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GeofÌsica Internacional (2005), Vol. 44, Num. 3, pp. 251-263

Geoelectrical characterization of an oil-contaminated site in Tabasco, Mexico
Vladimir Shevnin1, Omar Delgado-RodrÌguez1, Luis FernÀndez-Linares1, HÈctor Zegarra MartÌnez1, Aleksandr Mousatov1 and Albert Ryjov2
1 2

Mexican Petroleum Institute, Mexico, D. F., Mexico Moscow State Geological Prospecting Academy, Geophysical Faculty, Moscow, Russia

Received: September 21, 2004; accepted: January 27, 2005 RESUMEN
La caracterizaciÑn geoelÈctrica de un sitio contaminado por hidrocarburos, donde hace treinta aßos fue perforado un pozo petrolero, fue realizada utilizando el mÈtodo de Sondeo ElÈctrico Vertical (SEV) con la tecnologÌa de Imagen de Resistividad e InterpretaciÑn 2D de los datos adquiridos. La interpretaciÑn de los datos de SEV estuvo apoyada por mediciones de resistividad en muestras de agua y suelo. Las zonas contaminadas corresponden con anomalÌas de baja resistividad ubicadas encima del nivel freÀtico. Los resultados de la interpretaciÑn geoelÈctrica fueron comparados con los resultados del muestreo geoquÌmico. La correlaciÑn inversa existente entre la resistividad elÈctrica y la concentraciÑn de contaminantes confirma la existencia de contaminaciÑn madura, la misma que crea anomalÌas de baja resistividad. Mediante la modelaciÑn petrofÌsica es posible estimar el valor de resistividad que define la frontera entre zona limpia y contaminada. Los datos de resistividad son presentados a manera de secciones y mapas, ademÀs de haber sidos recalculados a secciones y mapas de parÀmetros petrofÌsicos, como herramientas Çtiles en la caracterizaciÑn mÀs precisa y detallada de las zonas contaminadas. PALABRAS CLAVE: Sondeo ElÈctrico Vertical, 2D tecnologÌa de resistividad, contaminaciÑn por hidrocarburos, modelaciÑn petrofÌsica.

ABSTRACT
A geoelectrical characterization of an oil-contaminated site, where thirty years ago an oil well was drilled, was carried out using vertical electrical sounding (VES) method on 2D Resistivity Imaging technology and 2D interpretation. VES data were added by water resistivity measurements and soil resistivity analysis. The contaminated zones feature with low resistivity anomalies situated above groundwater level. The geoelectrical interpretation was compared with geochemical sampling. An inverse correlation between electrical resistivity and concentration of contaminants confirms that the oil-contamination is mature and for this reason it creates low resistivity anomalies. Petrophysical modelling allowed estimating boundary resistivity value separating clean and contaminated zones. Resistivity data were visualized as cross-sections and maps and also were recalculated into sections and maps of petrophysical parameters, useful for more precise and detailed characterization of contaminated zones. KEY WORDS: Vertical Electrical Sounding, 2D Resistivity Imaging, oil contamination, petrophysical modelling.

INTRODUCTION The exploration and activity of production in the oil industry generates a large volume of wastes. It was estimated that 73% (125 867 tons) of hazardous wastes from oil processing by the Mexican oil industry per year 2000 consisted of drilling mud and cuttings (PEMEX, 2001). Unlike the US Petroleum Exemption (USEPA, 2002), Mexican environmental regulations establish that drilling mud and cuttings are hazardous wastes. Therefore intensive research and adaptation of technologies and methods are required to localize contaminated zones and to control remediation processes in the subsurface. Geophysical methods are frequently employed in the characterization of a contaminated site. Previous investigations have demonstrated the efficiency of geoelectrical meth251

ods in the delineation of contaminated zones, both in area and with depth (Abdel-Aal et al., 2001, Sauck, 1998); Vertical Electrical Sounding (VES) on Resistivity Imaging technology has proven to be an effective technique (Modin et al., 1997; Shevnin et al., 2002; Shevnin et al., 2003). Oil contamination of soil, after four months to one year after contamination, creates a low resistivity zone (Sauck, 1998; 2000). The chemical processes in oil-contamination have been described in detail by Bailey et al. (1973), Sauck (1998, 2000) and Atekwana et al. (2001) in terms of reactions and variations in the physical characteristics of the affected media. According to these authors, low resistivity anomalies result from an increase in the level of Total Dissolved Solids (TDS) created by bacterial action in the deeper part of the vadose zone or below the water table, in an acid environment.


V. Shevnin et al.

We present the results of VES application to the characterization of a polluted site in Mexico. The geoelectrical results are correlated with geochemical information. We used Resistivity Imaging technology (Griffiths and Barker, 1993), 2D VES data interpretation (Loke and Barker, 1995), soil resistivity tests in the laboratory and recalculation of resistivity data into petrophysical parameters, in order to obtain a better characterization of oil contamination. GENERAL SITUATION The study area is an abandoned oil well situated near Cardenas, Tabasco, Mexico, and measures 160 m x 180 m (Figure 1). The well was abandoned 13 years ago after 20 years of production. In the early 1970s, drilling mud and oilsoaked rock cuttings were disposed in a pit in the lower part of the area. For most of the year it is filled in with water (Figure 1). The study area can be divided into two zones: the swampy pit zone and the dry elevated zone.

FIELD INVESTIGATIONS Vertical Electrical Sounding (VES) A total of 246 VES points, distributed along 11 profiles, were measured in the study area (Figure 1) to determine the geoelectrical structure in the upper 10 metres, to delineate the oil contaminated zones both in plan and in depth, and to estimate the level of contamination. A VES survey was carried out using a great number of electrodes (~50) placed along each profile with a constant interval (2 m). AB/2 spacing of a Schlumberger array was from 2 to 20 m with the step 2 m. Spacing between sounding centres was 4 m. We used ERA resistivity meter developed in Russia, operating at a frequency of 4.88 Hz with a sensitivity of 1 µV, with a stabilized current of 10 mA.

1140

Y,m
1120

M-1
P2 P11

1100

P6 P3
1080 M-4 1060 S - 11 S - 12 S - 13

P7

P8
S-1

S - 10
P9
S-9

S-2
S-3

M-5

S-7 1040 S-8 1020 M-3 S-6 S-5 M-2 1000 S-4

P10

P4
M-6

P1

P5

980 900 M-4 920 940 960 980 1000 1020 1040

X,m
1060 1080

Monitoring well S-6 Sampling well Oil-well

P4 VES profile

LEGEND Outline of the pit for rainy season Fence Soil sampling point Water sampling points
Fig. 1. Map of the study area.

252


Geoelectrical characterization of an oil contaminated site in Tabasco, Mexico

Water resistivity measurement Water resistivity was measured in 44 water samples (Figure 1) in the study area. This survey included water from the regional aquifer and surface water (mainly from the pit). Six wells (M-1 to M-6) were drilled to 4 m depth to monitor ground water around the site. The surface water included uncontaminated and contaminated samples. The measurements were carried out using the resistivity equipment and a calibrated four-electrode cell (resistivimeter) designed for water resistivity measurements. Soil resistivity measurement We performed one reference soil sampling at location M-1 (Figure 1) for geoelectrical purposes. This location is uncontaminated and has high clay content. The soil sample was divided in four equal parts and measured with four resistivimeters. The soil was saturated with water of different salinity (0.23 ­ 17 g/l NaCl). We obtained (C) dependence to estimate clay content and some others parameters (Figure 7, Table 2). Two additional soil samples were obtained from the contaminated zone near boreholes S-2 and S-10 (Figure 1). These water and soil resistivity measurements established the eventual range of anomalous values resulting from pollution. Geochemical studies Soil samples were collected for geochemical analysis from three different depth levels between 0 - 2 m. Thirteen samples (S-l to S-13) were collected inside the pit and two were control samples collected from outside. The soil samples were collected with a split spoon sampler using percussion. They were immediately stored in clean 250 ml bottles with a Teflon-lined lid, and were preserved at 4°C for analysis, after USEPA (1995) guidelines. Total Pe-

troleum Hydrocarbon (TPH) concentration was determined using infrared spectroscopy after EPA418.1 (USEPA, 1983). Clay content and soil particle size distribution were determined after Bouyoucos (1962). Values of TPH concentration, clay content and soil electrical conductivity were used to control the geoelectrical anomalies and to correlate the geochemical and geoelectrical data. Geochemical studies were performed mostly inside the pit whereas geoelectrical measurements covered a wider area. RESULTS AND DISCUSSION It is not easy to recognise the effect of contaminants on resistivity when clay is present. However, the application of VES Resistivity Imaging technology, supported by measurements of water and soil resistivity and petrophysical modelling, allows discrimination between the contaminated and uncontaminated areas. Water resistivity The histogram in Figure 2A was obtained from 44 water samples. It shows the presence of several groups of samples, with mean resistivity values of 11, 15, 23, 46 and 72 m. After comparing water resistivity with location of sampling and geological information, we concluded that the value 46 m corresponded to surface water, the value 23 m to groundwater from the main aquifer (samples from monitoring wells) and the water of 11 and 15 m to polluted surface water. The water of 72 m was found in the concrete basin near the oil borehole. The map of Figure 2B shows the distribution of water resistivity in the area. The lowest values were found in the northern part of the pit and in the monitoring well M-5. A contamination map for the pit area (Figure 2C) shows maximal contamination in the northern (more than 8000 ppm) and southern (more than 2000 ppm) parts of the pit.

Fig. 2. Histogram (A) and map (B) of water resistivity; C ­ map of oil contamination for the pit area.

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VES interpretation The simplified geological model of the area consists of two horizontal layers: aquitard (above) and aquifer (below). Inside the first layer there are: artificial cover of sand and gravel in the east part of the area, products of drilling (mainly in the pit area) and contaminated zones (Table 1). Groundwater level is at the depth 1.5 -2 m inside the first layer (aquitard). That is why the contamination is situated above the groundwater level in aquitard. Table 1 Values of resistivity and clay content for different soils of the cross-section and for contaminated zones Part of section Upper part (aquitard) (0-4 m) Artificial cover of sand and gravel Drilling products Lower part (aquifer) Contaminated zones Rho, .m 15 - 30 Clay content, % 30 - 40

corresponding depth of penetration for a given electrode spacing. Block thickness is the same in each row but the deeper row is 10% thicker than the previous row. The program is limited to 24 model layers, and in our case, the model had 7 layers. Using X2IPI program, we adopt a model with a block column below each sounding point; thus the number of blocks in each row is equal to the number of soundings. In this case we may create vertical sections, horizontal maps and arbitrary surface maps in the survey area using the coordinates of the blocks as a reference. Examples of interpretation for profiles 5 and 9 are shown in Figure 4. A layer of minimum resistivity was found slightly above the groundwater level. We call this the "optimal layer", because it is the most favourable for a contamination study. Its existence at oil contaminated sites was confirmed by direct measurement, using Vertical Resistivity Probes (VRP) (Sauck, 1988). The low resistivity in this layer is due to intense bacterial action on residual hydrocarbons near the base of the vadose zone. The optimal layer may be recognized in several profiles and may be used to prepare the resistivity map of the site for the depth 2 - 4 m (Figure 5). The dashed line shows the pit outline. From petrophysical modeling (see below), the boundary between contaminated and not contaminated soil is 12 m. The 12 m contour is shown as a heavier line. Two anomalies in the northern and the southern part of the pit show probable contaminated zones in agreement with geochemical data (Figure 3C). Petrophysical modelling

70 - 400

0-5

15 - 20 50 - 100 < 12

10 - 40 0 - 10 > 50 (apparent value)

VES interpretation included qualitative analysis of all data and quantitative 2D VES interpretation. Typical VES curves were mainly of A and H types (Figure 3). High a values at small spacings are typical for a zone with sand and gravel cover. The deepest layer on VES curves with resistivity about 50 m is the main aquifer. The low resistivity layer (15-30 m) above is an aquitard. Contamination is clearly visible in the aquitard layer with the help of petrophysical modelling, which allows calculating resistivity of uncontaminated soils and estimating the difference between clean and contaminated soils. VES data interpretation was performed with the Res2DInv software (Loke and Barker, 1995; 1996) using IPI2Win and X2IPI programs for VES data transformation (Bobachev, 1994, 2003) and Surfer (Golden software) for visualization. The 2-D model created by the Res2DInv program consists of a number of rectangular blocks not exceeding the number of data points. The horizontal position and size of the blocks usually depend on the positions of data points. The depth of each row of blocks is approximately equal to 254

Petrophysical modelling leads to a theoretical calculation of soil resistivity based on geological information about water and soil in the survey area. Soil resistivity depends on groundwater resistivity (as in Archie's formula (Archie, 1942); thus groundwater resistivity measurements are important for resistivity surveys. The petrophysical approach used in this paper was developed by A. Ryjov in 1984 ­ 1990, and may be found in Ryjov (1987), Ryjov and Sudoplatov (1990) and Ryjov and Shevnin (2002). Petrophysical model and forward problem modelling There are many petrophysical models developed for the oil and gas industry (Archie, 1942; Waxman and Smits, 1968; Clavier et al., 1984; Johnson and Sen, 1988) and for near surface sediments (Rhoades et al., 1976; Ryjov, 1987; Ryjov and Sudoplatov, 1990; Tabbagh et al., 2002). The conductivity of sandy and clayey soils was considered by Ryjov (1987) and Ryjov and Sudoplatov (1990),


Geoelectrical characterization of an oil contaminated site in Tabasco, Mexico

The walls of capillary ducts are non-conductive and depend on sand or clay electrochemical parameters, e.g. EDL and cation exchange capacity (CEC). Thus, the electrical properties of the sand-clay mixture are defined by the effective conductivities of wide or fine capillary systems. Conductivities of sand (sand) and clay (clay) components may be described as follows:
sand

= m =

sand

sandcap
claycap

(1) (2)

clay

m clay

,

where sand = Vsandpor / Vsand and clay = Vclaypor / Vclay are the porosity values of sand and clay components; Vsandpor and Vsand are the pore and sand total volume; Vclaypor and Vclay are the pore and total volume of the clay component; sandcap and claycap are the conductivity of a single capillary of sand and clay, m is the cementation exponent.
The average conductivity c of a capillary filled with water is a radially varying function (r) that depends on the electrical double layer properties:
Fig. 3. First curve parameter indicates profile number, while second one the VES point.
rc

2 c = 2 rc


0

r (r )dr ,

(3)

taking into account the geometrical microstructure and electrochemical processes for a wide range of water salinity and clay content. The sand capillary radii are of 10-3­10-4 m exceeding greatly the thickness of the electrical double layer (EDL). The EDL thickness depends on water salinity. At near surface conditions the fluid mineralization varies from 0.02 to 2 g/l and the EDL thickness ranges between 10-8 ­ 10-10 m. The pores of the clay component are assumed to be narrow because the average radii lie in the interval of 10-7 ­ 10-8 m and are comparable with the EDL thickness.

where rc is the capillary radius. In the pore system of sand the average conductivity sandcap does not depend on the capillary radius and equals the free-water conductivity w. The conductivity of free water relates strongly to salt type and salt concentration. The water solution conductivity can be approximated by a function of cation and anion concentrations considering the hydration effect (Ryjov, 1987):

Fig. 4. Vertical resistivity cross-sections for profiles 5 and 9 after 2D interpretation with Res2DInv software.

255


V. Shevnin et al.

Fig. 5. Resistivity map for the optimal layer.

Cc w = F zcU cCc exp 1000 zn

Ca + z a U a C a exp 1000 zn

,

ity (CEC): Ci ( r ) = C cations or anions. (4)

DEL i

(r ) + C

CEC i

( r ) , where i is the index for

where zc, za are the charges of ions or valence; F is the Faraday number, 96485 Q/mol; Cc and Ca are the cation and anion concentrations in the solution; Uc and Ua are the cation and anion mobilities and n is the hydration number. This approach allows estimating the water conductivity for different salts such as NaCl, KCl, Ca(HCO3)2, CaCl2, MgCl2, CaSO4, NaHCO3, Na2SO4, etc., in a wide salinity range from 0.001 to 120 g/l at least. For fine clay capillaries we use an analog to equation (4), where the concentration of the charges C is a function of the capillary radius C(r) and the clay cation exchange capac256

The concentration Ci(x) of anions or cations with charge zi in an electrical double layer is based on the Boltzmann equation (Fridrihsberg, 1984):
Ci ( x ) = Coi exp(- zi F( x ) / RT ) ,

(5)

where: C oi is the cation or anion concentration in an electroneutral solution; x is the shortest distance from the given point in a liquid phase up to a solid phase; (x) is the electric potential in a fluid at a current distance x from the capillary wall; R is the gas constant and T ­ absolute temperature in °K. The microstructure of sandy ­ clayey soils may be described from the ideal packing concept for binary mixtures


Geoelectrical characterization of an oil contaminated site in Tabasco, Mexico

of fine and coarse particles of quasi-spherical shapes (McGeary, 1961). When the clay concentration is less than the sand porosity, the clay particles fit inside the sand pores and do not modify the structure. When the clay concentration exceeds the sand porosity, the sand grains are embedded in the clay matrix. The total porosity t of sandy-clayey soils may be approximated by the following expressions (Ryjov and Sudoplatov, 1990; Marion et al., 1992): = (sand - Cclay) + clay.Cclay, = Cclay. clay, where C
clay

ample of the template (Figure 6) for the following parameters: NaCl solution and full soil saturation, temperature 20°C, sand porosity 25%, clay porosity 55%, Cation Exchange Capacity (CEC) of clay 3 g/l. (Values from curves are clay content in % from 0 (sand) until 100 (clay)). The inclined dashed line is for water resistivity. If groundwater resistivity is 20 m, we can estimate from Figure 6, water salinity (0.3 g/l) and lithological legend, where each type of soil (sand, sandy loam, loam, light (C3), medium (C2) and heavy (C1) clay) has its own resistivity interval. Interpretation of laboratory soil resistivity measurements as function of water salinity If groundwater and soil resistivity at full saturation are known we may estimate from Figure 6 the soil lithology, the clay content and the porosity. This estimation was included in algorithm Petrofiz (Ryjov and Sudoplatov, 1990) from laboratory soil resistivity data =f(C), where C is the water salinity. For this purpose we divide the soil sample into several subsamples, saturated by different NaCl solutions with salt concentration from 0.6 to 100 g/l. We measured the resistivity of each subsample using identical calibrated resistivimeters. Interpreting (C) curve we are estimating clay content, soil porosity and cation exchange capacity. Some details of this technology may be found in Shevnin et al. (2004) The (C) graph for a soil sample obtained near monitoring well M-1 is presented in Figure 7, and its interpretation is in Table 2 together with two other samples obtained near sampling wells S-10 and S-2. The sample M-1 was taken in the reference area without contamination, whereas two other samples were taken in areas with strong and moderate contamination. Table 2 Results of sample resistivity measurements' interpretation Sampling Clay content site % M-1 S-10 S-2 43 41 41 Porosity % 33 31 31 CEC of Fitting clay, g/l error, % 0.12 21 8 2.1 4.6 2.8

Cclay < sand Cclay sand ,

(6) (7)

­ is the volumetric clay content in a mixture.

When Cclay>sand the conductivity corresponds to the effective conductivity clay of the clay component. It depends on the conductivity of narrow channels, clay porosity and concentration. = clayCclay
m clay.

(8)

When Cclay
Fig. 6. Theoretical graphs of sandy-clayish soil resistivity from water salinity at CEC=3.

Note that clay content is close to 40% and the main difference between samples is in the CEC values. They are small for the clean reference site and large for the strongly contaminated site. These CEC estimates are apparent not a real values, but may be good indicators of mature petroleum contamination. 257


V. Shevnin et al.

Sudoplatov, 1990). In 2003 a new algorithm was developed for recalculation of soil resistivity from VES interpretation of petrophysical parameters. For petrophysical modelling about 15 soil parameters are used: water salinity, including types of anions and cations with their valence, hydration number, sorption constant and mobility; porosity, capillary radii, humidity, cementation exponent m and cation exchange capacity for sand and clay components, and temperature of soil. If we know soil resistivity from VES and water salinity from water resistivity measurements we may solve the inverse problem to estimate three soil parameters, e.g. clay content, porosity, and cation exchange capacity. This algorithm allows recalculating vertical resistivity cross-sections and resistivity maps taking into account water salinity into sections and maps of petrophysical parameters.
Fig. 7. Petrophysical analysis of water (A) and soil resistivity (D) together with VES data (B, C).

Petrophysical analysis of experimental information The results of the petrophysical analysis are shown in Figure (7), based on a comparison of water and soil resistivity measurements, petrophysical modelling and VES resistivity statistical analysis. We intend to find a boundary resistivity value to distinguish between clean and contaminated soils. In Figure 7 is presented a theoretical template calculated for CEC value 0.12 g/l, estimated near the well M-1. A theoretical dependence of soil resistivity closer to the soil of the investigated site may be found from soil resistivity measurements for several different salinities (Figure 7). The theoretical palette of =f(C) for different clay contents was calculated for a cation exchange capacity (CEC) of clay of 0.12 g/l. A groundwater resistivity value of 27 m corresponds to the main aquifer. The salinity of this water (Figure 7, point A) is 0.22 g/1. The resistivity range between pure sand (Line 0) to sand with 40% of clay (Line 40) is from 14 to 100 m. From statistical VES data analysis the resistivity of uncontaminated soil ranges from 12 to 70 m. At water resistivity of 27 m this yields a clay content of 4 to 40 % (Figure 7, grey rectangle B an line D). We consider soil resistivity values below 12 m (line E) as indicating contaminated soil and accept this value as a boundary between clean and contaminated soils. Resistivity of contaminated soil is marked with rectangle C. Recalculation of VES resistivity data into petrophysical parameters Algorithm Petrofiz has been used for petrophysical modelling and soil resistivity data interpretation since 1984. Its applicability has been tested on many soils (Ryjov and 258

The cross-section of profile 6 in Figure 8 can be divided visually into two parts: an upper one down to the depth 3-5 m and a lower one below depth 3-5 m. This division is clear and contrasting. Profile 6 is situated in the pit area and crosses two contaminated zones. In these zones all parameters have anomalous values. The map in Figure 9A displays clay content for the optimal layer. From geological data the peak clay content in this area is 40%. With soil resistivity measurements and data interpretation we confirmed values close to 40%. But in Figure 9A there are zones with clay content up to 60%. These anomalous zones coincide with contaminated areas in the northern and southern parts of the pit. We successfully estimated the clay content in uncontaminated areas with the help of recalculation from resistivity data, but in the contaminated zones the clay content estimated from resistivity is not real and can be used only as a fairly good indicator of contamination. We assume that contamination can not change the clay content in a soil. But contamination does change the capillary properties and soil resistivity thus producing an apparent increase of clay content. A similar situation is encountered with cation exchange capacity recalculated from resistivity (Figure 9B). CEC anomalies with values more than 23 g/l show the same zones in the pit as did clay content (Figure 9A) or petroleum contamination anomalies (Figure 2C). Figure 10 presents three correlations between geological and geoelectrical parameters. (A) Correlation between hydrocarbon concentration and soil resistivity; (B) correlation between clay content from geological data and clay content estimated from resistivity; (C) correlation between cation exchange capacity estimated from soil samples and recalculated from resistivity data (1 = direct proportion and


Geoelectrical characterization of an oil contaminated site in Tabasco, Mexico

Fig. 8. Vertical cross-sections along profile 6 for the next parameters: A ­ resistivity; B ­ clay content; C ­ porosity; D ­ cation exchange capacity.

2 = practical approximation). The best correlation is shown between resistivity and contamination grade (A) that is the base for the resistivity method. Here the soil resistivity was measured on the same samples as for contamination. For graphs (B) and (C) the correlation is between direct estimation of clay content and CEC, and its integral estimation with the help of the resistivity method.

In Figure 11 the final results of the geoelectrical survey are presented. In the map there are different contours for resistivity, clay content, CEC and porosity. In the pit area there are three anomalous zones with crossings of four contours marked A, B, C. Outside of the pit only two parameters have crossed contours. Anomalies A and B coincide with geochemical anomalies, thus we consider these anomalies 259


V. Shevnin et al.

1140

1140

Y, m
1120

A

Clay content, %
1120

Y, m

B

CEC, g/l

1100

50

1100

33

1080

1080

20

40
1060 1060 12

30
1040 1040 7.4

1020

20

1020

4.5

1000

1000

980 900

X, m
920 940 960 980 1000 1020 1040 1060 1080

980 900

X, m
920 940 960 980 1000 1020 1040 1060 1080

Fig. 9. Maps of clay content (A) and cation exchange capacity (B) recalculated from resistivity for the optimal layer.

as true contaminated zones. Anomaly C is located between sampling wells S -11 (13375 ppm) and S-12 (3566 ppm) in the zone of the geochemical gradient. But from geoelectrical data this anomaly looks like an oil contaminated zone. Also from geoelectrical data, an additional conductive zone D at X = 980, Y = 1110 was found outside the pit. This anomaly seems related to anomaly A, although its origin is not clear. When integrating geochemical and geoelectrical information it is necessary to remember their differences. The geochemical method gives direct information about contamination, whereas the geoelectrical method gives indirect information. Geochemistry gives local data and geoelectric prospecting gives data integrated over a large volume. Productivity of the latter is nearly ten times greater than the former and the cost is 20 times lower. In this case a good integration of the two methods is very useful. Resistivity can be used first to outline contaminated zones, it can reduce the area for geochemical study to 20 - 40% of the total area. Results of a geoelectrical study are usually available in 3-5 days, whereas geochemical analysis in the laboratory may take as much as 2-3 weeks. A geoelectrical study does not affect the environment whereas drilling in contaminated zones can lead to aquifer contamination. It is important to obtain geoelectrical information before geochemical sampling to make this sampling more accurate in plan and depth. CONCLUSIONS 1. A geoelectrical characterization of a contaminated site was carried out using vertical electrical sounding method as 2D Resistivity Imaging together with water and soil resistivity measurements. Contaminated areas were localized in plan and in depth, as low resistivity zones. The 260

zones of maximum contamination were found in the pit area. 2. The correlation between soil resistivity and contamination shows that the VES method is effective in defining contaminated areas. The inverse correlation between geoelectrical parameters and concentration of contaminants confirms that oil-contamination in this site is "mature" and thus creates a low resistivity anomaly. 3. VES 2D interpretation was successful in finding an optimal layer in the lower part of the vadose zone. This layer is best for localizing contamination in plan. 4. Resistivity data can be recalculated into petrophysical parameters (clay content, porosity and cation exchange capacity). These parameters have standard values in uncontaminated zones and anomalous values in contaminated zones. They are useful for better differentiation of contaminated and uncontaminated zones. In contaminated zones the petrophysical parameters exhibit apparent anomalies caused by changes in soil capillaries. ACKNOWLEDGMENTS We are indebted to The Mexican Petroleum Institute, where this study was made. We acknowledge the valuable help of M. Sc. David Flores HernÀndez in the fieldwork. We sincerely thank Dr. A. Bobachev whose software was used in this study. We thank two reviewers for their comments that significantly improved the manuscript and Dr. Cinna Lomnitz the main editor of GeofÌsica Internacional for English corrections.


Geoelectrical characterization of an oil contaminated site in Tabasco, Mexico

Fig. 10. Correlation graphs between geological and geoelectrical parameters.

Fig. 11. Final result of geoelectrical survey, position of contaminated zones.

261


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Vladimir Shevnin1, Omar Delgado-RodrÌguez1, Luis FernÀndez-Linares 1 , HÈctor Zegarra MartÌnez1, Aleksandr Mousatov1 and Albert Ryjov2
1

Mexican Petroleum Institute, Eje Central LÀzaro CÀrdenas 152, 07730 MÈxico, D.F., MÈxico E-mail: vshevnin@imp.mx 2 Moscow State Geological Prospecting Academy, Geophysical faculty, Volgina str., 9, 117485, Moscow, Russia.

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