01 0
km mi 0.5
410
Height, m
550
Geomorphology Attribute from Remote Sensing
Moraines, hard rocks in fold belt
Locally compacted areas, swamps
River marshes, swamps
Infrastructural noise from built-up areas
> DEM resolution comparison of LiDAR and SRTM. The resolution of the aircraft-borne LiDAR data (left) is significantly better than that from the 2000 space shuttle mission (right). This small area corresponds to the white rectangle in the top figure on page 49.
These ecological and cultural observations are indicators of operational difficulties and risks. In addition, there are data-quality risks for seismic acquisition: The glacial moraines limit proper coupling, requiring large static corrections; swamps will generate resonance from trapped surface waves; multiple source types may be necessary since vibrators cannot be used in swamps; and the surface features may generate substantial levels of noise from surface waves scattered from escarpments.
RAG invested in a LiDAR DEM study to identify and mitigate potential problems before beginning seismic acquisition. Of the available remote-sensing sources, this aircraft-based survey provides the most accurate surface map (above left). It identified locations of increased scattering risk from abrupt changes in elevation. The steep slopes represent bound - aries that scatter energy in seismic surface-wave modes. Identifying the type and location of such surface changes helps geoscientists design a filter that eliminates noise scattered from a specific direction. Using the LiDAR survey and working with geoscientists from WesternGeco, RAG recon - structed the glacial and postglacial history of the survey area. From this survey, the geoscientists developed an elastic model for layer depths, thicknesses, velocities and attenuation, and then computed model-based surface static corrections and coupling corrections for the sources and receivers.
11. Gras R and Stanford N: “Integration of Surface Imagery with Subsurface Data,” paper P-115, presented at the EAGE 62nd Conference and Technical Exhibition, Glasgow, Scotland, May 29–June 2, 2000.
In general, local fluctuations of the seismic signal resulting from variations in coupling conditions are corrected by amplitude compensation. However, variations in coupling conditions are limited to certain frequencies, meaning that a general amplitude correction may introduce noise rather than attenuate it. The RAG study used a surface-consistent method that also included the correction of the spectral distortion at source and receiver resulting from the variations in coupling conditions here. To perform this task, RAG loaded the high- resolution DEM from the LiDAR, the seismic survey and the field data into a comprehensive GIS database.
Geomorphology maps from the remote- sensing study provided information about the local near-surface geology such as glacial moraines and swamps. These attributes derived from remote sensing were found to correlate with the frequency content of seismic attributes computed from surface-consistent spectral deconvolutions for the source, receiver and common midpoint (CMP) terms for one half of the survey (above right). From the remote- sensing attributes and the spectral seismic attributes, geoscientists predicted the seismic response for the other half of the survey, which compared well with the data and validated the procedure. Because of the detail and areal extent of the remote-sensing study, the company was able to ensure the consistency of corrections across the entire concession.
Surface-Consistent Spectral Seismic Attribute
Low-frequency source and CMP attributes
High-frequency source and CMP attributes
Low-frequency receiver attributes
High-frequency receiver and CMP attributes
> Correlations between geomorphology and spectral attributes. For example, where moraines and hard rock were present, the source and common midpoint (CMP) terms calculated by spectral deconvolution exhibited low frequencies.
The Richness of Remote Sensing Within the E&P industry, the use of remote sensing by satellite is not restricted to seismic survey planning. It is also used to find clues to the presence of hydrocarbons (see “Prospecting by Satellite,” previous page), and in reservoir surveillance, such as for subsidence monitoring and for planning and monitoring CO2 injection. The results of remote-sensing analysis are stored in a GIS database. These can be combined with subsurface information and models to generate 3D representations of the study area. Subsurface information and formation properties are often incorporated in modeling packages such as the Petrel seismic-to-simulation software.11 Integration of the surface and subsurface information into one package allows assessment of surface constraints within the context of a shared 3D space. As this article describes, such integration provides valuable insights for a seismic acquisition program. It helps link subsurface structure to its surface expression of faults and folds. Planning of drilling and production facilities and pipelines accounts for both surface and subsurface needs, including environmental constraints.
Satellite images that help locate businesses and friends’ homes are becoming useful tools in our daily lives because of their easy accessibility on the Internet. Similarly, the richer images from bands extending deep into the infrared spectrum are becoming increasingly indispensable for E&P activities.
—MAA
Winter 2008/2009
51
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