1UDC 550.837.31

Peinado H.(1), Delgado O.(2), Ryjov A.A.(3), Shevnin V.A.(4)

(1) Instituto de Geología, UNAM, Mexico(2) Mexican Petroleum Institute, Mexico(3) VSEGINGEO(4) Lomonosov Moscow State University, Geological faculty

JOINT ANALYSIS OF GEOLOGICAL AND GEOPHYSICAL CHARACTERISTICS OFSOIL IN SINALOA, MEXICO

In area of study near the channel Valle del Fuerte in Sinaloa state, Mexico, geological

and geoelectrical characteristics of soil were measured. In each of key area an electrical

resistivity tomography (ERT) profile was fulfilled, three boreholes were made with soil samples

collection in each borehole, filtration coefficients of soil, cation exchange capacity, porosity and

grain size analysis, groundwater salinity and ionic content in water were determined. ERT data

were interpreted and soil resistivity curves versus pore water salinity were measured. Joint

analysis of all data obtained was performed to find correlations between them to create

petrophysical soil model and to estimate hydraulic conductivity model of the area.

Introduction

This study was fulfilled near the channel Valle del Fuerte in Sinaloa state, Mexico (Fig.1).

The channel supplies water for population and agriculture needs. The purpose of study was in

soil characteristics determination to find their hydraulic conductivity and relation with other

characteristics.

Figure 1. Map of Mexico (1), Sinaloa state (2) and working area (3)

2

Field dataNear the channel were selected 7 key areas, and in each area short electrical resistivity

tomography (ERT) profiles were measured, each profile consisted of 11 VES points, three

boreholes in each key area were performed until the depth 4 m (21 boreholes), and soil samples

(3-4 samples from each borehole) were collected to determine different geological and electrical

properties in laboratory (73 samples). Groundwater probes (21) were obtained in each borehole

to determine total salinity, ionic content, electrical conductivity, pH. For soil probes grain size

analysis was made (percentage of sand, silt and clay particles), for some samples (25) porosity

was determined, filtration coefficient (73), ionic exchange capacity - CEC (73). Samples with

weight about 2 kg were used for soil resistivity measurements versus pore water salinity (4-5

different salinities) on technology developed in MPI [Shevnin et al., 2004].

0.1 1 100.2 0.3 0.5 0.7 2 3 5 7 20

0

10

20

30

40

50 f, %

Salinity, g/l

Figure 2. Histogram of groundwater salinity (n=21)

In water the predominant cation is Na+ and predominant anions are SO42-, Cl-, HCO3

-

with nearly equal content.

Grain size analysis of soil samples has shown 14% of clay, 46% of silt and 40% of sand.

0.0001 0.001 0.01 0.1 1 10 100

0

10

20

30

40

Kf(m/d)

f,%

0.04 0.50.2 2 5

1 2 3

Figure 3. Histogram of hydraulic conductivity Kf (n=73) divided in three groups

Taking into account form of Kf histogram (Fig.3) soil can be divided by the boundaries

0.04 m/d and 0.5 m/d, into loamy, silt and sandy soil.

3

0.2 0.3 0.4 0.5 0.6 0.7 0.8

0

10

20

30

40 f, %

soil porosity

Figure 4. Porosity histogram (n=25)

Porosity was determined on difference between weight of fully saturated and dried

samples.

103 4 5 7 20 30 40 50 600

5

10

15

20

25

CEC mg-eqv/100 g

f, %

n=73

Figure 5. Histogram of cation exchange capacity measured in laboratoryin mg-eqv/100 g units (n=73)

CEC in laboratory is measured in mg-eqv/100 g units. In Ryjov's program Petrowin CEC

is used in g/l units. It is possible to recalculate CEC units dividing values mg-eqv/100 g into 4

and obtain CEC in g/l. For more correct recalculation one need to take into account type of ions

in exchange process.

Application of electrical resistivity tomography

Use of ERT in our study was based on theory developed by Ryjov from 1990 and his

colleagues [Ryjov and Sudoplatov, 1990; Ryjov and Shevnin, 2002; Shevnin et al., 2007] for

calculation of soil resistivity on geological parameters and estimation of geological parameters

on soil resistivity and some additional parameters [Shevnin et al., 2006a]. Ryjov developed

Petrowin program, for different calculation between geological parameters and soil resistivity as

forward and inverse petrophysical problems [Shevnin et al., 2008]. This theory and field

technology was applied during several years in Mexico at many field sites, mainly on oil

contaminated zones and demonstrated its efficiency [Shevnin et al., 2005; 2006a]. In Sinaloa

state we made some attempts to use this technology from 2003, with less success than in other

areas of Mexico because of soil peculiarities in Sinaloa. That is why, obtaining great volume of

4

field and laboratory data near the channel Valle del Fuerte, we made one more attempt to

compare geological parameters and resistivity data that was the aim of this article.

ERT measurements along each profile were performed to allow interpretation with both

1D and 2D algorithms. Each profile included electrodes with linear step 2 m to perform 11 VES

with AB/2 from 3 to 21 m with step between VES points 4 m.

Figure 6. Result of 2D inversion for profile 1. Three layered type K model is evident

Let's consider technology of ERT-VES data processing on example of profile 1. First 2D

inversion is performed (Fig.6) with Res2DInv program [Loke and Barker, 1996]. Cross-section

is rather stable with horizontal layering. Mean VES curve was obtained and interpreted as 1D

model (Fig.7). Using this model as start model the whole profile was 1D interpreted (Fig.8) with

IPI2Win program [Electrical..., 1994].

1 102 3 4 5 7 20 300.70.5

10

20

30

5070 Rho, Ohm.m

AB/2, Z, m

Rho_a(AB/2)

Rho (Z)

Figure 7. Mean VES curve for profile 1 and 1D model for it

To recalculate resistivity cross-section into petrophysical cross-sections we need to know

groundwater salinity (Fig.2) and soil model. Soil model is determined after measuring soil

resistivity versus pore water salinity and quantitative interpretation of this curve with the

program Petrowin using technology described in [Shevnin et al., 2008].

5

One possible soil model has such parameters set:

Table 1.Component Capil. radius, m. Porosity CEC, g/l

Sand 0.500E-04 0.3 0Clay 0.300E-07 0.65 2.6

0 4 8 12 16 20 24 28 32 36 40-7

-5

-3

-1

7.4 12 20 33 55

0 4 8 12 16 20 24 28 32 36 40-7

-5

-3

-1

0 5 10 15 20 25 30 35 40 45 50

0 4 8 12 16 20 24 28 32 36 40-7

-5

-3

-1

0.05 0.14 0.37 1 2.7 7.3 20

Resistivity, Ohm.m

Clay content, %

Filtrationcoefficient, m/d

X, mZ,m

Z,m X, m

Z,m X, m

A

B

CFigure 8. Geoelectrical cross-sections for profile 1. А – resistivity cross-section after 1D

interpretation. B – clay content cross-section; C – filtration coefficient cross-section

1 10 1002 3 4 5 7 20 30 50 70 200 300

04

8

12

16

20 f, %

Rho, Ohm.m

Figure 9. Soil resistivity histogram for all 7 cross-sections after 2D inversion

6

Histogram maximum is on 17 Ohm.m. Left and right boundaries are 3.5 and 74 Ohm.m.

Petrophysical parameters for these three values are the next:

Table 2.Resistivity Clay content, %. Porosity, % CEC, g/l Kf interval, m/d

3.5 79 51 2 3*10-6 - 0.0117 13 25 0.34 1.3 - 0.3774 0 30 0 4.6 - 72

Analysis of relations between petrophysical parameters

In fig. 10 there are soil resistivity curves versus pore water salinity for 8 samples from 3

boreholes on profile 1. Curves belong to two different groups. Curves 2, 5 and 8 correspond to

soil samples taken from the second layer with probing depth 1.4-4 m (more sandy), other curves

with probing depth from 0 to 1.4 m correspond to soil samples taken from near surface layer –

with higher clay content. Boreholes did not enter to the third layer.

1

10

1_0-1.82_1.8-43_b0-14_b1-1.95_b1.9-46_c0-17_c1-1.48_c1.4-41

2

34

5

67

8

Soil resistivity, Ohm.m

0.7

2

5

20

50

0.1 1 10

Water salinity, g/l

0.2 0.5 2 5 20

Legend

30

Figure 10. Curves of soil resistivity versus pore watersalinity measured in the laboratory. Curve name

contains ordered number, borehole and sample depth

1 100.2 0.3 0.5 0.7 2 3 4 5 7 20

1

10

2

3

57

20

304050 Resistivity, Ohm.m

Salinity, g/l

A

B12

Figure 11. Theoretical soil curves (2)compared with experimental resistivity

values (1)A – sample 5:b1.9-4.B – sample 6: c0-1

Such curves were interpreted with the program Petrowin with estimation of some

petrophysical parameters of soil [Shevnin et al., 2008].

Table 3.Sample 5: b1.9-4 Clay content, % Porosity,% CEC, g/l Kf, m/d

Experimental 3 3.08Interpreted value 5 27.8 0.45 3.08 - 2

Table 4.Sample 6: c0-1 Clay content, % Porosity,% CEC, g/l Kf, m/dExperimental 25 0.75Interpreted value 44 38.4 0.88 0.74 - 0.04

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Calculation of filtration coefficient

For calculation of filtration coefficient with Petrowin program we need to calculate clay

content. Then two different algorithms of Kf calculation are used.

The first algorithm, developed by Ryjov is based on pores radii in different soils and clay

content, according to formulas (1-2)

ïïï

þ

ïïï

ý

ü

ïïï

î

ïïï

í

ì

÷÷ø

öççè

æ-×=

-×-K=

×+×=

pS

claySV

pCclaypSpsef

claypCclayVpseff

KC

RR

KCK

RKCRKK

1

),1(

,10)(136.6 10221

, at Сclay< Kps, (1)

1021 10)(136,6 ××= claypСclayf RKCK , at Сclay> Kps (2)

where Kf1 – is filtration coefficient of sample; Кpsef – coefficient of effective porosity of sample

which depends on level of sand pores filling by clay; КpS – sand porosity; КpС – clay porosity;

Сclay – volume clay content in soil; RS – sand pore radius; Rclay- clay pore radius. Porosity is in

relative units between 0 and 1, pore radius is in m.

The second algorithm of Kf2 determination was proposed in [Shevnin et al., 2006b] and

uses formula (3);2

20072.0 -×= clayf CK (3)

Both formulas give different results, but their advantages and disadvantages for Kf

determination are not clear yet that is why in Petrowin program both algorithms are used and

results (like tables 2 and 3) show two Kf values.

The main difference of Sinaloa soil for our analysis is the fact that we need to use fine

fraction content in soil (clay + silt), instead of clay content. Difference in Kf determined on two

algorithms (2-3 times) is not important, because pump tests in single boreholes also have similar

difference in comparison with more precise determination of Kf in group of boreholes. What Kf

values better for use for soil characterization we can decide by comparison calculated on

Petrowin values Kf with experimental values measured on soil samples.

Petrophysical parameters correlation

In fig.12 correlation between CEC and Kf on experimental data on soil samples is shown.

Kf is more when CEC is low and vice versa. In reality parameters depends on clay content (or

fine fraction content), the more is fine fraction content the higher is CEC and the lower is Kf.

8

10 20 30 40 509876543

0.0001

0.001

0.01

0.1

1

10 Kf, m/d

CEC, mg-eq./100 g

Figure 12. Distribution of 73 experimental values in coordinate system Kf – CEC,separated into two groups on Kf values

This relation is clear visible in fig.13, presenting correlation between fine fraction content

and CEC on experimental data.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

10

20

30

40

50

9876

5

4

3

CEC, mg-eq./100 g

Fine fraction content

Figure 13. CEC and fine fraction content correlation

9

In fig.14 Kf distribution versus fine fraction content (clay + silt) is shown. With letter A

points concentration was marked that is in good correlation with formula (3) [Shevnin et al.,

2006b]; with letter B separated points concentration with low Kf were marked that is in good

correlation with Ryjov formulas (1-2). We need to comment that usually we use clay content in

formulas 1 - 3 but in fig. 14 fine fraction content was used (clay + silt). That is why both graphs

were moved along horizontal axis taking into account clay content in fine fraction of soil of

Sinaloa.

In fig.15 results of measurements and theoretical calculations are presented in the same

coordinate system. This process was named petrophysical modeling and is used to verify

consistency of all data. Blue circles show soil samples ρ measurements at different salinities (in

interval between 0.028 and 30 g/l). Such resistivity versus pore water salinity values were

measured for every 73 soil samples from 21 boreholes for 7 key areas.

0.0001

0.001

0.01

0.1

1

10

0.1 10.05 0.07 0.2 0.3 0.5 0.7

Fine grains content

K ,m/d

f1

2 A

B

Figure 14. Correlation between measured Kf and fine fraction content in soil sample (clay + silt).1 – theoretical calculation on Ryjov's algorithm, 2 – on formula (3)

10

1

2

34

5

67

8

R

1

10

100

200300 Legend

Sand2%4%10%20%30%40%50%70%Clay 100%Water

2345678910

1Resistivity, Ohm.m

Water

0.5

2

5

20

50

0.1 1 10 100

Salinity, g/l

0.2 0.5 2 5 20 50

12

3

4

5678910

Figure 15. Petrophysical modeling

Blue dash line shows water resistivity versus salinity. Continues lines of different colors

for salinity interval 0.1 - 100 g/l show theoretical curves of resistivity for soils with definite clay

content in percent from 0 (pure sand) until 100 (pure clay) versus salinity.

Vertical continues line shows typical groundwater salinity 0.73 g/l. For this salinity all

experimental and theoretical values of soil resistivity are in interval from 2.5 to 40 Ohm.m. Soil

resistivity obtained from 2D inversion of 7 profiles shows by thick gray line R and is in interval

from 3.5 to 74 Ohm.m (look at fig. 9). Upper part of that line goes out of limits of sand line,

because upper part of soil is in vadose zone (depth of groundwater for the whole area is at 1.7 m,

and that for profile 1 at 1.3 m), and calculations in fig. 15 were performed for full saturation.

Inclined lines in salinity interval from 0.28 to 15 g/l are soil curves resistivity versus salinity for

all soil samples of profile 1 (look at fig. 10). Relatively good conformity of all data says that

experimental and calculated data are in good consistency.

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Conclusions

Soil in the area of channel Valle del Fuerte in Sinaloa state of Mexico consists mainly of

silt fraction (46%), fine grain sand (40%) and small amount of clay (14%). Clay can have high

CEC value (until 9 g/l or 30-40 mg/100g and even more until 25 g/l). CEC of soil depends

mainly of clay content, sand has low or zero CEC value.

Predominant groundwater salinity is 0.7-1 g/l, but sometimes salinity grows until 20 g/l.

Predominant cation is Na+, and anions are SO42-, Cl-, HCO3

-, in equal parts.

Filtration coefficient was measured for all 73 soil samples. Kf values are mainly in

interval 0.1 - 10 m/d with very low Kf values for clay samples from 0.01 to 0.001 m/d and less.

Petrophysical modeling showed that experimental and calculated soil parameters suited

well to each others and consequently the main purpose of our work to understand soil model of

the area was reached.

By using electrical soundings (SEV or ERT) and Petrowin program we can diminish 2-3

times quantity of soil samples for determination of petrophysical parameters (porosity and clay

content) and water-physical parameters (Kf and salinity) of soil in studied area and diminish

costs of field and laboratory works.

References

1. Electrical prospecting with resistivity method. Editors: Khmelevskoy V.K. and

Shevnin V.A. MSU edition, 1994. 160 pp. (In Russian).

2. Loke, M. H. and Barker R. D. 1996. Rapid least-squares inversion of apparent

resistivity pseudosections using a quasi-Newton method. Geophys. Prospect., 44, 131-152.

3. Ryjov A.A., Sudoplatov A.D. 1990. The calculation of specific electrical conductivity

for sandy - clayed rocks and the usage of functional cross-plots for the decision of hydro-

geological problems. // In book "Scientific and technical achievements and advanced experience

in the field of geology and mineral deposits research. Moscow, pp. 27-41. (In Russian).

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some examples of algorithm's application. Proceedings of SAGEEP-2002 conference.

5. Shevnin V., Delgado Rodríguez O., Mousatov A., Ryjov A., 2004, Soil resistivity

measurements for clay content estimation and its application for petroleum contamination study.

SAGEEP-2004, Colorado Springs. p. 396-408.

6. Shevnin V., Delgado Rodriguez O., Mousatov A., Zegarra Martinez H., Ochoa Valdes

J. and Ryjov A., 2005, Study of petroleum contaminated sites in Mexico with resistivity and EM

methods. SAGEEP-2005 Atlanta, Georgia, p.167-176.

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7. Shevnin V., Delgado Rodríguez O., Mousatov A., David Flores Hernández D., Zegarra

Martínez H. and Ryjov A. 2006a. Estimation of soil petrophysical parameters from resistivity

data: their application for oil contaminated sites characterization. Geofísica Internacional, Vol.

45, Num. 3, pp. 179-193.

8. Shevnin V., Delgado-Rodríguez O., Mousatov A. and Ryjov A. 2006b. Estimation of

hydraulic conductivity on clay content in soil determined from resistivity data. Geofísica

Internacional, Vol. 45, Num. 3, pp. 195-207.

9. Shevnin V., Mousatov A., Ryjov A. and Delgado-Rodriquez O. Estimation of clay

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Prospecting, 2007, 55, p.265-275

10. Shevnin V.A., Mousatov A.A., Ryjov A.A. & Delgado - Rodriguez O., Petrophysical

Analysis of Resistivity Data. – 14th European Meeting of Environmental and Engineering

Geophysics, Near Surface Geophysics 2008 Kraków, Poland, 15 - 17 September 2008, 4 pp.