well-n-calm

The well-n-calm is a resource to guide archiving various well data related to groundwater problems in California. Currently, it is focused to provide a consistent format to organize compiled well data in Envrionmental Geophysics group at Stanford. We share our lessons learned to compile well data in our group for past 5 years while solving groundwater problems in California. List of major projects incuding well data are:

  • Butte
  • Kaweah (Tulare)
  • Indian Wells Valley
  • San Louis Obispo

With the organization of well data in these projects, we also develop a basic python code base such that one can easily explore the existing data sets, import new data, and export the data sets to various formats (e.g. LAS, CSV).

_images/california_water_wells.png

Location of the water wells in California. Around milllon wells are plotted.

Contents:

Data Sources

WCR

Well Completion Report (WCR)

CASGEM

The California Statewide Groundwater Elevation Monitoring (CASGEM) Program

DOGGR

Division of Oil, Gas, and Geothermal Resources (DOGGR)

GAMMA

Groundwater Ambient Monitoring and Assessment (GAMMA) Program

SGMA

Sustainable Groundwater Management Act (SGMA).

EM resistivity

  • SkyTEM
  • towed-TEM

Data Format

To organize the well data, we consider two diffent file format:

  1. Interval file
  2. Collar file

Common Variables

  • Project:
  • ID:
  • ReferenceDepth:
  • From:
  • To:
  • Projection
  • Zone
  • X:
  • Y:

Data Types

  • Lithology log
  • Water level data
  • Water quality data (e.g. total dissoved solidds(TDS))

Data explorer

Data explorer is a python code base which allows us to explore the organized well data connected to specific projects in our Environmental Geophysics group.

Lessons Learned

Exploring well data in California, and convert them into a usuable format such that one can extract useful information about groundwater is not simple. Main roadblock for this is …

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Contributors:

  • Akash Ahamed
  • Alex Kendrick
  • Ian Gottschalk
  • Matt Lees
  • Meredith Goebel
  • Noah Dewar
  • Rosemary Knight
  • Seogi Kang (@sgkang)