Geophysical Journal International
Geophys. J. Int. (2011) 184, 729–745
doi: 10.1111/j.1365-246X.2010.04839.x
Thermal properties of rocks of the borehole Yaxcopoil-1
(Impact Crater Chicxulub, Mexico)
Yu. Popov,1 R. Romushkevich,1 D. Korobkov,1 S. Mayr,2, ∗ I. Bayuk,1 H. Burkhardt2
and H. Wilhelm3
1 Moscow
State Geological Prospecting University, Russia. E-mail: [email protected]
Angewante Geophysik, Technische Universitaet Berlin, Sekr. ACK 2, Ackerstrasse 71–76, D-13355, Berlin, Germany
3 Geophysical Institute, University Karlsruhe, Germany
Accepted 2010 October 1. Received 2010 October 1; in original form 2009 January 27
SUMMARY
The results of thermal property measurements on cores from the scientific well Yaxcopoil-1
(1511 m in depth) drilled in the Chicxulub impact structure (Mexico) are described. The thermal conductivity, thermal diffusivity, volumetric heat capacity, thermal anisotropy coefficient,
thermal heterogeneity factor, and, in addition, porosity and density were measured on 451
dry and water-saturated cores from the depth interval of 404–1511 m. The acoustic velocities were determined on a subgroup of representative samples. Significant vertical short- and
long-scale variations of physical properties related to the grade of shock-thermal metamorphism and correlations between thermal and other physical properties are established. Rocks
of the post-impact and impact complexes differ significantly in heterogeneity demonstrating
that the impact complex has larger micro- heterogeneity on sample scale. The pre-impact
rocks differ essentially from the impact and post-impact rocks in the thermal conductivity,
thermal diffusivity, density and porosity. The thermal anisotropy of rocks of all structurallithological complexes is very low (K = 1.02 . . . 1.08), which is similar to the situation in the
Puchezh-Katunk and Ries impact structures. Correlations are established between the thermal
conductivity and elastic wave velocities measured in laboratory. For limestone-calcarenites,
the thermal conductivity (λ) can be calculated from the compressional wave velocity (Vp )
using the formula λ = 0.346 Vp + 0.844, and for dolomite-anhydrites this relation has the
form λ = 0.998 Vp + 1.163 [for λ in W (m K)−1 and Vp in km s−1 ]. These correlations are used
for downscaling of the sonic velocities to the decimetre scale. The effective medium theory
is applied to invert the matrix thermal conductivity and pore/crack geometry from the thermal conductivity measured on the studied samples. Representative experimental data on the
thermal properties for all lithological groups encountered by the Yaxcopoil-1 well essentially
extend an existing database on the thermal properties of rocks of impact structures and can
be used for determination of the heat flow density, interpretation of temperature logging data,
theoretical modelling of heat and mass transfer processes and constructing thermal models of
the Chicxulub impact structure as well as for the lithological interpretation. The research results confirm the necessity of dense sampling for the thermal property measurements to obtain
reliable results in petrophysical and geothermal investigations of impact structure formations.
Key words: Heat flow; Acoustic properties; Impact phenomena; Crustal structure.
1 I N T RO D U C T I O N
The Chicxulub impact crater is the third impact structure studied
for thermal petrophysics using scientific deep drilling with dense
coring. A scientific well Yaxcopoil-1 has been drilled in the crater
∗ Now at: Fachrichtung Geophysik, Freie Universitaet Berlin, Malteserstrasse
74–100, 12249 Berlin, Germany.
C
2010 The Authors
C 2010 RAS
Geophysical Journal International down to a depth of 1511 m. Two other impact structures—Ries
(Germany, Bavaria) and Puchezh-Katunk (the European part of
Russia)—were studied earlier using cores from the scientific boreholes Noerdlingen-73 (1206 m) and Vorotilovo (5374 m) (Popov
et al. 1998; Popov et al. 2003a).
Principal goals of studying thermal properties in the Yaxcopoil-1
well included the differentiation of major lithological complexes of
the Chicxulub impact structure regarding the thermal conductivity (TC), thermal diffusivity and volumetric heat capacity (VHC)
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2 Fachgebeit
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Y. Popov et al.
since these thermal properties depend mainly on the mineralogical
composition, porosity and pore geometry of rocks. Thermal properties form a basis for the heat flow density determination (Wilhelm et al. 2005), interpretation of temperature logging data (Wilhelm et al. 2004; Safanda et al. 2007) and modelling the thermal
regime of the impact structure. To solve the problems mentioned a
set of the rock’s thermal properties—thermal conductivity, thermal
diffusivity, VHC, thermal heterogeneity factor, thermal anisotropy
coefficient—and porosity and density were measured on 451 cores
collected within the depth interval of 404–1511 m of the scientific
well Yaxcopoil-1. The performed thin-section analysis of samples
intends to substantiate the relation between these properties and
porosity. These results were complemented by the inversion of the
pore/crack geometry from the thermal conductivity measurements.
This inversion is based on the effective medium theory (EMT) and,
in addition to the pore/crack geometry, provides the thermal conductivity of mineral matrix. The first part of thermal property results
derived from the measurements on 120 cores recovered from depth
interval of 404.35–666.33 m was described earlier (Popov et al.
2004). Since previous experimental data for the Ries and PuchezhKatunk impact structures have demonstrated high variability in
the thermal properties (up to 30–40 per cent) even in short depth
Figure 1. The impact structure Chicxulub and the Yaxcopoil-1 well location (the map in the upper panel) and the structure cross-section (lower panel).
C 2010 The Authors, GJI, 184, 729–745
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9.50 (5.39)
0–25.6
2.58 (0.19)
2.10–3.44
2.49 (0.23)
2.02–3.42
Notes: Upper entry: average (arithmetic) value (rms); lower entry: min. value – max. value. λ|| – thermal conductivity tensor component parallel to bedding.
2.76 (0.23)
2.05–3.77
2.10 (0.21)
1.37–2.87
1.32 (0.42)
0.73–2.58
1.46 (0.48)
0.72–3.17
0.10 (0.04)
0.03–0.48
0.13 (0.05)
0.01–0.69
1.02 (0.03)
1.00–1.38
1.03 (0.04)
1.00–1.46
3.09 (1.13)
1.32–5.40
Pre-impact: displaced
sedimentary target
rocks with monomict
cataclastic breccias
and dyke breccias
(894.94–1510.97)
3.58 (1.02)
1.91–5.59
22.3 (6.12)
1.95–36.8
2.24 (0.11)
2.05–2.64
2.01 (0.16)
1.69–2.62
2.84 (0.24)
2.14–3.42
1.77 (0.16)
1.45–2.42
0.72 (0.11)
0.55–1.30
0.69 (0.11)
0.55–1.29
0.24 (0.09)
0.04–0.54
0.20 (0.08)
0.04–0.59
1.06 (0.07)
1.00–1.34
1.07 (0.07)
1.00–1.27
1.25 (0.29)
0.88–3.04
Impact
(794.63–894.94)
1.99 (0.17)
1.58–3.02
2.23 (0.14)
1.93–2.58
1.98 (0.20)
1.60–2.56
2.43(0.30)
1.55–3.19
1.53 (0.22)
0.94–2.32
0.84 (0.13)
0.59–1.58
0.89 (0.12)
0.67–1.52
0.08 (0.03)
0.02–0.27
0.12 (0.07)
0.01–0.62
1.02 (0.03)
1.00–1.26
1.08 (0.07)
1.00–1.58
1.99 (0.20)
1.57–3.03
1.37 (0.33)
0.65–3.22
Post-impact
(404–794.63)
ρ sat
ρ dry
(Cρ)sat
(Cρ)dry
a||sat
a||dry
β sat
β dry
K sat
K dry
λ||sat
Thermal diffusivity,
10–6 m2 s−1
Thermal inhomogeneity
factor
Anisotropy coefficient
Thermal conductivity,
W (m K)−1
The thermal conductivity, thermal diffusivity, VHC, thermal
anisotropy coefficient, thermal heterogeneity factor, and, in addition, porosity, density and acoustic compressional and shear wave
velocities were measured on 451 dry and water-saturated cores
collected from the depth interval of 404–1511 m with a uniform
sampling interval. The length range of cores studied was from 40 to
80 mm. For 30 semi-consolidated rock samples the measurements
could be performed only in dry state because of their possible disintegration during water saturation. For the other 421 cores the thermal
property measurements were performed for dry and water-saturated
state, and porosity and density were measured also.
The non-contact optical scanning technology is used for the thermal property measurements similar to the study of the first part of
Yaxcopoil-1 core analysis (Popov et al. 2004). The optical scanning
method is based on scanning a sample surface with a focused, movable heat source in combination with a temperature sensor (Popov
Lithologic complex
(depth, m)
3 CORE COLLECTION AND
MEASUREMENT METHODS
Table 1. Thermal properties, density and porosity of dry and water-saturated rocks within lithologic complexes.
The impact crater Chicxulub, located in the northern part of the
Yukatan peninsula (Mexico) (Fig. 1), was formed 65 Ma. Based on
geophysical pre-site observations (Morgan et al. 1997) the CSDP
borehole Yaxcopoil-1 was drilled from 2001 December to 2002
March to a depth of 1511 m in the southern part of the crater at
about 62 km radial distance from the impact centre, in the outer part
of an annular trough at the rim of the excavation cavity (Dressler
et al. 2004), see Fig. 1(b). The coring was provided within the depth
interval of 404 to 1511 m.
The Yaxcopoil-1 well encountered the following structurallithological blocks (Fig. 1 and Table 1). Post-impact complex I (404–
794.63 m) is presented by Tertiary carbonate rocks (mainly limestones). Impact complex II (794.63–895) includes allochthonous
polymict impact breccias. Pre-impact complex III (895–1511m)
is presented by displaced pre-impact target rocks with impactinduced dyke breccias and consists of bedded Cretaceous limestone, dolomitic autoclastic breccia and anhydrite (Stoeffler et al.
2004). Under pre-impact target rocks we understand the surface rocks that an asteroid or comet impactor smashes into in
a meteorite impact event. More details in lithology are given in
Figs 2–4 and Table 2. The lithological profile of the Yaxcopoil1 borehole in Fig. 2 corresponds to the data by Kenkmann et al.
(2004). A classification of impactites is suggested by Stoeffler
et al. (2004): lower sorted suevite (807.75–823.25 m), upper suevite (823.25–846.09 m), middle suevite (846.09–861.06 m), brecciated impact melt rock (861.06–884.96 m) and lower suevite
(884.96–894.94 m).
Volumetric heat
capacity, 106 J (m3 K)−1
2 GEOLOGICAL DESCRIPTION
O F C H I C X U L U B I M PA C T S T RU C T U R E
λ||dry
Density, 103 kg m−3
Porosity,
per cent
intervals (within several metres) average core spacing from 1 to
2.5 m was used for the measurements on Yaxcopoil-1 cores to study
in details vertical variations of the physical properties related to
the grade of shock-thermal metamorphism as well as correlations
between the thermal and other physical properties measured on
cores and with logging data. Supplementary petrophysical properties (e.g. acoustic velocities, electrical resistivity, permeability and
internal surface) were determined on a subgroup of representative
samples from the Yaxcopoil-1 well, selected according to the results
of thermophysical measurements (Mayr et al. 2008a).
23.4 (6.34)
1.67–36.6
Thermal properties/borehole Yax (Chicxulub)-1
731
732
Y. Popov et al.
Figure 2. Distribution of thermal conductivity and thermal inhomogeneity factor along the well for dry (solid rhombuses) and water-saturated (empty circles)
rock samples. Thermal inhomogeneity factor is the ratio (λmax −λmin )/λmean .
et al. 1999). The optical scanning provides a possibility to record
the distribution of the thermal conductivity λ and thermal diffusivity a along each core with a spatial resolution of about 1 and
1.5 cm, respectively. The thermal conductivity tensor components
λ|| and λ⊥ (parallel and perpendicular to the rock bedding, respectively) and corresponding thermal diffusivity tensor components a||
and a⊥ were measured on dry and water-saturated samples. The
anisotropy coefficient K was determined as K = λ|| /λ⊥ . VHC Cρ
(where C is specific heat capacity and ρ is rock density) was determined as Cρ = λ/a, and specific heat capacity was calculated from
formula C = λ/aρ after λ, a and ρ were determined independently
for dry and water-saturated samples.
The optical scanning technology has the following advantages
in comparison to other thermal property measurement methods for
our goals (Popov et al. 1999): (1) high precision (1.5 per cent) and
accuracy (1.5 per cent) (for a confidence probability of 0.95) of the
thermal conductivity measurements within the range of 0.2 . . . 50.0
W (m K)−1 for dry and fluid-saturated solids (rock samples), (2)
high precision (2.0 per cent) and accuracy (2.0 per cent) of the
thermal diffusivity measurements within the range of (0.1 . . . 5.0)
× 10−6 m2 s−1 for dry and fluid-saturated rock samples, (3) simultaneous determination of the thermal conductivity and diffusivity tensor components for every rock sample under study, (4)
non-destructive numerous measurements on cylindrical or flat surfaces of cores without any additional mechanical treatment of cores,
(5) thermal property measurements on water-saturated sedimentary
rock samples with high porosity without sample destruction, (6)
measurements on rock samples with a different size scale—from
1 cm up to 70 cm in length, (7) recording thermal conductivity and
thermal diffusivity distribution along a scanning line for every rock
sample studied to take into account the rock inhomogeneity and (8)
a short time of measurement (from 10 to 30 s) for every sample.
The thickness of core layer involved in the measurement due to heat
depth penetration depends on the thermal conductivity and diffusivity values and for the collection studied is roughly between 2 and
2.5 cm.
The profiles of thermal conductivity and thermal diffusivity values along each core were recorded. All cores were split axially
using a diamond-saw splitter. This preparation step could cause
some damage to the cores but an effect of this damage on wellconsolidated rocks practically does not influence the results of the
thermal property measurements since the heat penetration layer
mentioned above exceeds significantly the thickness of the layer
with the possible damage. The optical scanning along two directions provided the determination of thermal conductivity and diffusivity tensors components for 2-D anisotropy (Popov et al. 1999).
The thermal inhomogeneity factor β [β = (λmax– λmin )/λaver , where
λmax , λmin and λaver are maximum, minimum and average (arithmetic) thermal conductivities recorded along each scanning line]
was determined for each scanning line and for each core sample
to characterize the thermal inhomogeneity of rocks. All measurements of physical properties were performed on the same cores to
exclude an influence of rock heterogeneity on results of studying
correlations between the physical parameters. The measurements
were performed at ambient conditions.
The porosity and density ρ of rock samples were determined
using the water saturation method with basic error of ±0.005 and
5 kg m−3 , respectively. Samples were dried at a temperature of
105◦ C for 48 hr in a special oven. The rocks were saturated by
first evacuating samples for 12 hr and dropping samples into a
C 2010 The Authors, GJI, 184, 729–745
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Figure 3. Distribution of volumetric heat capacity and thermal diffusivity along the well for dry (solid rhombuses) and water-saturated (empty circles) rock
samples.
water reservoir while still at vacuum conditions. Samples remained
in the water reservoir under vacuum for another 12 hr before they
were transferred to the bottom of a vessel in which they were covered by a layer of water of 35 cm thickness. 10 hours later samples
were taken from the vessel for following preparations to the measurements of thermal properties. Sample surfaces were dried using
a soft tissue. After that a thin layer of black enamel (about 25 microns) was applied on the sample surface as a trip (1 cm in a width).
The enamel trip is necessary to provide stable optical characteristics
of the rock samples and smooth heat energy absorption and emission during the optical scanning method application (Popov et al.
1999). The enamel trip on rock sample surface prevents too water
desaturation and evaporation during the thermal property measurements which lasted from 1 to 1.5 min. Measurements on individual
water-saturated samples were repeated three to four times to check
whether the saturation state changes during optical heating. During
these repetitions of a total duration of 12–15 min the results did
2010 The Authors, GJI, 184, 729–745
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not change indicating that the saturation state is not affected by the
heating on this timescale.
Acoustic compressional (vp ) and shear (vs ) wave velocities in
water-saturated samples (saturation using evacuation) were determined with a pulse transmission technique in the ultrasonic frequency range (Mayr & Burkhardt 2006). Velocities were measured
in radial direction of the samples, that is, parallel to the horizontal layering, without sample preparation. The maximum error of
measured velocities is 5 per cent. For eight anhydrite samples the
velocities under fully saturated conditions were determined by BiotGassmann modelling using measurements on dry and wet samples
(cf. Mayr & Burkhardt 2006; Mayr et al. 2008b).
4 M E A S U R E M E N T R E S U LT S
The statistical characteristics of the physical properties determined
for different lithological complexes of the Chicxulub impact crater
734
Y. Popov et al.
Figure 4. Distribution of density for dry (solid rhombuses) and water-saturated (empty circles) cores and rock porosity along well.
are shown in Tables 1–3. The parallel components of rock’s thermal
conductivity and diffusivity are shown in Table 1. The studied rocks
are characterized by a large range of thermal properties as well of
density and porosity as it is seen from Table 1. The thermal conductivity of dry (λdry ) and water-saturated rocks (λsat ) ranges from 0.65
to 5.40 W (m K)−1 and from 1.57 to 5.59 W (m K)−1 correspondingly. The rock’s thermal diffusivity (adry and asat ) ranges from 0.55
× 10−6 to 3.17 × 10−6 m2 s−1 and from 0.55 × 10−6 to 2.58 ×
10−6 m2 s−1 for dry and water-saturated state of rock samples, respectively. The VHC is (0.94 . . . 2.87) × 106 J (m3 K)−1 for dry rocks
and (1.55 . . . 3.77) × 106 J (m3 K)−1 for water-saturated rocks. The
density values are (1.60 . . . 3.42) × 103 kg m−3 and (1.93 . . . 3.44)
× 103 kg m−3 for dry and water-saturated rocks correspondingly.
The porosity range is found to be 0 . . . 36.8 per cent.
The average values and statistical parameters of thermal conductivity, inhomogeneity factor, anisotropy coefficient, thermal diffusivity, porosity and density of water-saturated rocks for different
rock types are given in Tables 2 and 3, respectively. Distributions
of the rock’s thermal conductivity λsat (a thermal conductivity tensor component parallel to bedding) and inhomogeneity factor along
the well for dry and water-saturated cores are illustrated in Fig. 2.
Fig. 3 demonstrates a distribution of rock’s VHC and thermal diffusivity along the well. These data provide information necessary
for modelling the transient heat and mass transfer processes in the
formation crossed by the well. Vertical distributions of density (‘ρ’)
and porosity measured on cores are given in Fig. 4. Density values
allow us to estimate the specific heat capacity ‘C’ from the data on
VHC using the equation VHC = Cρ.
The thermal anisotropy of rocks of all lithological complexes
was found to be very low (K = 1.02 . . . 1.08), that is similar to the
situation for thermal anisotropy established earlier for the PuchezhKatunk and Ries impact structures (Popov et al. 1998; Popov et al.
2003a). An increase in the anisotropy coefficient K for several rock
samples could be caused by oriented fracturing or layering with high
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Table 2. Average values of thermal conductivity, inhomogeneity factor, anisotropy coefficient, thermal diffusivity, porosity and density
of water-saturated rocks for different rock types.
λ|| sat ,
W (m K)−1
β sat
K sat
a||sat ,
10−6 m2 s−1
Porosity,
per cent
Density,
103 kg m−3
1–4
1.99 (0.24)
1.57–2.73
0.08 (0.04)
0.02–0.27
1.02 (0.03)
1.00–1.19
0.83 (0.16)
0.59–1.56
23.3 (6.40)
1.67–36.6
2.24 (0.16)
1.93–2.58
1d
2.68 (–)
2.46–3.00
0.08 (–)
0.03–0.12
1.02 (–)
1.00–1.07
0.99 (–)
0.87–1.11
8.74 (–)
3.19–11.6
2.52 (–)
2.47–2.62
5
6
1.78 (–)
1.58–1.99
0.28 (–)
0.15–0.48
1.05 (–)
1.00–1.20
0.71 (–)
0.68–0.73
29.8(–)
18.7–36.8
2.11 (–)
2.05–2.23
4
7
1.90 (0.08)
1.81–2.01
0.28 (0.10)
0.17–0.47
1.03 (0.06)
1.00–1.15
0.64 (0.04)
0.58–0.71
23.7 (5.41)
16.6–35.8
2.12 (0.03)
2.07–3.17
8
8
1.95 (0.08)
1.83–2.04
0.23 (0.08)
0.10–0.34
1.06 (0.07)
1.00–1.18
0.65 (.03)
0.60–0.69
21.34 (3.84)
17.4–28.2
2.25 (0.08)
2.17–2.36
6
9
1.94 (0.05)
1.88-2.03
0.17 (0.08)
0.04-0.29
1.11 (0.12)
1.00-1.34
0.66(0.04)
0.60-0.72
26.6 (3.55)
21.7-31.3
2.24 (0.05)
2.18-2.35
9
10
2.56 (–)
2.09–3.02
0.27 (–)
0.14–0.54
1.00 (–)
1.00
1.09 (–)
0.74–1.30
7.36 (–)
1.95–13.1
2.51 (–)
2.34–2.64
5
11
2.42 (0.21)
1.91–3.27
0.09 (0.05)
0.03–0.25
1.02 (0.04)
1.00–1.19
0.88 (0.09)
0.73–1.28
14.1 (3.24)
2.56–20.0
2.39 (0.10)
2.26–3.05
84
11a, 11d
3.19 (0.43)
2.47–3.92
0.16 (0.15)
0.04–0.48
1.04 (0.09)
1.00–1.38
1.12 (0.25)
0.79–1.70
10.5 (4.02)
4.38–24.0
2.51 (0.08)
2.36–2.63
17
12
5.04 (0.30
4.15–5.59
0.08 (0.03)
0.03–0.16
1.02 (0.05)
1.00–1.24
2.01 (0.22
1.50–2.47
2.89 (0.09)
2.69–3.44
57
13, 14
4.01 (0.43)
2.68–4.83
0.10 (0.04)
0.04–0.24
1.02 (0.04)
1.00–1.11
1.37 (0.21)
0.86–1.77
2.57 (0.12)
2.10–2.88
43
13a, 14a
4.32 (0.28)
3.72–4.80
0.12 (0.06)
0.05–0.25
1.01 (0.02)
1.00–1.09
1.48 (0.15)
1.23–1.79
2.66 (0.07)
2.49–2.79
17
Type of rocks
1.34 (0.52)
0–5.57
10.8 (3.51)
3.52–25.6
7.92 (2.50)
2.38–14.9
N
161
Notes: Upper entry: average (arithmetic) value (rms); lower entry: max. value–min. value. N: number of samples.
1-4 = Limestone, turbidite, para- and orthoconglomerate; 1d = dolomitic limestone; 6-9 = lower suevite, upper suevite, middle suevite
and brecciated impact melt rock; 10 = lower suevite; 11 = calcarenite; 11a = calcarenite with lens or streak of anhydrite; 11d =
dolomitic calcarenite, brecciated calcarenite; 12 = anhydrite; 13 = dolomitic autoclastic breccia; 13a = dolomitic autoclastic breccia is
with lens or streak of anhydrite; 14 = dolomite; 14a = dolomite with lens or streak of anhydrite.
Table 3. Thermal conductivity, inhomogeneity factor, anisotropy coefficient, thermal diffusivity, porosity and density of
water-saturated suevites.
Porosity, per cent
Density, 103 kg m−3
N
0.66 (0.04)
0.58–0.73
25.0 (5.61)
16.6–36.8
2.19 (0.09)
2.05–2.36
27
1.09 (–)
0.74–1.30
7.36 (–)
1.95–13.1
2.51 (–)
2.34–2.64
5
λ||sat
β sat
K sat
a||sat
6–9
1.91 (0.11)
1.58–2.04
0.24 (0.10)
0.04–0.48
1.07 (0.09)
1.00–1.34
10
2.56 (–)
2.09–3.02
0.27 (–)
0.14–0.54
1.00 (–)
1.00
Rock type
Notes: Upper entry: average value (rms); lower entry: min. value–max. value.
N: number of samples.
6: lower sorted suevite (807.75–823.25 m); 7: upper suevite (823.25–846.09 m); 8: middle suevite (846.09–861.06 m); 9:
brecciated impact melt rock (861.06–884.96 m); 10: lower suevite (884.96–894.94 m) (acc. to Stoeffler et al. 2004).
thermal heterogeneity β (as an example, water-saturated core from
the depth 683.1 m has K sat = 1.26 and β = 0.27 that corresponds
to siliceous layers in limestone). The oriented fracturing in the rock
samples could be caused by the decompressional effect and the rock
damage associated with drilling (and in particular the effect of the
stress relief).
It is seen from Table 1 that the post-impact complex (Tertiary
limestones) and impact complex (suevites and brecciated impact
melt rocks) have similar λsat values [1.99 W (m K)−1 ] but they differ
significantly in heterogeneity factor values (β sat was found to be 0.08
for post-impact rocks and 0.24 for impactites). This demonstrates
that the impact complex has larger microheterogeneity (on sample
scale).
The non-parametric Wilcoxon’s criterion (Davis 1986) was used
for the statistical analysis of results of thermal property measure2010 The Authors, GJI, 184, 729–745
C 2010 RAS
Geophysical Journal International C
ments. It is established that suevites belonging to lower sorted suevite, upper and middle suevite and brecciated impact melt rock
(rock type 6–9, Table 2) should be merged to one petrothermal
group according to the λdry and λsat data. Lower suevites (rock type
10, Table 2) differ essentially in λdry and λsat values from suevites
of rock type 6–9 (Table 2). The rocks of the post-impact and impact complexes differ statistically significantly in thermal diffusivity
and VHC (Table 1). The non-parametric statistical Wilcoxon’s criteria was used for estimation of significance in difference between
rock groups in thermal properties since amounts of sampling for
rock types mentioned are small (rock types 6–10, Table 2) and the
statistical distribution of thermal diffusivity and VHC of rocks
of the post-impact (amount of sampling is 43 cores) and impact
complexes (178 cores, Table 1) contradicts the normal law (Davis
1986).
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The cretaceous carbonate and sulphate pre-impact target rocks of
the pre-impact complex are highly heterogeneous at the macroscale
(formation scale). These rocks are characterized by significant rms
value within the rock group for λsat [rms = 1.02 W (m K)−1 ] and
for asat (rms = 0.42 × 10−6 m2 s−1 ), but at the microscale (rock
sample level) they are relatively homogeneous. The same applies
for the post-impact rocks (thermal inhomogenety factor β sat ≤ 0.10
for all rock groups). It is seen from Table 1 that the pre-impact rocks
differ essentially from the impact and post-impact rocks in thermal
conductivity, thermal diffusivity, density and porosity.
5 DISCUSSION
The observation of low thermal anisotropy shows that the oriented
fracturing is not essential in our case. Nevertheless, although the
two components of the thermal conductivity tensor (parallel and perpendicular to the bedding plane) were measured only the parallel
component λ|| was used for the correlation analysis. Such a methodological approach usually helps to exclude a possible influence of
the horizontal cracks which can disturb the thermal conductivity
component λ⊥ (perpendicular to bedding plane) to a larger extent
(although not so much, since the anisotropty coefficient is small)
than a porosity value as it was shown by Popov et al. (2003b).
The distribution of the rock’s thermal conductivity λsat (a thermal
conductivity tensor component parallel to bedding) along the well
(Fig. 2, Table 2) demonstrates a general increase from 1.6 to 5.0 W
(m K)−1 within the depth interval of 404–1300 m followed a decrease to 3.0 W (m K)−1 down to the well bottom. These variations
in the thermal conductivity are caused by variations in rock lithology. High values of λsat for some rock samples from the depth
interval of 931–1044 m can be explained by lenses and streakes of
anhydrite in calcarenites [the thermal conductivity of anhydrite single crystal varies from 4.8 to 5.4 W (m K)−1 ; Clark 1969; Dortman
1992]. Significant variations in λsat [2.2 . . . 5.6 W (m K)−1 ] within
the depth interval of 1044–1127 m can be explained by interlayering of low-conductive calcarenites (thermal conductivity of single
crystal of calcite ranges from 3.1 to 3.2 W (m K)−1 ; Popov et al.
1987) and highly conductive anhydrites. Higher values of λsat and its
significant variations [3.6 . . . 5.4 W (m K)−1 ] within a depth interval
of 1154–1298 m are caused by interlayering of highly conductive
anhydrites and dolomites [thermal conductivity of dolomite varies
from 5.7 to 6.3 W (m K)−1 ; Popov et al. 1987].
Anhydrites encountered in the Yaxcopoil-1 well have low porosity
(0–5.6 per cent) and very dense grain packing that explains the high
values of λsat [arithmetic average value is 5.0 W (m K)−1 ] and λdry
close to the matrix thermal conductivity. The porosity of dolomitic
autoclastic breccias varies significantly—from 3.52 to 25.6 per cent
with an average value of 10.8 per cent, therefore the thermal conductivity of dolomites including dolomitic autoclastic breccias from
the Chicxulub impact crater [rock type 13, Table 2, λsat = 4.0 W
(m K)−1 ] is lower compared to the respective values of anhydrites
and calcarenites. A decrease of the thermal conductivity within the
depth interval of 1296–1511 m is caused by predominance of calcarenites and dolomitic autoclastic breccia here. Higher values of
λsat in a depth interval of 1432–1454 m are due to the presence of
thin layers of anhydrites.
The thermal inhomogeneity factor β has an obvious zonality
along the well (Fig. 2). The statistical processing of the original
data provides more visible differentiation of the formation on β dry
and β sat . Several depth intervals, including the impact zone, have
values of β dry > 0.15. It is seen from Fig. 2 that β sat decreases in
all these intervals excluding the impact zone. The decrease in β
means that the rock porosity is a main reason for this effect. The
thermal inhomogeneity of porous rocks decreases after water saturation since contrast in the thermal conductivity of rock matrix
and fluid in pores decreases significantly [thermal conductivity is
∼0.024 W (m K)−1 for air and ∼0.60 W (m K)−1 for water and
ranges 2.5 . . . 4.7 W (m K)−1 for rock matrix]. The impact zone
shows another behaviour of β: β does not decrease after water saturation (Fig. 2). Such a behaviour of β in the impact zone can be
explained by the fact that the thermal inhomogeneity of rocks in this
zone is caused mainly by textural and mineralogical peculiarities
of rocks (inhomogeneity in mineralogical composition, variations
in grain size, etc.) as it follows from our thin-section analysis of
the cores. Macro- and microanalysis demonstrates also that studied rock samples contain clasts of carbonate and crystalline rocks,
silicate minerals and glasses, which vary in size from 0.1 mm to
3 cm.
The upper contact of the impactites with the post-impact rocks
(the depth 794.63 m) is clearly distinguished in λdry and λsat values
(Fig. 2). The lower contact of the impact and post-impact rocks
(894.94 m) is not so certain, caused by similar values of λsat for
lower suevite (depth interval of 884.96–894.94 m) and subjacent
calcarenites [2.6 and 2.5 W (m K)−1 correspondingly]. The impact
rocks in the depth interval of 794.53–884.96 m are characterized by
high homogeneity at the macroscale level [for the formation, rms
of λsat is 0.10 W (m K)−1 ] and high thermal heterogeneity at the
microscale level (from 0.04 to 0.59, averaging β = 0.20). We can
conclude that the impact rocks are distinguished well in the thermal
property distributions (λdry , λsat and β) along the well.
It is seen from Table 2 that rock types 6–9 have close mean values
of the thermal conductivity. The statistical analysis using criteria
χ 2 based on non-parametric statistics by Wilcoxon demonstrates
that lower suevite, upper suevite, middle suevite and brecciated
impact melt rocks can not be distinguished in mean values of thermal conductivity and should be related to the same petrothermal
group. The lower suevites (rock type 10, depth interval of 884.96–
894.94 m) differ essentially from rock types 6–9 in the thermal
conductivity values (Table 3) that can be explained by lower porosity and a different mineralogical composition of the lower suevites
(Stoeffler et al. 2004).
6 C O R R E L AT I O N S B E T W E E N T H E R M A L
P R O P E RT I E S A N D P O R O S I T Y
Our previous measurements showed that for studying correlations
between the thermal and other physical properties of sedimentary
rocks it is important (1) to use component λ|| (parallel to bedding
plane), and (2) to measure all physical parameters studied on the
same rock samples (Popov et al. 2003b; Popov et al. 2004). The
thermal conductivity component parallel to the rock bedding was
taken into account to minimise an influence of cracks which do not
change porosity practically but can disturb significantly the thermal
conductivity, acoustic velocity and electric resistivity values (Popov
et al. 2004). This component was earlier found to be the most
representative to study correlations between different physical properties because of the less possible influence of cracks (oriented parallel to the bedding plane) in the rock samples (Popov et al. 2003b).
These cracks can be caused by unloading of the core during its
extraction. Nevertheless, in our case the low thermal anisotropy
established (see below) for the Chicxulub core collection under
study shows that for the Chicxulub cores the oriented fracturing
is not essential. From the measurements, a very close correlation
between λ|| of dry samples and porosity was found for limestone,
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Figure 5. Correlation between the thermal conductivity of dry rocks and porosity. Regression lines for different lithotypes are marked as follows: 1: limestone;
2: dolomitic autoclastic breccia; 3: calcarenite and 4: suevite.
calcarenite, suevite and dolomitic autoclastic breccias (Fig. 5). Regression equations are shown in Table 4. Regression equation type
has been chosen for every correlation field to correspond to the
highest value of correlation coefficient. An important point is that
the correlation coefficient for the vertical component λ⊥ of thermal
conductivity tensor versus porosity is lower than for the horizontal
component λ|| for all studied lithotypes as it was established earlier
(Popov et al. 2003b). For example, the correlation coefficients for
limestone are –0.96 and –0.78 for horizontal λ|| and vertical λ⊥
thermal conductivity components correspondingly. A reason of this
discrepancy is that the rare horizontal and subhorizontal cracks do
not affect the porosity and λ|| essentially but cause a significant
change in λ⊥ values. For water-saturated rocks this effect is much
smaller because of a lower contrast in the thermal conductivity of
a rock matrix and water compared to the case of dry rock samples.
The correlation between λ|| (dry) and porosity () can be used to
determine the thermal conductivity λM of rock matrix when →
0. According to the regression equations for correlation between λ||
and (Table 4) the matrix thermal conductivity can be estimated
as 2.61 W (m K)−1 for limestones, 2.59 W (m K)−1 for calcarenites,
2.52 W (m K)−1 for suevites and 4.70 W (m K)−1 for dolomitic
autoclastic breccias.
The correlation between λII and for water-saturated rock samples is shown in Fig. 6, R values and regression equations are shown
in Table 4. The correlation coefficients of water-saturated samples
are lower than those for dry samples. This can be explained by
the smaller contrast in the thermal conductivity of mineral skeleton and fluid [TC is around 0.024 W (m K)−1 for air, around
0.60 W (m K)−1 for water and rock matrix TC ranges from 2.52
to 4.70 W (m K)−1 ]. The clay content also significantly disturbs this
correlation in water-saturated rock samples (see above). Therefore
for water-saturated rocks other factors—content of high thermal
conductivity minerals, difference in porous medium geometry and
clay content—can suppress the porosity influence. Nevertheless
the correlation between thermal conductivity and porosity is close
in general for dry and water-saturated samples, which is in good
Table 4. Regression equations for correlations established.
Correlation
type
Limestone
(N = 161)
Lithotype (number of samples)
Dolomite and dolomitic
autoclastic breccias
Suevite
(N = 43)
(N = 32)
Calcarenite
(N = 83)
λdry = f ()
λdry = −0.052 + 2.61
(R = –0.96)
λdry = 0.0015 2 –0.15 + 4.7
(R = –0.83)
λdry = 2.52 e−0.0308
(R = −0.84)
λdry = −0.054 + 2.59
(R = –0.84)
λsat = f ()
λsat = –0.028 + 2.65
(R = –0.82)
λsat = 4.87 e−0.018
(R = –0.72)
λsat = –0.029 + 2.66
(R = –0.78)
λsat = –0.023 + 2.73
(R = –0.49)
λsat /λdry = f ()
λsat /λdry = 0.89 e0.0216
(R = 0.92)
λsat /λdry = 0.024 + 0.99
(R = 0.73)
λsa t/λdry = 1.045 e0.0183
(R = 0.82)
λsat /λdry = 0.025 + 0.98
(R = 0.78)
VHCdry = f ()
VHCdry = −0.028 + 2.21
(R = –0.77)
VHCdry = −0.033 + 2.53
(R = −0.50)
VHCdry = −0.019 + 2.22
(R = –0.70)
VHCdry = –0.026 + 2.34
(R = –0.57)
adry = f (λdry )
adry = 0.57e0.282λdry (R = 0.96, N = 453)
asat = f (λsat )
asat = 0.44e0.293λsat (R = 0.93, N = 419)
Notes: λ: thermal conductivity component parallel to bedding plane [W (m K)−1 ]; a: thermal diffusivity component parallel to bedding plane (10−6 m2 s−1 );
VHC: volumetric heat capacity [106 J (m3 K)−1 ]; : porosity (per cent), N: number of rock samples studied.
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Figure 6. Correlation between thermal conductivity of water-saturated rocks and porosity. Regression lines for different lithotypes are marked as follows: 1:
limestone; 2: dolomitic autoclastic breccia; 3: calcarenite and 4: suevite.
agreement with results by Cosenza et al. (2003). It is seen from
Figs 5 and 6 and Table 4 that correlations between the thermal conductivity and porosity and regression equations are quite similar for
different rock types when porosity is high enough (at > 15 per
cent) and content of minerals with high thermal conductivity is not
essential.
Fig. 7 illustrates a close correlation between the ratio δλ =
λsat /λdry and for studied rocks. The correlation coefficients are
0.92, 0.78, 0.82 and 0.73 for limestones, calcarenites, suevites
and dolomitic autoclastic breccias correspondingly. The regression
equations are given in Table 4. It is important to note that the regression equations are very similar for different rock types, which
allows us to assume a possible universal character of the correlation
between δλ and for different sedimentary rocks.
A correlation between VHC of dry samples and porosity is observed (Fig. 8). The correlation coefficients are –0.77, –0.57, –0.70
and –0.50 for limestones, calcarenites, suevites and dolomitic autoclastic breccias correspondingly (Table 4). For water-saturated rocks
this correlation is not as good as for limestones (Fig. 9). This fact can
be explained by the thermal contrast between rock matrix VHC and
water VHC for water-saturated rocks that is smaller compared to dry
rock samples [rock matrix VHC ranges (1.9 . . . 2.1) 106 J (m3 K)−1
when air and water VHC values are 0.00124 × 106 J (m3 K)−1 and
4.20 × 106 J (m3 K)−1 correspondingly). Similar significant correlations for anhydrite are not observed, that can be explained by small
values and narrow range of porosity (Fig. 8).
A high correlation between the thermal conductivity and thermal diffusivity is observed for dry (R = 0.96) and water-saturated
samples (R = 0.93) when all rock types are united in a single
statistical sampling (Fig. 10). Since the thermal conductivity and
thermal diffusivity values are connected by a = λ/ VHC these
data allow us to estimate regularities in VHC variations for rock
Figure 7. Correlation between the parameter λsat /λdry (relative changes of thermal conductivity after water saturation) and porosity. Regression lines for
different lithotypes are marked as follows: 1: limestone; 2: dolomitic autoclastic breccia; 3: calcarenite and 4: suevite.
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Figure 8. Correlation between the volumetric heat capacity of dry samples and porosity. Regression lines for different lithotypes are marked as follows: 1:
limestone; 2: dolomitic autoclastic breccia; 3: calcarenite and 4: suevite.
Figure 9. Correlation between the volumetric heat capacity of dry and water-saturated limestones.
types studied. It is seen from Fig. 10 that the dependence of thermal diffusivity on thermal conductivity is non-linear. As follows
from the previous equation, VHC decreases with increasing thermal
conductivity.
7 D E T E R M I N AT I O N O F P O R E / C R A C K
G E O M E T RY O F R O C K S
The pore/crack geometry for anhydrite, dolomite, limestone and
suevite rock samples from the depth range from 677.79 to
1486.5 m of the Yaxcopoil-1 well were found with the help of
inversion based on the EMT. The EMT allows one to take into
account the effect of rock’s inner structure, inclusion shape and
orientation. The inclusions are mineral grains, pores and cracks.
In addition to the pore/crack geometry, the inversion provides the
thermal conductivity of mineral matrix.
According to the EMT, the macroscopic (effective) thermal con2010 The Authors, GJI, 184, 729–745
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ductivity λ∗ (a tensor of the second rank) is defined as follows:
q(r )
= −λ ∗ ∇T (r )
,
(1)
where r is the coordinate vector, q(r) and T(r) are, respectively, the
coordinate-dependent vector of heat flow density and temperature.
The angular brackets indicate the volume averaging. If the medium
is statistically homogeneous, the volume averaging can be replaced
by the statistical averaging.
Determination of the effective physical properties is a manybody problem that, in the general case, can be solved only approximately. In this work, we apply one of the most advantageous methods
of EMT—the self-consistent method to find the effective thermal
conductivity (Shermergor 1977; Willis 1977; Popov et al. 2003b).
According to this method, each inclusion (mineral grain or crack)
assumed to be of elliptical shape (general ellipsoid) is embedded in
a medium having the effective properties. For inclusions, differing
in shape and orientation, the effective TC can be calculated by the
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Y. Popov et al.
Figure 10. Correlation between the thermal diffusivity and thermal conductivity.
formula
λ∗ =
N
φ j λ( j)
P(F ( j) ; θ, ϕ, ψ)[λ∗ (I − F ( j) )
j=1
( j) −1
+λ F ]
( j)
×
N
φj
dF
( j)
dθ dϕ dψ
P(F ( j) ; θ, ϕ, ψ)[λ∗ (I − F ( j) )
j=1
−1
+ λ( j) F ( j) ]−1 dF ( j) dθ dϕ dψ
.
(2)
Here λ∗ and λ(j) are, respectively, the effective TC and TC of the
j-th component, θ, ϕ and ψ are the Euler angles specifying the
rotation of inclusions in space, φ j is volume concentration of the
j-th component; I is the unit tensor of the second rank, F(j) is the
depolarization tensor depending on the ellipsoid shape and matrix
properties. For ellipsoids of revolution, the tensor can be written in
the explicit form
α2 − 1
2 Arh(e) − e
, e=
for oblate spheroid
F3 = (1 − e )
3
e
α2
(X 1 = X 2 ≥ X 3 )
and
F3 = (1 + e2 )
e − artg(e)
,
e3
e=
1 − α2
α2
1/2
for prolate spheroid (X 1 = X 2 ≤ X 3 ); α = XX 31 , F1 + F2 + F3 =
1, F1 = F2 . The function P(F ( j) ; θ, ϕ, ψ) is the distribution
function (DF) of volume over the shape and orientation for the
j-th component, and N is the number of components. Component is considered to be different, if they have different thermal
conductivity.
Note that a formula similar to (2) can also be derived for the effective tensors of elasticity, electrical and hydraulic conductivity and
dielectric permittivity. Thus, Bayuk & Chesnokov (1998) presented
a unified derivation of the formula for elasticity and electrical and
hydraulic conductivity. The authors also successfully checked applicability of the formula for the case of elasticity for dry and watersaturated samples of artificial sandstone having known pore/crack
geometry. The results presented by Popov et al. (2004) can serve
as a validation of the EMT-based approach for the effective TC determination. The behaviour of DF’s describing the pore/crack shape
and inverted with the help of formula (2) form TC measurements
was in good agreement with thin-section photos of sedimentary
rock samples.
The inverse problem formulation and technique of its solution
are addressed in Popov et al. (2003b, 2004). According to this technique the matrix thermal conductivity and distribution of pore/crack
volume in dependence of their aspect ratio can be found from measurements of bulk thermal conductivity on dry and water-saturated
samples. The shape of mineral grains in the inversion is assumed to
be spherical. The inverse problem solved is based on minimization
of misfit between measured and theoretical values of thermal conductivity. Since this inverse problem is non-unique and has many
solutions, bounds are imposed on the wanted thermal conductivity
of matrix to obtain a physically plausible solution. The values of
matrix TC provided by the regression curves for different rock types
(Table 4) were used as initial guesses in the inversion.
The thermal conductivity of matrix for rocks described in previous sections is shown in Fig. 11. The limestones and calcarenites
are united in one group, and will subsequently be addressed as
limestones-calcarenites. The thermal conductivity of matrix varies
from 4.3 to 5.15 W (m K)−1 for dolomites (Fig. 11a), from 4.6
to 5.8 W (m K)−1 for anhydrites (Fig. 11b), from 2.2 to 3.6 W
(m K)−1 for limestones and calcarenites (Fig. 11c), and from 1.8
to 3.2 W (m K)−1 for suevites (Fig. 11d). The variation of matrix’s
thermal conductivity for dolomite is explained by the fact that the
samples are not pure dolomite but a mixture of dolomite with limestone with dominating dolomite. Similarly, the enhanced values
for limestones-calcarenites >3.1 W (m K)−1 (thermal conductivity for pure calcite) are due to the presence of dolomite. Suevites
samples have the lowest thermal conductivity of matrix compared
to other rocks. The lower value of matrix thermal conductivity of
limestones-calcarenites at small porosity can be attributed to cracks
that do not contribute to porosity but lower the matrix thermal
conductivity. Average values of matrix thermal conductivity estimated for rock types from correlations between thermal conductivity and porosity at → 0 (see above) are within the range of matrix
thermal conductivity found from theoretical modelling (Fig. 11).
The matrix thermal conductivity values calculated for individual
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Figure 11. Matrix thermal conductivity inverted for (a) dolomite, (b) anhydrite, (c) limestone and calcarenite and (d) suevite.
rock samples (Fig. 11) differ from average values of matrix thermal conductivity found from the correlations ‘thermal conductivity
versus porosity’ that can be caused by the individual peculiarities
in sample properties and uncertainties caused by approximations in
theoretical modelling.
The distribution of pore/crack volume over their aspect ratio is
described by the two-parametric Beta-DF. We take this distribution
because it is very flexible and depending on its parameters gives
maxima in the domain of both very thin and very long fractures,
and at some parameters it resembles the frequently used log-normal
distribution. If α = β = 1, the Beta-distribution transforms into the
uniform distribution. The ability of the inversion to reliably predict
the pore/crack geometry was verified by comparison of pore/crack
shape seen on thin section photos with the shape of the inverted
DFs. Fig. 12 demonstrates examples of DFs for each rock type
under study.
Figure 12. Distribution functions of aspect ratio inverted for (a) dolomite (depth 1378.9 m), (b) anhydrite (depth 1165.3 m), (c) limestone (depth 709.6 m)
and (d) suevite (depth 845 m).
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Figure 13. Statistical parameters of distribution functions found for different rock types: a: anhydrite; b: dolomite; c: suevite and d: limestone and calcarenite.
To describe the shape of the DFs found at each depth we calculate
four statistical parameters [mean value of aspect ratio, standard
deviation, asymmetry (skewness) and excess] for the DFs and show
how they vary with depth (Figs 13a–d). The standard deviation is
a measure of the data scatter. The asymmetry characterizes which
values dominate—greater than mean values (asymmetry >1) or
smaller than mean value (asymmetry <1). The excess indicates how
sharp or round the peak of the distribution is. The excess >1 means
that the peak is sharper than the peak of the normal distribution,
and excess <1 means that the peak is rounder than that.
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As follows from the analysis of Figs 13(a)–(d) where the pore
shapes are described, the smallest aspect ratio values are observed
for suevites. This fact allows us to conclude that the suevites are
more cracked than the other rocks. The most isometric voids are
inherent in anhydrite. The limestone and calcarenite samples have
more variable mean aspect ratio.
8 D O W N S C A L I N G O F E L A S T I C WAV E
VELOCITIES USING THERMAL
CONDUCTIVITY MEASUREMENTS
Since the thermal conductivity and seismic velocity are both influenced by microcracks (shape and density), quality of grain contacts and pore and grain size distribution (e.g. Popov et al. 2004;
Hartmann et al. 2005; Mayr & Burkhardt 2006), once knowing the
geometry, it is in principle possible to estimate seismic velocity
from thermal conductivity. The approach allowing the prediction of
one type of physical properties from another with the help of the
EMT based on the rock’s inner structure is described in Bayuk &
Chesnokov (1998). In this work, the EMT-based relation between
elastic wave velocities, electrical conductivity and permeability is
analysed. A similar approach is valid for establishing the EMT-based
relation between elastic wave velocities and thermal conductivity.
These facts give a possibility to predict the seismic velocity from
measurements of thermal conductivity using the pore and crack
geometry and vice versa. When performing such a prediction it
should be taken into account that relations between different physical properties established for laboratory measurements can be different for field data. This is so-called upscaling problem. Several
reasons exist for such a scale problem. The elastic wave velocities
are measured in laboratory on samples whose size is around a few
centimetres, whereas, the length of sonic probes is around a few
metres. The second reason is the frequency dependency of elastic
properties, which may be caused by scattering or intrinsic attenuation. These facts require one to use special upscaling techniques to
predict the physical properties at larger scale (see e.g. Tiwary et al.
2009).
Correlations between velocity and thermal conductivity values
can be used to predict the thermal conductivity from velocity. However, the correlations can be described by different formulae at
different scales. To predict a distribution of thermal conductivity
along a well from sonic velocities, a rather dense sampling (through
20–30 cm) is needed to upscale the dense thermal conductivity
and velocity values measured at the decimetre scale to the scale of
logging measurements (metre scale). In this work we use correlations found between thermal conductivity and ultrasonic velocity
values measured in laboratory for limestone, calcarenite, dolomite
and anhydrate water-saturated samples to predict the distribution
of ‘ultrasonic’ velocities along Yaxopoil-1well, that is, at the scale
of laboratory measurements or decimetre scale. Specifically, this
is important to answer a question: ‘Which errors can we expect if
laboratory measurements are directly used to mimic field data?’
The correlations established under laboratory conditions on cores
are shown in Fig. 14. In this analysis, we merge dolomites and anhydrites to one group (dolomite-anhydrite), since they have similar thermal conductivity and velocity. For limestone-calcarenite
samples both compressional and shear wave velocities correlate
well with thermal conductivity. Using the regression equations established the thermal conductivity (λ) can be calculated from the
compressional wave velocity (Vp ) using the formula λ = 0.346
Vp + 0.844 for limestone-calcarenite [for λ in W (m K)−1 and Vp
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Figure 14. Correlations established between the thermal conductivity of
limestone and calcarenite and dolomite (water-saturated samples) with elastic wave velocities measured at the laboratory conditions: (a) correlation
between thermal conductivity and vp for limestone and calcarenite, (b) correlation between thermal conductivity and vs for limestone and calcarenite
and (c) correlation between thermal conductivity and vp for dolomite (black
squares) and anhydrite (crosses are measurements, and empty circles are
calculations with the Biot-Gassman theory).
in km s−1 ]. For dolomite-anhydrites this relation has the form λ =
0.998 Vp + 1.163.
Fig. 15 shows the derived ‘ultrasonic’ velocity together with
the sonic (logging) velocity. As seen, generally, the ‘ultrasonic’
velocity overestimates high sonic values typical of the dolomiteanhydrites and underestimates low values observed in sonic logs for
the limestone-calcarenites. This tendency is seen almost everywhere
along the well except a small depth interval (1475–1490 m).
9 C O N C LU S I O N S
(1) Sedimentary rocks crossed by the Yaxcopoil-1 drillhole are
characterized by significant short- and long-scale variations in the
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Y. Popov et al.
ties of impact rocks (inhomogeneity in mineralogical composition,
variations in grain size, etc.).
(4) The thermal conductivity of rock matrix and statistical parameters of the DF of pore aspect ratio, which were established from
the combination of experimental data on rock’s thermal conductivity and theoretical modelling of effective thermal conductivity of
inhomogeneous media, were found to be different for dolomite, anhydrite, limestone and calcarenite, and suevite encountered by the
Yaxcopoil-1 well.
(5) A close correlation between the ratio δλ = λsat /λdry and porosity were established for all rocks studied. Regression equations are
very similar for different rock types, which allows one to assume
a possible universal character of the correlation for different sedimentary rocks.
(6) The different lithologies in the section are characterized by
the different regression equations between different thermal properties and porosity.
(7) Correlations found between thermal conductivity and velocity values measured in laboratory on core samples are used to predict
a high-resolution (of decimetre scale) velocity distribution along
Yaxopoil-1 well. Commonly, the decimetre-scale velocity overestimates high sonic values and underestimates low sonic values.
(8) The general approach of petrophysical investigations developed and used within the research of the Chicxulub impact structure provides valuable information for determination of the heat
flow density, interpretation of temperature logging data, theoretical modelling heat and mass transfer processes and constructing
thermal models of geological structures as well as for lithological
interpretation.
AC K N OW L E D G M E N T S
Figure 15. Comparison of the sonic velocity values (‘Vp_sonic’) with the
decimetre-scale (ultrasonic) values (‘Vp_US’) predicted from the thermal
conductivity measurements
thermal properties, which are caused by variations in porosity and
rock’s lithology and demonstrate a division of the formation into the
pre-impact and post-impact sections with respect to their thermal
properties and porosity.
(2) Established significant variations in the rock thermal properties demonstrate necessity of dense sampling for the measurements to obtain a reliable database for petrophysical and geothermal studying of formations with complex lithologies. Such an approach was not possible with previous traditional thermophysical
equipment and can be provided now with the new optical scanning
technique.
(3) Studying the inhomogeneity factor values (at the rock sample
scale) and its variations at rock sample fluid-saturation provides further discrimination of the impact complex (suevites and brecciated
impact melt rocks) from pre-impact and post-impact layers in mineralogical composition and rock structure. The impact complex is
characterized by larger values in thermal inhomogeneity factor of
rocks that is caused mainly by textural and mineralogical peculiari-
The research was funded by the Deutsche Forschungsgemeinschaft
(grants BU 298/16 and 17 within the ICDP-Chicxulub project)
and by the Russian Foundation for Basic Research (grant 08–0500977). The authors also wish to acknowledge the generous support
from Schlumberger Oilfield Services. They thank Dr. J. UrrutiaFucugauchi and A.-M. Soler-Arechalde from UNAM (Mexico) for
their help in core collection preparation and delivery. Authors thank
two unknown reviewers for their very helpful comments and recommendations to improve the manuscript.
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