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

Stage: Development
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 v.1 (CR-2418) 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.

Building on the capabilities of EMGeo v.1, EMGeo v.2 (CR-2688) includes an algorithm that improves the software’s ability to analyze joint three-dimensional electromagnetic and magnetotelluric field simulations and to perform inverse modeling.

EMGeo v.3 builds on the capabilities of EMGeo v.2. EMGeo v.3 (CR-2981) includes a module to model and image process seismic data, which is a new type of geophysical measurement not previously included in earlier versions of this software. To analyze the seismic data, additional pre-processing of the seismic data is required. Specifically, the seismic data will need to be transformed into the Laplace-Fourier Domain via a Laplace-Fourier transformation. Once transformed, the data can be modeled and imaged with EMGeo v3.

Applications and Industries

* 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