EMGeo (ElectroMagnetic Geological Mapper) wins 2009 R&D100 Award
Seismic imaging methods have a long and established history in hydrocarbon reservoir exploration. Yet they have not proven effective in discriminating different types of reservoir fluids, such as brines, oil, and gas. Over time, because of this inability to discriminate, billions of dollars per year are lost in drilling dry holes—up to 100 million dollars per each unsuccessful drilling, and typically 2 to 6 months of unrecoverable labor costs for hundreds of people—while significant hydrocarbon reservoirs not revealed by conventional seismic-based methods remain undiscovered. This limitation has led to the development of new geophysical technologies, specifically the use of low-frequency electromagnetic energy to complement seismic methods. In contrast to seismic data, electromagnetic measurements have been shown to be highly sensitive to changes in fluid types and hence the location of hydrocarbons. However, successfully extracting and processing the information from electromagnetic data has proved up to now to be a formidable problem. The problem is especially significant in the search for hydrocarbon energy in highly complex offshore geological environments, where many of the world’s oil and gas deposits remain to be found. Such offshore hydrocarbon exploration is an especially arduous task because reservoirs generally reside in highly complex geological environments, often beneath miles of ocean. Deep-water reservoirs are exceedingly difficult to successfully locate without recourse to imaging them and the background geology in three spatial dimensions (3D). To provide a maximally consistent electromagnetic data interpretation to geologists, such imaging requires large-scale modeling, spatially exhaustive survey coverage, and multicomponent data volumes.
The EMGeo ElectroMagnetic Geological Mapper, the interpretive software developed by Gregory A. Newman and Michael Commer at Lawrence Berkeley National Laboratory, answers this challenge.
EMGeo overcomes the technological problems involved in interpreting offshore hydrocarbon reservoirs by exploiting 21st century computing power—massively parallel computing resources—and combining that power with advanced electromagnetic measurement techniques, to provide a unique imaging capability for hydrocarbon deposits. This capability has also proven very useful in solving other emerging problems, such as finding sources of alternative (specifically geothermal) energy and conducting optimally effective environmental remediation. Using sophisticated parallelization schemes, EMGeo can be scaled up to tens of thousands of computing processors, providing a unique advantage over comparable technologies in treating large-scale (“industrial”) 3D data sets (Commer and Newman, 2008). It enables investigators to “see what is there,” in the offshore subsurface, as never before, leading to new detection ability (both in area coverage and resolution), new efficiency, and new savings.
To map the oceanic subsurface at a scale and resolution previously unknown, EMGeo unites the latest in computing power with EM measuring techniques. Among such measurement techniques, the key emergent EM technology with respect to hydrocarbon exploration is controlled source electromagnetics (CSEM). The CSEM technique senses regions of enhanced resistivity that can be associated with oil or gas deposits. This technique interrogates down to reservoir depths as deep as 4 km beneath the ocean floor with the current technology. EMGeo processes the data provided by CSEM to create images of hydrocarbon reservoirs at an unmatched level of detail and spatial extent. EMGeo also has an unmatched capability for processing other types of electromagnetic data. Previously considered a source of noise when measuring CSEM data, magnetotelluric (MT) fields—arising primarily from the interaction of the solar wind with the Earth’s magnetosphere—complement CSEM fields, since they provide greater depth information than CSEM data. EMGeo can join the strengths of CSEM and MT for never-before-possible unambiguous imaging of subtle reservoir targets in complex geological media. The benefits of joint imaging of MT and CSEM data are illustrated in this example: (a) The earth model, showing the reservoir at 1 km depth and the salt deposit (2 to 6 km depth), both rendered in blue; (b) CSEM image; (c) MT image; and (d) joint CSEM and MT image. When imaging only the CSEM data, the reservoir is indicated, but not the deeper salt. When MT data are imaged, the salt structures show up, but there is no indication of the reservoir. Finally, when both types of data are analyzed together, the reservoir and salt structures are imaged at much better resolution than what could be obtained otherwise (Commer and Newman, 2008).
Related Publication citation
Commer, M. and G.A. Newman, New advances in three-dimensional controlled-source electromagnetic inversion. Geophysical Journal International, 2008. 172(2): p. 513-535. DOI: 10.1111/j.1365-246X.2007.03663.x (LBNL-63010)