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EMGeo: Risk Minimizing Software for Finding Offshore Fossil Fuels by Fluid Identification



    Plots of electrical conductivity over the Troll Field in the North Sea produced by analyzing 3D electromagnetic field data.  
  • Discovering and mapping offshore fossil fuel deposits by fluid identification


  • Minimizes the risk of drilling unprofitable oil wells
  • Augments seismic exploration mapping methods
  • Direct, non seismic indicator of hydrocarbons
  • Can be scaled for rapid analysis


Berkeley Lab researchers Greg Newman and Michael Commer have developed advanced software for discovering and mapping offshore fossil fuel deposits. When combined with established seismic methods, this software makes possible direct imaging of reservoir fluids.


Seismic methods have a long and established history in hydrocarbon exploration, and are proven very effective in mapping oil-bearing formations. However they are not good at discriminating the different types of reservoir fluids, such as brines, water, oil and gas. This has encouraged the development of new geophysical imaging technologies that can be combined with established seismic methods to directly image fluids. One technique that has recently emerged uses low frequency electromagnetic (EM) energy to map variations in subsurface electrical conductivity of offshore oil and gas prospects.

With the marine controlled source electromagnetic (CSEM) measurement technique, a deep-towed electric dipole transmitter is used to excite a low frequency (~0.1 to 10Hz) electromagnetic signal that is measured on the sea floor by electric and magnetic field detectors, where the largest transmitter-receiver offsets can exceed 15 km.

EM measurements are highly sensitive to changes in pore fluid types and the location of hydrocarbons, given that hydrocarbons are far less electrically conductive than brine or water.  However, the Earth is a poor medium, and low frequency EM waves (< 1Hz) are needed to interrogate down to reservoir depths - as deep as 4 km with the current technology. The result is a tradeoff; achievement of greater depths of penetration is accompanied by a loss of resolution. Hence incorporation of a priori information from seismic imaging to delineate the bulk reservoir and surrounding geological structure is critical to constrain the CSEM method, thereby allowing one to extract valuable information on fluid and rock properties of the reservoir.

Exploration with this technology in the search for hydrocarbons now extends to highly complex offshore geological environments. These geometries are exceeding difficult to map without recourse to 3D EM imaging experiments, requiring fine model parameterizations, spatially exhaustive survey coverage and multi-component data. The resulting processing requirements for 3D imaging are enormous.


To cope with this problem the Berkeley Lab researchers have developed a parallel 3D imaging algorithm called Electromagnetic Geological Mapper (EMGeo). EMGeo can scale up to the tens of thousands of processors so that an inversion of a 3D field data set can be carried out in days rather than months. The EMGeo software is now aiding oil and natural gas companies minimize the economic risk and environmental damage of drilling unprofitable wells.

EMGeo simulates 3D electromagnetic field data for subsurface electrical conductivity properties and then images conductivity using a non-linear conjugate gradient optimization scheme which minimizes the misfit between field data and model data using a least squares criteria. The 3D models generated from the data augment the seismic mapping methods that traditionally inform fossil fuel development decisions.


  • Copyrighted. Available for licensing.


Commer M., Newman G.A., Carazzone J.J., Dickens T.A., Green K.E., Wahrmund L.A., Willen D.E., and Shiu J., Massively parallel electrical conductivity imaging of hydrocarbons using the Blue Gene/L supercomputer, IBM Journal of Research and Development, 52, 93–103, 2008.

Commer M., and Newman G.A., New advances in three-dimensional controlled-source electromagnetic inversion, Geophysical Journal International, 172, 513–535, 2008.

Commer M., and Newman G.A., Three-dimensional controlled-source electromagnetic and magnetotelluric joint inversion, Geophysical Journal International, 178, 1305–1316, 2009.

Newman, G. A., and Boggs, P. T., 2004, Solution accelerators for large-scale three-dimensional electromagnetic inverse problems: Inverse Problems, 20, S151-S170.

Newman, G. A., and Commer, M., 2005, New advances in transient electromagnetic inversion: Geophysical Journal International, 160, 5-32.

Newman G. A., Commer M., and Carrazzone J. J., Imaging CSEM data in the presence of electrical anisotropy, Geophysics, 75, F51-F61, 2010.

Newman G.A., Gasperikova E., Hoversten G.M., and Wannamaker P.E., Three-dimensional magnetotelluric characterization of the Coso Geothermal Field, Geothermics, 37, 369–399, 2008.

Newman G.A., Recher S., Tezkan B., and Neubauer F. M., 3D inversion of a scalar radio magnetotelluric field data set, Geophysics, 68, 791-802, 2003.




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