GSA Annual Meeting in Indianapolis, Indiana, USA - 2018

Paper No. 22-8
Presentation Time: 9:00 AM-5:30 PM

ANALYSIS OF TDS CONCENTRATION IN RELATION TO OIL AND GAS PRODUCED WATER DISPOSAL PONDS IN KERN COUNTY, CALIFORNIA


PETELA, Valerie1, VARADHARAJAN, Charuleka2 and JORDAN, Preston D.2, (1)Lawrence Berkeley National Laboratory, Berkeley, CA 94720; California State University, Sacramento, Sacramento, CA 95819, (2)Lawrence Berkeley National Laboratory, Berkeley, CA 94720

In 2016, California ranked 3rd in the nation for petroleum production and approximately 72% of that production occurred in Kern County. Kern County is located in the Central Valley of California, which has a population of over 6.5 million people and is one of the most productive agricultural regions in the world. Groundwater supplies about 40-50% of California’s water needs. Because California is so dependent upon groundwater, it’s important to assess possible sources of groundwater quality degradation. One potential source is unlined ponds used for disposal of water that is incidentally extracted along with oil during production. For every barrel of oil produced, there can be up to 17 barrels of produced water extracted with it. Produced water is of poor quality, with typically high salinity, some hydrocarbons and potentially other constituents from formation waters. In California, forty percent of produced water not injected into the subsurface is disposed in unlined ponds specifically for percolation or evaporation, which may result in contamination of fresher groundwater below. There are large data gaps regarding produced water quality and the impacts of produced water disposal ponds on groundwater quality. This study analyzed data from the State’s Geotracker GAMA database to determine if produced water disposal ponds influenced groundwater quality in nearby wells. Of 1,956 wells with more than 3 TDS measurements, Mann-Kendall tests revealed 190 increasing TDS trends in the study area. These wells were then analyzed for potential petroleum chemical indicators and spatial correlation with disposal ponds. The spatiotemporal statistical analyses were conducted using python programming scripts in Jupyter notebooks made publicly available on a Github site for increased transparency and accountability.
Handouts
  • Petela_GSA2018_LBL.pdf (37.0 MB)