extracted from the calibrated 3D-model (red) isshown for comparison on the Croix-Neyrat temperature profile (grey + orange).

Fig. 13. Estimated recoverable heat from the S1 Reservoir aquifer. The depth of the topExtended in m.

cs 51 (2014) 496–508 507

the meantime, gravimetric and/or magnetic data could be used inforward and inverse modelling for refining the shapes of the geo-logical model. Moreover, the present study was limited by a lack ofsufficient thermal and hydraulic data.

Our modelling results show that conduction cannot be assumedas the only process explaining the temperature distribution in thebasin, especially in the shallower part; advection may be neededto explain the recorded temperatures. First, new temperature logscould validate/discard the shapes of the available logs that seem tobe inconsistent with the low permeability values measured on rocksamples. This should allow a better temperature fit of the numericalmodel. Second, if the existing logs were correct, it would be neces-sary to consider some water circulation in the basin in the thermalnumerical model. In that case, a groundwater-flow model would beneeded. Such a model would provide interesting information con-cerning the basin’s behaviour, but would need data about waterlevels in the aquifers and on the hydraulic parameters – such aspermeability – of the different layers, in order to establish a validhydrogeological concept. The permeability of the different reser-voirs could then be taken into account in computing the recoverableenergy of the reservoirs.

The occurrence of many thermal springs emerging from themain border faults is a clue for water movements at a Limagne-basin scale. Thus, any new calculation of recoverable energy willneed a new hydrogeological concept for the rocks that would fitwith the characteristics of not only porous but also fractured clasticreservoirs. Moreover, natural brine circulation within deep frac-tured clastic rocks and/or the underlying fractured basement are

of S1 Reservoir (in m b.s.l.) is shown on the map. Coordinate system: Lambert II

5 therm

tidptwc

EeGSEu

GFirst Well and Results. In: Third European Geothermal Review (TEGR), 24–26

08 P. Calcagno et al. / Geo

uch nearly-vertical permeable structures cross-cut mainly deeprittle reservoir rocks from clastic to granitic composition. Bynalogy with the URG, hydrothermal convective cells, developedithin a nearly vertical fracture system, could exist in the Limagne

asin.In conclusion, the 3D geological model, the thermal model and

he quantification of the geothermal potential at depth as presentedn this study, are guidelines for resource exploration in the Limagne’Allier basin. In particular, our R&D will benefit the awarding ofermits for the geothermal exploration that is currently active inhe area. Furthermore, the geothermal-exploration methodologye propose here is independent of the location of the study and

ould be replicated elsewhere.

cknowledgements

This work is supported by ADEME (French Agency for Energy andnvironment): contract no. 0805C0093 – CLASTIQ-2, and has ben-fited from the ENGINE European coordination action (Enhancedeothermal Innovative Network for Europe, http://engine.brgm.fr).ample measurements were carried out in collaboration with thenvironment and Resources DTU, Technical University of Denmark,nder the supervision of Dr Molenaar.

The authors thank the two anonymous reviewers for their fruit-ul comments and Dr Kluijver for reviewing the English of the

anuscript.

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