Документ взят из кэша поисковой машины. Адрес оригинального документа : http://geophys.geol.msu.ru/shevnin/publ/187.pdf Дата изменения: Wed Oct 17 15:56:43 2012 Дата индексирования: Sat Feb 2 23:50:21 2013 Кодировка: Поисковые слова: m 33

ESTIMATION OF SOIL SUPERFICIAL CONDUCTIVITY IN A ZONE OF MATURE OIL CONTAMINATION USING DC RESISTIVITY
Vladimir Shevnin (vshevnin@imp.mx), Mexican Petroleum Institute, Mexico city, D.F. Omar Delgado-RodrМguez, Mexican Petroleum Institute, Mexico city, D.F. Aleksandr Mousatov, Mexican Petroleum Institute, Mexico city, D.F. Albert Ryjov, Moscow State Geological Prospecting Academy, Moscow, Russia

Abstract
At several SAGEEP conferences we presented resistivity technology for oil contamination study. Soil resistivity and groundwater salinity measurements were recalculated into petrophysical parameters: clay content, porosity and cation exchange capacity. For uncontaminated sandy-clay soil these parameters are close to real ones. For contaminated soil these parameters are apparent ones, but can be used as good contamination indicators. The real cause of petrophysical anomalies is an increase of superficial conductivity greater than electrolytic conductivity change in pore space of contaminated soil, as it was found by Abdel Aal et al. (2004) with spectral IP method. We suppose that DC resistivity also can give information about superficial conductivity, which is the main cause of resistivity and petrophysical anomalies at contaminated sites. Superficial conductivity for soil is estimated on clay content (calculated on soil resistivity), groundwater salinity and clay conductivity. Clay conductivity depends on pore water salinity and cation exchange capacity of clay. Anomalous clay content reflects an increase of internal surface area whereas anomalous cation exchange capacity reflects an increase of surface charges at the mineral grain electrolyte interface. Practical examples of superficial conductivity estimation at oil contaminated sites are demonstrated and compared with resistivity and petrophysical anomalies.

Introduction
Contamination study with application of geoelectrical methods is now recognized and applied in many countries. But some aspects of such study are now in research and development stage. Several models of electrical properties' changes of contaminated soils were developed and published (Sauck, 1998, Atekwana et al., 2001, 2003, Abdel Aal et al., 2004). Abdel Aal et al. (2004) with the help of spectral IP method in laboratory investigated changes of superficial and electrolytic conductivity of soil in biodegradation process. Authors concluded that an increase of superficial conductivity is the main factor of soil conductivity changes due to biodegradation in contaminated zones. Shevnin et al. (2004, 2005) studied soil resistivity changes in contaminated zones using vertical electrical sounding (VES) method with recalculation of soil resistivity (taking into account groundwater salinity) into petrophysical parameters: clay content, porosity and cation exchange capacity. Comparing the results of these studies we found similarity in superficial conductivity and petrophysical parameters' anomalies in mature contaminated sites. That allowed developing algorithm of superficial conductivity determination using clay content and CEC values of contaminated soil. The only difference between two approaches that for direct estimation of superficial conductivity the spectral IP measurements are needed, whereas for calculation of this parameter on petrophysical data we can use only resistivity measurements.

1514


Changes of soil properties due to mature oil contamination
In the table 1 there is a list of changes in chemical, physical (including electrical) and biological parameters of contaminated soils, collected from publications of Sauck (1998), Atekwana et al., (2001, 2003), Abdel Aal et al., (2004); Modin et al., (1997); Shevnin et al., (2004, 2005). Table 1.: Changes of soil properties due to mature oil contamination. Soil Property Changes in mature Soil Property Changes in mature contamination zone contamination zone Bacterial population Increase Soil resistivity Decrease Nutrients Decrease Water resistivity Decrease Total dissolved salts Increase Electrolytic Increase conductivity in pores Contaminants Increase Superficial Increase density conductivity in pores Biofilms Increase Clay content Increase (apparent) Surfactants Increase Porosity (apparent) Increase or decrease Cation exchange Increase capacity (apparent) An idea of clay content estimation on soil resistivity and water salinity

,
1000
0

Ohm.m
Legend ( Clay content, %)

1000



,

Oh m.m

Water
2

100

Water

100 50 20 10 5 2 1

4 10 20 30 40 50 70 100

SandPor=55% 10
0.1 CEC ( g/l) 0.2 1 0.5 8 1 2 3 5 7 9 15 30 50 10 0

1

T=20oC CECClay=3

Salinity, g/l 0.1 1 10 100

0.1
0.1 0.01 0.1 1

C(NaCl), g/l
10 100

0.01

Figure 1.: Theoretical graphs of soil resistivity Figure 2.: Theoretical graphs of soil resistivity versus water salinity for different values of clay versus water salinity for pure sand and pure clay content. Dash line is for water resistivity. with 55% of porosity and different clay CEC. Soil porosity for sand - clay mixture is in interval 0.16 - 0.55 for the case of fig.1. Sand porosity is in this case 0.25, clay porosity - 0.55. When clay content is equal to sand porosity all pores of sand are

1515


filled with clay, soil porosity is minimal and equal 0.16. Grains of sand and clay particles have very high resistivity in comparison with pore water resistivity. Line for sand in figure 1 is situated above and in parallel of water line and distance between these lines depends on sand porosity. Clay porosity is higher than sand porosity. That is why the line for pure clay is closer to water line (at high salinity). But the line for clay is parallel to water line only for high water salinity. For fresh water clay resistivity line is not parallel to water line and is below water line. This is result of the influence of electric double layer (EDL) in clay pores. Intermediate soil lines between pure sand and pure clay are between sand and clay for fresh water and distributed in accordance with soil porosity for high water salinity. That is why soil line for minimal porosity (0.16 for clay content 20%) is above all lines at salinity more than 5 g/l when the EDL influence is negligible. The difference between sand line and any line with some clay content shows the EDL influence on soil conductivity. The more is clay content the higher is EDL influence. Using figure 1 we can estimate clay content in soil if we know soil resistivity and groundwater salinity (at full saturation). In reality sand line position depends on sand porosity and clay line position depends on cation exchange capacity (CEC) of clay. That is why more accurate clay content estimation we can obtain if we know sand component porosity and clay component CEC value, or if we use computer program which can estimate all above mentioned parameters. In figure 2 the line for sand resistivity with porosity 55% equal a pure clay porosity is shown. This line can be considered also as line for clay without EDL and CEC influence. The difference between sand and clay lines depends on EDL influence and also on CEC values of clay. The main idea of figure 2 is to demonstrate only influence of different CEC values.

Estimation of petrophysical parameters on resistivity data
At SAGEEP conferences we presented resistivity technology for oil contamination study (Shevnin et al., 2004, 2005). Soil resistivity and groundwater salinity measurements were recalculated into petrophysical parameters: clay content, porosity and cation exchange capacity. The advantage of using petrophysical parameters · For uncontaminated soil petrophysical parameters (clay content, porosity and cation exchange capacity) estimated on resistivity are close to true petrophysical parameters, found with traditional methods in laboratory. · For contaminated soils we receive anomalous parameters, but these parameters help to localize contamination. · Joint usage of resistivity and petrophysical parameters helps receiving more detailed characterization of non-contaminated and contaminated soils. · Petrophysical parameters have more separation ability to resolve clean and contaminated soil. Mature oil contamination changes soil petrophysical parameters in such a way as it looks like an increase of clay content together with CEC of clay. We call this effect - quasi-clay. In table 2 there are some examples of quasi-clay appearance. Table 2.: Clay content, estimated from VES resistivity and water salinity for some contaminated sites. Site name Background values, % Anomalous values, % Ratio Paredon-31 40 100 2.5 Km-42 2 6 3 Km-124 Layer 1 20 50 2.5 Km-124 Layer 2 2.5 18 7.2 Km-124 Layer 3 2.5 22 8.8 Mecatepec 25 100 4

1516


Comparison of two contamination indicators: clay content and superficial conductivity · Sometimes it is difficult to explain that increase of clay content in contaminated zone isn't real, it is quasi-clay effect, created by biodegradation processes. Clay content is well known geological term. There is no real increase of clay content in contaminated site. · Superficial (interfacial) conductivity is the term not so well known, and its usage is more close to real geoelectrical situation than usage of clay content. · Quasi-clay is intermediate term related to both clay content and superficial conductivity. Table 3.: Comparison between our approach and results of Abdel Aal et al. (2004) Our approach Abdel Aal et al., 2004 approach DC resistivity method measures only real Spectral IP (SIP) method measures real and resistivity, which conductivity is the combination imagine conductivity component. Real component of electrolytic and superficial conductivities. is a sum of electrolytic and superficial conductivities. Clay mainly has superficial conductivity, which The relative increase of interfacial (or superficial) depends on the area between solid and liquid and conductivity above electrolytic conductivity was also on EDL and CEC (electrical charges). In found in experiment with SIP method. Interfacial contaminated soil (including sandy soil) quasi-clay electrical properties were modified due to is appeared (apparent increase of clay content), due microbial activity. An increase of surface area to physical - chemical changes caused by exerts a primary control on interfacial electrical biodegradation. Increase of quasi-clay is followed properties. An additional factor is enhanced by increase of CEC (like increase of surface and mineral weathering, which releases ions into charges). solution increasing water conductivity. What method or parameter is the best to detect contamination? In this work we consider and compare several ideas: Resistivity and Induced Polarization methods, determination petrophysical parameters from B A resistivity, determination superficial conductivity (or resistivity) from clay content and CEC. Induced polarization is further development of resistivity method. It measures several parameters, including resistivity. But IP instruments cost more; IP field measurements are more complicated. We suppose that resistivity method with determination of petrophysical parameters can give valuable information. That is why C we are trying to compare these two methods. The next D question is comparison of different parameters estimated on resistivity: clay content, porosity, CEC, superficial resistivity. Below we are trying to estimate their separation index or resolution between clean and contaminated soil (figure 9). Some models of contamination in pores With the help of figure 3 we compare some possible models explaining quasi-clay and superficial conductivity increase in case of mature contamination. If biofilms, which are products of biodegradations, are distributed on mineral grains Figure 3.: A ­ sand model; B ­ central part of sand model with biofilms on the surface of sand grains; C ­ model with biofilms on the surface of sand grains and inside pore space; D ­ model with oil emulsion in pore space, caused by surfactants.

1517


surface as in figure 3, B, then small biofilm thickness in comparison with capillary diameter can not noticeably change capillary conductivity. To obtain noticeable effect, biofilms should be distributed on mineral grains and in capillary volume (Fig.3, C). In this case the influence of EDL on all surfaces (grains and biofilms) would be noticeable. Another model (Fig.3, D) shows capillary path filled with oil products, separated into small drops under the influence of surfactants, produced by bacteria (Atekwana et al., 2001). Each drop has high resistivity, but spaces between them are very thin and influence of EDL is high. Is it possible that real situation can include combination of models C and D. The principal difference between models B and C (or D) is in pore radius and the grade of EDL influence. Dependence of electrical conductivity () of NaCl aquatic solution from a distance up to capillary wall. 10 Figure 4 shows the change of water conductivity in Curves legend: EDL. Conductivity in EDL for fresh water increases until 30 sal t c oncentration in neutral solution, g/l: times (Fig.4, line for salinity 0.059 g/l). When all capillaries are fine, soil conductivity depends greatly on conductivity in 5.9 1 EDL of capillaries. In case of wide capillaries EDL does not influence on soil conductivity. 1.18 Looking on fig. 2 we can conclude that sand and clay 0.59 resistivities are equal at the same porosity at high salinity and 0.1 different at low salinity due to superficial conductivity. Soil superficial conductivity depends on clay content in soil (or quasi-clay for contaminated soil), on cation exchange 0.059 capacity and groundwater salinity. If we know these 0.01 parameters we can determine superficial conductivity. X 1000 1 10 1 00 Dista nce fr om ca pi lla ry wa ll, an gstro m Superficial conductivity of soil is conductivity of the solution in clay (or quasi-clay) pores multiplied to clay Figure 4.: Change of conductivity in content in soil. To estimate conductivity of water solution in electric double layer as function of the clay pores, we need to use clay conductivity and divide it to distance from the solid phase. (1 -10 clay porosity. We calculate clay conductivity theoretically angstrom = 10 m, 100 е=1 mkm), like function of pore water salinity and cation exchange capillary radius for clay is 1-10 A, for capacity (according to algorithm of Ryjov) (Ryjov and sand - > 100 mkm. Sudoplatov, 1990). In figure 2 dependence of clay resistivity versus water salinity is shown for different values of clay CEC. Using these data we can find dependence between clay conductivity and CEC for every salinity value to use it for clay conductivity determination. For practical application we use resistivity instead of conductivity. Three steps of superficial resistivity calculation: 1. Calculation of Clay (f (CEC,Salin.)) using data presented in figure 2. For example X=ln(CEC) and Y=ln(Clay): lnY=a ­ b*X - c*X2 (1), where a, b, c ­ approximating coefficients, different for each salinity (see example in figure 5). Equation for salinity 0.01g/l: Y = 1.384397357 - 0.4938222434 * X - 0.05359104356 * pow (X, 2); (1, A) Equation for salinity 0.1g/l: Y = 1.32372928 - 0.4454906043 * X - 0.06208606125 * pow (X, 2); (1, B) Equation for salinity 1 g/l: Y = 1.051064232 - 0.3276935254 * X - 0.07539715295 * pow (X, 2). (1, C)
Electrical conductivity of liquid, Sim/m

1518


= exp(Y ) . (2) 2. Resistivity of the solution in clay pores is multiplication of clay resistivity on clay porosity W , Clay = Clay Clay ( f (CEC , Salin.)) (3) 3. Superficial resistivity is resistivity of the solution in clay pores divided on clay content.
Clay



8 5

cla

y

2

C Clay C Clay Using these formulas we calculated superficial resistivity for several contaminated sites and compared new maps with maps of soil resistivity and petrophysical parameters.



SC

=



W , Clay

=



1 0.5

Clay



Clay

( f (CEC , Salin.))

(4)

Salinity 0.1
Ryjov Approximation

0.2 0.1

CEC, g/l
1 10 100

Practical examples of superficial conductivity
Y, m

In each contaminated site we performed vertical electrical sounding measurements along profiles on multiX, m Ohm.m electrodes technology. Profiles were placed in studied 110 134 164 200 245 300 365 445 545 Y, m area in such a way to have possibility to draw resistivity 25 20 maps. Each profile data we interpreted using Res2DInv 15 B 10 program (Loke and Barker, 1995) and then with the help 5 0 of X2IPI and IPI software (Bobachev, 1994, 2003) we -5 converted the data into data cube to present interpretation -10 -64 -56 -48 -40 -32 - 24 -16 -8 0 8 16 24 32 40 48 56 64 results as vertical cross-sections and maps for different X, m Clay, % depths. Normally maps have higher resolution to find fine 2 2.5 3 3.5 4 4.5 5 5.5 6 Y, m 25 anomalies from contamination (Shevnin et al., 2005). 20 Interpreted resistivity data we recalculated into 15 C 10 petrophysical parameters: clay-content, porosity, cation 5 0 exchange capacity, taking into account groundwater -5 -10 salinity of the study area. -64 -56 -48 -40 -32 - 24 -16 -8 0 8 16 24 32 40 48 56 64 X, m In figure 6 we present a series of maps for sandy CEC g/l 1.5 2 2.5 3 3.5 aquifer of the site km 42 in Tabasco, Mexico. Survey was Y, m performed 9 months after the oil products leakage. 20 Resistivity anomalies caused by contamination are weak, D 10 while petrophysical anomalies are more definite and have 0 more contrast. Mean value of sand resistivity (saturated -10 by water with resistivity 110 Ohm.m) is 280 Ohm.m. -64 -56 -48 -40 -32 -24 -16 -8 0 8 16 24 32 40 48 56 64 X, m Superficial resistivity (RSC) estimated for the sand layer RSC, 18 22 27 33 40 49 60 74 90 110 134 164 oh m.m is in interval from 15 to 170 Ohm.m. In contaminated Figure 6.: Maps of resistivity (A), clay zone quasi-clay content is 5-6% in comparison with clean content (B), CEC (C) and superficial zone with 2-3% of clay. resistivity RSC (D) of sandy aquifer for the In figure 7, B we presented additional map of site km. 42.
-64 - 56 -48 -40 -32 -24 -16 -8 0 8 16 24 32 40 48 56 64

25 20 15 10 5 0 -5 -10

A

Figure 5.: Approximation of interrelation between clay resistivity and cation exchange capacity for definite salinity.

0.1

(resistivity) determination.

1519


normalized superficial resistivity. This parameter was 20 estimated on the next formula: A 10 Norm _ RSC = (RSC - BGV ) / BGV (5) BGV means background value of superficial 0 resistivity. There isn't clear procedure to estimate -10 -64 -56 -48 -40 -32 -24 -16 -8 0 8 16 24 32 40 48 56 64 background value. We used BGV=30 Ohm.m. Normalized RSC, 18 22 27 33 40 49 60 74 90 110 134 164 oh m.m superficial resistivity shows the total interval (between maximum and minimum, -0.9 - +2.6 in the case of fig.7, 20 C) and probably should be useful at comparison of data B 10 for different contaminated site. On the histogram of normalized superficial resistivity (figure 7, C) the gray 0 -10 rectangular marks values interval for contaminated zone. -64 -56 -48 -40 -32 -24 -16 -8 0 8 16 24 32 40 48 56 64 In figure 8 several maps for the first layer (0-1 m) -0. 4 0 0.4 0.8 1.2 1.6 2 2.4 Nor mR CS in the site km124 in Tabasco, Mexico are presented. Soil 50 f, % resistivity changes from 30 to 330 Ohm.m. Superficial 40 resistivity (RSC) of the same layer changes from 0.4 to 40 30 C 20 Ohm.m. Outlines of contaminated zone are very similar for 10 No rmRC S resistivity, clay content and superficial resistivity. For 0 -1 -0. 8 -0. 6 -0. 4 -0. 2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 localization of contamination all 4 parameters are similar, Figure 7.: Maps of RSC (A), normalized but for explanation, as we think, superficial resistivity or RSC (B) and histogram of normalized RSC quasi-clay concept is better. (C).
Y, m X, m Y, m X, m

.

.

.

260

Y, m
250



260

, Ohm .m

Y, m
250 240

Cl ay content, %
35 30 25

245 240 150

230

230

PE2
220 90

PE2
220

20 15

210 55 200

210

10

A
360 370 380 390 400 410 420 430 440 450

200
33

B
360 370 380 390 400 410 420 430 440

5

X, m
450

X, m

0

260

Y, m
260 250

Y, m
250

CEC, g/l
20
240 230 220

RSC , Ohm .m
40 25 15 9

240 230

PE2
220 210 200

12

PE 2

5.5 3.3 2

7.4

210 200

C
360 370 380 390 400 410 420 430 440 4 50

4.5

D
360 370 380 390 400 410 420 430 4 40 450

1.2 0.7 4 0.4 5

X, m

X, m

Figure 8.: Maps of resistivity (A), clay content (B), CEC (C) and RSC (D) for the site 124 km in Tabasco.

1520


f, % Resolution between contaminated and non-contaminated soil for SI=dX/stnd=2.52 10 different parameters. Resistivity A , Ohm.m In Shevnin et al. (2005) we proposed to use separation 0 index between clean and contaminated soil to compare different 10 20 30 50 70 100 200 500 1000 20 SI=4.36 f, % parameters, estimated on the next formula: SI=dX/stnd (6). Clay Where dX is the distance between distribution maximums for 10 B Cl ay content, % clean and contaminated soil and stnd ­ is a standard. 0 Using this formula for the 1st layer of the site Km 124 we 40 0 5 10 15 20 25 30 35 40 45 50 55 60 f, % found SI values (Fig. 9) for resistivity SI= 2.52, for clay content ­ SI=3.02 C 20 Porosity 4.36, for porosity ­ 3.02, for CEC ­ 2.46 and for superficial Poros ity, % resistivity RSC ­ 6. For RSC calculation we used clay content and 0 CEC. Probably because of that SI for RSC has greater value in 20 14 16 18 20 22 24 26 28 f, % SI=2.46 comparison with clay content and CEC. We plan to continue 10 calculate separation index for different contaminated sites with CEC D CEC, g/l different geological situation. 0 0. 01 0.02 0.05 0.1 0. 2 0. 5 1 In figure 10 maps of resistivity and superficial resistivity f, % for the site Paredon in Tabasco, Mexico are shown. Resistivity for SI=6 Norm_RCS the contamination indicator layer changes from 1 to 54 Ohm.m. E Superficial resistivity of the same layer changes from 0.14 to 55 Ohm.m. Outlines of contaminated zones are very similar for Figure 9.: Histograms and resistivity and superficial resistivity. Zone of high superficial separation between clean and resistivity near the oil borehole corresponds (on our opinion) to contaminated soil for several fresh contamination due to nowadays oil leakage from closed parameters. borehole. In figure 11 several maps for the third layer (2-3 m) for the site Mecatepec in Veracruz, Mexico are presented. Soil resistivity changes from 4 to 400 Ohm.m. Superficial resistivity of the same layer changes from 0.08 to 150 Ohm.m.
50 40 30 20 10 0 -1 -0.8 -0.6 -0. 4 -0. 2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6

20

Y, m

114 0 MW-1

Rho, Ohm- m
54 33 Y, m
1100

112 0

110 0 S - 13 S - 10 10 80 MW-4 10 60 S - 11 10 40 S- 8 10 20 MW-3 S- 6 S- 5 MW-2 10 00 S- 7 S- 4 S - 12 S- 9 S- 3 S- 1 S- 2

20 12 7.4 4.5
1040 1080

1060

2.7 1.6 1 MW-6
1020

98 0 90 0

A
920 940

1000

B
920 940 960 980 1000 1 020 1040 0.14 0.22 0.37 0.6 1 1.6 2.7 4.5 7.4 12 20 33 55

X, m
1060

X, m
980 1000 1020 1040 1 060 1080

960

RSC, O hm.m

MW-4 M onitoring we ll

LE GEN D S-2 Fenc e

Sampling well

Figure 10.: Maps of resistivity and RSC for the site Paredon in Tabasco.

Oil borehole

1521


Y,m
1050 1040 1030 1020 1010 1000 990 980 970 960 950

Rho,

Ohm-m


400 245 90 55 33 20 12 7.4 4.5

Y,m
1050 1040 1030

Clay, %

Y,m

%

1050

100 1040 90 80 70 60 50 40
1020 1010 1000 990 980 970 960 1030

RSC,

Ohm.m

RSC,

Oh m.m
150 90 55 33 20 12 7.4 4.5 2.7 1.6 1 0.6 0.37 0.22 0.14

150 1020
1010 1000 990 980 970 960

30 20 10

A
X,m
110 120 130 140 15 0 160 170 180 190 200

B
110 120 130 140 150 160 17 0 180

0

950

X,m
190 200

C
110 120 130 140 150 160 170 180

0.08

950

X,m
190 200

Figure 11.: Maps of resistivity (A), clay content (B) and RSC (C) for the site Mecatepec in Veracruz. Table 4.: Comparison of ratio between resistivity and RSC values: ratio intervals and mean ratios. Mean Rho / RSC Site Interval Rho / RSC Paredon 2-30 7.5 Km 42 3-6 4.7 Km 124 10-33 22 Mecatepec 15-55 34 In table 4 values of ratio between resistivity and superficial resistivity (RSC) for different sites and their mean ratio are shown, to estimate the input of superficial resistivity created by soil lithology and biodegradation.

Conclusions
DC resistivity can give information about superficial conductivity, which is the main cause of resistivity anomalies and petrophysical anomalies at contaminated sites. Superficial conductivity can be estimated on clay content and CEC values, received from soil resistivity and water salinity data. Several parameters visualized as maps or cross-sections can characterize contamination of a site: resistivity, clay content, porosity, cation exchange capacity, superficial resistivity. An advantage of superficial resistivity is that it is the real cause of changes in other soil properties in contaminated area. This parameter probably has more resolution, taking into account separation index. Another possibility consists in using quasi-clay concept - that is such effect of contamination that is equivalent to the appearance of additional clay in soil. Quasi-clay allows explaining changes of soil resistivity, porosity, CEC and even changes of filtration coefficient. The disadvantage of the quasi-clay term is that real clay content does not change due to contamination.

References
Abdel Aal, G. Z.; Atekwana, E. A.; Slater, L. D.; Atekwana, E. A., 2004, Effects of microbial processes on electrolytic and interfacial electrical properties of unconsolidated sediments. Geophys. Res. Lett., Vol. 31, No. 12, L12505 10.1029/2004GL020030. Atekwana, E., Cassidy, D. P., Magnuson, C., Endres A. L., Werkema, Jr., D. D. and Sauck W. A., 2001, Changes in geoelectrical properties accompanying microbial degradation of LNAPL. in

1522


Proceedings of the Symposium on the Application of Geophysics to Engineering and Environmental Problems. 1 ­ 10. Atekwana E A., Atekwana E. A., Rowe R. S., 2003, Relationship between Total Dissolved Solids and Bulk Conductivity at a Hydrocarbon-Contaminated Aquifer. SAGEEP Proceedings, 2003. p. 228 - 237. Bobachev, A.A., 1994, IPI2Win software: http://geophys.geol.msu.ru/rec_labe.htm . Bobachev, A.A., 2003, X2IPI software: http://geophys.geol.msu.ru/x2ipi/x2ipi.html . Loke, M.H. and Barker, R.D., 1995, Least-squares deconvolution of apparent resistivity pseudosectious. Geophysics, 60, 1682-1690. Modin, I. N., Shevnin, V. A., Bobatchev, A. A., Bolshakov, D. K., Leonov, D. A. and Vladov, M. L. 1997, Investigations of oil pollution with electrical prospecting methods, in Proceedings of the 3rd EEGS-ES Meeting. Aarhus, Denmark, 267-270. 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 hydrogeological problems, In the book "Scientific and technical achievements and advanced experience in the field of geology and mineral deposits research. Moscow, pp. 27-41. (In Russian). Sauck, W. A., 1998, A conceptual model for the geoelectrical response of LNAPL plumes in granular sediments. In Proceedings of the Symposium on the Application of Geophysics to Engineering and Environmental Problems, 805-817. Shevnin V., Delgado RodrМguez O., Mousatov A., Ryjov A., 2004, Soil resistivity measurements for clay content estimation and its application for petroleum contamination study. SAGEEP Proceedings, Colorado Springs. p. 396-408. 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 Proceedings, Atlanta, 167-176.

1523