Mapping Salinity

Determining where protected groundwater resources are in relation to oil and gas resources and production activities is a key step in answering the question What lies between oil and gas operations and protected water?

We are using a number of different approaches

  • Compiling water-quality sampling data from existing records and plotting them in 3D
  • Expanding spatial coverage by using borehole measurements made when oil and water wells are drilled to calculate salinity
  • Expanding spatial coverage beyond drilled wells and oil fields using ground-based and aerial measurements

The descriptions below will lead you through our process for defining the study area and mining information from existing oil and water well records.

Compiling Existing Water-Quality Sample Data and Mapping It in 3D

Data are obtained from oil and water well records at each oil field.

However, we do not have wells completed and water sample data for much of the area where protected groundwater resources likely exist.

map of an oil field showing that the oil and gas wells are not evenly distributed across the oil field

Map of oil and gas wells clustered in the northern and southern ends of an oil field (DOGGR, 2015).

Using Geophysical Log Measurements Obtained When an Oil Well Is Drilled to Fill Gaps in Water-Quality Data

When oil wells are drilled, several different kinds of information may be collected, including information about the rocks and sediments and borehole geophysical logs. These geophysical logs measure various characteristics of the rocks and fluids and often include resistivity (or conductivity) data. Fluid salinity strongly affects resistivity values, and resistivity logs, in conjunction with porosity, self-potential, and gamma ray logs, can be used to calculate changes in salinity. Borehole geophysical log data from oil wells commonly span the depth intervals between where water and oil well casings are perforated and can be used to estimate salinity in these gaps.

Scanned images of borehole geophysical logs from most oil wells are available from the DOGGR; shown here are examples from different wells with scanned logs of lithology and geophysical sensor responses.

Example of a geophysical log

Image of a driller's log.

Example of a driller's log

Example of an oil well geophysical log.

Using the Traced Geophysical Logs

To calculate salinity along the depth profile of an individual well, we trace the resistivity lines on the scanned image to create a numerical record. We then calculate TDS values from selected intervals on the resistivity line that reflect the right conditions for applying salinity equations. Once we have calculated TDS points for multiple wells, we apply statistical techniques to generate a 3DTDS model across the landscape (Gillespie and others, 2017; Shimabukuro and Ducart, 2016; Shimabukuro and others, 2016; Shimabukuro and Stephens, 2016; Stephens and others, 2018).

The basic procedure for digitizing scanned images is labor-intensive and finding more efficient techniques is important.

Using Ground-Based and Aerial EM Surveys to Map Subsurface

After compiling existing water-quality data and calculating salinity profiles from digitized oil well-log information, there are still gaps in groundwater salinity data between wells, especially in the buffer areas outside the oilfield footprint where there are fewer wells. Using EM techniques, subsurface resistivity can be surveyed from the surface or air, even for areas without wells (Fitterman and Stewart, 1986). The resulting resistivity model is somewhat similar to a low-resolution borehole resistivity log. By collecting these data near wells with logs, the interpretation of the EM data is grounded in independent data on lithology and salinity. We can then make interpretations about how the geologic structure and salinity change between wells are guided by the airborne and surface data collected between wells.

These EM surveys can be completed using geophysical instruments on the ground or suspended from aircraft. Ground measurements are useful to collect data in strategic locations at single sites, while airborne systems are able to efficiently collect high resolution data over large regions in a series of survey flight lines, usually arranged in blocks of evenly spaced lines.

Airborne EM data can be used to create high-resolution cross sections and depth-slice maps of resistivity over large regions. These resistivity sections and maps can then be tied to salinity values from compiled water-quality data and digitized well-log information to map salinity changes between wells.

plots of depth versus resistivity and depth versus specific gravity

These two plots compare a resistivity model from an airborne EM survey of Paradox Valley, Colorado, to a measurement of salinity by depth at a nearby well (Ball and others, written commun., 2017). (Depth is in meters and resistivity is in Ohm-meters.)

Electormagnetic induction being taken on the ground, Mojave Desert near Twentynine Palms, California. Photo taken by L. Ball, United States Geological Survey, 2014

Ground-based EM system (Ball, L.B., written commun., 2017).

Electromagnetic survey being taken from air, Poplar, Montana. Photo taken by L. Ball, United States Geological Survey, 2014

Airborne EM system in flight (Ball, L.B., written commun., 2017).

cross-section view of resistivity values

In this resistivity cross section, we can see the very low resistivity values (pink and red) associated with highly saline groundwater such as the brine observed at the river and in the well (TDS concentrations are given in mg/L). Using these correlations, we can map the brine-saturated parts of the aquifer and map the freshwater-brine interface (Ball and others, written commun., 2017).

References

1982

Division of Oil, Gas, and Geothermal Resources, 1982, California Oil & Gas Fields Volume III - Northern California: California Department of Conservation, report CD-1, 330 p.

1986

Fitterman, D., and Stewart, M., 1986, Transient electromagnetic sounding for groundwater: Geophysics, v. 51, p. 995-1005, DOI: 10.1190/1.1442158

1992

Division of Oil, Gas, and Geothermal Resources, 1992, California Oil & Gas Fields Volume II -- Southern, Central Coast, and Offshore California Oil and Gas Fields: California Department of Conservation, report CD-1, 645 p.

1998

Division of Oil, Gas, and Geothermal Resources, 1998, California Oil & Gas Fields Volume I -- Central California: California Department of Conservation, report CD-1, 499 p.

2014

Ball, L.B., Bloss, B.R., Bedrosian, P.A., Grauch, V.J.S., and Smith, B.D., 2015, Airborne electromagnetic and magnetic survey data of the Paradox and San Luis Valleys, Colorado, Chapter G of Buesch, D.C., Geology and geophysics applied to groundwater hydrology at Fort Irwin, California: U.S. Geological Survey Open-File Report 2015–1024, 19 p., DOI: 10.3133/ofr20151024

2015

Ball, L.B., Bloss, B.R., Bedrosian, P.A., Grauch, V.J.S., and Smith, B.D., 2015, Airborne electromagnetic and magnetic survey data of the Paradox and San Luis Valleys, Colorado: U.S. Geological Survey Open-File Report 2015–1024, 19 p., DOI: 10.3133/ofr20151024

California Department of Water Resources, 2015, Water quality data reports: Digital spatial data with water quality data for groundwater wells; data received June 2015 via email.

California State Water Resources Control Board, 2015, Electronic Data Transfer Library, accessed February 2015 at: https://www.waterboards.ca.gov/drinking_water/certlic/drinkingwater/EDTlibrary.html

Division of Oil, Gas, and Geothermal Resources, 2015, DOGGR Well Finder: Digital spatial data for oil wells in California; California Department of Conservation, accessed February 28, 2015 at: https://maps.conservation.ca.gov/doggr/wellfinder/#/-118.94276/37.12009/6

U.S. Geological Survey, 2015, National Water Information System - Web interface: U.S. Geological Survey water database, DOI: 10.5066/F7P55KJN

2016

Blondes, M.S., Gans, K.D., Rowan, E.L., Thordsen, J.J., Reidy, M.E., Thomas, B., Engle, M.A., Kharaka, Y.K., and Thomas, B., 2016, U.S. Geological Survey National Produced Waters Geochemical Database v2.2 (provisional): U.S. Geological Survey web page accessed March 1, 2016 at

Shimabukuro, D.H. and Ducart, A., 2016, Converting scanned oil well data into usable digital data: in California Oil, Gas, and Groundwater 2016 Symposium, Bakersfield, Calif., November 2-3, 2016: Groundwater Resources Association, unpaginated.

Shimabukuro, D.H., Stephens, M.J., Ducart, A., and Skinner, S.M., 2016, Rapid estimation of aquifer salinity structure from oil and gas geophysical logs, in American Geophysical Union Fall 2016 Meeting, San Francisco, Calif., December 12-16, 2016: American Geophysical Union, unpaginated.

2017

Gillespie, J.M., Kong, D., and Anderson, Stephen D., 2017, Groundwater salinity in the southern San Joaquin Valley: American Association of Petroleum Engineers Bulleting, v. 101, no. 8, p. 1239-1261, DOI: 10.1306/09021616043

2018

Stephens, Michael J, Shimabukuro, David H., Gillespie, Janice M., Chang, Will, 2018, Groundwater salinity mapping using geophysical log analysis within the Fruitvale and Rosedale Ranch oil fields, Kern County, California, USA: Hydrogeology (online v.), p. 1-16, DOI: 10.1007/s10040-018-1872-5