geophysical investigation of petroleum contaminated … · deq cyril project geophysical...

DEQ CYRIL PROJECT GEOPHYSICAL INVESTIGATION OF PETROLEUM CONTAMINATED GROUND WATER USING ELECTRICAL RESISTIVITY December 16, 2009 Prepared for: Oklahoma Department of Environmental Quality Land Protection Division Site Remediation Section Oklahoma City, Oklahoma Prepared by: Todd Halihan, Chris Mace, Tim Sickbert Oklahoma State University School of Geology 105 Noble Research Center Stillwater, OK

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Table of Contents List of Figures ........................................................................................... iv List of Tables...........................................................................................viii Acknowledgements .................................................................................. ix

1.0 Introduction ..........................................................................................1 1.1 Purpose of Report ...............................................................................................................1 1.2 Site Background ..................................................................................................................1 1.3 Regional Geology................................................................................................................2 1.4 Local Geology .....................................................................................................................2

2.0 Methods ................................................................................................3

2.1 Theory Overview .................................................................................................................3 2.2 ERI Data Collection .............................................................................................................5 2.3 ERI Inverse Modeling ..........................................................................................................5 2.4 OhmMapper Data Collection ...............................................................................................7 2.5 OhmMapper Data Modeling ................................................................................................8 2.6 Spatial Data - GPS ..............................................................................................................9 2.7 Data Interpretation...............................................................................................................9

3.0 Results ................................................................................................12 3.1 ERI Data Results...............................................................................................................12 3.2 OhmMapper Data Results.................................................................................................15

3.2.1 OhmMapper Model Data ............................................................................................15 3.2.2 OhmMapper Raw Data ...............................................................................................17

4.0 Discussion..........................................................................................19

4.1 ERI Data............................................................................................................................19 4.1.1 DEQAS01-03 ..............................................................................................................19 4.1.2 DEQCS01-03..............................................................................................................20 4.1.3 DEQNW01-02.............................................................................................................21

4.2 OhmMapper Data..............................................................................................................22 4.3 Summary of Interpretations and Recommendations .........................................................23

5.0 Conclusions........................................................................................24 6.0 References..........................................................................................25

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7.0 Appendices.........................................................................................26 Appendix A. Copy of Site Notes .............................................................................................26 Appendix B. Copy of Field Photos..........................................................................................26 Appendix E1. ERI processed dataset (EXCEL format) ..........................................................26 Appendix E2. OhmMapper raw dataset (EXCEL format) .......................................................26 Appendix E3. OhmMapper processed dataset (EXCEL format) ............................................26 Appendix E4. GIS database for data......................................................................................26

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List of Figures Figure 1. Map view of the ORC site in Cyril, OK. The ORC is outlined in red...........................F1

Figure 2. Map view of Cyril, OK with the ORC site outlined in red. The locations of previous waste disposal pits are highlighted in green. ..............................................................................F2

Figure 3. Map view of Cyril, OK with the ORC site outlined in black. The locations of previous waste disposal pits are highlighted in green. DEQ monitoring wells are indicated on the map by yellow and red circles. Red indicates new wells; yellow indicates older wells.. .........................F3

Figure 4. Map view of Cyril, OK with the ORC site outlined in black. DEQ monitoring wells are located on the map by yellow and red circles. OSU ERI lines are highlighted on the map by yellow lines..................................................................................................................................F4

Figure 5. Map view of Cyril, OK with the ORC site highlighted in black. DEQ monitoring wells are located on the map by yellow and red circles. The green lines represent data collected by the OhmMapper. Grids 1 and 2 are identified by the solid black rectangles..............................F5

Figure 6. Picture of the OhmMapper during setup. The transmitter is electrically separated from the fifth receiver by a 4 m non-conductive rope. The dipole cables and rope length can be longer for deeper penetration, but the field conditions limited the geometry of the instrument...F6

Figure 7a. ERI line DEQAS01 Data: Inverse model of electrical resistivity created from ERI line DEQAS01 data. (Left) Map view of ORC showing (bottom) location of DEQAS01 within the site, and (top) relative to nearby wells and ERI lines. (Right top) Full range of model colored to show resistivity; (bottom) limited range of model colored to highlight areas of interest. Vertical elevation and horizontal line offsets are in meters.. ..................................................................F7a

Figure 7b. ERI line DEQAS01 Interpretation: Inverse model of electrical resistivity created from ERI line DEQAS01 data. (Left) Map view of ORC showing (bottom) location of DEQAS01 within the site, and (top) relative to nearby wells and ERI lines. (Right) Full range of model colored to show resistivity; additional available data and interpretations shown in blue. Vertical elevation and horizontal line offsets are in meters.. .................................................................................F7b

Figure 8a. ERI line DEQAS02 Data: Inverse model of electrical resistivity created from ERI line DEQAS02 data. (Left) Map view of ORC showing (bottom) location of DEQAS02 within the site, and (top) relative to nearby wells and ERI lines. (Right top) Full range of model colored to show resistivity; (bottom) limited range of model colored to highlight areas of interest. Vertical elevation and horizontal line offsets are in meters.. ..................................................................F8a

Figure 8b. ERI line DEQAS02 Interpretation: Inverse model of electrical resistivity created from ERI line DEQAS02 data. (Left) Map view of ORC showing (bottom) location of DEQAS02 within the site, and (top) relative to nearby wells and ERI lines. (Right) Full range of model colored to show resistivity; additional available data and interpretations shown in blue. Vertical elevation and horizontal line offsets are in meters ...................................................................................F8b

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List of Figures (con’t) Figure 9a. ERI line DEQAS03 Data: Inverse model of electrical resistivity created from ERI line DEQAS03 data. (Left) Map view of ORC showing (bottom) location of DEQAS03 within the site, and (top) relative to nearby wells and ERI lines. (Right top) Full range of model colored to show resistivity; (bottom) limited range of model colored to highlight areas of interest. Vertical elevation and horizontal line offsets are in meters.. ..................................................................F9a

Figure 9b. ERI line DEQAS03 Interpretation: Inverse model of electrical resistivity created from ERI line DEQAS03 data. (Left) Map view of ORC showing (bottom) location of DEQAS03 within the site, and (top) relative to nearby wells and ERI lines. (Right) Full range of model colored to show resistivity; additional available data and interpretations shown in blue. Vertical elevation and horizontal line offsets are in meters. ..................................................................................F9b

Figure 10a. ERI line DEQCS01 Data: Inverse model of electrical resistivity created from ERI line DEQCS01 data. (Left) Map view of ORC showing (bottom) location of DEQCS01 within the site, and (top) relative to nearby wells and ERI lines. (Right top) Full range of model colored to show resistivity; (bottom) limited range of model colored to highlight areas of interest. Vertical elevation and horizontal line offsets are in meters.. ................................................................F10a

Figure 10b. ERI line DEQCS01 Interpretation: Inverse model of electrical resistivity created from ERI line DEQCS01 data. (Left) Map view of ORC showing (bottom) location of DEQCS01 within the site, and (top) relative to nearby wells and ERI lines. (Right) Full range of model colored to show resistivity; additional available data and interpretations shown in blue. Vertical elevation and horizontal line offsets are in meters... ...............................................................F10b


Figure 11b. ERI line DEQCS02 Interpretation: Inverse model of electrical resistivity created from ERI line DEQCS02 data. (Left) Map view of ORC showing (bottom) location of DEQCS02 within the site, and (top) relative to nearby wells and ERI lines. (Right) Full range of model colored to show resistivity; additional available data and interpretations shown in blue. Vertical elevation and horizontal line offsets are in meters.. ................................................................F11b


Figure 12b. ERI line DEQCS03 Interpretation: Inverse model of electrical resistivity created from ERI line DEQCS03 data. (Left) Map view of ORC showing (bottom) location of DEQCS03 within the site, and (top) relative to nearby wells and ERI lines. (Right) Full range of model colored to show resistivity; additional available data and interpretations shown in blue. Vertical elevation and horizontal line offsets are in meters.. ................................................................F12b

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List of Figures (con’t)

Figure 13a. ERI line DEQNW01 Data: Inverse model of electrical resistivity created from ERI line DEQNW01 data. (Left) Map view of ORC showing (bottom) location of DEQNW01 within the site, and (top) relative to nearby wells and ERI lines. (Right top) Full range of model colored to show resistivity; (bottom) limited range of model colored to highlight areas of interest. Vertical elevation and horizontal line offsets are in meters.. ................................................................F13a

Figure 13b. ERI line DEQNW01 Interpretation: Inverse model of electrical resistivity created from ERI line DEQNW01 data. (Left) Map view of ORC showing (bottom) location of DEQNW01 within the site, and (top) relative to nearby wells and ERI lines. (Right) Full range of model colored to show resistivity; additional available data and interpretations shown in blue. Vertical elevation and horizontal line offsets are in meters.. ................................................................F13b

Figure 14a. ERI line DEQNW02 Data: Inverse model of electrical resistivity created from ERI line DEQNW02 data. (Left) Map view of ORC showing (bottom) location of DEQNW02 within the site, and (top) relative to nearby wells and ERI lines. (Right top) Full range of model colored to show resistivity; (bottom) limited range of model colored to highlight areas of interest. Vertical elevation and horizontal line offsets are in meters.. ................................................................F14a

Figure 14b. ERI line DEQNW02 Interpretation: Inverse model of electrical resistivity created from ERI line DEQNW02 data. (Left) Map view of ORC showing (bottom) location of DEQNW02 within the site, and (top) relative to nearby wells and ERI lines. (Right) Full range of model colored to show resistivity; additional available data and interpretations shown in blue. Vertical elevation and horizontal line offsets are in meters.. ................................................................F14b

Figure 15. Map view of Cyril, OK with the ORC site outlined in black and the inverse model for OhmMapper data at a depth of 0.2 m. ......................................................................................F15










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List of Figures (con’t)

Figure 25. OhmMapper receiver 1 data: (left) Raw point data collected by OhmMapper receiver 1, and (right) the interpolated raster of raw point data. ............................................................F25





Figure 30. This image presents an OhmMapper psuedosection created during the process of inverse modeling. The upper image includes receiver point data with an approximate depth. The lower is an interpolation of inverse model values. .............................................................F30

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List of Tables Table 1. Measured resistivities for selected materials. Values from Telford, et al. (1990).........10 Table 2. Wells selected for correlation to geophysical results. ..................................................15

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Acknowledgements We would like to thank the Department of Environmental Quality for the opportunity to work on this project. We would also like to thank Katie Jackson, Kathy Thompson, and Shannon Jeffries for their hard work in the field, without which this project would not have succeeded. Dr. Halihan has a managed conflict of interest with Oklahoma State University regarding the use of ERI developments.

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1.0 Introduction

This section of the report introduces the project and the equipment used for the geophysical survey, a brief history of the Oil Refinery Company field location, and description of the regional and local geology.

1.1 Purpose of Report This geophysical research project, conducted by the Oklahoma State University School of Geology in conjunction with the Department of Environmental Quality (DEQ), tests whether electrical resistivity imaging (ERI) and electrical resistivity scanning provide a cost-effective method to find and document the source of petroleum-contaminated ground water at an historical petroleum processing site. The investigation focused on the source of a contaminated groundwater discharge zone (seep) in Cyril, Oklahoma.

The following equipment was used to collect geophysical data at the Cyril site:

• Geometrics OhmMapper (capacitively-coupled resistivity meter, CC-EM), and

• Advanced Geosciences, Inc., SuperSting R8 Earth Resistivity Meter (ERI)

(SuperSting).

The OhmMapper was used to shallowly scan large areas of land quickly to identify areas of interest; the SuperSting was used to collect deeper and higher resolution vertical subsurface images along lines of potential interest.

1.2 Site Background The Oklahoma Refining Company Superfund Site (EPA ID: OKD091598870) located in Cyril, Oklahoma, was originally owned by the Anderson-Pritchard Oil Corporation (APCO) (Figure 1). APCO produced a variety of petroleum products. In 1978, the Oklahoma Refining Company, (ORC), purchased APCO. ORC operated on the 220 acre site for several years, adding unlined product and waste storage pits, wastewater treatment ponds, bulk storage tanks, and a land treatment area (DEQ, 2008) (Figure 2). ORC split the site into two sections: north and south. ORC declared bankruptcy in 1984, and in 1986 they abandoned the site. The refinery was repeatedly sold and eventually all operations ceased in 1994 with Cayman Resources. Extensive contamination of the site occurred during refining operations. (DEQ, 2008). After a Remedial Investigation/Feasibility Study, the Oklahoma Department of Environmental Quality (DEQ) remediated the southern side of the site. Upon completion, the soil, sediment, surface water, and surface waste requirements of the 1992 ROD were met (DEQ, 2008). LNAPL extraction and groundwater remediation were postponed until additional site characterization was conducted.

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In August of 2004, the northern portion of the ORC site was deferred to Superfund authority. The United States Environmental Protection Agency (EPA) removed process towers, vessels, asbestos-containing materials, and cooling towers. The EPA completed the cleanup by February 2006 (DEQ, 2008). Currently, the north side still contains contaminated soils, and the LNAPL and groundwater contamination is still located throughout the site. Adjacent to the south side of the site, several seeps have been located flowing into Gladys Creek, ranging from caustic to acidic. Many ground water monitoring wells have been installed across the site to help determine the extent of contamination (Figure 3).

1.3 Regional Geology The ORC site lies on top of the axis of a major structural basin: the Anadarko syncline (ORC, 1991). This region is bounded to the north by the Central Kansas Uplift, to the east by the Nemaha Ridge, the southwest by the Amarillo-Wichita Mountains, and to the west by the Cimarron Uplift-Keys Dome (ORC, 1991). Since the Permian-aged bedrock now present at the surface was deposited after the structures were formed, the stratum directly underlying the ORC site is relatively horizontal.

1.4 Local Geology The youngest Permian stratum at the ORC site is the Weatherford Member of the Cloud Chief Formation (Lloyd, 2007). The Weatherford is primarily a massive gypsum unit with local anhydrite and dolomite. This unit underlies Quaternary-age clay deposits on the northwest area of the site, but it crops out in other areas of the site. The thickest measurement of the Weatherford within the site is 31.5 feet, estimated from the drill records (9.6 m) (Lloyd, 2007). Since it is an erosional surface, the thickness can vary from zero to its thickest measurement. Loss of circulation during drilling suggests solution cavities exist within the gypsum unit (Lloyd 2007). The Rush Springs Sandstone conformably underlies the Weatherford Member. The Rush Springs Sandstone is a fine-grained, red, silty sandstone with traces of gypsum in the matrix at the Weatherford/Rush Springs contact, probably due to diagenesis. The sandstone contains cross-bedded and flat-bedded depositional structures. The total thickness across the ORC site is approximately 250 feet (Lloyd, 2007). Conformably underlying the Rush Springs Sandstone in descending order are the Marlow Formation, Dog Creek Shale Formation, the Blaine Formation, and the Flowerpot Shale Formation. Most wells drilled at this site are either completed in the Rush Springs Formation or the Quaternary alluvium. (Lloyd, 2007).

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2.0 Methods

This section provides information on the theory behind the AGI SuperSting and the Geometrics OhmMapper, descriptions of the data collection processes for the two instruments, and descriptions of the data reduction processes for the two instruments.

2.1 Theory Overview There are several methods of electrical surveying. Some make use of the natural (telluric) currents that are produced by the earth, and others artificially introduce a current. These latter methods generally use direct currents or low frequency alternating currents to investigate the electrical properties of the subsurface (Kearey, 2002). Resistivity is defined as the resistance in ohms between the opposite faces of a unit cube of material. For a conducting cube of resistance dR, length dL, and cross-sectional area dA, the resistivity ρ is given by

dLdAdR

=ρ

The SI unit of resistivity is ohm-meters (ohm-m), and the inverse of resistivity is conductivity with units of Siemens per meter (S-m-1); sometimes called a ‘mho-m-1’ (Kearey, 2002). Resistivity is one of the most variable geophysical properties (Kearey, 2002). Certain minerals such as native metals and graphite efficiently conduct electricity via the transport of electrons. However, most rock forming minerals are insulators. Current is carried through these rocks through pore waters. Thus, most rocks conduct electricity through electrolytic rather than electronic processes. Generally, as a rock’s porosity decreases, its resistivity increases. The effective resistivity can also be expressed in terms of the resistivity and volume of the pore water within the matrix according to an empirical formula given by Archie (1942)

wcb fa ρφρ −−=

Where φ is the porosity, f is the fraction of pores containing water of resistivity ρw, and a, b and c are empirical constants. The fluid resistivity, ρw, can vary widely depending on the amount and the resistivities of dissolved solids (Kearey, 2002). The AGI SuperSting is a multichannel DC resistivity instrument that directly couples with the earth to pass an artificial current into the subsurface (AGI, 2002). The basic method for passing a current into the subsurface is using a 3/8” stainless steel electrode. The circuit is completed by using another electrode as current sink at a large distance away from the current electrode. Theoretically, the electrical current flows radially from the point at which the current enters the subsurface such that the current distribution is uniform over hemispherical shells, each of which

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has a surface area of 2πr2 (Kearey, 2002). Two voltage electrodes at the surface measure the potential drop between them. This measurement is used to calculate the apparent resistivity. The apparent resistivity is calculated through various equations dependent upon the electrode configuration used. For instance, the apparent resistivity is calculated from the Wenner configuration (the current and potential electrode spacing are constant) by:

IV

aΔ

= παρ 2

Where ρa is the apparent resistivity, α is the electrode spacing, ΔV is the potential drop between the potential electrodes, and I is the current. For the Schlumberger configuration (the inner potential electrodes have a smaller, non-constant spacing between the two current electrodes) the apparent resistivity is calculated by:

( )( ) I

VxLxL

laΔ

+−

= 22

222

2πρ

Where L is half the distance between the current electrodes, l is half the distance between the potential electrodes, and x is the distance between the mid-points of the potential and current electrodes (Kearey, 2002). Data collected using the SuperSting is used to inversely model (described below) the distribution of resistivity in the subsurface. The inverse model domain is a vertical plane extending along a line between the endpoint electrodes, to a depth approximately one-fifth the distance between the endpoint electrodes, and with horizontal and vertical spacing of model points one-half the electrode spacing. A Geometrics OhmMapper TR1 capacitively-coupled resistivity system was also used to measure the subsurface resistivity of the site (Geometrics, 2001). The OhmMapper consists of an ungrounded dipole transmitter and five ungrounded dipole receivers. The transmitter uses the capacitance of its coaxial antenna to send an approximately 16.5 kHz AC signal into the ground. The capacitance of the antenna varies directly with the length of the cable; therefore greater cable length has greater capacitance and induces more current in the subsurface. The AC signal passes through the ground and capacitively creates AC voltages in receivers’ antennas. Receivers measure the resultant voltage. The strength of the signal at the receiver declines with the cube of the receiver’s distance from the transmitter, limiting the effective depth of investigation to less than 20 m. The OhmMapper is pulled either by person or an all-terrain vehicle (ATV) for near continuous data collection. For any significant distance with more than one or two receivers, an ATV is required and was therefore used for this project. The OhmMapper has three modes of operation: search, simple survey, and mapped survey. The simple survey mode was used for this study. The simple survey mode allows the instrument to track start of line, waypoints and end of line without preprogramming. The mapped survey mode was not utilized because it was difficult to accommodate field variability in surveying with the programmed approach.

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The depth of investigation using an OhmMapper depends on the spacing between the transmitter and the receivers, and antenna length. The distance from the transmitter to the first receiver (the rope separation) is chosen empirically on site to determine the maximum separation possible without losing signal in the receiver furthest from the transmitter. The distance between each receiver dipole center is the length of the antenna, twice the length of each pole. For this work, one transmitter and five receivers were arranged with 10-meter spacing and a 4-meter rope separation. The instrument is commonly used with multiple receivers to simulate a dipole-dipole resistivity array. The data can be converted to a dipole-dipole resistivity dataset that can be used to create an inverse model, similar to electrical resistivity imaging (ERI) data. As the depth of investigation varies with the properties of the earth for this type of circuit, it is more difficult to determine the depth of the material being measured.

2.2 ERI Data Collection An Advanced Geosciences, Inc. SuperSting R8 Earth Resistivity Meter (SuperSting) direct-coupled resistivity system was used to collect 8 transects of ERI data at the study site (Figure 4). The system consisted of 56 stainless steel electrodes (3/8-inch diameter) driven into the ground along straight lines with two-meter spacing between electrodes. The total length of each line was 110 meters. The two-meter spacing was selected to provide the appropriate depth of imaging for the study area along with sufficient linear distance to meet the project objectives. The depth of imaging at the site was approximately 21 meters below the surface. The electrodes were connected via geophysical cables and the cables were connected to the SuperSting. Once each of the survey lines were laid out in the field, the SuperSting field computer measured apparent resistivity between electrodes using the Halihan/Fenstermaker (Halihan et al, 2005) algorithm. Eight ERI datasets were collected during October 11-13, 2008. Copies of field notes from the ERI data collection work are contained in Appendix A. The processed data sets for the ERI are contained in electronic Appendix E1. Three-dimensional electrode position coordinates were collected with a TopCon HiperLite+ differential GPS system in real-time kinematic mode providing ±3 cm XYZ precision relative to the base station for ≥ 95% of electrodes, and ±4 cm precision for ≥ 99% of electrodes.

2.3 ERI Inverse Modeling Inverse modeling is the process of artificially creating a distribution of material resistivity in a model domain which, if the distribution existed and were measured by the same technique, would provide the same values as the field observations. Resistance observations and inverse models are non-unique: more than one resistivity distribution would yield the same set of observations, and more than one inverse model can match the measured values. Single-property models—electrical, seismic, magnetic, gravity, etc.—require knowledge of their limitations, awareness of context, and additional observations to validate the model and support meaningful interpretation. Multiple independent models can, as in this report, co-validate. Each

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ERI line in this report was collected and modeled independently, that is, without reference to or calibration against other ERI data and models. The resulting model set shows physically and geologically sensible distributions of resistivity within each model, and consistency with adjacent models. Inverse modeling is an iterative process. On the first iteration, the algorithm uses the complete dataset to create a trial model and the result is evaluated. The evaluation identifies anomalies—individual observational values far different than spatially adjacent values. The operator removes the most extreme data errors from the dataset to be processed by the next iteration. This cycle continues until the data errors are minimized. Typically, a total of no more than five percent of observations are removed; that is, typically greater than 95% of the original data remain in the final model. For ERI data collected at the ORC site, the Halihan/Fenstermaker technique (Halihan et al, 2005) was used to develop the electrical resistivity images. Golden Software Surfer 8 was then used to create contoured images representing modeled electrical resistivity values of the subsurface for each survey. Inverse modeling requires accurately locating electrodes’ positions in three-dimensional space. Location coordinates for all geophysical data were measured and recorded with a TopCon HiperLite+ GPS system operating in RTK mode, described below. Final images were created by contouring and plotting the inverse model of each survey line using a consistent color scheme for the site to allow for evaluation of the results of all surveys on a comparative basis. The models are illustrated using two color schemes. The first color scheme, shown below, illustrates low-resistivity features using the warm colors red and orange. Greens and blues highlight more resistive features, and the gray shades show features with apparent resistivity of 500 ohm-meters or greater.

The second color scheme, shown below, highlights low-resistivity features with apparent resistivities of 10 ohm-meters or less. Any feature with an apparent resistivity greater than 10 ohm-meters is represented as gray. Resistivities below 10 ohm-meters can be considered very low-resistivity relative to most sites. Resistivities below 1 ohm-meter are extremely low (seawater resistivity is 0.25 ohm-meters).

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As a part of overall data quality control process, the resistivity data for the entire site were compiled and then a normalized color scheme for the images was created. This allows consistency in the color scheme so the results can be correlated across the site.

2.4 OhmMapper Data Collection A Geometrics OhmMapper TR1 capacitively-coupled resistivity system was used to measure subsurface resistivity in selected areas. The OhmMapper consisted of an ungrounded dipole transmitter and five ungrounded dipole receivers. The transmitter used the capacitance of an antenna to induce an approximately 16.5 kHz AC signal in the ground. A longer cable would have a greater capacitance which would induce a larger current into the subsurface. However, the longest cable length may not be appropriate for the given field conditions. This occurs when the length of the cables increases the total length of the OhmMapper array such that the skin effect becomes a negative factor in data collection. The AC signal passed through the ground and the receivers measured the AC voltage (Geometrics, 2001). The OhmMapper is pulled either by person or an all-terrain vehicle (ATV) for near continuous data collection, but for any significant distance with more than one or two receivers, an ATV is required. Two areas, Grid 1 and Grid 2, were selected for OhmMapper surveys (Figure 5). The grids were designed to collect data in areas presumptively upstream of the seeps based on hydrogeological principles using local topography. Grid 1 is to the west and northwest, and upslope, of the acidic seep; Grid 2 is to the west and northwest, and upslope, of the caustic seep. Prior to data collection, straight lines running north-south and east-west were laid out using the high-resolution TopCon HiperLite+ GPS system (described below). Grid boundaries were constrained by site extent and physical obstacles (e.g., fences). Gaps within the grids occur where physical obstacles (fences, trees, large rubble piles, and buildings) prevented data collection. For this project, the OhmMapper array was towed behind an ATV at a walking speed along survey grid lines (Figure 5). The OhmMapper allows for large scale scans in less time than the SuperSting, but at a shallower depth of investigation. The depth of investigation using an OhmMapper varies depending on the maximum spacing between the transmitter and the most distant receiver. Skin effects also affected the depth of investigation. This occurs when signal is lost because the resistivity of a material is too low for an EM unit operating at a particular frequency. The configuration for this work consisted of one transmitter and five receivers each at 5 meter dipole (10 meter center-to-center) spacing and a receiver-transmitter non-conductive rope spacing of 4 meters. The receivers were connected to the Geometrics OhmMapper console along with a Topcon differential GPS system. The console was connected to a tow link and cable that connected to an optical isolator wand. The optical isolator wand was connected through a weight to the receivers. A 4-meter non-conductive rope was placed in between the transmitter and the closest receiver; this rope allowed for the desired transmitter and receiver

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separation (Figure 6). The 4-meter rope separation increased the depth of investigation and insured that the maximum transmitter/receiver spacing was not exceeded for the local conditions. The extremely high contrast in the resistivity of the subsurface at this site limited the receiver-transmitter separation. The OhmMapper data were collected at a rate of approximately 2 samples per second. For this project, the simple survey data collection mode was used. The simple survey mode allows the instrument to keep track of key the start, waypoints and end of line without preprogramming. The GPS data are matched with the OhmMapper data based on the synchronization between the two instruments during data collection when it is downloaded into the Geometrics MagMap2000 software program. The OhmMapper unit collected between 10 and 20 thousand data points for each of the five receivers. These were collected in a set of .BIN data files during the period of September 20-21, 2008 and November 15-16, 2008. Copies of field notes from the OhmMapper data collection work are contained in Appendix A. The raw data sets are contained in electronic Appendix E2, and the processed OhmMapper data sets are contained in Appendix E3. Site photos are contained in Appendix B.

2.5 OhmMapper Data Modeling Data were transferred from the OhmMapper console into MapMap2000 software in binary (.BIN) format. The .BIN files were converted by MagMap2000 into ASCII (.STN) format. The ASCII format allowed for text editing if needed. Once the data were converted, MagMap2000 Locator was used to interpolate the readings between the known position points or “marks” (Geometrics, 2001). This ensured the proper positions of the data in the X-Y coordinate system. The raw ASCII data were converted to .DAT files compatible with the ArcMap/ArcInfo 9.2 software. Since five receivers were used for data collection, it was possible to use the EarthImager 2D software to create inverse models of apparent resistivity distributions. Inverse model data were then plotted, interpolated, and contoured as plan-view rasters by ArcMap for each survey grid. The interpretation of the inverted OhmMapper results was one of the main objectives for this project; however, the OhmMapper field data were used to evaluate the inverted data to determine consistency between the model and observed measurements as significant data data losses occurred at depth. To illustrate the observed data, the .DAT files that were exported from MagMap 2000 were used to group data points by receiver. Data from each receiver were extracted from the MagMap 2000 *.DAT file into individual files which were then plotted, interpolated, and contoured as a plan-view rasters by ArcMap. These raw data rasters are colored to match the inverted map rasters and ERI images. The result of this process is two sets of data and three sets of images. The first set of images represents the inverse model derived from the data. Images showing raw data points grouped by receiver demonstrate losses in data and variations with depth. These provide key information on data quality and variability. The third set of images showing interpolations of the raw data are interpolations and are available to be compare with images created from the inverse model.

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2.6 Spatial Data - GPS The geographic coordinates of all geophysical observations were measured with a TopCon HiperLite+ GPS system using two GPS receivers operating in real-time kinematic (RTK) mode, with post-processed corrections provided by the National Geodetic Survey’s (NGS) Online Positioning User Service (OPUS) applied to field observations (NGS OPUS, 2009). A single two-frequency (L1, L2) GPS receiver operating with Wide-Area Augmentation System (WAAS) differential GPS (DGPS) provides a theoretical maximum accuracy on the order of three meters. Any two observations with similarly capable independent GPS receivers will have an assumed potential error of at least three meters. Real-Time Kinematic (RTK) GPS processing between two GPS receivers provides precision on the order of tens of millimeters. That is, a GPS base station receiver at a fixed location calculates and transmits satellite signal error to a second, rover, receiver. The rover applies the calculated error as a correction to its satellite-based position. Each such calculated position is very consistent with respect to the rover’s position relative to the base station. Thus, RTK GPS provides very precise—i.e., self-consistent—position data. By itself, the single two-frequency GPS receiver operating with WAAS can theoretically locate itself within about three meters. When that GPS receiver records the satellite data it receives for a sufficiently long period, that recorded data can be processed along with similar records from multiple fixed-location GPS receivers whose positions are known: this reduces the error to within tens of millimeters. This post-processing provides very accurate position, typically to within less than 100 millimeters. The techniques employed here should provide ±3 cm XYZ precision relative to the base station for ≥ 95% of electrodes, and ±4 cm precision for ≥ 99% of electrodes; the base-station position is precise to within ±2 cm in the horizontal plane and ±3 cm vertical (extreme range of eight observation periods totaling 36.8 hours on six different days): base-station three-dimensional position standard deviation = 0.4 cm. . All coordinates were adjusted to CORS NAD83 as provided by National Geodetic Survey (NGS) Online Positioning User Service (OPUS) and presumably as accurate as they are precise. The GPS receiver that collected coordinate data for the OhmMapper was mounted on the ATV pulling the OhmMapper array. The physical distance between the GPS rover receiver and the OhmMapper receivers, the flexibility of the connection between them, the terrain, and motion all degrade coordinate precision. This is adjusted for in processing, but the locations are not as precise. Qualitatively, raw coordinate data collected with the GPS rover connected to the OhmMapper are in general along straight, parallel, equidistant lines.

2.7 Data Interpretation The magnitude of subsurface resistivity values will vary from site to site based on a number of factors related to the geologic materials, fluid chemistry, and any buried debris and structures. Table 1 provides textbook values of measured resistivities, demonstrates how widely materials can vary, and how minimal amounts of water can affect measured values. The variability among these data illustrates that it would be inappropriate to base interpretations on generalized values of material resistivity: interpretations must be based in the characteristics of the local geology, local water quality and chemistry, and anthropogenic effects. As a first-order

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approximation for a typical site, fine materials such as clay and silt are generally less resistive than coarse sand and gravel; and dry material will be more resistive than the same material when wet. Table 1. Measured resistivities for selected materials. Values from Telford, et al. (1990).

Material wet / dry / unspecified р (Ω-m) Granite porphyry 4500 - 1,300,000 Lava 100 - 50,000 Slate 600 - 40,000,000 Quartzite 10 - 200,000,000 Sandstone 1 - 640,000,000 Limestone 50 - 10,000,000 Clays 1 – 100 Material (% wt H2O) р (Ω-m) siltstone (0.38 %) 560,000,000 - (0.54%) 15,000 coarse-grained sandstone (0.18 %) 100,000,000 - (0.39%) 960,000 medium-grained sandstone (0.10 %) 140,000,000 - (1%) 4200

Water tables may or may not appear as distinct features in electrical resistivity surveys. The water table, or piezometric surface, is the elevation at which hydrostatic head equals atmospheric pressure. Rarely is water absent above the water table. Rather, capillary forces retain water in pores, often to elevations well above the water table. Above the water table, these pore waters can provide low-resistivity current paths and obscure the water table itself. Also, fluctuating water tables carry solutes upward with them and can preferentially deposit contaminants. Light non-aqueous-phase liquids (LNAPLs) may be lifted by a rising water table and remain as a significant quantity adhering to the matrix when the water table falls. Further, the resistivity of the ground water is often similar to the resistivity of the soil matrix. Thus, the water table is often not easily seen in survey images. Bedrock fractures often appear as vertically-oriented anomalies and may be either conductive or resistive depending on the fluid within the fracture (e.g., clean groundwater and/or unweathered/weathered contamination. Buried tanks or other man-made structures typically show up in survey images as either low resistivity (metallic construction) or high resistivity (fiberglass, concrete or other construction) anomalies. Areas where tanks or sumps have been removed or replaced may or may not appear as anomalies depending on whether native or non-native material was used as backfill. On contaminated sites, resistivity surveys generally indicate areas that appear to differ from the background geology. Either the contrasting areas have shapes that are different from the surrounding geology, either strong vertical features or strongly circular features. Additionally, impacts are often very resistive or very conductive. Fresh DNAPL impacts can be thousands of ohm-meters, but could be confused with competent granite or gypsum bedrock if it were present. Fresh LNAPLs such as gasoline often present elevated resistivities of 150-500 ohm-meters, but can be obscured by rocks of a similar range. Weathered NAPLs of either density

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can become extremely conductive similar to seawater (0.25 ohm-meters). These impacted zones often are below 10 or often below 1.0 ohm-meters. These strongly conductive areas are generally unexpected for freshwater areas. Electrical surveys do not independently identify the nature or composition of anomalies. Variations in geology, moisture content, natural voids, fractures, and contaminants all create anomalies. Meaningful data interpretation is only possible by calibrating or benchmarking electrical resistivity images against direct observations from well logs, measured from well samples, or observed in nearby outcrops. As with all geophysics, both capacitively-coupled electromagnetic-methods (CC-EM) and ERI measure a single, non-unique property. No single property is adequate to identify any feature or material. Ideally, confirmation work is performed as near in time as possible following geophysical data collection to minimize the possibility of changes in subsurface material chemistry or distribution. For this project, the confirmation data collected previously from on-site wells was used to evaluate the efficiency of the technique following review of the preliminary interpretations provided in this report.

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3.0 Results

This section describes the ERI data collected by the SuperSting. Then it describes the raw data points and rasters, and the inverted data rasters for the OhmMapper. This section also contains information regarding groundwater sampling wells near the survey areas, and trends that may be associated with the lithology and fluid contents of the wells and the data collected by each instrument. Section 4 provides interpretations of the data and models described here.

3.1 ERI Data Results Of the methods used, ERI provided the deepest subsurface imaging, along with the best lateral control over electrical properties. The method is, however, limited in the area it can cover compared to the OhmMapper relative to cost and speed. The ERI resistivity of the site was generally very low with extremely low-resistivity areas at the caustic and acidic seeps, and at the central refinery locations. Descriptions of the inverse models are given below. Information regarding wells is drawn from drillers’ well logs as provided for this report by the Oklahoma DEQ; information regarding water quality analysis and water chemistry is drawn from data supplied for this report by the Oklahoma DEQ. Results are presented as the ERI data images in two different color schemes as Figure #a, and with an interpretation based on site data as Figure #b. ERI Line DEQAS01 (Figures 7a and 7b): This image represents the inverse model created from the dataset collected directly east of the large rubble pile near the acidic seep. This survey line crossed wells SBB-21 and SGW-2. A distinct, low resistivity zone is continuous across the image. The top of this low resistivity zone ranges from the surface to 12 meters deep; resistivity ranges from 0.79 ohm-meters to 10 ohm-meters. Resistivity values are lowest toward the northwest and increase toward the southeast. Well SBB-21 contains dry, loose sand that is approximately 1 m thick at the surface. The sand grades into a dark gray clay which had a mild odor of hydrocarbon at the time of well construction. The clay changes color at approximately 2 m below the surface. Below this point, no odor of hydrocarbon is present. Well data collected by the DEQ provides no evidence of LNAPL in the well. This well is located very close to the end of the image which contains fewer data points and decreases the resolution of the image. Well SGW-2 was completed 6 m below the surface. The first meter of the well contains fine-grained, silty sand which grades into a silt to an approximate depth of 2 m. This silt contains odorous traces of hydrocarbon. The silt layer grades into a clay layer to a depth of 3.5 m. This clay grades into a sand unit to a depth of 4.5 m. There is evidence from the core of hydrocarbon staining at the bottom of this sand unit. Below 4.5 m, the lithology changes to a layer of alluvium in which the well is completed in at 6 m. This alluvium contains hydrocarbon toward the bottom of the well, as noted in the driller’s log. Laboratory analysis of water samples drawn in May 2008, about five months prior to the geophysical survey, report arsenic, beryllium, cadmium, chromium, lead, and nickel at concentrations significantly higher than the

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environmental limitations (RAO concentrations). The field-measured pH at the time the sample was drawn was 1.5. ERI Line DEQAS02 (Figures 8a and 8b): This image shows a distinct low resistivity zone continuous across the image at an elevation of 390 to 400 meters. Resistivity in this zone ranges from 1.45 ohm-meters to 10 ohm-meters with the lowest resistivity toward the north and increasing to the south. No wells were located near this survey line. ERI Line DEQAS03 (Figures 9a and 9b): This image represents the inverse model created from data collected along a line oriented south to north, west of the large rubble pile near the acidic seep. It illustrates a nearly continuous shallow, low-resistivity layer with values between 0.96 and 10 ohm-meters. The lowest-resistivity area is located at approximately 70 - 80 meters along the survey line, and resistivity increases toward the south and north. No wells were located near this survey line. ERI Line DEQCS01 (Figures 10a and 10b): This image represents the inverse model created from data collected from east to west along a line oriented roughly parallel to the road south of the gypsum knoll, near the caustic seep at the southeast corner of the site. This survey line crosses four water sampling wells: SBB-20, SBB-9, RMW-3S, and RMW-3D. This image shows a relatively low-resistivity layer that is distributed more broadly than what was illustrated at the acidic seep. The lowest resistivity area is located at approximately 4 meters west of well SBB-20. This layer has apparent resistivity values that range from 2.92 to 10 ohm-meters. Well SBB-20 contains a sandy-clay that changes to a silty-sand at approximately 0.75 meters below the surface. The sandy-clay contains traces of asphalt. The silty-sandy contains no evidence of contamination. The Rush Springs formation is located at approximately 1.8 m below the surface. The well is completed at approximately 6 m below the surface. The sandstone contains traces of hydrocarbon residue which increase with depth. The sandstone is stained completely black with hydrocarbon near the depth at which the well was completed. This well does not currently contain LNAPL. This well is located near the edge of the image, limiting correlation with the ERI model. Well SBB-9 contains a very thin layer of overburden at the surface. The well is completed approximately 8.5 m below the surface, and the Rush Springs formation is found throughout the well. The driller logged two very thin zones of core saturated with hydrocarbon. This well does not currently contain LNAPL. The thin layers of hydrocarbon would be too small to be detected by the ERI because the electrode spacing used for the survey was too large. Wells RMW-3S and RMW-3D are drilled into the Rush Springs formation and do not currently contain any LNAPL. ERI Line DEQCS02 (Figures 11a and 11b): This image represents the inverse model created from data collected along a line running south to north sub-parallel to Gladys Creek, just east of the gypsum knoll. The image illustrates a low-resistivity feature in the shallow subsurface. The feature in this image is less extensive than DEQCS01 but is located at approximately the same elevation. This feature has its lowest resistivities starting at the southwestern end and continuing to the 40 meter mark along the survey line.

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Well SBB-20 contains sandy-clay that changes to silty-sand at approximately 0.75 meters below the surface. The sandy-clay contains traces of asphalt; the silty-sand contains no evidence of contamination. The Rush Springs formation is located at approximately 1.8 m below the surface. The well is completed at approximately 6 m below the surface. The sandstone contains traces of hydrocarbon residue which increase with depth. The sandstone is stained completely black with hydrocarbon near the depth at which the well was completed. This well does not currently contain LNAPL. This well is located near the edge of the image, limiting correlation with the ERI model. Well SGW-1 contains clay to a depth of 3 m. When the well was drilled, there were small intervals that had no recovery. From 3 m to 4.5 m, the well consists of silty clay with 5% fine sand. After 4.5 m, the material grades to silty clay with 15% sand at a depth of 5.5 m. The well is completed at 7.6 m, with no recovery below about 5.5 m. The driller’s log does not report contamination. Water samples collected from the well contain 1,2-dichloroethane, arsenic, benzene, and beryllium concentrations which are significantly higher than their respective RAO concentrations. The water in this well has a measured pH of 7.09. ERI Line DEQCS03 (Figures 12a and 12b): This image represents the inverse model created from data collected along a southwest-to-northeast line located to the west of the gypsum outcrop near the caustic seep. At approximately 398 meters in elevation, a clearly-defined low-resistivity layer is present that has model resistivities that range from 4.86 ohm-meters to 10 ohm-meters. This feature is more broadly distributed compared to the low-resistivity feature in DEQCS02; however, this feature is located at approximately the same elevation as the previous dataset. ERI Line DEQNW01 (Figures 13a and 13b): This image represents the inverse model created from data collected along a west-to-east line on the northern section of the site where the refinery was once located. The center of this line was placed directly between groundwater sampling wells SBB-37 and SBB-24. The image displays a very large range of apparent resistivity readings that range from 0.14 ohm-meters to 32,338 ohm-meters. A very resistive feature, greater than 2500 ohm-meters, is located at approximately 20 meters from the western end of the model. A vertical, low-resistivity feature is located at approximately 38 meters from the western end of the model. All of the low-resistivity units have apparent resistivity values of 10 ohm-meters or less. The first 3 meters of well SBB-24 contains Quaternary-age deposits which range from clay and silt, to sand stained with hydrocarbon. At 3 m, the well core contains the Weatherford gypsum which is stained with hydrocarbon in places. The well is completed at approximately 4.2 m below the surface. The upper half of the Weatherford gypsum is weathered, with hydrocarbon staining in the fractures. The bottom half of the Weatherford gypsum contains crystal growths within the fractures, and some fractures contain siltstone. This well contained a thin layer of LNAPL in the sampling interval prior to the survey. Well SBB-37 is located just a few meters to the north of well SBB-24, and the core was not sampled continuously. At approximately 8 m below the subsurface, a gypsiferous sandstone with no open fractures or visible porosity is encountered. At 9 m below the surface, a fine-grained sandstone is encountered with low angle crossbedding and a zone of contamination. This well contained a thick layer of LNAPL in the sampling interval prior to the survey. ERI Line DEQNW02 (Figures 14a and 14b): This image represents the inverse model created from data collected along a southwest-northeast line intersecting line DEQNW01 between wells

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SBB-24 and SBB-37. This image shows similar features to DEQNW01. It illustrates very resistive materials in several areas in the image. Low-resistivity features in this image, with apparent resistivities of 10 ohm-meters or less, are discontinuous. The low-resistivity feature at approximately 20 meters from the southwestern point appears parabolic. The modeling algorithm can create this geometry when a very low-resistivity feature is located between two very high-resistivity features. Wells SBB-24 and SBB-37 are described under ERI Line DEQNW01, above.

3.2 OhmMapper Data Results

3.2.1 OhmMapper Model Data As noted in section 2.5, OhmMapper Data Modeling, collecting data with multiple receivers supports inverse modeling similar to that performed with ERI data collected using multiple-electrode arrays. For this study, OhmMapper data were divided into straight-line segments. Each straight-line segment was used to create a 2D-vertical-plane inverse model. Inverse model values were then extracted by depth. Values at selected depth ranges from each of the models were then combined. These model values were then interpolated to create plan-view raster images illustrating the resistivity distribution within the depth range. Figures 15 through 24 present these model-value plan-view interpolated images. Wells within the boundaries of the OhmMapper survey grids, and for which data were available, were correlated with OhmMapper data and model values.

Table 2Error! Reference source not found.

Well Name LNAPL Present? Well Name LNAPL

Present? DLR-1 No DLR-7 Yes DLR-2 No DLR-10 Yes DLR-3 Yes SGW-2 Yes* DLR-4 Yes SGW-3 Yes* DLR-5 No SBB-13 No DLR-6 No

* refers to wells that were not sampled on June 2008.

The data used for the well correlations were collected by the DEQ during June 2008. This time period was selected because that event was the closest in time to the geophysical investigation. Water samples were not collected for two wells, SGW-2* and SGW-3*, during that event. However, the well log information, provided by DEQ, provides some basic information regarding whether the wells contain contaminated soils and/or waters. Well correlations for the OhmMapper data focus on whether the wells contain LNAPL. This well information was compared with the inverted/raw rasters to determine whether trends exist.

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Further discussion of the well correlations with the OhmMapper data will be discussed in section 4. The interpretations of the interpolated inverse model value rasters follow: Group 1, 0.2-0.7 meters below the surface (Figures 15 and 16): This group includes three images representing interpolated model values at depths 0.2 and 0.7 m below the surface. The images show two low-resistivity features in Grid 1 (Features 1 and 2). Feature 1 is an extensive area of generally diffuse, irregular low resistivity with isolated small areas of very low resistivity near RMW-3, SGW-3, near the north-northeast edge, and, especially, near the southeast edge; and with scattered areas of higher resistivity along the west-southwest edge, the north to northwest edge, just southeast of the center, and adjacent to the southeast very low resistivity feature. Feature 2 shows a moderately large very-low-resistivity feature immediately south of SGW-2. The resistivity distribution in Grid 2 shows an east-west linear feature in the middle of the eastern half of the grid. Adjacent lines (c.f. Figure 26) only weakly correlate with this feature, and data loss becomes significant in this area for receivers 4 and 5 (cf. Figures 28 and 29). The linear feature itself persists in the data collected with receiver 4. This anomaly is very local; its significance cannot be determined by OhmMapper data alone, but was neither large enough by itself nor part of a larger pattern sufficient to justify investigation with ERI. Group 2, 1.2-2.2 meters below the surface (Figures 17, 18, and 19): This group includes three images representing interpolated model values at depths 1.2, 1.7, and 2.2 meters below the surface. Feature 1 shows a general increase in resistivity with depth. Feature 2 remains relatively unchanged with depth. Grid 2 contains sporadic low resistivity features that change very little with depth. Group 3, 2.7-3.7 meters below the surface (Figures 20, 21, and 22): This group includes three images representing interpolated model values at depths 2.7, 3.2, and 3.7 meters below the surface. In Grid 1, Feature 1 continues to show a general increase in resistivity with depth, with the greatest apparent change on the east and west edges of the feature. In contrast, Feature 2 shows a general decrease in resistivity with depth. In Grid 2, low-resistivity features at 2.7 m depth are very similar to shallower data, but become more numerous at 3.2 and 3.7 m. Group 4, 4.2-4.8 m below the surface (figures 23 and 24): This group includes two images representing interpolated model values at 4.2 and 4.8 meters below the surface. In Grid 1, Feature 1 has changed very little with depth. Feature 2 has slightly increased in size with depth, and the low-resistivity area of Feature 2 encroaches on Feature 1. In Grid 2, the sporadic, low-resistivity features remain relatively unchanged compared with shallower images; however, as the depth increases to 4.8 m, the sporadic features become much less defined. Feature 3 also increases in size and decreases in resistivity with an increase in depth. Wells DLR-1, DLR-2, DLR-5, DLR-6, and SBB-13 do not contain LNAPL. DLR-1, DLR-6, and SBB-13 are located in a relatively resistive material. As the depth increases, low-resistivity material can be observed near the wells, but it does not completely surround the well locations. Wells DLR-2 and DLR-5 are located in or near very-low-resistivity areas. Wells DLR-3, DLR-4, DLR-7, and DLR-10 have LNAPL, as observed in ground water samples collected by the DEQ. Wells SGW-2 contains metal-impacted waters with a pH of 1.5; SGW-3 contains metal-impacted waters with high concentrations of organic compounds with a pH of 11.7. Model values indicate that SGW-2 and SGW-3 are surrounded by low-resistivity material

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from the surface to the modeled depth. Wells DLR-3 and DLR-4 are in or near a very low-resistivity material; however, the low-resistivity material does not completely surround the wells at approximately 1.7 m below the surface. Well DLR-10 is in or near a low-resistivity material at approximately 4.2 m below the surface. DLR-7 is not located in or near any low-resistivity material throughout the models. Wells SGW-2 and SGW-3 contain contaminated water and are found in very low-resistivity areas in all the models. However, these wells represent only two of the 11 wells selected for correlation; therefore, there is no observable trend for the correlation of the models to the well data.

3.2.2 OhmMapper Raw Data With the capacitively-coupled OhmMapper system, the material being measured may so attenuate the transmitter’s signal that receivers fail to detect it. This causes data loss which leads to low data density and poor coverage. Models of poorly-covered areas are less useful than models with good coverage. Data loss and/or spurious observations occur with any geophysical method; modeling capacitively-coupled EM can obscure the loss, requiring special attention be given to its occurrence. To evaluate coverage and data density for each of the five receivers, Figures 25 – 29 show (left) the locations and color-scaled values of observations, and (right) interpolated rasters of the observations. The depth of investigation increases with the receiver’s distance from the transmitter, with receiver 1 closest. Thus, the receiver data very roughly corresponds to the grouping of model values by depth, above. Receiver 1 (Figure 25): The density of data points for this receiver is consistent across the survey area: the interpolated raster can be interpreted with little ambiguity. In Grid 1, Receiver 1 recorded generally low to very low resistivities in two extensive areas: Feature 1 and Feature 2. Feature 1 encompasses extremely low resistivities at the southwest edge, the west edge, at SGW-3, and especially to the east and southeast. Feature 2 encompasses low to very low resistivities northwest of SBB-10 and SBB-35. In Grid 2, Receiver 1 recorded low resistivities in several areas, including very low to extremely low resistivities along a line running between DLR-06 and DLR-02. Low resistivities are weakly concentrated in the central part of the south half of the grid. Note, also, the extremely low resistivities along the north edge of the cutout at the southeast corner of Grid 2. Receiver 2 (Figure 26): The density of data points for this receiver is generally consistent across the survey area: the interpolated raster can be interpreted with little ambiguity. Note, however, data gaps in the southeast corner of Grid 1. In Grid 1, Receiver 2 recorded resistivities similar to those recorded by Receiver 1. Notably different are Features 1 and 3 with visibly higher resistivities, and Feature 2, with visibly lower resistivities.

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Receiver 3 (Figure 27): Data loss is significant along the eastern margin of both Grids 1 and 2, and in the central part of the south half of Grid 2: interpret data, interpolations, and models of these areas with caution. Elsewhere, data point density is generally consistent. In Grid 1 Feature 1, Receiver 3 recorded higher resistivities than did Receivers 1 and 2: the low-resistivity feature appears smaller. In contrast, the receiver recorded lower resistivities in Feature 2. Feature 3 is almost unchanged. In Grid 2, an extensive area (Feature 4) of low measured resistivity appears for the first time. Receiver 4 (Figure 28): Data loss is significant in the southeastern quadrant of Grid 1, and deeply along the east and south margins of Grid 2: the usefulness of interpolations and models in these areas is probably limited. Data recorded with Receiver 4 continues trends of increased resistivity in Feature 1 and decreased resistivity in Feature 2 and Feature 4. Low-resistivity areas in the central part of the south half of Grid 2 appear more extensive, connected, and continuous; however, this is an area of low data density and interpretations are tenuous. Receiver 5 (Figure 29): Data loss is too significant in all low-resistivity areas to support reliable interpretation.

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4.0 Discussion

This section discusses the data collected by the SuperSting and the OhmMapper, the models created from those data, and interpretations based on the data and the models. This section further addresses well correlations, and provides an overall summary and recommendations for future work. The two instruments employed in this investigation measure the same subsurface property, the electrical resistivity of materials. However, each instrument measures that property using a different physical approach, and each method has a somewhat different theory behind it. Resistivity is a property of material that is independent of the size of the material. Many people are familiar with resistance, but resistance changes with scale. Resistivity is a measurement of resistance that accounts for geometry to allow measurements to be taken at a range of scales and to yield similar values. In environmental work, resistivity is often measured using the geophysical methods described here. The same property of subsurface fluids is also often measured either in situ by placing an instrument in a monitoring well, or in vitro (in glass), measuring the resistivity of fluid sample drawn from the subsurface. When describing fluids, the property is generally reported as conductivity in units of microsiemens per centimeter (μS/cm) where one siemen per meter is the inverse of one ohm-meter. Geophysical methods measure bulk properties, in this case the net electrical resistivity of solid materials and the fluids filling spaces between the solids. Here, “fluids” includes water and everything dissolved within it, liquid hydrocarbons, some organic material, and gasses. Geophysical electrical resistivity measurement methods cannot by themselves distinguish what part of the measurement is the solid material and what part is fluid. Any calibration of geophysical measurements to fluid contaminant concentration, for example, is site specific and requires effectively simultaneous collection of geophysical and direct physical measurements.

4.1 ERI Data

4.1.1 DEQAS01-03 DEQAS01 (Figure 7b): There is an extremely low resistivity material that is located where well SGW-2 is completed and between 70-80 m on the x-axis. This feature can be interpreted to be a fluid effect on the bulk resistivity values for two reasons. First, the water sample collected from the well contains high concentration of metals and a pH of 1.5 with the drilling record indicating a presence of hydrocarbons. The high concentrations of metals in solution will lead to a much lower resistivity signature. Secondly, wells SGW-2 and SBB-21 are completed at approximately the same elevation, but they are completed in different lithologies. Well SGW-2 is completed in a gravelly, fine sand; whereas, well SBB-21 is completed in a clay layer. Dry, gravelly sand has a higher resistivity (expected to be approximately 50-500 ohm-m) than a clay layer (expected to be 15-30 ohm-meters) under normal circumstances; however, at this location,

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both exhibit a very low resistivity signature. Therefore, the pore fluids are providing the strongest electrical signatures and are overshadowing the lithologic signature. Gravelly fine sand is a relatively high permeable unit when compared to clay and silt, so fluid flow will be concentrated in areas that contain this material. With a combination of the fluid and lithologic data, the interpretation is that the very low resistivity feature located at 60-85 m on the x-axis near the completion point of well SGW-2 is a possible paleochannel which could be a flow path for metal and degraded hydrocarbon contaminated fluids. The clay layer in SBB-21 at approximately the same elevation as the sand layer can be interpreted to be the flood plain of the paleochannel. The ERI data combined with the drilling and fluid data indicate that remediation can be accelerated by locating remediation systems in the paleochannel, and focusing efforts to the south to capture fluids that appear to have migrated out of the interpreted paleochannel. DEQAS02 (Figure 8b): A low- to extremely-low-resistivity feature extends from about the middle to the right side of this model. The resistivity distribution correlates well with the resistivity distribution of DEQAS01. Considered with the DEQAS01 model, the feature appears to be a buried stream channel trending roughly west-to-east. DEQAS03 (Figure 9b): A low- to extremely-low-resistivity feature extends across this model, but the extremely-low-resistivity is less extensive than in the DEQAS01 and DEQAS02 models. The resistivity distribution correlates well with the resistivity distributions of DEQAS01 and DEQAS02: the correlation supports the conceptual model of a preferential flow path for contaminated fluid along a buried stream channel. Two plausible models for the limited extent of extremely-low-resistivity are: 1) a change in grain size of the preferential flow path, or 2) tailing of the contaminant plume with lower conductive solute concentrations. In all three models at this seep area, the low-resistivity features dip downward at the edges (north and south). Note especially the north (right) ends of DEQAS01 and DEQAS03, and the south (left) ends of DEQAS02 and DEQAS03. This may be an effect of the boundary conditions of the processing, and may not indicate downward movement of fluids or sediment layers. However, the vertical conductive feature on the right side of DEQAS03 appears pervasive and is not likely a boundary condition effect. Confirmation drilling or perpendicular ERI imaging of this area with a larger depth objective would be required to confirm this issue.

4.1.2 DEQCS01-03 DEQCS01 (Figure 10b): The drilling logs for the wells located along this line are completed in the Rush Springs Sandstone. None of the wells located along this line contained LNAPL; however, there is a very-low-resistivity material located at approximately 392 m in elevation throughout the plane of the image. These values are not typical for water-saturated or dry sandstone, so it can be interpreted that this is an impacted layer. DEQCS02 (Figure 11b): The water data collected from well SGW-1 is similar to the water data collected from well SGW-2. Well SGW-1 contains high concentrations of metals and a measured pH of 7.09. This data suggest that this may be a metals-impacted location. At approximately 58-85 m along this line, the very low-resistivity material is located at or near the surface. This feature corresponds to the previous location of a waste disposal pit; therefore, it

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can be interpreted that the previous pond is a source for the contamination located at approximately 395 m in elevation. DEQCS03 (Figure 12b): This model displays the similar sandstone weathering front feature which was observed with DEQCS01. Line DEQCS01 has two wells, RMW-3D and RMW-3S which are completed entirely in the Rush Springs Sandstone. There is a slight decrease in resistivity as the depth increases, so it is interpreted that this gradual decrease in resistivity is a weathering front of the sandstone down until the resistivity decreases to values which are uncharacteristically low for saturated sandstone. Line DEQCS03 exhibits a similar trend which is interpreted to be the weathering front of the Rush Springs sandstone. There is a very-low-resistivity feature located at approximately 392 m elevation. This feature is interpreted to be a potential zone of contamination. This survey line was located at or near an area which contained a waste disposal pit; however, this historic pit does not appear to be a source for the low resistivity feature since it does not extend to the surface.

4.1.3 DEQNW01-02 DEQNW01 and DEQNW02 (Figures 13b and 14b): Well SBB-37 is drilled into the Weatherford Gypsum, but at 8 m below the surface, a gypsiferous, fine-grain sandstone with low angle cross bedding is encountered. This can be observed in the images since there is a large contrast in resistivity between a gypsum and sandstone bed. Non-weathered gypsum will always have a very high resistivity electrical signature and similar results have been obtained elsewhere in Oklahoma (Tarhule et al, 2003). Sandstone, wet or dry, will typically have a much lower resistivity compared to a gypsum unit. Weathered gypsum can have a lower resistivity than non-weathered gypsum because of the presence of fractures which can fill with fluid and clay-rich sediment. The sandstone noted in the drillers log acts as a relatively high-permeability unit. Very-low-resistivity material can be found near the area where the sandstone is encountered. Similar electrical signatures have been detected in previous images which have been interpreted to be very low pH fluids with high metal or weathered hydrocarbon concentrations. Image DEQNW001 has a very low resistivity, vertical feature at approximately 38 m along the line. This feature has been interpreted to be a vertical dissolution feature within the gypsum that may contain contamination. A similar feature is observed in DEQNW002 at approximately 34 m along the line. This feature was interpreted to be a vertical dissolution feature within the gypsum, but due to the increased resistivity, no or less contamination is believed to be present at that location. It should be noted that the cross lines of these two lines do not correlate well. This is likely due to some 3D effects generated by the extremely strong horizontal resistivity gradient at this point going from a conductive impacted area below 10 ohm-meters to a competent bedrock above 2500 ohm-m. On line DEQNW01, a potential target zone for further investigation is at 65-70 m along and approximately 14 m below the surface. There are three potential target zones along DEQNW02 at 20 m, 65 m, and 95 m lateral distance at elevations of 406 m, 406 m, and 402 m, respectively. All of these conductive areas are suspected to be impacted by weathered hydrocarbons and possibly metals. The high resistivity material that is interpreted as competent gypsum bedrock on both lines should be sampled if there are expected to be any possible

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DNAPL impacts to the site as unweathered or lightly weathered DNAPL products can be strong insulators in the subsurface.

4.2 OhmMapper Data The raw data rasters, described in section 3.2, do not correlate well with the petroleum impacted well data that are available. As depth increases, the amount of data available for interpolation dramatically decreases. The decrease in data collected at depth greatly skews the images created. However, the raw point data can be used for interpretation. In Grid 1, a large amount of data is lost with depth (Figures 5, 25-29). The areas surrounding the zone of data loss contain a large amount of very low resistivity data points. Since the zone of data loss is bounded on the east and west sides by very low resistivity data, it is interpreted that the data loss occurs due to a decrease in resistivity which limits the skin depth of the OhmMapper. There is a large amount of data that is lost for Grid 2, none of which is bounded by well defined data; therefore, it is impossible to determine the cause for data loss in the area. Receivers 1 and 2 were the only two receivers which did not have significant data losses, so the depth of penetration for the OhmMapper was very limited in areas. However, the OhmMapper did collect high density data for the landfill areas for all 5 receivers. The loss of data with depth can be observed in the inverted data rasters as well (Figure 15-24). For example, the image of model data at 4.8 m below the surface contains long, rectangular, low-resistivity features (Figure 24). This occurs when there is a large distance between two data points during the interpolation process. This can skew the images which greatly reduces the ability to provide effective interpretations. Though the OhmMapper was limited by its skin depth, it did collect good data for the first two receivers. For the inverted data, this is equivalent to depths of approximately 2.3 m and 2.8 m respectively (Figure 30). This depth of investigation was approximated using the inversion process. The data points collected by each receiver are illustrated here with an approximated depth and contoured by apparent and model resistivity. The low resistivity features discussed in section 3.2 are observed in the raw data rasters for the first two receivers and in the inverted data rasters at depths of 2.2 m and 2.7 m as well (Figures 19 and 20) . The large, low resistivity feature in grid one is located near well SGW-3. Well SGW-3 contains water with high metal concentrations and pH of 11.7. Unlike SGW-1 and SGW-2, this well contains high concentrations of organic materials as well. The high concentrations of metals within the samples will cause a very low resistivity signature, so the very-low-resistivity signatures in Grid 1 can be interpreted to be a low-resolution image of the metal impacted waters. In Grid 2 (Figure 5), a similar interpretation can be made as in Grid 1, however, the data loss with depth is more significant in many areas. Feature 3 (Figures 15-24) does have relatively stable data and may be mapping an impacted area of the site. However, feature 3 is lacking the cross lines available in Features 1 and 2 due to landfill boundaries. A set of ERI lines in this area would assist in defining the extent of impact and areas for locating potential remediation strategies.

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4.3 Summary of Interpretations and Recommendations The ERI technique worked well at the site, and is only limited by the limited areal coverage that the technique provides. In low-resistivity environments such as this one, the instrument collects excellent quality data and can provide targeting of preferential flow paths that have been imaged on this site. The OhmMapper did not work as well for this site as the resistivity was so low that the penetration depth for the instrument was not enough to observe the water table. The signal was absorbed by material in the vadose zone, generally correlating to areas that contained former waste pits. This problem has been an issue for the instrument previously on conductive areas due to the method used to obtain the resistivity data. The instrument uses capacitive coupling to determine the electrical properties of the subsurface, but this approach can be problematic under some conditions such as the one encountered over much of the Cyril site. Addtionally, the method of dragging the instrument along the ground became limiting at the site in many areas due to the large amount of debris present on the surface which presented a risk to both the instrument and the survey personnel. Due to the limitations for the OhmMapper, future work for the central refinery location could utilize an EM-31 instrument or similar inductively couple EM instrument. This instrument collects data at a depth similar to that of the skin depth of the OhmMapper, i.e. the first two receivers. The EM-31 is carried by hand and will be a much safer instrument to run, both for the worker and the instrument, since the central refinery location contains numerous hazards. If an EM-31 instrument was utilized in areas that were too difficult for using the OhmMapper, some of the OhmMapper area should be covered as well to confirm that the instruments responded to the site similarly. This instrument will be problematic in areas with a significant amount of metal in that the method will strongly correlate with metal debris as well as subsurface impacts. Future work on the site could use EM-31 and ERI techniques to define flowpath locations and allow precise placement of remediation systems. The extremely low-resistivity of the contaminated areas makes them relatively easy to detect using these techniques. Similar to other plumes with metals and aged hydrocarbons, the signal that will be detected will not be related to undegraded hydrocarbon products, but to degradates and to other ions in solution (metals in this case).

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5.0 Conclusions This geophysical research project with the Department of Environmental Quality (DEQ) tested whether electrical resistivity imaging (ERI, SuperSting, aka multielectrode resisitivity) and electrical resistivity scanning (using an OhmMapper) provided a cost-effective method to find and document the sources of impacted ground water in an area containing an historical petroleum site. The investigation focused on the source of an impacted groundwater discharge zone (i.e. seeps) located in Cyril, Oklahoma. The electrical resistivity imaging using an AGI SuperSting was able to identify contaminated zones at the ORC Superfund site located in Cyril, Oklahoma. The instrument was limited to the amount of area that could be covered in a particular period of time due to the amount of time it required for data acquisition. However, the data quality for this instrument was very good which allowed for faster processing times for each survey line. When the ERI data were compared to well information from the Cyril site, potential flow paths and sources could be detected. Though the ERI approach was limited to the area that could be covered, the overall data quality and processing times made ERI an effective method for documenting the sources at this particular site. The capacitively-coupled EM approach using Geometrics OhmMapper was used primarily to cover large areas quickly and obtain data at a lower resolution and shallower depth than the ERI approach. The data collected by the OhmMapper were to be used for locating areas of interest to be further investigated with the AGI SuperSting at higher data resolution and greater depth of investigation. However, the OhmMapper did not allow data collection at the expected depth. The field conditions in Cyril, OK were not satisfactory for this instrument as the vadose zone had very low resistivity and did not allow the signal to penetrate to the water table. The low resistivities encountered at this site far exceeded the skin depth limitations of the instrument. This limited the depth of investigation to less than 3 m. The amount of time required to construct the grid, collect and process the data was far too great for the quality of data collected by the OhmMapper. Field conditions around the central refinery location were too dangerous for this instrument due to the debris scattered throughout the area which posed a hazard to the instrument and personnel. This limited data collection for a large section of the site. Overall, this EM approach was not an effective method for documenting sources at the site at water table depths. An EM-31 or similar inductively-coupled EM instrument could provide a similar result with increased safety and acquisition speed over the remainder of the site.

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6.0 References

Advanced Geosciences, Inc., 2002, AGI EarthImager 2-D Instruction Manual, Austin, TX, 88 p. Department of Environmental Quality, 2008. “Oklahoma Refining Company Superfund Site,

Community Involvement Plan.” <http://www.deq.state.ok.us/LPDnew/SF/ Remediations/ORC/Community%20Involvement%20Plan%20-%20Final.pdf>

Geometrics, Inc., 2001, OhmMapper TR1 29005-01 Rev. F Operation Manual, Geometrics, Inc.

San Jose, California, USA, 147 p. Halihan, T., Paxton, S.T., Graham, I., Fenstermaker, T.R. and Riley, M., 2005. Post-remediation

evaluation of a LNAPL site using electrical resistivity imaging. Journal of Environmental Monitoring, 7: 283-287.

Kearey, P., Brooks, M, Hill, I. 2002. An Introduction to Geophysical Exploration. Blackwell Science, Malden MA.

Lloyd Meghan, 14 Nov. 2007, Third Quarter 2007 LNAPL Monitoring Event Report. OK DEQ,

pp 7. National Geodetic Survey Online Positioning User Service. http://www.ngs.noaa.gov/OPUS/ ORC Superfund Site, September 1991. RI Report.,OK DEQ. Tarhule, A., T. Dewers, R. Young, A. Witten, and T. Halihan, 2003, Integrated subsurface-

imaging techniques for detecting cavities in the gypsum karst of Oklahoma, in Johnson, K.S. and Neal, J.T. (eds.), Evaporite karst and engineering/environmental problems in the United States: Oklahoma Geological Survey Circular 109, p. 77-84.

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7.0 Appendices

Appendix A. Copy of Site Notes

Appendix B. Copy of Field Photos

Appendix E1. ERI processed dataset (EXCEL format)

Appendix E2. OhmMapper raw dataset (EXCEL format)

Appendix E3. OhmMapper processed dataset (EXCEL format)

Appendix E4. GIS database for data

geophysical investigation of petroleum contaminated … · deq cyril project geophysical...

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